Abstract The search for biomarkers associated with obesity-related diseases is ongoing, but it is not clear whether plasma and serum can be used interchangeably in this process. Here we used high-throughput screening to analyze 358 proteins and 76 lipids, selected because of their relevance to obesity-associated diseases, in plasma and serum from age- and sex-matched lean and obese humans. Most of the proteins/lipids had similar concentrations in plasma and serum, but a subset showed significant differences. Notably, a key marker of cardiovascular disease PAI-1 showed a difference in concentration between the obese and lean groups only in plasma. Furthermore, some biomarkers showed poor correlations between plasma and serum, including PCSK9, an important regulator of cholesterol homeostasis. Collectively, our results show that the choice of biofluid may impact study outcome when screening for obesity-related biomarkers and we identify several markers where this will be the case. Subject terms: Translational immunology, Biomarkers, Metabolic disorders Introduction Obesity-related illness is an increasingly important global health issue that places a tremendous economic burden on society^[56]1. The negative health effects of prolonged obesity are partly fuelled by chronic low-grade inflammation, which contributes to cardiometabolic and kidney pathophysiology^[57]2–[58]4. However, the exact mechanisms that link obesity with cardiometabolic and kidney diseases are unclear and remain a subject of intensive research. The search for biomarkers that assist in the identification of novel disease-related pathways is critical to develop new therapies that are tailored to subpopulations particularly prone to obesity-related pathophysiology. Disease-related biomarkers are often identified and quantified in blood-derived plasma or serum^[59]5,[60]6. Preparation of plasma and serum requires the removal of cellular components by centrifugation. Generation of plasma is preceded by the addition of an anti-coagulant (e.g. EDTA, heparin or citrate) to the whole blood. By contrast, the blood used for serum is allowed to clot before centrifugation, resulting in lower concentrations of clotting factors (such as fibrinogen and coagulation cascade proteins) in serum than in plasma. The World Health Organization generally recommends using plasma as this more accurately reflects the physiological and/or pathophysiological state of the patient^[61]7. However, biomarkers are often reported to have better detectability in serum^[62]8 despite the fact that serum has a slightly lower total protein concentration than plasma^[63]9. Indeed, some intracellularly stored proteins and lipids are only detectable upon coagulation-induced release from leukocytes and platelets, and serum is preferred in assays detecting, for example, cardiac troponins^[64]10–[65]12. Importantly, the choice of biofluid is not merely a question of detectability, but it may also affect the conclusions drawn from a study. For example, Alsaif et al. showed that of 16 proteins (identified in either plasma or serum) that were differentially expressed between healthy controls and subjects with bipolar disorder, only two showed differential expression in both serum and plasma^[66]13. The aim of our study was to determine whether the use of plasma or serum would yield different results when screening for obesity-related biomarkers. We analyzed proteins and lipids that have previously been suggested to play a role in obesity-related cardiometabolic diseases in plasma and serum from age- and sex-matched groups of lean and obese humans. Our results show that the use of plasma or serum may have an effect on study outcome when screening for obesity-related biomarkers and we identify key markers that highlight this issue. Results and Discussion Detectability of proteins in plasma versus serum We used four Olink multiplex protein panels (inflammation, cardiometabolic, cardiovascular II, cardiovascular III) selected on the basis of their relevance to obesity-related diseases to measure protein concentrations in plasma and serum from 11 obese subjects and 11 age- and sex-matched lean controls. The characteristics of the human cohort are presented in Table [67]1. Of the 368 proteins analyzed (10 of which were measured in duplicate panels, see Supplementary Table [68]S1 for the full list), one protein (BDNF) was excluded due to technical issues, nine proteins (IL-1 alpha, IL-2, TSLP, IL-22 RA1, IL-13, TNF, IL-20, IL-33, IFN-gamma) were excluded because they were undetectable in both plasma and serum, and 23 additional proteins were excluded because values were missing in >30% of the samples in all of the four groups (lean plasma, lean serum, obese plasma, obese serum; Supplementary Table [69]S1). Detectability issues with one of the excluded proteins, NT-proBNP, have previously been reported^[70]14. In total, 335 proteins were included in the comparative analyses (Supplementary Fig. [71]S1). Table 1. Summary of cohort demographics. Lean Obese Men Women All Men Women All Sex 3 8 3♂/8♀ 3 8 3♂/8♀ BMI (kg/m^2) 23.3 ± 0.9 22.0 ± 1.0 22.4 ± 2.4 41.0 ± 2.1 44.4 ± 1.5 43.5 ± 4.1 Age (years) 42.3 ± 4.3 40.0 ± 5.3 40.6 ± 13.0 45.0 ± 3.1 40.4 ± 5.6 41.6 ± 13.6 Hormone replacement therapy — 0/8 — — 0/8 — Hormonal contraceptive pill — 2/8 — — 0/8 — Intrauterine contraceptive device — 0/8 — — 2/8 — [72]Open in a new tab Data are shown as mean ± SEM. For the majority of proteins, their concentrations were similar between plasma and serum (Supplementary Fig. [73]S2a,b). After adjusting for multiple comparisons using the stringent Holm-Bonferroni test, we found significantly different concentrations between plasma and serum for 23.5% and 33.4% of proteins in the lean and obese cohorts, respectively [adjusted (adj.) p < 0.05, Fig. [74]1]. Most of these proteins were present at higher concentrations in serum, which may partly be explained by the clotting-induced volume displacement effect^[75]15,[76]16 and by the fact that coagulation elicits release of platelet granules and intracellularly stored cytokines^[77]17–[78]19. The intracellularly stored protein MCP-1, for example, exhibited significantly higher concentrations in serum compared with plasma (in both the inflammation and the cardiovascular III panels) in the lean and obese groups. Of note, we did not record female menstruation cycle and/or menopausal state, which may affect platelet activation, although conflicting results have been shown^[79]20–[80]25. Figure 1. [81]Figure 1 [82]Open in a new tab Detectability of proteins in plasma versus serum. Heatmaps showing protein biomarkers that exhibited significantly different concentrations in plasma versus serum in (a) lean subjects (n = 11) and (b) obese subjects (n = 11) after adjustment for multiple comparisons using the method of Holm-Bonferroni at adj. p < 0.05. Proteins that are significantly different in only one of the groups (lean or obese) are marked in bold. For proteins that are present in duplicate protein panels, the panel is indicated in parentheses: I, inflammation; CVII, cardiovascular II; and CVIII, cardiovascular III. Relative protein concentrations are reported as z-scores. A subset of proteins with significantly different concentrations in plasma and serum (including HSP-27, PAR-1, 4E-BP1 and SRC) exhibited lower concentrations in serum (Fig. [83]1). HSP-27 has been proposed as a biomarker for both cardiometabolic disease and cancer^[84]26, although controversial results have been reported^[85]27. Of note, a recent study showed that the concentration of HSP-27 increased by about three-fold with just one freeze-thaw cycle in plasma but was more stable in serum^[86]28. All of our samples underwent two freeze-thaw cycles, which could explain the higher detectability of HSP-27 in plasma. During coagulation, PAR-1 is cleaved^[87]29 and SRC^[88]30 and 4-EBP1^[89]31,[90]32 become prone to degradation through proteolytic pathways, which likely explains their lower concentrations in serum. Furthermore, two proteins, AXIN1 and STK4, passed our cut-off criteria for detection in plasma but not in serum (Supplementary Table [91]S1). Enrichment analysis of all the proteins that were significantly altered between plasma and serum confirmed the enrichment of pathways involved in neutrophil chemotaxis and platelet activation (Supplementary Fig. [92]S2c, Supplementary Table [93]S3). Sensitivity of plasma versus serum when screening for obesity-related protein biomarkers Most of the biomarkers that showed significantly different concentrations between the obese and lean groups were present at higher levels in the obese group; however, a small number (including IGFBP-1 and GH) showed lower concentrations in the obese group, with significant differences observed in both plasma and serum (Table [94]2). The number of proteins with significantly different concentrations between the lean and obese groups was greater in serum (Table [95]2), in agreement with (1) an earlier study that reported higher sensitivity of serum to detect diabetes-associated differences in metabolite concentration^[96]33 and (2) the fact that obesity is associated with higher leukocyte and platelet counts and increased platelet activation^[97]34,[98]35. MCP-3 was present at higher concentrations in the obese versus lean group in serum but not plasma (Table [99]2), and showed low detectability in plasma in both groups (Supplementary Table [100]S1). However, concentrations of PAI-1 were only significantly higher in the obese versus lean group in plasma despite showing higher detectability in serum (Table [101]2). This difference in sensitivity versus detectability for PAI-1 was confirmed by ELISA (Supplementary Fig. [102]S3). PAI-1 inhibits fibrinolysis and has been proposed to be an important biomarker in cardiometabolic and diabetes research, although, as recently reviewed, conflicting results have been reported^[103]36. A possible explanation for this discrepancy, at least in part, may be due to the interchangeable use of plasma versus serum; indeed, studies comparing lean versus obese and/or diabetic groups have reported differences in PAI-1 levels when using plasma^[104]37 but not serum^[105]38. Coagulation-induced secretion of intracellular PAI-1 is likely responsible for the high serum levels of PAI-1, which may mask the differences between the lean and obese groups. Table 2. Proteins that exhibited significant differences in concentrations between obese and lean groups in plasma and/or serum. Protein Plasma Serum Lean Obese Obese vs lean log[2] ratio H-B Adj. p value FDR Adj. p value Lean Obese Obese vs lean log[2] ratio H-B Adj. p value FDR Adj. p value NPX (mean ± SEM) NPX (mean ± SEM) 4E-BP1 6.57 ± 0.21 7.54 ± 0.10 0.97 ns 0.0106 4.57 ± 0.10 6.18 ± 0.33 1.62 ns 0.0081 ADAM-TS13 5.17 ± 0.06 4.94 ± 0.04 −0.22 ns ns 6.38 ± 0.05 6.19 ± 0.03 −0.18 ns 0.0336 ADM 6.33 ± 0.06 7.11 ± 0.09 0.78 0.0002 0.0001 6.03 ± 0.08 6.82 ± 0.11 0.79 0.0032 0.0004 AGRP 3.64 ± 0.11 3.12 ± 0.07 −0.52 ns 0.0136 3.74 ± 0.13 3.10 ± 0.08 −0.64 ns 0.0081 AMBP 5.52 ± 0.02 5.72 ± 0.04 0.20 ns 0.0037 5.53 ± 0.05 5.78 ± 0.03 0.26 ns 0.0058 CCL3 (CVD II) 3.18 ± 0.04 3.67 ± 0.05 0.49 0.0002 0.0001 3.65 ± 0.07 4.13 ± 0.07 0.48 0.0241 0.0018 CCL4 6.04 ± 0.07 6.67 ± 0.13 0.63 ns 0.0095 7.33 ± 0.12 7.76 ± 0.19 0.43 ns ns CCL18 5.28 ± 0.22 6.09 ± 0.19 0.81 ns ns 5.36 ± 0.21 6.25 ± 0.18 0.89 ns 0.0259 CCL19 8.98 ± 0.15 9.66 ± 0.13 0.68 ns 0.0246 9.16 ± 0.15 9.82 ± 0.12 0.66 ns 0.0222 CDCP1 1.56 ± 0.14 2.16 ± 0.17 0.60 ns ns 1.64 ± 0.14 2.37 ± 0.18 0.73 ns 0.0274 CES1 1.41 ± 0.05 2.04 ± 0.20 0.63 ns ns 1.31 ± 0.04 2.02 ± 0.16 0.71 ns 0.0136 CHI3L1 5.27 ± 0.21 6.21 ± 0.27 0.94 ns ns 5.60 ± 0.13 6.61 ± 0.27 1.01 ns 0.0266 CHL1 2.47 ± 0.09 2.14 ± 0.04 −0.33 ns 0.0406 2.64 ± 0.12 2.20 ± 0.05 −0.43 ns 0.0366 CSF-1 7.03 ± 0.06 7.30 ± 0.07 0.27 ns ns 7.13 ± 0.05 7.43 ± 0.08 0.30 ns 0.0349 CSTB 3.65 ± 0.14 4.27 ± 0.12 0.62 ns 0.0321 3.65 ± 0.13 4.54 ± 0.21 0.89 ns 0.0155 CTSD 3.76 ± 0.09 4.61 ± 0.11 0.85 0.0025 0.0003 4.14 ± 0.05 4.94 ± 0.14 0.79 0.0400 0.0026 CTSZ 3.81 ± 0.13 4.33 ± 0.12 0.52 ns ns 3.98 ± 0.06 4.46 ± 0.14 0.47 ns 0.0373 CXCL10 8.82 ± 0.17 9.51 ± 0.15 0.69 ns ns 8.70 ± 0.18 9.59 ± 0.15 0.89 ns 0.0127 CXCL11 6.36 ± 0.16 6.96 ± 0.18 0.60 ns ns 6.87 ± 0.16 7.94 ± 0.26 1.07 ns 0.0213 ENG 1.47 ± 0.07 1.40 ± 0.06 −0.07 ns ns 1.54 ± 0.05 1.31 ± 0.03 −0.23 ns 0.0183 FABP4 3.65 ± 0.30 5.57 ± 0.15 1.93 0.0115 0.0010 3.78 ± 0.26 5.72 ± 0.16 1.93 0.0024 0.0004 FCN2 4.48 ± 0.16 5.22 ± 0.10 0.74 ns 0.0106 4.04 ± 0.14 4.87 ± 0.10 0.83 0.0422 0.0026 FGF-21 (CVD II) 4.56 ± 0.43 7.12 ± 0.48 2.56 ns 0.0105 4.54 ± 0.43 7.03 ± 0.48 2.49 ns 0.0105 FGF-21 (I) 3.50 ± 0.41 5.98 ± 0.44 2.48 ns 0.0086 3.59 ± 0.40 6.03 ± 0.44 2.44 ns 0.0081 Gal-9 6.96 ± 0.04 7.51 ± 0.07 0.55 0.0019 0.0003 7.07 ± 0.06 7.64 ± 0.08 0.57 0.0045 0.0005 GH 9.51 ± 0.69 6.38 ± 0.66 −3.13 ns 0.0318 9.63 ± 0.69 6.46 ± 0.64 −3.17 ns 0.0222 GLO1 3.29 ± 0.10 3.78 ± 0.18 0.49 ns ns 4.66 ± 0.17 5.56 ± 0.25 0.90 ns 0.0415 HAOX1 2.90 ± 0.30 4.71 ± 0.41 1.80 ns 0.0220 2.97 ± 0.31 4.81 ± 0.42 1.84 ns 0.0183 HB-EGF 3.84 ± 0.09 3.94 ± 0.08 0.10 ns ns 5.32 ± 0.13 6.52 ± 0.16 1.20 0.0030 0.0004 HGF 6.76 ± 0.07 7.62 ± 0.14 0.86 0.0249 0.0019 7.58 ± 0.09 8.60 ± 0.14 1.02 0.0026 0.0004 IGFBP-1 3.85 ± 0.20 1.47 ± 0.26 −2.38 0.0002 0.0001 3.98 ± 0.18 1.55 ± 0.27 −2.44 0.0002 0.0002 IGFBP-2 6.65 ± 0.25 5.81 ± 0.12 −0.85 ns ns 6.84 ± 0.21 5.95 ± 0.11 −0.89 ns 0.0146 IL-1ra 5.33 ± 0.09 7.05 ± 0.21 1.72 0.0013 0.0003 5.80 ± 0.10 7.47 ± 0.20 1.67 0.0009 0.0004 IL-6 2.36 ± 0.17 4.17 ± 0.34 1.81 ns 0.0044 2.46 ± 0.16 4.22 ± 0.33 1.75 ns 0.0043 IL-10RB 6.34 ± 0.09 6.70 ± 0.08 0.36 ns 0.0473 6.53 ± 0.08 6.94 ± 0.08 0.41 ns 0.0146 IL-18 7.75 ± 0.14 8.47 ± 0.20 0.72 ns ns 7.88 ± 0.15 8.67 ± 0.22 0.79 ns 0.0396 IL-18R1 6.61 ± 0.11 7.16 ± 0.13 0.55 ns 0.0360 6.80 ± 0.09 7.37 ± 0.13 0.57 ns 0.0188 KIT 3.31 ± 0.08 2.84 ± 0.09 −0.47 ns 0.0095 3.29 ± 0.08 2.99 ± 0.10 −0.30 ns ns LAP TGF-β−1 5.64 ± 0.11 6.01 ± 0.08 0.38 ns ns 6.94 ± 0.09 7.28 ± 0.08 0.34 ns 0.0417 LEP 4.06 ± 0.32 6.66 ± 0.11 2.60 0.0019 0.0003 4.09 ± 0.34 6.81 ± 0.11 2.72 0.0018 0.0004 LILRB2 2.18 ± 0.09 2.62 ± 0.08 0.44 ns 0.0225 2.11 ± 0.11 2.68 ± 0.08 0.58 ns 0.0083 LTBR 1.75 ± 0.12 2.02 ± 0.08 0.27 ns ns 1.84 ± 0.03 2.10 ± 0.05 0.26 ns 0.0043 MCP-1 9.35 ± 0.07 9.87 ± 0.05 0.52 0.0058 0.0005 10.52 ± 0.13 11.02 ± 0.13 0.51 ns ns MCP-3 1.39 ± 0.00 1.48 ± 0.04 0.09 ns ns 1.42 ± 0.02 2.07 ± 0.10 0.65 0.0254 0.0018 MCP-4 2.19 ± 0.16 2.72 ± 0.12 0.53 ns ns 3.56 ± 0.17 4.36 ± 0.22 0.80 ns 0.0450 MIP-1 alpha (I) 3.35 ± 0.03 3.83 ± 0.06 0.48 0.0027 0.0003 3.72 ± 0.07 4.28 ± 0.08 0.56 0.0107 0.0009 MPO 2.30 ± 0.23 2.91 ± 0.08 0.61 ns ns 2.92 ± 0.13 3.52 ± 0.12 0.59 ns 0.0222 NCAM1 2.27 ± 0.09 1.89 ± 0.06 −0.38 ns 0.0191 2.26 ± 0.10 1.90 ± 0.08 −0.36 ns ns NEMO 3.40 ± 0.17 3.76 ± 0.22 0.36 ns ns 1.58 ± 0.04 2.27 ± 0.18 0.69 ns 0.0223 OSM 2.40 ± 0.12 3.39 ± 0.21 1.00 ns 0.0106 3.95 ± 0.16 5.14 ± 0.29 1.19 ns 0.0208 PAI-1 3.49 ± 0.31 5.80 ± 0.23 2.31 0.0030 0.0003 7.21 ± 0.09 7.60 ± 0.07 0.39 ns 0.0274 PLC 5.00 ± 0.14 5.45 ± 0.07 0.45 ns ns 5.23 ± 0.06 5.61 ± 0.03 0.38 0.0064 0.0006 PON3 5.45 ± 0.26 4.29 ± 0.27 −1.17 ns 0.0406 5.43 ± 0.21 4.22 ± 0.26 −1.22 ns 0.0146 PRCP 0.78 ± 0.07 1.11 ± 0.06 0.33 ns 0.0231 0.68 ± 0.05 1.11 ± 0.06 0.43 0.0067 0.0006 PRSS8 8.75 ± 0.08 9.20 ± 0.08 0.45 ns 0.0106 8.93 ± 0.09 9.43 ± 0.08 0.50 ns 0.0073 RARRES2 9.60 ± 0.13 10.22 ± 0.07 0.62 ns 0.0106 9.97 ± 0.09 10.44 ± 0.05 0.48 ns 0.0036 SCGB3A2 2.12 ± 0.26 0.96 ± 0.12 −1.15 ns 0.0130 2.23 ± 0.26 0.98 ± 0.13 −1.25 ns 0.0081 SELE 2.10 ± 0.13 2.81 ± 0.12 0.70 ns 0.0095 2.26 ± 0.12 2.93 ± 0.14 0.67 ns 0.0146 SPON2 9.78 ± 0.04 10.01 ± 0.04 0.24 ns 0.0036 10.31 ± 0.05 10.54 ± 0.03 0.23 ns 0.0105 STAMPB 3.16 ± 0.12 3.56 ± 0.15 0.41 ns ns 1.84 ± 0.04 2.38 ± 0.16 0.54 ns 0.0450 t-PA 4.06 ± 0.20 5.23 ± 0.09 1.17 0.0276 0.0019 4.28 ± 0.24 5.89 ± 0.09 1.61 0.0082 0.0007 TGM2 6.03 ± 0.13 6.35 ± 0.08 0.32 ns ns 3.85 ± 0.08 4.66 ± 0.17 0.81 ns 0.0082 TNF-R1 4.69 ± 0.14 5.27 ± 0.06 0.58 ns 0.0220 5.01 ± 0.06 5.53 ± 0.06 0.52 0.0019 0.0004 TNF-R2 3.24 ± 0.13 3.64 ± 0.04 0.41 ns ns 3.42 ± 0.07 3.77 ± 0.07 0.35 ns 0.0146 TNFRSF10A 2.06 ± 0.05 2.27 ± 0.07 0.22 ns ns 2.13 ± 0.07 2.42 ± 0.07 0.29 ns 0.0462 TNFRSF11A 4.04 ± 0.09 4.60 ± 0.08 0.56 ns 0.0036 4.40 ± 0.12 4.94 ± 0.08 0.54 ns 0.0146 TNFSF14 2.96 ± 0.08 3.73 ± 0.08 0.77 0.0004 0.0001 4.23 ± 0.12 4.97 ± 0.19 0.74 ns 0.0274 TR-AP 3.40 ± 0.14 3.89 ± 0.08 0.49 ns ns 3.54 ± 0.14 4.12 ± 0.10 0.58 ns 0.0266 TRAIL-R2 4.40 ± 0.09 4.66 ± 0.05 0.26 ns ns 4.59 ± 0.09 4.89 ± 0.05 0.30 ns 0.0481 TRAIL 7.25 ± 0.09 7.61 ± 0.08 0.36 ns ns 7.49 ± 0.09 7.92 ± 0.09 0.43 ns 0.0213 U-PAR 3.28 ± 0.15 3.64 ± 0.06 0.36 ns ns 3.78 ± 0.07 4.26 ± 0.10 0.48 ns 0.0104 VEGF-A 9.19 ± 0.06 9.58 ± 0.05 0.40 0.0196 0.0016 9.85 ± 0.13 10.40 ± 0.09 0.55 ns 0.0213 vWF 3.18 ± 0.18 3.55 ± 0.11 0.37 ns ns 6.21 ± 0.15 6.95 ± 0.18 0.73 ns 0.0349 [106]Open in a new tab Differences in mean normalized protein expression (NPX) values between obese and lean groups are reported as a log[2] ratio. p values were adjusted for multiple comparisons using either Holm-Bonferroni (H-B) or false discovery rate (FDR); ns, not significant (adj. p > 0.05). For proteins that are present in duplicate protein panels, the panel is indicated in parentheses: I, inflammation; CVII, cardiovascular II; and CVIII, cardiovascular III. Protein correlations in plasma versus serum For the correlation analysis, 316 proteins survived the cut-off criteria (Supplementary Fig. [107]S1). We observed significant correlations between plasma and serum samples for most (68.8%) of the proteins analyzed in the lean and obese groups combined (Table [108]3), although fewer significant correlations were seen when dividing the cohort into obese and lean (Supplementary Table [109]S4). Of the 10 proteins that were measured in duplicate panels, eight displayed similar correlations between plasma and serum. However, MCP-1 and uPA only showed a significant correlation between plasma and serum in one of the duplicate panels. Table 3. Correlations of protein concentrations in plasma versus serum in all subjects. Inflammation Cardiometabolic Cardiovascular II Cardiovascular III Protein r H-B Adj. p value Protein r H-B Adj. p value Protein r H-B Adj. p value Protein r H-B Adj. p value FGF-21 1.00 1.57E-20 MBL2 0.99 2.79E-15 GH 1.00 1.47E-25 IGFBP-1 0.98 4.93E-13 FGF-19 0.99 1.11E-15 FCGR2A 0.98 6.92E-14 FGF-21 1.00 1.82E-22 Ep-CAM 0.97 1.47E-11 CCL20 0.99 7.03E-15 LILRB5 0.97 5.69E-12 LEP 1.00 2.85E-21 FABP4 0.96 1.32E-10 IL-18 0.98 3.69E-14 FCN2 0.97 9.41E-11 HAOX1 1.00 8.14E-20 CHIT1 0.96 1.85E-10 MMP-10 0.98 1.01E-13 LYVE1 0.96 6.02E-10 SERPINA12 0.99 1.00E-17 TFF3 0.96 2.79E-10 CXCL9 0.98 2.95E-13 CCL18 0.95 1.43E-09 IL-6 0.99 1.90E-16 SCGB3A2 0.95 3.62E-09 CDCP1 0.97 2.68E-11 COMP 0.95 6.47E-09 FABP2 0.99 8.91E-16 IGFBP-2 0.94 2.47E-08 TRANCE 0.97 6.59E-11 TIMD4 0.95 9.36E-09 KIM-1 0.99 2.38E-14 CCL24 0.94 2.85E-08 MCP-2 0.97 1.05E-10 THBS4 0.94 1.10E-08 IL-18 0.98 3.44E-13 PON3 0.94 3.53E-08 CCL19 0.96 4.09E-10 IGLC2 0.94 2.73E-08 GIF 0.98 1.35E-12 TR 0.93 9.55E-08 IL-12B 0.96 1.30E-09 REG1A 0.94 3.16E-08 CTRC 0.98 2.26E-12 CCL22 0.92 1.88E-07 OPG 0.95 4.06E-09 CR2 0.94 3.88E-08 MMP-12 0.98 4.02E-12 t-PA 0.92 2.80E-07 PD-L1 0.95 4.80E-09 FCGR3B 0.93 5.93E-08 REN 0.97 1.18E-11 CPA1 0.92 5.11E-07 CXCL10 0.95 9.69E-09 PRSS2 0.93 7.84E-08 IL-1ra 0.97 1.28E-11 DLK-1 0.91 8.47E-07 IL-18R1 0.94 1.68E-08 ANGPTL3 0.93 1.56E-07 SCF 0.97 4.73E-11 CPB1 0.91 9.00E-07 Flt3L 0.93 7.43E-08 SAA4 0.93 1.61E-07 ADM 0.96 1.70E-10 TNFRSF10C 0.90 1.99E-06 uPA 0.93 8.58E-08 TNC 0.92 2.47E-07 ACE2 0.96 1.05E-09 CHI3L1 0.89 4.47E-06 CD6 0.93 1.38E-07 NRP1 0.92 2.75E-07 LPL 0.95 2.47E-09 CCL15 0.89 6.88E-06 SCF 0.93 1.45E-07 DPP4 0.92 5.55E-07 MMP-7 0.95 2.92E-09 MMP-3 0.89 7.27E-06 CCL23 0.92 2.06E-07 CRTAC1 0.90 2.52E-06 PRSS8 0.95 3.92E-09 TIMP4 0.88 1.43E-05 TNFB 0.92 2.87E-07 APOM 0.89 5.45E-06 XCL1 0.95 7.68E-09 LDL receptor 0.87 2.59E-05 TRAIL 0.91 6.72E-07 GP1BA 0.89 6.21E-06 VEGF-D 0.95 8.75E-09 SELE 0.87 3.29E-05 CD244 0.91 6.72E-07 LILRB2 0.89 7.09E-06 TNFRSF13B 0.94 1.13E-08 ST2 0.84 0.0002 CCL25 0.90 2.94E-06 FETUB 0.89 8.64E-06 HO-1 0.94 1.23E-08 IL-6RA 0.84 0.0002 OSM 0.87 2.65E-05 CDH1 0.88 1.36E-05 BMP-6 0.93 6.58E-08 CTSZ 0.83 0.0003 CCL11 0.87 3.92E-05 TIE1 0.88 1.95E-05 IgG Fc R II-b 0.93 7.84E-08 SHPS-1 0.81 0.0008 CST5 0.86 5.29E-05 NCAM1 0.87 2.06E-05 IL16 0.93 8.80E-08 CTSD 0.81 0.0009 CCL28 0.85 0.0001 TCN2 0.87 2.34E-05 RAGE 0.93 1.07E-07 CCL16 0.80 0.001 IL-10RB 0.85 0.0001 AOC3 0.87 3.12E-05 TIE2 0.93 1.26E-07 GDF-15 0.80 0.001 HGF 0.85 0.0001 VCAM1 0.85 0.0001 MERTK 0.92 3.06E-07 Gal-4 0.79 0.002 CCL4 0.84 0.0001 TGFBI 0.84 0.0002 TF 0.92 4.20E-07 CD93 0.79 0.002 TNFRSF9 0.84 0.0002 F7 0.84 0.0002 TRAIL-R2 0.92 4.51E-07 CD163 0.78 0.003 CSF-1 0.83 0.0003 C2 0.84 0.0002 IL27 0.92 4.63E-07 RARRES2 0.77 0.004 IL-8 0.83 0.0004 ANG 0.84 0.0002 Gal-9 0.91 6.07E-07 IL2-RA 0.75 0.007 MIP-1α 0.82 0.0005 SERPINA7 0.83 0.0004 IL1RL2 0.91 9.77E-07 RETN 0.75 0.008 CXCL11 0.79 0.002 OSMR 0.83 0.0004 AGRP 0.91 1.06E-06 BLM hydrol. 0.75 0.008 ADA 0.78 0.003 IGFBP6 0.82 0.0005 CTSL1 0.91 1.53E-06 MPO 0.74 0.009 TWEAK 0.78 0.003 ICAM3 0.81 0.0008 TNFRSF11A 0.90 2.86E-06 IL-17RA 0.73 0.01 MCP-4 0.78 0.003 PROC 0.81 0.0008 CD4 0.89 7.67E-06 TNF-R1 0.73 0.01 CD5 0.77 0.004 ICAM1 0.80 0.001 TM 0.88 1.09E-05 ICAM-2 0.73 0.01 CD40 0.77 0.004 QPCT 0.79 0.002 MARCO 0.88 1.12E-05 TLT-2 0.73 0.02 4E-BP1 0.76 0.006 PRCP 0.79 0.002 FS 0.88 1.15E-05 IL-18BP 0.72 0.02 LIF-R 0.76 0.007 IL7R 0.79 0.002 DCN 0.87 3.66E-05 IL-1RT2 0.72 0.02 DNER 0.75 0.007 C1QTNF1 0.78 0.003 SOD2 0.86 5.53E-05 PI3 0.71 0.02 IL-10 0.75 0.008 CHL1 0.78 0.003 CCL3 0.85 9.10E-05 PAI-1 0.71 0.02 MMP-1 0.73 0.01 SERPINA5 0.77 0.004 hOSCAR 0.84 0.0002 COL1A1 0.71 0.02 MCP-1 0.70 0.03 SPARCL1 0.77 0.004 PD-L2 0.83 0.0004 MEPE 0.71 0.02 TNFSF14 0.70 0.03 IGFBP3 0.76 0.005 THBS2 0.82 0.0005 TFPI 0.71 0.02 EN-RAGE 0.69 0.04 NID1 0.76 0.006 PlGF 0.82 0.0005 OPG 0.70 0.03 β-NGF 0.69 0.04 SELL 0.76 0.007 Protein BOC 0.82 0.0005 MB 0.69 0.04 VEGF-A 0.68 0.048 PCOLCE 0.75 0.008 PAR-1 0.81 0.0009 TR-AP 0.69 0.04 SLAMF1 0.65 ns CST3 0.75 0.008 PRELP 0.77 0.004 PLC 0.64 ns CXCL6 0.62 ns CD59 0.74 0.01 AMBP 0.76 0.005 vWF 0.64 ns LAP TGF-β-1 0.59 ns GAS6 0.74 0.01 SORT1 0.76 0.006 MMP-9 0.63 ns CXCL1 0.56 ns CFHR5 0.73 0.01 VSIG2 0.76 0.006 CDH5 0.63 ns CXCL5 0.56 ns ST6GAL1 0.72 0.02 SPON2 0.73 0.01 PSP-D 0.63 ns CX3CL1 0.55 ns LILRB1 0.71 0.03 CCL17 0.73 0.01 uPA 0.62 ns STAMPB 0.47 ns F11 0.64 ns CD84 0.70 0.03 GRN 0.62 ns FGF-23 0.43 ns CA4 0.62 ns THPO 0.64 ns ITGB2 0.62 ns FGF-5 0.42 ns TIMP1 0.62 ns IDUA 0.64 ns Gal-3 0.61 ns CASP-8 0.25 ns LCN2 0.62 ns FGF-23 0.63 ns AXL 0.60 ns ST1A1 0.23 ns PAM 0.56 ns GLO1 0.63 ns PGLYRP1 0.60 ns IL-7 0.18 ns VASN 0.54 ns PSGL-1 0.61 ns CSTB 0.59 ns TGF-α 0.18 ns KIT 0.52 ns CXCL1 0.60 ns PECAM-1 0.59 ns ARTN # # CNDP1 0.51 ns PRSS27 0.53 ns TNF-R2 0.58 ns AXIN1 # # TNXB 0.51 ns ANG-1 0.52 ns U-PAR 0.58 ns GDNF # # ENG 0.50 ns LOX-1 0.52 ns TNFRSF14 0.58 ns IL-10RA # # MET 0.49 ns ADAM-TS13 0.48 ns CNTN1 0.56 ns IL-15RA # # GNLY 0.48 ns TGM2 0.48 ns Notch 3 0.53 ns IL-17A # # TGFBR3 0.48 ns PTX3 0.48 ns FAS 0.53 ns IL-17C # # CD46 0.47 ns CEACAM8 0.44 ns IGFBP-7 0.52 ns IL-20RA # # CA1 0.45 ns GDF-2 0.42 ns TNFSF13B 0.51 ns IL-22RA1 # # PLXNB2 0.44 ns CD40-L 0.39 ns IL-1RT1 0.51 ns IL-24 # # EFEMP1 0.43 ns Dkk-1 0.36 ns AP-N 0.51 ns IL-2RB # # CCL14 0.39 ns HB-EGF 0.31 ns SELP 0.50 ns IL-4 # # CA3 0.38 ns PDGF-B 0.31 ns CXCL16 0.49 ns IL-5 # # COL18A1 0.36 ns SRC 0.26 ns MMP-2 0.49 ns IL-6 # # NOTCH1 0.35 ns PIgR 0.24 ns OPN 0.46 ns LIF # # PTPRS 0.32 ns IL-17D 0.24 ns PRTN3 0.44 ns MCP-3 # # CCL5 0.22 ns HSP 27 0.20 ns LTBR 0.38 ns NRTN # # MFAP5 0.21 ns BNP # # SPON1 0.36 ns NT3 # # MEGF9 0.11 ns CA5A # # MCP-1 0.34 ns SIRT2 # # CES1 # # DECR1 # # ALCAM 0.28 ns IFN-γ nd nd DEFA1 # # GT # # PCSK9 0.27 ns IL-1α nd nd FAP # # IL-4RA # # JAM-A 0.24 ns IL-13 nd nd ITGAM # # ITGB1BP2 # # CASP-3 0.20 ns IL-2 nd nd LTBP2 # # NEMO # # PDGF-A 0.16 ns IL-20 nd nd PLA2G7 # # PAPPA # # KLK6 0.15 ns IL-33 nd nd PLTP # # PARP-1 # # AZU1 0.10 ns TNF nd nd REG3A # # SLAMF7 # # EGFR −0.02 ns TSLP nd nd SOD1 # # STK4 # # EPHB4 # # BDNF Ϯ Ϯ UMOD # # TNFRSF10A # # NT-Pro-BNP # # [110]Open in a new tab Pearson correlations (r) between NPX values in plasma and serum samples from the total cohort (n = 22) are shown. p values were adjusted by the Holm-Bonferroni (H-B) multiple comparison test; ns, not significant (adj. p > 0.05). nd, not detected. ^#Excluded due to too many missing values. Ϯ, removed due to technical issue. We observed good correlations between plasma and serum samples for leptin (r = 1.00, adj. p < 0.001) and IGFBP-1 (r = 0.98, adj. p < 0.001), which are proteins that exhibited obesity-associated differences in concentration (Fig. [111]2a,b). Some proteins showed poor correlations, such as PCSK9 (r = 0.27, ns) and FGF-23 (r = 0.43 and 0.64 in the inflammation and cardiovascular II panels, respectively, both ns) (Fig. [112]2c,d). PCSK9 binds to the receptor for low-density lipoprotein and PCSK9 inhibitors are therefore of intense interest to pharmaceutical companies^[113]39,[114]40. Studies interchangeably measure PCSK9 in plasma^[115]41,[116]42 and serum^[117]43,[118]44, but our result indicates that the choice of biofluid could potentially have a significant impact on the conclusions drawn. Our panels also included the FDA-approved biomarkers KIM-1 and osteopontin, which are used to monitor kidney disease^[119]45,[120]46. KIM-1 was well correlated between plasma and serum (r = 0.99, adj. p < 0.001) but osteopontin displayed a poor correlation (r = 0.46, ns) (Fig. [121]2e,f). Figure 2. [122]Figure 2 [123]Open in a new tab Protein correlations in plasma versus serum. Pearson correlations (r) between normalized protein expression (NPX) values for proteins in plasma and serum samples. Each data point is from one individual (open triangles: obese; closed triangles: lean). p values were adjusted by the Holm-Bonferroni multiple comparison test. Lipids in plasma versus serum, and in lean versus obese groups We also performed targeted lipidomics of inflammation-related lipids in plasma and serum from the lean and obese groups. Of the 76 lipids analyzed (see Supplementary Table [124]S2), two were excluded as they did not survive the cut-off criteria for the comparative analysis (Supplementary Fig. [125]S4). For most of the lipids, there were no major differences in concentration between plasma and serum (Supplementary Fig. [126]S5). We observed that concentrations of 21.6% of the lipids in the lean cohort and 18.9% of the lipids in the obese cohort were significantly higher in serum than in plasma (after FDR adjustment, adj. p < 0.05); none of the lipids showed lower concentrations in serum (Fig. [127]3a,b). In total, 73 lipids survived the cut-off for the correlation analyses; we observed significant correlations between plasma and serum for 64% of the analyzed lipids when analyzed in the lean and obese groups combined (Table [128]4), and fewer significant correlations were seen when dividing the cohort into obese and lean (Supplementary Table [129]S5). Figure 3. [130]Figure 3 [131]Open in a new tab Oxylipins in plasma versus serum, and in lean versus obese groups. Heatmaps showing lipids that exhibited significantly different concentrations in plasma versus serum in (a) lean subjects (n = 11) and (b) obese subjects (n = 11) after adjustment for multiple comparisons using the false discovery rate (FDR) test at adj. p < 0.05. Lipids that are significantly different in only one of the groups (lean or obese) are marked in bold. Relative lipid concentrations are reported as z-scores. (c) Lipids that showed significantly different concentrations between the obese and lean groups in plasma and/or serum after FDR adjustment. Table 4. Correlations of lipid concentrations in plasma versus serum in all subjects. Lipid r H-B Adj. p value 9,10-DiHOME 0.99 1.78E-18 9,10-DiHODE 0.99 9.18E-18 13-HODE 0.99 2.59E-15 12,13-DiHOME 0.99 3.83E-15 15,16-DiHODE 0.98 4.77E-13 19,20-DiHDoPA 0.97 1.26E-12 9-HOTE 0.97 3.93E-12 9-HODE 0.97 1.18E-11 13-HOTE 0.96 1.48E-10 12(13)-EpOME 0.96 2.04E-10 15(16)-EpODE 0.94 7.71E-09 DHA 0.93 1.60E-08 C16:1n7 0.93 3.84E-08 EPA 0.90 5.81E-07 C18:2n6 0.90 6.28E-07 C14:0 0.89 1.40E-06 AA 0.89 1.83E-06 C18:1n9 0.89 2.17E-06 C12:0 0.88 2.80E-06 DHEA 0.88 2.94E-06 C18:1n7 0.87 5.36E-06 ALA 0.87 7.22E-06 C14 Ceramide 0.87 8.78E-06 C18:3n3 0.85 2.07E-05 C20:5n3 0.84 4.24E-05 PGF2a 0.84 4.75E-05 LEA 0.84 5.45E-05 NA-Gly 0.83 9.19E-05 aLEA 0.83 9.19E-05 LA 0.82 0.0001 C16 Ceramide 0.82 0.0001 C24 dihydroceramide 0.81 0.0002 AEA 0.79 0.0005 1/2-LG 0.78 0.0008 17,18-DiHETE 0.75 0.002 C18:1 Ceramide 0.75 0.002 C24 Ceramide 0.75 0.002 C15:0 0.75 0.002 9(10)-EpOME 0.72 0.005 C18 Ceramide 0.71 0.007 1/2-AG 0.70 0.01 C17:0 0.68 0.01 11,12-DiHETrE 0.67 0.02 C16:0 0.67 0.02 4-HDoHE 0.66 0.02 C20 Ceramide 0.64 0.04 1/2-OG 0.64 0.04 C16:1n7t 0.59 ns 14,15-DiHETrE 0.59 ns Dihomo GLA EA 0.56 ns C20:1n9 0.55 ns C20:2n6 0.54 ns 5-HETE 0.53 ns 12(13)-Ep-9-KODE 0.51 ns 5-HEPE 0.50 ns DEA 0.49 ns TXB2 0.45 ns 9c 0.44 ns 15-HETE 0.39 ns OEA 0.37 ns 18:1 Sphingosine 0.32 ns 12-HEPE 0.31 ns 5,6-DiHETrE 0.27 ns C20:4n6 0.25 ns C20:3n6 0.19 ns 9,10-e-DiHO 0.13 ns C18:0 0.12 ns 11-HETE 0.10 ns 12-HETE 0.08 ns NO-Gly 0.04 ns C20:0 0.04 ns 9-KODE 0.01 ns 9,10-EpO −0.27 ns C22:4n6 # # C22:5n3 # # 14-HDoHE # # [132]Open in a new tab Pearson correlations (r) between lipid concentrations in plasma and serum samples from the total cohort (n = 22) are shown. p values were adjusted by the Holm-Bonferroni (H-B) multiple comparison test; ns, not significant (adj. p > 0.05). ^#Excluded due to too many missing values. Four lipids showed significantly different concentrations between the obese and lean groups in plasma and/or serum (Fig. [133]3c). Concentrations of AEA and 19,20-DiHDoPA were significantly different (higher for AEA and lower for 19,20-DiHDoPA in the obese group) in both plasma and serum, but concentrations of 15-HETE and 11-HETE were significantly different (both higher in the obese group) only in plasma (Fig. [134]3c). Concluding remarks In this study, we investigated whether the use of plasma or serum would yield different results when screening for obesity-related biomarkers. For most of the proteins and lipids, their concentrations showed good correlations between plasma and serum. However, it is important to note that PCSK9 concentrations did not correlate between plasma and serum, indicating that caution must be taken when comparing studies that use different biofluids. Although most of the protein and lipids had similar concentrations in plasma and serum, those that did differ were generally present at higher concentrations in serum. Importantly, we observed significantly higher concentrations of the key disease-associated biomarker PAI-1 in the obese group only in plasma and not in serum, despite the protein showing higher detectability in serum. This result highlights that sensitivity does not necessarily parallel detectability. Furthermore, some obesity-induced changes, for example of MCP-3 concentrations, were only detected in serum. Collectively, these findings show that care should be taken when choosing biofluids for the study of biomarkers, particularly those for which we report differences in sensitivity/detectability between plasma and serum. Methods Study participants We recruited obese subjects [body mass index (BMI) 35–55 kg/m^2, aged 18–65 years] from a cohort scheduled to undergo gastric bypass surgery, as well as age- and sex-matched lean subjects (BMI 18.5–24.9 kg/m^2). Subjects were excluded if they were taking anti-inflammatory and/or immunosuppressive drugs, currently smoked, or had been diagnosed with significant gastrointestinal disease or inflammatory bowel disease. Study participants were enrolled in accordance with the Helsinki Declaration and provided written informed consent. The study was approved by the Gothenburg Ethical Review Board #682-14 (ClinicalTrials.gov [135]NCT02322073). Blood collection Venous blood samples obtained from study participants after an overnight fast were collected in eitherplasma tubes spray coated with K[2]EDTA (Greiner Bio One) or serum tubes containing inert separator gel and silica particles as clot activator (Greiner Bio One). Plasma samples were centrifuged immediately whereas serum samples were allowed to clot for 30 min at room temperature before centrifugation (10 min at room temperature, 3,000 rpm Hettich EBA200). Samples were snap-frozen in liquid nitrogen and stored at −80 °C until analysis. Multiplex protein assay Protein biomarkers were analyzed using the proximity extension assay, using four protein panels (inflammation, cardiometabolic, cardiovascular II and cardiovascular III) (Olink Proteomics, Uppsala, Sweden) at the Clinical Biomarkers Facility at Science for Life Laboratory (Uppsala University, Sweden) according to the manufacturer’s instructions. Briefly, 1 µl plasma or serum was incubated with a mixture of 92 proximity antibody pairs tagged with oligonucleotides in a 96-well plate. In this assay, once a pair of antibodies binds to their corresponding antigens in close proximity, linked oligonucleotides hybridize into double stranded DNA, which is further extended and amplified, and ultimately quantified by high-throughput real-time PCR (BioMark™ HD System, Fluidigm Corporation). To avoid intra-assay variability, plasma and serum samples were analyzed on the same plate. ELISA Plasma and serum PAI-1 levels were measured using a commercially available ELISA for Human Total Serpin E1/PAI-1 (#DY9387-05, R&D), according to the manufacturer’s instructions. To ensure that the protein was quantified within the linear range of the standard curve, plasma and serum were diluted 1:100 and 1:500, respectively. Measurements of oxylipins, endocannabinoids and ceramides Oxylipins, endocannabinoids, and ceramides in plasma and serum were isolated and quantified using modifications of published protocols^[136]47–[137]49. Briefly, plasma or serum aliquots (40 µl) were spiked with deuterated oxylipin, endocannabinoid and ceramide surrogates, mixed with butylated hydroxyl toluene and ethylene diamine tetraacetic acid, and extracted with 200 µl isopropanol containing the internal standards 1-cyclohexyl ureido, 3-dodecanoic acid and 1-phenyl ureido 3-hexanoic acid in isopropanol. The homogenate was then centrifuged (10 min, 4 °C, 15,000 g) and the isopropanol supernatant was collected and stored at −20 °C until analysis. Analytes were separated using a Waters Acquity ultra-performance liquid chromatography (UPLC; Waters, Milford, MA) on a 2.1 mm × 150 mm, 1.7 µm BEH C18 column (Waters) for analysis of oxylipins and endocannabinoids, and 2.1 mm × 150 mm, 1.7 µm BEH C8 column (Waters) for analysis of ceramides. Separated analytes were detected by tandem mass-spectrometry, using electrospray ionization with multi reaction monitoring on an API 6500 QTRAP (Sciex, Redwood City, CA) for oxylipins and endocannabinoids, and an API 4000 QTRAP (Sciex) for ceramides. Analytes were quantified using internal standard methods and 7–9 point calibration curves of authentic standards. Measurement of non-esterified fatty acids Non-esterified fatty acids in plasma and serum were isolated and converted to fatty acid methyl esters (FAMEs) as previously reported^[138]47. Briefly, plasma or serum aliquots (50 µl) were spiked with lipid class surrogates, mixed with 410 µl isopropanol, followed by 520 µl cyclohexane and 570 µl 0.1 M ammonium acetate. Samples were then centrifuged (5 min, 4 °C, 15,000 g), the upper organic phase was collected, and the remainder was re-extracted with a second 520 µl cyclohexane aliquot. The samples were then dried by vacuum centrifugation and reconstituted in 100 µl toluene and 180 µl methanol. To prepare FAMEs, 280 µl of toluene/methanol extracts were enriched with 20 µl methanol containing 60 µM C15:1n5 and incubated with 45 µl 2 M TMS-diazomethane in hexane (Sigma-Aldrich, St. Louis MO) for 30 min at room temperature. Samples were dried under vacuum and the residue was dissolved in 100 µl hexane containing 4 µM C23:0, which acted as an internal standard. Samples were then stored at −20 °C until analysis. FAMEs were separated on a 30 m × 0.25 mm × 0.25 µm DB-225 ms column in a 6890 gas chromatogram interfaced with a 5973A mass selective detector (Agilent Technologies, Santa Clara, CA). All fatty acids were quantified against a 7-point calibration curves of authentic standards. Peak identifications were based on retention times and m/z ratios, with peak confirmation by inspection of simultaneously acquired full scan spectra collected from 50–400 m/z. Calibrants and internal standards were purchased from NuchekPrep (Elysian, MN), Sigma-Aldrich, or Avanti Polar Lipids. Data were quantified using Chemstation vE.02.14 (Agilent Technologies) against 6–8 point calibration curves. Statistical analysis Data are reported for proteins and lipids that had <30% missing values in: (1) at least one of the four groups (lean plasma, lean serum, obese plasma, obese serum) for the comparative analyses or (2) all of the four groups for the plasma-serum correlations. Statistical analysis of the protein multiplex data was done in the R environment (version 3.5.1) using packages gplots (3.0.1) and gdata (2.18.0)^[139]50. For the proteins reported, missing values were replaced with limit of detection (LOD) values. Hierarchical clustering with Pearson correlation distance and complete linkage confirmed that the dataset did not include outliers. Concentrations of proteins are reported as normalized protein expression (NPX) values, an arbitrary unit on a log[2] scale. Heatmaps were generated using hierarchical clustering based on correlation distance and Ward’s (ward.D2) clustering. Comparisons of protein levels using Student’s t-test were paired when comparing individual donor plasma versus serum values and unpaired when comparing the lean versus obese groups; p values were adjusted for multiple comparisons using either the stringent Holm-Bonferroni test or the commonly used false discovery rate (FDR) test as indicated (adjusted p values < 0.05 were considered significant). Pearson coefficient of correlation (r) values were calculated and p values were adjusted by the Holm-Bonferroni multiple comparison test. The pathway enrichment analysis for proteins was done using Metascape^[140]51. Briefly, Gene IDs corresponding to significantly altered proteins were analysed, using the 325 unique proteins that survived the cut-off criteria as the background list. A Gene Ontology category was deemed significantly enriched if the p value was lower than 0.01 and displayed a minimum enrichment of 1.5. Statistical analysis of the lipidomics data was done in MetaboAnalyst^[141]52. For the lipids reported, missing values were replaced with half of the lowest reported value. Fatty acid data normalization was optimized in Jmp Pro v 12.0 and confirmed using the Shapiro-Wilk normality test. For statistical analysis, data points underwent log transformation and pareto scaling. Heatmaps were generated using hierarchical clustering based on Euclidean’s method of distance calculation and Ward’s clustering. Unadjusted p values were adjusted using FDR (adjusted p values < 0.05 were considered significant). Supplementary information [142]41598_2019_51673_MOESM1_ESM.pdf^ (529.8KB, pdf) Supplementary figures and table descriptions [143]Supplementary Table S1.^ (39.1KB, xlsx) [144]Supplementary Table S2.^ (20.2KB, xlsx) [145]Supplementary Table S3.^ (58.4KB, xlsx) [146]Supplementary Table S4.^ (52.6KB, xlsx) [147]Supplementary Table S5.^ (14KB, xlsx) Acknowledgements