The association of the lipid profile with knee and hand osteoarthritis severity: the IMI-APPROACH cohort

OBJECTIVE

To investigate the association of the lipidomic profile with osteoarthritis (OA) severity, considering the outcomes radiographic knee and hand OA, pain and function.

DESIGN

We used baseline data from the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) cohort, comprising persons with knee OA fulfilling the clinical American College of Rheumatology classification criteria. Radiographic knee and hand OA severity was quantified with Kellgren-Lawrence sum scores. Knee and hand pain and function were assessed with validated questionnaires. We quantified fasted plasma higher order lipids and oxylipins with liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based platforms. Using penalised linear regression, we assessed the variance in OA severity explained by lipidomics, with adjustment for clinical covariates (age, sex, body mass index (BMI) and lipid lowering medication), measurement batch and clinical centre.

RESULTS

In 216 participants (mean age 66 years, mean BMI 27.3 kg/m², 75% women) we quantified 603 higher order lipids (triacylglycerols, diacylglycerols, cholesteryl esters, ceramides, free fatty acids, sphingomyelins, phospholipids) and 28 oxylipins. Lipidomics explained 3% and 2% of the variance in radiographic knee and hand OA severity, respectively. Lipids were not associated with knee pain or function. Lipidomics accounted for 12% and 6% of variance in hand pain and function, respectively. The investigated OA severity outcomes were associated with the lipidomic fraction of bound and free arachidonic acid, bound palmitoleic acid, oleic acid, linoleic acid and docosapentaenoic acid.

CONCLUSIONS

Within the APPROACH cohort lipidomics explained a minor portion of the variation in OA severity, which was most evident for the outcome hand pain. Our results suggest that eicosanoids may be involved in OA severity.

AB0172 METABOLIC AND LIPOPROTEIN PROFILING IN RHEUMATOID ARTHRITIS WITH THE USE OF NMR-BASED METABOLOMICS

Background

Rheumatoid arthritis (RA) remains a disease with high morbidity, due to the greater prevalence of cardiovascular disease. In contrast to the general population, systemic inflammation in RA lowers the circulating levels of lipids, a phenomenon called “the lipid paradox”. In addition to that, therapy is withdrawn in 50% of patients within 5 years, due to loss of efficacy or side effects. Momentarily predictive biomarkers for drug efficacy or side effects are missing, while a personalized approach in RA therapy is imperative. Several reports support the notion that specific metabolomic profiles are good predictors for response to MTX and anti-TNF therapy.

Objectives

The aim of this work is to depict in detail the metabolic profile of RA patients at different time-points of therapy DMARDs/bDMARDs in order to retrieve biomarkers related to their response to a given therapy, to monitor the disease progression and to predict the optimal disease management approaches.

Methods

Plasma was collected from fasted RA patients according to their therapy timepoint and organized in the following groups: a) newly diagnosed, without therapy (Naïve, n=15); b) patients having received therapies previously, with unstable disease, who were evaluated before receiving a new therapy (RAb, n=23); c) patients after having received a new therapy (RAa, n=14); and d) patients receiving any standard therapy (RAs, n=54), either DMARD or bDMARD, being in a stable condition. Finally, healthy subjects were enrolled as controls (n=33). Metabolomic profiling was carried out firstly with untargeted 1H NMR spectroscopy, and secondly with in vitro diagnostic (IVDr) NMR spectroscopy with the lipoprotein subclass analysis (B.I.LISA), to quantify absolute concentrations of metabolites and lipoproteins. The acquired data were subjected to univariate and multivariate statistical analysis to investigate clustering of the groups and define the responsible molecules. Clinical parameters, including inflammation markers, DAS28, and comorbidities, were also included in the analysis, and Spearman correlation coefficient was calculated.

Results

Untargeted NMR data were analyzed with multivariate supervised approach (PLS-DA) revealing distinct metabolic signatures for the 6 groups under investigation. The most defined groups being RAb and RAs, compared to controls, which indicated changes in alanine, tyrosine, lactate and acetone. Besides small molecule, significant changes were also observed in various plasma lipoproteins. For the thorough investigation of these findings, a targeted lipoprotein subclass analysis was conducted and highlighted significantly higher lipoprotein subclass concentrations, including free cholesterol (FC), cholesterol (CH), phospholipids (PL) and apolipoprotein A1 subfractions in RAs compared to controls and Naïve. Concerning metabolite differentiations, RAs patients exhibited reduced ketone bodies and organic acids compared to RAb and control individuals, respectively. All RA groups had lower concentrations of sarcosine. Correlation analysis highlighted the association of DAS28, ESR and CRP with ketone body acetoacetate (p<10-4) and sarcosine (p<10-2). VAS correlated with HDL triglyceride subfractions (H1TG and H2TG, p<10-5) and sarcosine (p=1.8x10-4). All therapies were found to correlate with lipoproteins; MTX with LDL-2 subfractions (p=5x10-4), intermediate-density lipoprotein (p=2.7x10-5) and acetate (p=5.9x10-6), Anti-IL-6R with VLDL cholesterol subfraction V1CH, (p=1.9x10-3) and V2CH (p=1.5x10-3), and Anti-CD20 with triglyceride fractions, IDTG and TPTG (p<10-3).

Conclusion

Overall, these data reveal that RA patients have a distinct metabolic signature depending on the time-point of therapy. Clinical parameters correlated with changes in ketone bodies, amino and organic acids, while therapies correlated with lipoproteins. The above analysis indicates that biomarkers revealed by metabolomic profiling can be useful in RA therapy monitoring.

Disclosure of Interests

None declared

Acetyl-CoA carboxylase inhibition alters tubulin acetylation and aggregation in thrombin-stimulated platelets

Introduction

Acetyl-CoA carboxylase (ACC), the first enzyme regulating lipid synthesis, promotes thrombus formation by increasing platelet phospholipid content. Inhibition of its activity decreases lipogenesis and increases the content in acetyl-CoA which can serve as a substrate for protein acetylation. This posttranslational modification plays a key role in the regulation of platelet aggregation, via tubulin acetylation.

Purpose

To demonstrate that ACC inhibition may affect platelet functions via an alteration of lipid content and/or tubulin acetylation.

Methods

Platelets were treated 2 hours with CP640.186, a pharmacological ACC inhibitor, prior to thrombin stimulation. Platelet functions were assessed by aggregometry and flow cytometry. Lipogenesis was measured via 14C-acetate incorporation into lipids. Lipidomics analysis was carried out on the commercial Lipidyzer platform. Protein phosphorylation and acetylation were evaluated by western blot.

Results

Treatment with CP640.186 drastically decreased platelet lipogenesis. However, the quantitative lipidomics analyses showed that preincubation with the compound did not affect global platelet lipid content. Interestingly, this short-term ACC inhibition was sufficient to increase tubulin acetylation level, at basal state and after thrombin stimulation. It was associated with an impaired platelet aggregation, in response to low thrombin concentration, while granules secretion was not affected. Mechanistically, we highlighted a decrease in Rac1 activity, associated with a reduced phosphorylation of its downstream effector PAK2. Surprisingly, actin cytoskeleton was not impacted but we evidenced a significant decrease in ROS production which could result from a decreased NOX2 activity.

Conclusion

Pharmacological ACC inhibition decreases platelet aggregation upon thrombin stimulation. The mechanism depends on increased tubulin acetylation, with subsequent alteration of the Rac1/PAK2/NOX2 signaling pathway

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): Fonds pour la formation à la Recherche dans l'Industrie et l'Agriculture (FRIA)

Platelet-specific deletion of acetyl-CoA carboxylase 1 decreases phospholipid content and impairs platelet functions

Background

Acetyl-CoA carboxylase (ACC), the first enzyme regulating lipid synthesis, promotes thrombus formation by increasing platelet phospholipid content and thromboxane A2 generation.

Purpose

Our study sought to evaluate whether ACC1 platelet-specific deletion may affect platelet functions by decreasing phospholipid content.

Methods

We generated a new Cre transgenic mouse strain that allows megakaryocyte/platelet specific ACC1 deletion (GpIbCre+/− x ACC1 flx/flx mouse). In vitro, platelet functions were assessed by aggregometry and flow cytometry. In vivo, hemostasis was assessed via the measurement of bleeding time. Lipidomics analysis was carried out on the commercial Lipidyzer platform. Thromboxane A2 secretion was evaluated by ELISA.

Results

As expected, ACC1 deletion was restricted to the megakaryocytic lineage. Hematological parameters in platelet-specific ACC1 knockout mice showed a decrease in platelet count by 30% and an increase in platelet volume by 31%, compared to ACC1 flx/flx platelets. In vitro, platelets from platelet-specific ACC1 knockout mice displayed a decrease in thrombin and CRP-induced platelet aggregation, associated with impaired dense granules secretion. In contrast, ADP-induced platelet aggregation was higher in the absence of ACC1. In vivo, platelet-specific ACC1 knockout mice showed a normal bleeding time. In agreement with our hypothesis, lipidomics analyses showed that ACC1 deletion in platelets was associated with a significant decrease in arachidonic acid-contaning phosphatidylethanolamine plasmalogen, and subsequently with a reduced production of thromboxane A2 upon thrombin or CRP stimulation.

Conclusion

Platelet-specific ACC1 deletion led to a decrease in phospholipid content which, in turn, decreased platelet thromboxane A2 generation, dense granules secretion and aggregation upon thrombin and CRP, but not ADP stimulation. Further studies are needed to elucidate the impact of ADP on platelet functions

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): Fonds pour la formation à la Recherche dans l'Industrie et l'Agriculture (FRIA)

POS1087 USING LIPIDOMICS TO PREDICT PREDNISOLONE TREATMENT RESPONSE IN PATIENTS WITH INFLAMMATORY HAND OSTEOARTHRITIS: THE HOPE STUDY

Background:

Lipidomics analysis has become a valuable technology for understanding patho-physiological mechanisms and may aid the identification of biomarkers of therapeutic responsiveness.

Objectives:

To explore the use of lipidomics for prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis.

Methods:

The Hand Osteoarthritis Prednisolone Efficacy (HOPE) study is a blinded, randomized placebo-controlled trial, that investigated the effect of prednisolone treatment in patients with painful, inflammatory hand OA, fulfilling the American College of Rheumatology criteria. The present analyses comprised only patients randomized to daily 10 mg prednisolone treatment for six weeks. Response to prednisolone treatment was defined according to the OARSI-OMERACT responder criteria at six weeks. Baseline blood samples were obtained non-fasted. Lipid species were quantified in erythrocytes with the LipidyzerTM platform (Sciex). After pre-processing of the data, 286 lipids species were available for further analyses (nmol/mL). In addition, we used an in-house LC-MS/MS platform to analyse oxylipins in plasma, identifying 25 oxylipins (area ratios). Elastic net regularized regression was used to predict prednisolone treatment response. A 10-fold cross-validation (CV) was performed for selection of the optimal tuning parameters based on the smallest CV mean prediction error. First, a model was fit with commonly assessed patient characteristics and patient reported outcomes, measured at baseline (model 1). Second, we fitted model 2 by adding the LipidyzerTM platform lipids to model 1. Third, we fitted model 3 by adding the oxylipins to model 1. The discriminatory accuracy of the model was estimated by receiver operating characteristic (ROC) analyses. The area under the curve (AUC) and corresponding 95% confidence intervals (CI) were calculated using 1,000 bootstrap replications.

Results:

Among the 40 patients included, 31 (78%) fulfilled the OARSI-OMERACT responder criteria. From the included general patient characteristics (Table 1), elastic net selected baseline hand function as only predictor of treatment response, with an AUC of 0.78 (95% CI 0.60;0.96) (Figure 1). In model 2, we added the 286 LipidyzerTM platform variables to model 1. In addition to hand function, two lipids were selected: diacylglycerol(DAG)(16:0/16:0) and phosphatidylethanolamine(PE)(O-18:0/20:4), which improved the discriminatory accuracy to an AUC of 0.92 (0.83;1.02). Lastly, model 3 was fit with patient characteristics as well as oxylipins, resulting in selection of AUSCAN function and three oxylipin predictors: 9-hydroxy-octadecatrienoic acid (HOTrE), 5-hydroxy-eicosapentaenoic acid (HEPE) and 10-hydroxy-docosahexaenoic acid (HDHA), with an AUC of 0.85 (0.69;1.02).

Conclusion:

The patients’ lipid profile improved the discriminative accuracy of the prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis compared to prediction by commonly measured patient characteristics alone. This exploratory study suggests that lipidomics is a promising field for biomarker discovery for prediction of anti-inflammatory treatment response.

Table 1.

Baseline characteristics

All prednisolone treatedn = 40Respondersn = 31 (78%)Non-respondersn = 9 (23%)General characteristicsAge, year62.4 (9.3)62.9 (9.4)60.8 (9.4)Sex, % women858489BMI, kg/m227.4 (4.4)27.8 (4.2)26.2 (5.0)Education, % high464256Disease duration6.7 (7.1)7.2 (7.4)4.9 (5.8)Erosive OA, %717456Kellgren-Lawrence sum score, 0-12035.1 (16.4)34.1 (16.5)37.5 (14.7)Ultrasound synovitis sum score, 0-9016.2 (6.6)15.5 (6.4)18.7 (7.2)VAS global assessment, 0-10052.3 (20.6)54.2 (16.8)45.6 (30.8)AUSCAN pain, 0-2011.0 (3.3)11.3 (2.4)10 (5.4)AUSCAN function, 0-3617.7 (7.6)19.6 (6.6)11 (7.5)

Numbers represent mean (SD) unless otherwise specified. AUSCAN = Australian/Canadian Hand Osteoarthritis Index, BMI = body mass index, VAS = visual analogue scale

Disclosure of Interests:

Marieke Loef: None declared, Tariq Faquih: None declared, Johannes von Hegedus: None declared, Mohan Ghorasaini: None declared, Andreea Ioan-Facsinay: None declared, Féline Kroon: None declared, Martin Giera Shareholder of: Pfizer, Consultant of: Boehringer Ingelheim Pharma, Margreet Kloppenburg: None declared.

POS0371 BIOLOGICAL REPRODUCIBILITY OF TARGETED LIPIDOME ANALYSES IN PLASMA AND ERYTHROCYTES OVER A 6-WEEK PERIOD

Background:

Lipidomics analysis has become a valuable technology for understanding patho-physiological mechanisms and the identification of candidate biomarkers in rheumatic musculoskeletal disorders. Variability in within-subject repeated measurements may lead to bias towards the null when estimating the association between biomarkers and a disease or treatment. Hence, information regarding the stability of the metabolite levels over time is essential.

Objectives:

We aimed to assess the lipid composition and biological reproducibility of lipid measurements in plasma and erythrocytes.

Methods:

Plasma and erythrocyte samples from 42 osteoarthritis patients (77% women, mean age 65 years, mean BMI 27 kg/m2), obtained non-fasted at baseline and six weeks, were used for the quantitative measurement of up to 1000 lipid species across 13 lipid classes with the LipidyzerTM platform in nmol/mL. Data was processed based on the relative standard deviation of quality controls, taking batch effects into account. Intraclass correlation coefficients (ICCs) and corresponding 95% confidence intervals (CI) were calculated to investigate the variability of the lipid concentrations between timepoints. The ICC distribution of lipid metabolites in plasma and erythrocytes were compared using two-sided paired Wilcoxon tests.

Results:

We measured 778 lipids in plasma, compared to 916 lipids in erythrocytes. After data processing, the analyses included 630 lipids in plasma, and 286 in erythrocytes. From these, 243 lipids overlapped between sample types. Major differences were observed between the sample types in the number of lipids per lipid class and the total concentration of the lipids within a class. Triacylglycerols (TAG) and cholesteryl esters (CE) were more abundant in plasma. Conversely, phosphatidylethanolamines (PE), sphingomyelins (SM) and ceramides (CER) were less abundant in plasma compared to erythrocytes (table 1). In plasma 78% of lipid measurements were good to excellently reproduced, with an overall median ICC 0.69. Compared to plasma, a considerably lower amount (35%) of lipids were well reproduced in erythrocytes. Median reproducibility of lipids in erythrocytes was 0.51. Figure 1 shows the ICC score distribution in plasma with erythrocytes, with a significantly better reproducibility in plasma (p-value<0.001). However, while overall reproducibility was better in plasma, this was not observed for all lipid classes. At class-level, reproducibility in plasma was superior for TAGs and CEs, while CERs, DAGs, (L)PEs and SMs showed better reproducibility in erythrocytes.

Table 1.

Number of individual lipids per class and class concentrations in plasma and erythrocytes

PlasmaErythrocytesNumber of lipid speciesClass concentration (nmol/mL)Number of lipid speciesClass concentration (nmol/mL)Triacylglycerols4821579.4 (1064.9-3195.2)1346.5 (5.6-9.4)Diacylglycerols913.3 (8.4-22.2)105.8 (4.7-6.2)Free fatty acids20745.3 (552.0-1202.9)20486.9 (379.2-669.2)Cholesteryl esters244571.6 (4065.1-5521.3)51.2 (0.9-1.7)Phosphatidylcholines314013.7 (3203.1-4661.6)423899.2 (3723.0-4296.6)Phosphatidylethanolamines26156.2 (120.9-180.3)423954.6 (3721.9-4323.3)Lysophosphatidylcholines9385.9 (335.6-442.9)7119.8 (109.7-168.9)Lysophosphatidylethanolamines24.2 (3.5-4.9)48.6 (6.8-9.7)Sphingomyelins121204.6 (1037.0-1351.9)82695.8 (2434.8-2815.6)Ceramides614.1 (11.9-17.4)7163.0 (133.3-186.4)Dihydroceramides21.0 (0.8-1.3)11.8 (1.4-2.1)Hexosylceramides55.1 (4.7-5.9)45.6 (5.0-7.4)Lactosylceramides23.4 (2.7-3.8)223.8 (20.6-33.5)

Numbers represent median (interquartile range) unless otherwise specified. Data represents baseline measurements.

Conclusion:

In plasma biological reproducibility was good for most lipid measurements. Although overall reproducibility was better in plasma compared to erythrocytes, notable differences were observed at individual- and lipid class-level that may favour the use of a particular sample type.

Disclosure of Interests:

Marieke Loef: None declared, Johannes von Hegedus: None declared, Mohan Ghorasaini: None declared, Féline Kroon: None declared, Martin Giera Shareholder of: Pfizer, Consultant of: Boehringer Ingelheim Pharma, Andreea Ioan-Facsinay: None declared, Margreet Kloppenburg: None declared

Amino acid analysis using chromatography-mass spectrometry: An inter platform comparison study

The analysis of amino acids has become a central task in many aspects. While amino acid analysis has traditionally mainly been carried out using either gas chromatography (GC) in combination with flame ionization detection or liquid chromatography (LC) with either post-column derivatization using ninhydrin or pre-column derivatization using o-phthalaldehyde, many of today's analysis platforms are based on chromatography in combination with mass spectrometry (MS). While derivatization is mandatory for the GC-based analysis of amino acids, several LC platforms have emerged, particularly in the dawn of targeted metabolite profiling using hydrophilic interaction liquid chromatography (HILIC) coupled to MS, allowing the analysis of underivatized amino acids. Among the numerous analytical platforms available for amino acid analysis today, we here compare three prominent approaches, being GC-MS and LC-MS after amino acid derivatization using chloroformate and HILIC-MS of underivatized amino acids. We compare and discuss practical issues as well as performance characteristics, e.g., the use of (13)C-labeled internal standards, of the different platforms and present data on their practical implementation in our laboratory. Finally, we compare the real-life applicability of all three platforms for a complex biological sample. While all three platforms are very-well suited for the analysis of complex biological samples they all show advantages and disadvantages for some analytes as discussed in detail in this manuscript.

Haloperidol disrupts lipid rafts and impairs insulin signaling in SH-SY5Y cells

Haloperidol exerts its therapeutic effects basically by acting on dopamine receptors. We previously reported that haloperidol inhibits cholesterol biosynthesis in cultured cells. In the present work we investigated its effects on lipid-raft composition and functionality. In both neuroblastoma SH-SY5Y and promyelocytic HL-60 human cell lines, haloperidol inhibited cholesterol biosynthesis resulting in a decrease of the cell cholesterol content and the accumulation of different sterol intermediates (7-dehydrocholesterol, zymostenol and cholesta-8,14-dien-3beta-ol) depending on the dose of the drug. As a consequence, the cholesterol content in lipid rafts was greatly reduced, and several pre-cholesterol sterols, particularly cholesta-8,14-dien-3beta-ol, were incorporated into the cell membrane. This was accompanied by the disruption of lipid rafts, with redistribution of flotillin-1 and Fyn and the impairment of insulin-Akt signaling. Supplementing the medium with free cholesterol abrogated the effects of haloperidol on lipid-raft composition and functionality. LDL (low-density lipoprotein), a physiological vehicle of cholesterol in plasma, was much less effective in preventing the effects of haloperidol, which is attributed to the drug's inhibition of intracellular vesicular trafficking. These effects on cellular cholesterol homeostasis that ultimately result in the alteration of lipid-raft-dependent insulin signaling action may underlie some of the metabolic effects of this widely used antipsychotic.