Plasma Lipidomics Investigation of Hemodialysis Effects by Using

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Plasma lipidomics investigation of hemodialysis effects by using liquid chromatography-mass spectrometry Lichao Wang, Chunxiu Hu, Shuxin Liu, Ming Chang, Peng Gao, Lili Wang, Zaifa Pan, and Guowang Xu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00170 • Publication Date (Web): 06 May 2016 Downloaded from http://pubs.acs.org on May 7, 2016

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Journal of Proteome Research

Plasma lipidomics investigation of hemodialysis effects by using liquid chromatography-mass spectrometry †,‡

Lichao Wang, †

‡

§

§

‡,¶

Chunxiu Hu, Shuxin Liu, Ming Chang, Peng Gao, †

Wang, Zaifa Pan, Guowang Xu*

Lili

,‡

†

College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China

‡

Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China

§

Nephrology Department, Dalian Municipal Central Hospital, 826 Xinan Road, Dalian 116033, China

¶

Clinical laboratory, Dalian sixth People’s Hospital, 269 Lugang Huibai Road, Dalian 116031, China

*Address correspondence to: Prof. Dr. Guowang Xu, Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China. Tel./fax: 0086-411-84379530. E-mail: [email protected].

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Abstract Chronic kidney disease (CKD) has been a global health problem which has a great possibility of being developed into uremia in the end. Hemodialysis (HD) is the most commonly used strategy for treating uremic patients. However, the patients still have a high risk of suffering various complications. It is well recognized that lipid disorder usually occurs in maintenance HD patients. To systemically study the effects of HD on lipid metabolism associated with uremia, an ultra-performance liquid

chromatography-quadrupole-time

of flight

mass spectrometry

(UPLC-Q-TOF/MS)-based lipidomics method was employed. A total of 87 human plasma samples from patients with pre-hemodialysis (pre-HD)/post-hemodialysis (post-HD) treatment and the healthy controls were enrolled in the study. As compared to pre-HD patients, many plasma lipids showed significant changes (p < 0.05) in patients receiving HD therapy. Specifically, sum of free fatty acids (FFA) as well as saturated FFA and eicosanoids, sums of lyso-phosphatidylinositols and lyso-phosphatidylethanolamines, FFA 16:1/FFA 16:0 and FFA 18:1/FFA 18:0 were obviously higher in pre-HD group than in the controls while significantly lower in patients after HD. These results indicated UPLC-Q-TOF/MS-based lipidomics is a promising approach to investigate lipid alterations in relation to uremia and is helpful to understand complex complications involved in HD patients. Keywords: Chronic kidney disease; lipidomics; hemodialysis; liquid chromatography-flight mass spectrometry; plasma lipids

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1. INTRODUCTION Chronic kidney disease (CKD) has become a worldwide health problem. CKD is 10.2-11.3% in China,

2

1

The prevalence of

which means that around 119.5 million population is suffering

from CKD. Many CKD patients ultimately progress to end-stage renal disease (ESRD or uremia), which is characterized by pronounced metabolic disturbance. Nowadays hemodialysis (HD) has still been a pervasive and efficient treatment for uremia. In clinic, Kt/Vurea (product of dialyzer urea clearance (K, mL/min) and dialysis time (t, min) divided by body urea volume (V, mL)) is a criterion to estimate adequacy of HD treatment in terms of urea reduction;

3-5

however, the high

error rate is mentioned in patients with HD schedules more than 3 times per week.

5

Several

researches have demonstrated HD time is positively correlated to the clearance of uremic toxins, such as the urea, creatinine, phosphorus and β2-Microglobulin.

6, 7

Moreover, some complex

complications (e.g., inflammation 8 and cardiovascular disease 9, 10) involved in HD patients have been investigated. Although numerous studies were focused on ESRD and HD, the detailed alterations of plasma metabolites between pre- and post- HD are still rare. Lipidomics is a newly emerging field on lipids, and aims to account for the mechanisms of lipids in regulating various phenomena of life. 11 It has been widely applied to disease biomarker discovery, 12-14 disease mechanism research, 15-17 drug development 18-20 and so on. In continuous HD patients, Allon et al. have reported that higher levels of saturated fatty acids in serum have a positive relevance to sudden cardiac death.

21

Others also have found triacylglycerides (TG) are

significantly increased, while high density lipoprotein cholesterols (HDL-C) showed an obvious decline in uremic patients. 22, 23 As most previous researches were mainly focused on several lipid species, a global lipid profiling study (lipidomics method) in maintenance HD patients is highly needed to comprehensively evaluate effects of HD on plasma lipid metabolism in uremic patients and understand the pathophysiological process of uremia. In this study, plasma lipidomic profiling from hemodialysis (HD) patients (pre-HD and 3



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post-HD) and healthy controls was studied using ultra-performance liquid chromatographyquadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS). Multivariate and univariate statistical analyses were used to evaluate HD’s effects on plasma lipid metabolism to get a better grasp of comorbidities associated with uremia. 2. MATERIALS AND METHODS 2.1. Materials Liquid chromatography grade acetonitrile, methanol and isopropanol were purchased from Merck (Darmstadt, Germany), ammonium acetate and tert-butyl methyl ether (MTBE) were purchased from Sigma-Aldrich (St. Louis, Mo, USA), and chloroform was purchased from Merck (Darmstadt, Germany). Ultrapure Water was prepared by Milli-Q system (Millipore, Billerica, MA,

USA).

Internal standards

(IS)

containing

phosphatidylcholine

PC

(19:0/19:0),

phosphatidylethanolamine PE (17:0/17:0), lysophosphatidylcholine LPC (19:0), sphingomyelin SM (d18:1/12:0), triacylglycerol TAG (15:0/15:0/15:0), ceramide Cer (d18:1/17:0) and free fatty acid FFA standards of d3-stearic acid (C18:0) were obtained from Avanti Polar Lipids (Alabaster, AL, USA). ISs were prepared in methanol as stock solution and stored in -20 oC. The detail concentrations of ISs were as follows: PC (19:0/19:0) 1.6 µg/mL, PE (17:0/17:0) 0.8 µg/mL, LPC (19:0) 0.8 µg/mL, SM (d18:1/12:0) 0.8 µg/mL, TAG (15:0/15:0/15:0) 1.25 µg/mL, Cer (d18:1/17:0) 0.5 µg/mL, and d3-FFA(C18:0) 0.8 µg/mL. 2.2. Sample Preparation Plasma samples were firstly thawed on ice. 300 µL ice-cold methanol containing ISs was mixed with 40 µL plasma in 2 mL Eppendorf tube to remove the protein. After thoroughly vortexed 30 s, 1 mL MTBE was added and the mixture was vibrated at room temperature for 1 h to extract the lipids as entirely as possible. Another 300 µL water was added followed by vortexing 30 s and settling at 4 oC for 10 min. After centrifugation at 14000 g for 15 min at 4 oC, the supernatant was transferred to two new Eppendorf tubes (400 µL for each) and freeze-dried in CentriVap 4


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Centrifugal Vacuum Concentrators (Labconco, MO). 20 µL solution A (chloroform: methanol, 2 : 1 (v /v)) and 80 µL solution B (water : isopropanol : acetonitrile, 5 : 30 : 65 (v/v/v) with 5 mmol/L ammonium acetate) were added to dried residue as the reconstitution solution for negative ion detection, and 20 µL solution A and 680 µL solution B were added to dried residue as the reconstitution solution for positive ion detection. After being vortexed for 30 s and centrifuged at 14000 g for 5 min at 4 oC, the supernatant was analyzed by UPLC-Q-TOF/MS. Notably, pooled quality control (QC) samples that consisted of equal volume of plasma from each sample were pretreated in the same way as the real biological samples. These QC samples were used to appraise the reliability of the whole experiment including sample preparation and UPLC-Q-TOF/MS sequences run. 2.3. UPLC-MS-based Plasma Lipidomics Profiling Ultra-high-performance liquid chromatography (Waters, Milford, MA) coupled with AB SCIEX TripleTOF™ 5600 plus mass spectrometer system (AB SCIEX, Framingham, MA) system was applied to plasma lipidomics analysis. Lipids were separated on a reversed-phase UPLC ACQUITY C8 BEN column (2.1 mm × 100 mm × 1.7 µm, Waters, Milford, USA) maintained at 55 oC in electrospray positive and negative ionization (ESI+ and ESI-) modes. 60 % acetonitrile in water containing 10 mM ammonium acetate was used as mobile phase A. 90 % isopropanol in acetonitrile containing 10 mM ammonium acetate was used as mobile phase B. The flow rate was 0.26 ml/min, with the elution gradient as follows: 32% B was firstly maintained for 1.5 min, then linearly increased to 85% B in 14 min, linearly increased to 97% B from 15.5 min to 15.6 min, finally maintained for 2.4 min followed by equilibration with 32% B in next 2 min. Full scan and MS/MS were performed in both ESI+ and ESI- modes for date acquisition and lipid identification, respectively. Ion spray voltage was set at 4500 V (-) and 5500 V (+), respectively. The declustering potential was at 100 V. Information-dependent acquisition (IDA) was used for MS/MS analysis with collision energy settings at 10 V. Curtain gas was set 35 psi 5



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and interface heater temperature was at 600 ℃ for ESI- and 500 ℃ for ESI+. 2.4. Lipid Identification Lipid identification in this study was performed based on MS/MS fragments and the accurate masses. In detail, MS/MS data from UPLC-Q-TOF/MS were subjected to a qualitative software Lipid View (AB SIEX, Framingham, MA). By applying MS/MS experimental pattern, data of putative assignments including lipid name, m/z and retention time were exported. And then the data were imported into Peak View software (AB SCIEX, Framingham, MA) where lipid assignments were assured by their characteristic ions (e.g., in positive mode, product ions of 252.27 or 264.27 for Cer, 184.07 for LPC, PC, and SM; and in negative mode, product ions of 196.04 for LPE and PE, 241.01 for PI etc.). For those which did not have fragments, the observed accurate masses were used to search lipid candidates via online lipid database Lipid Maps (www.lipidmaps.org) with an appropriate mass tolerance (< 15 ppm). Notably, retention time was often used to to figure out the lipid identities in such cases. According to MS/MS data and observed accurate masses, a total of 302 lipid molecular species were identified covering 17 sub-lipid classes including FFA, LPC, LP O, LPE, LPE O, PC, PC O, PE, PE O, SM, LPI, PI, Cer, HerCer, CE, DG, and TG. 2.5. Date Collection and Data analysis After lipid identification, a lipid quantitative method was created including lipid name, m/z, retention in Lipid View software. The method was applied for further quantitative analysis in Multiquant 2.0 (AB SCIEX, Framingham, MA). Peak areas were obtained from initial total ion chromatogram (TIC). After normalized by corresponding ISs, the data of all detected lipids in QCs were evaluated by their relative standard deviation (RSD) and only those with RSD below 30% were kept for further analysis (Table S1 showed the detailed information of lipid IS and lipid normalization). Partial least squares discriminant analysis (PLS-DA) with unit variance (UV) scaling was used to handle the data in SIMCA-P software (version 11.0; Umetrics). Paired 6


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nonparametric test by Matlab software (The MathWorks, USA) and nonparametric Mann-Whitney U test by SPSS 18 (SPSS Inc., Chicago, USA) were used to evaluate the statistic difference of lipids among groups. And p value < 0.05 was regarded as existing of significant statistical difference.

3. RESULTS 3.1. Study Population and Their Characteristics The plasma samples were sampled from 41 uremia patients undergoing HD treatment in Dalian Municipal Central Hospital Affiliated of Dalian Medical University and 46 healthy controls in Dalian Physical Examination Center. This study was approved by the ethics committee of Dalian Medical University. All the participants wrote informed consents voluntarily after reviewing a written plan of the whole study. 25 patients (male/female: 13/12) and 30 controls (male/female: 15/15) were allocated to the discovery set and 16 patients (male/female: 8/8) and 16 controls (male/female: 9/7) to the validation set. Uremia was histopathologically validated and all the patients were treated by a four hour HD with capillary low-flux dialyzer (FXclass 8). Plasma samples of patients were respectively acquired at the start of HD and at the end of HD. Moreover, patients were also fasting during the whole HD process. So all related plasma samples were collected under fasting conditions. Notably, the plasma samples were immediately stored at -80 o

C prior to sample preparation. A total of 41 consecutive stable HD patients (male/female: 21/20) and 46 healthy controls

(male/female: 22/24) were involved in this study. The mean age of all study subjects showed no significant difference between HD patients (61.44 ± 15.08) and the controls (60.17 ± 15.23). The basic clinical parameters of 41 patients in discovery set and validation set are shown in Table 1. Age and gender were well matched in both discovery set and validation set. The other clinical parameters, e.g., the level of creatinine, urea, K+, Ca2+ and phosphorus exhibited significant differences in pre-HD group as compared to post-HD group in both sets. HCO3- and body weight 7



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showed a significant lower level in the pre-HD group in the validation set. Previous report showed the similar change tendencies of clinical parameters in HD process 6. 3.2. Plasma lipid profiling of UPLC-Q-TOF/MS The whole workflow of this study is shown in Figure 1. Firstly, a non-targeted lipidomics method based on UPLC-Q-TOF/MS technology was applied to discover plasma lipid changes upon HD therapy. The findings were then verified by a validation set. A total of 302 lipids covering 17 different lipid sub-classes were determined by the established lipidomics analytical platform. The repeatability of the method was assessed by RSDs of the IS normalized data of all detected lipids (176 lipids in ESI+ mode and 126 lipids in ESI-mode) in QCs. The result of %RSD calculation of all IS normalized lipids in QC samples showed that 98.34% lipid molecular species possessed a %RSD less than 30% and the sum of the intensities of these lipids reached 99.27% (Figure 2A). The results suggested that this experiment had a good repeatability and stability. In the end, a total of 297 lipids from 17 different sub-classes were subjected to the following study. In order to obtain a direct overview of systemic differences of plasma lipid profiles among pre-HD, post-HD and the control groups, PLS-DA as a multivariate pattern recognition analysis model was applied to analyze the final discovery dataset. Figure 2B shows PLS-DA score plot of the plasma lipid profiles of three study groups in discovery set. The PLS-DA model possessed two principal components in which R2 and Q2 were 0.35 (R2X), 0.55 (R2Y) and 0.49 (Q2), respectively. It is very obvious that the three groups are clearly separated, which indicates distinct changes existed not merely between patients and the healthy controls, but also between pre-HD and post-HD therapy patient groups. Moreover, the validation plots exhibit YR2 = 0.131, YQ2 = -0.200 (Figure S1), which demonstrates the established model is legitimate without overfitting. Further univariate statistical analyses were performed by SPSS and MATLAB for investigating the alterations in lipid species.

8


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3.3. Exploration of Altered Lipid Metabolites Lipids of plasma from patients of pre-HD and post-HD were compared by paired nonparametric test conducted in MATLAB. In total, 124 individual lipid molecular species showed significant differences (p < 0.01) between pre-HD and post-HD groups. Volcano plot of pronouncedly altered lipids was performed and presented in Figure 2C in which changes in relative contents of differential lipids could be directly seen. As the volcano plot described, a lot of lipids presented decline to different degrees after HD therapy. Among them, free fatty acids (FFAs) altered most remarkably. Meanwhile, the relatively larger molecular weight lipids such as SM and TAG, had not been efficiently removed from plasma during the process of HD. Generally speaking, most lipids were decreased after a 4-hour HD treatment, but the degrees were highly dependent on the lipid classes. To better understand the effects of HD on plasma lipids and further on complex uremic syndrome, we investigated the variation in the sum of relative content of each lipid class. Differential lipids in pre-HD group or post-HD group versus healthy controls were defined by Nonparametric Mann-Whitney U test, and Paired Nonparametric test was used for verifying whether significant differences existed between pre- and post-HD groups. Fourteen classes of lipids significantly changed upon HD treatment vs. pre-HD treatment (Fig. 3A-F). Among them, FFA showed the greatest reduction and phosphatidylcholine (PC) was the most abundant lipid class (Fig.3A and B). Taking FFA as an example, its relative intensity dramatically decreased from 64.87 ± 4.93 in pre-HD patients to 20.47 ± 1.85 after 4-hr HD treatment, which was even lower than the normal level (23.95 ±0.90). And thus, significant differences in FFA existed between the pre-HD and post-HD (p < 0.001), between the pre-HD and the controls (p < 0.001), as well as between post-HD and the controls (p = 0.014). Among FFA, sum of saturated fatty acids (SFA), which significantly accumulated in pre-HD patients vs. the healthy controls (p < 0.0001), was reduced from 21.62 ± 1.43 to 9.79 ± 0.61 (p < 0.001 in pre-HD vs. post-HD); and 9



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eicosanoids (sum of FFA20:2, FFA20:3, FFA20:4 and FFA22:6) was reduced around 70% after 4-hr HD (i.e., post-HD vs. pre-HD FC = 0.24, p < 0.001), lower level than the normal value (post-HD vs. the controls FC = 0.77, p = 0.013). Other lipid classes including lyso-phosphatidycholine (LPC) (post-HD vs. the controls FC = 0.74, p = 0.001), ether lyso-phosphatidycholine (LPC O) (0.72, 0.005), ether lyso-phosphatidylethanolamine (LPE O) (0.60, 0.002), ether phosphatidylethanolamine (PE O) (0.63, < 0.001), hexosylceramides (HexCer) (0.80, 0.001) and cholesteryl esters (CE) (0.77, < 0.001), had significantly lower levels in post-HD vs. the controls. Lipid classes including LPE (pre-HD vs. the controls FC = 1.95, p < 0.001), phosphatidylethanolamine (PE) (1.50, 0.009), lyso-phosphatidylinositols (LPI) (2.06, < 0.001) and phosphatidylinositols (PI) (1.21, 0.017) were significantly higher in pre-HD than those in the controls. Our results showed that HD therapy can efficiently eliminate these lipid classes back to or even lower than the normal levels. Specifically, the relative levels of PE O and CE were significantly lower in pre-HD than the controls. Moreover, ceramides (Cer), PC and PC O showed no significant changes in both pre-HD and post-HD versus the controls. Interestingly, PE significantly increased after HD treatment (post-HD vs. pre-HD FC= 1.13, p = 0.013). Several specific lipid metabolites with important biological functions in human beings are exhibited in Figure 4 (A and B). Mono-unsaturated fatty acids (MUFA), such as FFA16:1 and FFA18:1, were separately compared with their saturated fatty acids to get the fold changes in three groups. The ratios, associated with activity of Stearoyl-CoA desaturase24, were the highest in pre-HD patients, significantly decreased to lower than those in the healthy subjects (p < 0.05) after treatment. 3.4. Validation of the differential lipid metabolites A total of 16 patients and 16 controls were used as the validation set to further verify the results obtained in the discovery set. The same analytical methods and statistical approaches as those used in discovery set were applied to achieve the goal. The PLS-DA score plot of the validation 10


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dataset was shown in Supplementary Figure S2A. There were two principal components in the PLS-DA mode, and its R2 and Q2 were respectively 0.39 (R2X), 0.52 (R2Y) and 0.41 (Q2). As compared with that of discovery dataset, we observed that the locations of three groups in these two datasets were very similar. The validation plots in validation set revealed YR2 = 0.165, YQ2 = -0.207 (Figure S2B). And discriminant analysis announced error rate was 18.75%, so the established PLS-DA model in the discovery set was reliable. Detailed alterations of 108 differential lipids in post-HD vs. pre-HD groups are summarized in Supplementary Table S2. These 108 lipids showed similar change trends as those in discovery set. The detailed information about changes in lipid classes upon HD treatment was shown in supplementary Table S3. Thirteen lipid classes all found in discovery set, significantly reduced in post-HD vs. pre-HD with post-HD/pre-HD FC ratios from 0.44 to 0.89. Similar to the discovery set, FFA showed the largest reduction upon HD treatment (post-HD vs. pre-HD FC = 0.44, p = 0.003) and significantly accumulated in pre-HD (pre-HD vs the controls FC = 2.42, p < 0.001). In addition, lipid classes such as SFA (pre-HD vs. the controls FC = 2.05, p < 0.001) and eicosanoids (2.13, < 0.001), LPE (2.11, < 0.001), and LPI (2.11, < 0.001) also displayed a significant increase in pre-HD group compared with the controls. Also, the change of LPC (post-HD vs. the control FC= 0.71, p = 0.006), LPC O (0.64, 0.003), LPE-O (0.59, 0.004), PE-O (0.68, 0.003) and CE (0.75, < 0.001) as well as CE (pre-HD vs. the controls FC = 0.85, p = 0.008) were successfully confirmed in validation set as expected. Besides, FFA16:1/FFA 16:0 and FFA18:1/FFA 18:0 had also the same trends as described in discovery set (Figure 4C and 4D).

4. DISCUSSION The present study investigated the therapeutic effects of hemodialysis on plasma lipids in a cohort of 41 patients with uremia to know what lipids have been removed during the HD, in the meantime to know whether the altered lipids are associated with complex complications. Although there are many reports about hemodialysis studies,

7, 25, 26

this is the first one to 11



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characterize detailed changes of a large scale of lipid molecular species from 17 different sub-lipid classes upon HD therapy. Our results demonstrated that FFAs were the most efficiently cleared metabolites by HD. After 4-hr HD treatment, about 60% FFAs were eliminated from plasma. FFAs have many biological functions in individuals. High levels of FFAs

27

and the redundant SFAs

21

are all

strongly linked to higher risk of cardiovascular disease in patients with kidney disease. Many studies

27, 28

have reported that the level of FFAs and SFAs obviously rise in plasma of kidney

disease patients. Moreover, FFA C20:2, FFA C20:3 and FFA C20:4 (eicosanoids) as well as FFA C22:6 (the precursor of eicosanoids) have essential functions in regulating physiological processes, e.g., pro-inflammatory, dilating vessel, signal molecules to regulate nervous system and even cancer. 29-31 Huang et al. 26 found partial precursors of eicosanoids were accumulated in uremic patients and were associated with increased inflammation symptom. In this study, significantly higher level of FFAs, SFAs, eicosanoids were found in pre-HD group than those in the control group. It indicated that uremic patients may have increased risks of suffering inflammation and cardiovascular disease.

8, 32

Impaired function of kidney to clear body wastes

may be the main reason for these. Moreover, the ratios of FFA 16:1/FFA 16:0 and FFA 18:1/FFA 18:0 were considered as important reference values to estimate the activity of Stearoyl-CoA desaturase (SCD).

24

SCD is an important enzyme interacted with hormones, such as insulin,

leptin, sex hormone and so on.

33-35

It has already been proven that the activity of SCD is very

high in patients with diabetes, cardiovascular disease, hypertension, immunological derangement and cancer.

36, 37

We found that the ratios of FFA 16:1/FFA 16:0 and FFA 18:1/FFA 18:0 were

larger in pre-HD than those in the control group. It can be argued that high incidence of cardiovascular disease may be closely correlated with increased activity of SCD in uremia. CEs are synthesized from cholesterol by the means of lecithin cholesterol acyl transferase (LCAT) and they have vital functions in cell signaling, energy storage and so on. 38, 39 LCAT has a 12


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very crucial role in esterifying cholesterol in discoidal HDLs to produce mature HDL, 40, 41 so it is positively associated with HDL level in vivo.

42

Furthermore, deficiency of HDL has been

demonstrated to be related to high risk of CVD morbidity.

43, 44

Calabresi et al.

45

found that the

activity of LCAT distinctly declined in nephropathy patients, as well as the level of HDLs, and HD can aggravate the situation. Significantly lower levels of CEs in pre-HD than the controls were found in our experiment, and CEs were further declined in post-HD group. Therefore, decreased activity of LCAT with the development of the uremia may be one of the main reasons for the reduced CEs in this study. LPCs are an important modulator in LDL and cell signaling46, 47 and generated from PCs with catalysis of LCAT. infections in sepsis.

48

41

And LPCs are suggested to have the ability to against microbial

In our study, though there was no significant difference of LPC between

pre-HD group and control group, LPC in post-HD group was much lower than the healthy controls (p < 0.05). The increased predisposition of uremia to inflammation may partially result from decreased level of LPC after HD therapy. Moreover, it has been reported that HD patients with low serum LPC have a higher risk of CVD than those with higher serum LPC. 49 In addition, although PCs and LPCs in pre-HD showed no significant difference as compared to the control group, it might further support the previous result that activity of LCAT was inhibited in kidney disease patients. 45 Therefore, some special adjuvant remedies are recommended to alleviate these side effects caused by HD therapy. LPI and LPE are subspecies of lysophospholipids and their mechanism in kidney disease is still unclear. Previous researches have reported that LPI could increase intracellular calcium level in HD patients, calcification.

10

50

and the enhanced concentration of serum calcium can stimulate the vascular

Another study found LPE was accumulated in ischemic heart patients.

30

In our

study, contents of LPI and LPE were almost doubled in pre-dialysis patients versus the healthy controls and then recovered to a normal level after 4-hr HD treatment. However, the pathologies 13



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are not clear yet, so much work is still needed to uncover this. It must be taken into consideration that the main mechanism of low-flux HD is diffusion interaction. Our results showed that a 4-hr maintenance HD treatment is an efficient way to remove some toxic metabolites. Specifically, the level of FFAs in combination with Kt/Vurea may be a novel idea for evaluating HD adequacy.

5. CONCLUSION In the present study, a UPLC-Q-TOF/MS-based lipidomics approach was applied to investigate HD’s effects on plasma lipids in uremic patients. Pronounced lipid alterations were found between pre-HD and post-HD in both discovery and validation sets. FFA, LPI and LPE were significantly accumulated in uremia vs. controls, and their levels changed back to normal or even less than

normal in patients after receiving a four-hour maintenance HD. CE were

significantly lower in pre-HD than the healthy, further decreased after HD. And LPC showed pronouncedly lower in post-HD than the control, though there were no significant differences between pre-HD and the control. Our results indicated that lipidomics can be used to evaluate maintenance HD’s effects on plasma lipid metabolism in uremic patients and may provide some useful hints on lipid involved complications.

ASSOCIATED CONTENT Supporting Information Supplementary Figure S1 Validation of PLS-DA model of discovery data set with 100 random permutations. Permutation test parameters: R2 intercept = 0.131, Q2 intercept = -0.200. Supplementary Figure S2 Discriminant analysis of lipidomics data in validation set. (A) PLS-DA score plot of plasma lipidomics data in three groups with unit variance (UV) scaling pretreatment. Black dot means control group; blue triangle means post-HD group; red square means pre-HD group. (B) 14


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Validation of PLS-DA model of validation data with 100 random permutations. Permutation test parameters: R2 intercept = 0.165, Q2 intercept = -0.207. Supplementary Table S1 Detailed information of lipid internal standards (I.S.) and lipid normalization Supplementary Table S2 Significantly changed 108 lipids among three study groups in validation set which were with the same change trends as described in volcano plot of discovery set (p ＜0.01). Supplementary Table S3 Relative levels of 13 significantly altered lipid classes (obtained in post-HD vs. pre-HD) among three study groups in validation set.

AUTHOR INFORMATION Corresponding Authors G.X.: Tel/Fax: +86-411-84379530. E-mail: [email protected]. Notes The authors declare no competing financial interest.

ACKNOWLEDGEMENTS The study has been supported by the key foundation (No. 21435006) and the creative research group project (No. 21321064) from the National Natural Science Foundation of China.

REFERENCES 1.

El Nahas, A. M.; Bello, A. K. Chronic kidney disease: the global challenge. Lancet 2005, 365, (9456), 331-340.

2.

Zhang, L.; Wang, F.; Wang, L.; Wang, W.; Liu, B.; Liu, J.; Chen, M.; He, Q.; Liao, Y.; Yu, X.; Chen, N.; Zhang, J.-e.;

Hu, Z.; Liu, F.; Hong, D.; Ma, L.; Liu, H.; Zhou, X.; Chen, J.; Pan, L.; Chen, W.; Wang, W.; Li, X.; Wang, H. Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet 2012, 379 (9818), 815-822. 3.

Gotch, F. A.; Sargent, J. A. A mechanistic analysis of the National Cooperative Dialysis Study (NCDS).pdf. Kidney

Int 1985, 28, 526-534. 4.

Daugirdas, J. T.; Depner, T. A.; Greene, T.; Levin, N. W.; Chertow, G. M.; Rocco, M. V.; Group, F. H. N. T. Standard

Kt/Vurea: a method of calculation that includes effects of fluid removal and residual kidney clearance. Kidney Int 2010, 77 (7), 637-644. 5.

Daugirdas, J. T.; Leypoldt, J. K.; Akonur, A.; Greene, T.; Depner, T. A.; Group, F. H. N. T. Improved equation for 15



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 25

estimating single-pool Kt/V at higher dialysis frequencies. Nephrol Dial Transplant 2013, 28 (8), 2156-2160. 6.

Eloot, S.; Van Biesen, W.; Dhondt, A.; Van de Wynkele, H.; Glorieux, G.; Verdonck, P.; Vanholder, R. Impact of

hemodialysis duration on the removal of uremic retention solutes. Kidney Int 2008, 73 (6), 765-770. 7.

Cornelis, T.; van der Sande, F. M.; Eloot, S.; Cardinaels, E.; Bekers, O.; Damoiseaux, J.; Leunissen, K. M.;

Kooman, J. P. Acute hemodynamic response and uremic toxin removal in conventional and extended hemodialysis and hemodiafiltration: a randomized crossover study. Am J Kidney Dis 2014, 64 (2), 247-256. 8.

Zimmermann, J.; Herrlinger, S.; Pruy, A. Inflammation enhances caediovascular risk and mortality in

hemodialysis patients.pdf. kidney Int 1999, 55, 648-658. 9.

Blacher, J.; Guerin, A. P.; Pannier, B. Arterial Calcifications, Arterial Stiffness, and Cardiovascular Risk in

End-Stage Renal Disease.pdf. Hypertension 2001, 38, 938-942. 10. Raggi, P.; Boulay, A.; Chasan-Taber, S. Cardiac Calciﬁcation in Adult Hemodialysis Patients.pdf. J Am Coll Cardiol 2002, 39 (4), 695-701. 11. Han, X.; Gross, R. W. Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. J Lipid Res 2003, 44 (6), 1071-1079. 12. Castro-Perez, J. M.; Kamphorst, J.; DeGroot, J. Comprehensive LC-MSE Lipidomic Analysis using a Shotgun Approach and Its Application to Biomarker Detection and Identification in Osteoarthritis Patients.pdf. J Proteome Res 2010, 9 (5), 2377-2389. 13. Jove, M.; Ayala, V.; Ramirez-Nunez, O.; Serrano, J. C.; Cassanye, A.; Arola, L.; Caimari, A.; Del Bas, J. M.; Crescenti, A.; Pamplona, R.; Portero-Otin, M. Lipidomic and metabolomic analyses reveal potential plasma biomarkers of early atheromatous plaque formation in hamsters. Cardiovasc Res 2013, 97 (4), 642-652. 14. Loomba, R.; Quehenberger, O.; Armando, A.; Dennis, E. A. Polyunsaturated fatty acid metabolites as novel lipidomic biomarkers for noninvasive diagnosis of nonalcoholic steatohepatitis. J Lipid Res 2015, 56 (1), 185-192. 15. Stegemann, C.; Drozdov, I.; Shalhoub, J.; Humphries, J.; Ladroue, C.; Didangelos, A.; Baumert, M.; Allen, M.; Davies, A. H.; Monaco, C.; Smith, A.; Xu, Q.; Mayr, M. Comparative lipidomics profiling of human atherosclerotic plaques. Circ Cardiovasc Genet 2011, 4 (3), 232-242. 16. Tam, V. C.; Quehenberger, O.; Oshansky, C. M.; Suen, R.; Armando, A. M.; Treuting, P. M.; Thomas, P. G.; Dennis, E. A.; Aderem, A. Lipidomic profiling of influenza infection identifies mediators that induce and resolve inflammation. Cell 2013, 154 (1), 213-227. 17. Vorkas, P. A.; Isaac, G.; Holmgren, A.; Want, E. J.; Shockcor, J. P.; Holmes, E.; Henein, M. Y. Perturbations in fatty acid metabolism and apoptosis are manifested in calcific coronary artery disease: An exploratory lipidomic study. Int J Cardiol 2015, 197, 192-199. 18. Kihara, Y.; Matsushita, T.; Kita, Y.; Uematsu, S.; Akira, S.; Kira, J.; Ishii, S.; Shimizu, T. Targeted lipidomics reveals mPGES-1-PGE2 as a therapeutic target for multiple sclerosis. Proc Natl Acad Sci 2009, 106 (51), 21807-21812. 19. Ji, J.; Kline, A. E.; Amoscato, A.; Samhan-Arias, A. K.; Sparvero, L. J.; Tyurin, V. A.; Tyurina, Y. Y.; Fink, B.; Manole, M. D.; Puccio, A. M.; Okonkwo, D. O.; Cheng, J. P.; Alexander, H.; Clark, R. S.; Kochanek, P. M.; Wipf, P.; Kagan, V. E.; Bayir, H. Lipidomics identifies cardiolipin oxidation as a mitochondrial target for redox therapy of brain injury. Nat Neurosci 2012, 15 (10), 1407-1413. 20. Morita, M.; Kuba, K.; Ichikawa, A.; Nakayama, M.; Katahira, J.; Iwamoto, R.; Watanebe, T.; Sakabe, S.; Daidoji, T.; Nakamura, S.; Kadowaki, A.; Ohto, T.; Nakanishi, H.; Taguchi, R.; Nakaya, T.; Murakami, M.; Yoneda, Y.; Arai, H.; Kawaoka, Y.; Penninger, J. M.; Arita, M.; Imai, Y. The lipid mediator protectin D1 inhibits influenza virus replication and improves severe influenza. Cell 2013, 153 (1), 112-125. 21. Friedman, A. N.; Yu, Z.; Denski, C.; Tamez, H.; Wenger, J.; Thadhani, R.; Li, Y.; Watkins, B. Fatty acids and other risk factors for sudden cardiac death in patients starting hemodialysis. Am J Nephrol 2013, 38 (1), 12-18. 22. Chan, M. K.; Persaud, J.; Vaghese, Z. Pathogenic roles of post-heparin lipases in lipid abnormalities in 16


Page 17 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


hemodialysis patients. Kidney Int 1984, 25, 812-818. 23. Piperi, C.; Kalofoutis, C.; Tzivras, M. Effects of hemodialysis on serum lipids and phospholipids of end-stage renal failure patients. Mol. Cell. Biochem. 2004, 265 , 57-61. 24. Attie, A. D. Relationship between stearoyl-CoA desaturase activity and plasma triglycerides in human and mouse hypertriglyceridemia. J Lipid Res 2002, 43 (11), 1899-1907. 25. Kalim, S.; Clish, C. B.; Wenger, J.; Elmariah, S.; Yeh, R. W.; Deferio, J. J.; Pierce, K.; Deik, A.; Gerszten, R. E.; Thadhani, R.; Rhee, E. P. A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients. J Am Heart Assoc 2013, 2 (6), e000542-e000552. 26. Huang, X.; Stenvinkel, P.; Qureshi, A. R.; Riserus, U.; Cederholm, T.; Barany, P.; Heimburger, O.; Lindholm, B.; Carrero, J. J. Essential polyunsaturated fatty acids, inflammation and mortality in dialysis patients. Nephrol Dial Transplant 2012, 27 (9), 3615-3620. 27. Madsen, T.; Christensen, J. H.; Toft, E. Effect of Intravenous ω-3 Fatty Acid Infusion and Hemodialysis on Fatty Acid Composition of Free Fatty Acids and Phospholipids in Patients With End-Stage Renal Disease.pdf. J Parenter Enteral Nutr 2011, 35 (1), 97-106. 28. Varga, Z.; Karpati, I.; Paragh, G. Relative abundance of some free fatty acids in plasma of uremic patients: Relationship between Fatty Acids, Lipid Parameters, and disease. Nephron 1997, 77, 417-421. 29. Holgate, S. T.; Peters-Golden, M.; Panettieri, R. A.; Henderson, W. R. Roles of cysteinyl leukotrienes in airway inflammation, smooth muscle function, and remodeling. J Allergy Clin Immunol 2003, 111 (1), S18-S36. 30. Wang, D.; DuBois, R. N. Pro-inflammatory prostaglandins and progression of colorectal cancer. Cancer Lett 2008, 267 (2), 197-203. 31. Harizi, H.; Corcuff, J. B.; Gualde, N. Arachidonic-acid-derived eicosanoids: roles in biology and immunopathology. Trends Mol Med 2008, 14 (10), 461-469. 32. GO, A. S.; Chertow, G. M.; Fan, D. Chronic Kidney Disease and the Risks of Death, Cardiovascular Events, and Hospitalization. N Engl J Med 2004, 351, 1296-1305. 33. Ntambi, J. M. Regulation of stearoyl-CoA desaturase by polyunsaturated fatty acids and cholesterol.pdf. J Lipid Res 1999, 40, 1549-1558. 34. Ntambi, J. M.; Miyazaki, M.; Stoehr, J. P.; Lan, H.; Kendziorski, C. M.; Yandell, B. S.; Song, Y.; Cohen, P.; Friedman, J. M.; Attie, A. D. Loss of stearoyl-CoA desaturase-1 function protects mice against adiposity. Proc Natl Acad Sci 2002, 99 (17), 11482-11486. 35. Dobrzyn, A.; Ntambi, J. M. The role of stearoyl-CoA desaturase in body weight regulation. Trends Cardiovasc Med 2004, 14 (2), 77-81. 36. Mar-Heyming, R.; Miyazaki, M.; Weissglas-Volkov, D.; Kolaitis, N. A.; Sadaat, N.; Plaisier, C.; Pajukanta, P.; Cantor, R. M.; de Bruin, T. W.; Ntambi, J. M.; Lusis, A. J. Association of stearoyl-CoA desaturase 1 activity with familial combined hyperlipidemia. Arterioscler Thromb Vasc Biol 2008, 28 (6), 1193-1199. 37. Brown, J. M.; Chung, S.; Sawyer, J. K.; Degirolamo, C.; Alger, H. M.; Nguyen, T. M.; Zhu, X.; Duong, M. N.; Brown, A. L.; Lord, C.; Shah, R.; Davis, M. A.; Kelley, K.; Wilson, M. D.; Madenspacher, J.; Fessler, M. B.; Parks, J. S.; Rudel, L. L. Combined therapy of dietary fish oil and stearoyl-CoA desaturase 1 inhibition prevents the metabolic syndrome and atherosclerosis. Arterioscler Thromb Vasc Biol 2010, 30 (1), 24-30. 38. Francone, O. L.; Gurakar, A.; Fielding, C. Distribution and Functions of Lecithin Cholesterol Acyltransferase.pdf. J Biol Chem 1989, 264 (12), 7066-7072. 39. Schwartz, C. C.; VandenBroek, J. M.; Cooper, P. S. Lipoprotein cholesteryl ester production, transfer, and output in vivo in humans. J Lipid Res 2004, 45 (9), 1594-1607. 40. Kaysen, G. A. Dyslipidemia in chronic kidney disease: Causes and consequences. Kidney Int 2006, 70, S55-S58. 41. Rye, K. A.; Barter, P. J. Regulation of high-density lipoprotein metabolism. Circ Res 2014, 114 (1), 143-156. 17



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 25

42. Calabresi, L.; Pisciotta, L.; Costantin, A.; Frigerio, I.; Eberini, I.; Alessandrini, P.; Arca, M.; Bon, G. B.; Boscutti, G.; Busnach, G.; Frasca, G.; Gesualdo, L.; Gigante, M.; Lupattelli, G.; Montali, A.; Pizzolitto, S.; Rabbone, I.; Rolleri, M.; Ruotolo, G.; Sampietro, T.; Sessa, A.; Vaudo, G.; Cantafora, A.; Veglia, F.; Calandra, S.; Bertolini, S.; Franceschini, G. The molecular basis of lecithin:cholesterol acyltransferase deficiency syndromes: a comprehensive study of molecular and biochemical findings in 13 unrelated Italian families. Arterioscler Thromb Vasc Biol 2005, 25 (9), 1972-1978. 43. Barter, P.; Gotto, A. M.; Phil, D. HDL Cholesterol, Very Low Levels of LDL Cholesterol, and Cardiovascular Events. N Engl J Med 2007, 357, 1301-1310. 44. Vaziri, N. D.; Norris, K. Lipid disorders and their relevance to outcomes in chronic kidney disease. Blood Purif 2011, 31 (1-3), 189-196. 45. Calabresi, L.; Simonelli, S.; Conca, P.; Busnach, G.; Cabibbe, M.; Gesualdo, L.; Gigante, M.; Penco, S.; Veglia, F.; Franceschini, G. Acquired lecithin:cholesterol acyltransferase deficiency as a major factor in lowering plasma HDL levels in chronic kidney disease. J Intern Med 2015, 277 (5), 552-561. 46. Steinberg, D.; Parthasarathy, S.; Carew, T. E. Beyond Cholesterol Modifications of Low-Density Lipoprotein That Increase Its Antherogenicity. N Engl J Med 1989, 320 (14), 915-924. 47. Oestvang, J.; Johansen, B. PhospholipaseA2: a key regulator of inflammatory signalling and a connector to fibrosis development in atherosclerosis. Biochim Biophys Acta 2006, 1761 (11), 1309-1316. 48. Drobnik, W.; Liebisch, G.; Audebert, F. X.; Frohlich, D.; Gluck, T.; Vogel, P.; Rothe, G.; Schmitz, G. Plasma ceramide and lysophosphatidylcholine inversely correlate with mortality in sepsis patients. J Lipid Res 2003, 44 (4), 754-61. 49. Lee, Y.-K.; Lee, D. H.; Kim, J. K.; Park, M.-J.; Yan, J.-J.; Song, D.-K.; Vaziri, N. D.; Noh, J.-W. Lysophosphatidylcholine, Oxidized Low-Density Lipoprotein and Cardiovascular Disease in Korean Hemodialysis Patients: Analysis at 5 Years of Follow-up. J Korean Med Sci 2013, 28, 268-273. 50. Grzelczyk, A.; Gendaszewska-Darmach, E. Novel bioactive glycerol-based lysophospholipids: New data – New insight into their function. Biochimie 2013, 95 (4), 667-679.

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Figure Legends Figure 1. Flow chart used to display the whole process of the study

Figure 2. Statistical analysis of lipidomics dataset in discovery set. (A) Coefficient of variation (CV) distribution of all detected lipids in 16 QCs. Number%, percentage of number of lipids within a defined range of CV value. Sum of response%, percentage of total response of lipids below a special criteria of CV value. (B) PLS-DA score plot of lipid profiles of plasma in three groups after unit variance (UV) scaling pretreatment. Black dot means control group; red square means pre-HD group; blue triangle means post-HD group. (C) 124 significantly altered lipids’ volcano plot (p < 0.01) in the discovery set. X axis: log2 (ratio of post-HD to pre-HD); Y axis: log10 (p value). P values of lipids were calculated by the Nonparametric Paired test.

Figure 3. Fourteen significantly changed lipid classes (obtained in comparison of post-HD vs. pre-HD groups) among three study groups. Data are presented as mean ± S.E. *: p < 0.05; **: p < 0.01; ***: p < 0.001 compared with control; #: p < 0.05; ##: p < 0.01; ###: p < 0.001 compared with the controls.

Figure 4. Fold changes of some specific lipids with great importance in human. A and B show the alterations in the discovery set; C and D show the alterations in the validation set.

19



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Table 1. Basic clinical characteristics of the samples in the discovery set and validation set*. discovery set group

pre-HD

post-HD

validation set pre-HD

post-HD

Gender (male/female)

13/12

8/8

Age (years)

61.92±15.56

60.69±14.77

Leukocyte (10*9/L)

6.26±1.60

7.04±2.29

PLT (10*9/L)

179±70.27

201.69±68.84

Albumin (g/L)

38.71±4.05

38.16±6.62

GPT (IU/L)

11.83±6.49

9.13±2.47

ALP (U/L)

101.25±58.33

86.06±48.97

Kt/V

1.37±0.31

1.17±0.34

Creatinine (µmol/L)

796.67±263.73 289.04±100.08*** 838.44±399.61 322.38±147.70***

Urea (mmol/L)

21.67±6.69

6.87±2.56***

22.34±7.91

8.10±2.71***

Na+ (mmol/L)

133.93±3.36

134.14±2.27

133.33±4.20

134.25±2.79

K+ (mmol/L)

4.77±0.93

3.98±0.51***

4.86±1.01

3.77±0.37**

Ca2+ (mmol/L)

2.36±0.17

2.57±0.21***

2.33±0.22

2.51±0.21***

Phosphorus (mmol/L)

1.66±0.52

0.84±0.18***

1.98±0.69

1.05±0.38***

HCO3- (mmol/L)

19.62±3.01

20.05±1.81

17.9±2.30

20.88±1.99***

Weight (kg)

70.16±20.16

70.4±20.53

69.53±15.90

70.00±15.75**

* Data are presented as mean ± SD;

**, p < 0.01; ***, p < 0.001 post-HD compared with pre-HD.

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Figure 1. Flow chart used to display the whole process of the study 198x185mm (300 x 300 DPI)



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Figure 2. Statistical analysis of lipidomics dataset in discovery set. (A) Coefficient of variation (CV) distribution of all detected lipids in 16 QCs. Number%, percentage of number of lipids within a defined range of CV value. Sum of response%, percentage of total response of lipids below a special criteria of CV value. (B) PLS-DA score plot of lipid profiles of plasma in three groups after unit variance (UV) scaling pretreatment. Black dot means control group; red square means pre-HD group; blue triangle means postHD group. (C) 124 significantly altered lipids’ volcano plot (p < 0.01) in the discovery set. X axis: log2 (ratio of post-HD to pre-HD); Y axis: log10 (p value). P values of lipids were calculated by the Nonparametric Paired test. 200x173mm (300 x 300 DPI)


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Figure 3. Fourteen significantly changed lipid classes (obtained in comparison of post-HD vs. pre-HD groups) among three study groups. Data are presented as mean ± S.E. *: p < 0.05; **: p < 0.01; ***: p < 0.001 compared with control; #: p < 0.05; ##: p < 0.01; ###: p < 0.001 compared with the controls. 204x239mm (300 x 300 DPI)



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Figure 4. Fold changes of some specific lipids with great importance in human. A and B show the alterations in the discovery set; C and D show the alterations in the validation set. 210x240mm (300 x 300 DPI)


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For TOC only 177x71mm (300 x 300 DPI)


Plasma Lipidomics Investigation of Hemodialysis Effects by Using

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