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 Plasma Erythrocytes Number of lipid species Class concentration (nmol/mL) Number of lipid species Class concentration (nmol/mL) Triacylglycerols 482 1579.4 (1064.9-3195.2) 134 6.5 (5.6-9.4) Diacylglycerols 9 13.3 (8.4-22.2) 10 5.8 (4.7-6.2) Free fatty acids 20 745.3 (552.0-1202.9) 20 486.9 (379.2-669.2) Cholesteryl esters 24 4571.6 (4065.1-5521.3) 5 1.2 (0.9-1.7) Phosphatidylcholines 31 4013.7 (3203.1-4661.6) 42 3899.2 (3723.0-4296.6) Phosphatidylethanolamines 26 156.2 (120.9-180.3) 42 3954.6 (3721.9-4323.3) Lysophosphatidylcholines 9 385.9 (335.6-442.9) 7 119.8 (109.7-168.9) Lysophosphatidylethanolamines 2 4.2 (3.5-4.9) 4 8.6 (6.8-9.7) Sphingomyelins 12 1204.6 (1037.0-1351.9) 8 2695.8 (2434.8-2815.6) Ceramides 6 14.1 (11.9-17.4) 7 163.0 (133.3-186.4) Dihydroceramides 2 1.0 (0.8-1.3) 1 1.8 (1.4-2.1) Hexosylceramides 5 5.1 (4.7-5.9) 4 5.6 (5.0-7.4) Lactosylceramides 2 3.4 (2.7-3.8) 2 23.8 (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 Citation: Ann Rheum Dis, volume 80, supplement 1, year 2021, page 416Session: Osteo arthritis, aetiology, pathology and animal models (POSTERS only)