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Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism's role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.

Patients with non-alcoholic steatohepatitis (NASH) show significantly faster progress in the stages of fibrosis compared to those with non-alcoholic fatty liver (NAFL) disease. The non-invasive diagnosis of NASH remains an unmet clinical need. Preliminary data have shown that sphingolipids, especially ceramides, fatty acids, and other lipid classes may be related to the presence of NASH and the histological activity of the disease. The aim of our study was to assess the association of certain plasma lipid classes, such as fatty acids, acylcarnitines, and ceramides, with the histopathological findings in patients with NASH. The study included three groups: patients with NASH (N = 12), NAFL (N = 10), and healthy [non non-alcoholic fatty liver disease (NAFLD)] controls (N = 15). Plasma samples were collected after 12 h of fasting, and targeted analyses for fatty acids, acylcarnitines, and ceramides were performed. Baseline clinical and demographic characteristics were collected. There was no significant difference in baseline characteristics across the three groups or between NAFL and NASH patients. Patients with NASH had increased levels of several fatty acids, including, among others, fatty acid (FA) 14:0, FA 15:0, FA 18:0, FA 18:3n3, as well as Cer(d18:1/16:0), compared to NAFL patients and healthy controls. No significant difference was found between NAFL patients and healthy controls. In conclusion, patients with NASH exhibited a distinctive plasma lipid profile that can differentiate them from NAFL patients and non-NAFLD populations. More data from larger cohorts are needed to validate these findings and examine possible implications for diagnostic and management strategies of the disease.

Untargeted metabolomic studies require an extensive set of analyte (metabolic) information to be obtained from each analyzed sample. Thus, highly selective, and efficient analytical methodologies together with reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) are usually applied in these approaches. Here, we present a performance comparison of five different chromatographic columns (C18, C8, RP Amide, zicHILIC, OH5 HILIC phases) to evaluate their sufficiency of analysis for a large analyte library, consisting of 817 authentic standards. By taking into account experimental chromatographic parameters (i.e. retention time, peak tailing and asymmetry, FWHM, signal-to-noise ratio and peak area and intensity), the proposed column scoring approach provides a simple criterion that may assist analysis in the select of a stationary phase for those metabolites of interest. RPLC methods offered better results regarding metabolic library coverage, while the zicHILIC stationary phase delivered a bigger number of properly eluted compounds. This study demonstrates the importance of choosing the most suitable configuration for the analysis of different metabolic classes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691−0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

In this study, we demonstrate the presence of a transacetylase activity in human plasma low-density lipoprotein (LDL) that transfers short-chain fatty acids from platelet-activating factor (PAF) and its close ether- and ester-linked analogues to ether/ester-linked lysophospholipids (lyso-PL). We show evidence that both PAF acetylhydrolase (PAF-AH) and transacetylase activities are inhibited to the same extent by serine esterase inhibitors, are resistant to heat treatment, and exhibit identical distributions in lipoprotein classes and in LDL subfractions. Additionally, the competitive inhibition of PAF-AH by lyso-PL, and the evidence that the recombinant PAF-AH also showed a similar transacetylase activity, suggest that PAF-AH is responsible for both activities. Using PAF as a donor molecule and lyso-PAF (1-O-alkyl-sn-glycero-3-phosphocholine) as an acceptor, the transacetylase activity showed typical allosteric kinetics, due to the positive co-operativity of the substrates, with apparent Vmax=19.6+/-3.4 nmol/min per mg of protein, apparent h=2.0+/-0.3 and apparent [S]0.5=9.4+/-2.3 microM at saturation for the concentration of lyso-PAF. The substrate specificity of the donor molecules was decreased by increasing the chain length of the acyl moiety in the sn-2 position of the glycerol. The ether linkage in the sn-1 position of the substrate was 30% more effective than the ester bond; cholesteryl acetate was inactive as an acetyl donor. The two acceptors tested, lyso-PAF and the ester-linked lyso-PC (1-acyl-sn-glycero-3-phosphocholine), showed similar specificity. Addition of exogenous lyso-PAF induced the transient formation of PAF-like aggregating activity predominantely in small dense LDL subfractions upon oxidation. We conclude that PAF-AH possesses both transacetylase and acetylhydrolase activities which remove PAF and its ether-linked analogues from LDL particles upon LDL oxidation. However, in atherogenic small dense LDL-5 particles, the transacetylase activity may acetylate extracellular lyso-PAF into biologically active PAF.

An increased amount of phospholipids remained attached on delipidated apolipoprotein B originated from oxidized low density lipoprotein (LDL). 31P nuclear magnetic resonance analysis of such apolipoprotein showed an organic phosphorus peak at -0.55 ppm, which suggests the formation of adducts (most probably Schiff bases) of oxidized phospholipids with apolipoprotein B. The above reaction occurs in parallel with the hydrolysis of oxidized phospholipids, catalyzed by the LDL-attached platelet-activating factor acetylhydrolase, and may contribute to the proatherogenic effect of oxidatively modified low density lipoprotein.

Free radical-mediated oxidation of cholesterol-rich LDL plays a key role in atherogenesis and involves the formation of oxidized phospholipids with proinflammatory biological activity. We evaluated the production of platelet-activating factor (PAF), a potent inflammatory mediator, in human LDL subspecies on copper-initiated oxidation (4 mumol/L CuCl2, 80 micrograms/mL for hours at 37 degrees C). PAF formation was determined by biological assay of HPLC-purified lipid extracts of copper-oxidized lipoproteins; chemical identity was confirmed by gas chromatographic and mass spectrometric analyses. PAF, characterized as the C16:0 molecular species, was preferentially produced in intermediate LDL (d = 1.029 to 1.039 g/mL) (8.6 +/- 5.7 pmol PAF/3 h per mg LDL protein) and light LDL (d = 1.019 to 1.029 g/mL), but was absent from dense LDL particles (d = 1.050 to 1.063 g/mL). As PAF:acetylhydrolase inactivates PAF and oxidized forms of phosphatidylcholine, we evaluated the relationship of lipoprotein-associated PAF:acetylhydrolase to PAF formation. We confirmed that PAF:acetylhydrolase activity was elevated in native, dense LDL (41.5 +/- 9.5 nmol/min per mg protein) but low in LDL subspecies of light and intermediate density (d 1.020 to 1.039 g/mL) (3.5 +/- 1.6 nmol/min per mg protein) [Tselepis et al, Arterioscler Thromb Vasc Biol. 1995;15:1764-1773]. On copper-mediated oxidation for 3 hours at 37 degrees C, dense LDL particles conserved 20 +/- 14% of their initial enzymatic activity; in contrast, PAF:acetylhydrolase activity was abolished in light and intermediate LDL subspecies. Clearly, the elevated PAF:acetylhydrolase activity of dense LDL efficiently diminishes the potential inflammatory role of endogenously formed PAF; nonetheless, formation of proatherogenic lysophospholipids results. In contrast, LDL particles of the light and intermediate subclasses can accumulate PAF on oxidative modification.

The distribution of PAF-acetylhydrolase (PAF-AH) activity in 3 LDL subfractions prepared by density gradient ultracentrifugation as well as the rate of phosphatidylcholine (PC) hydrolysis during oxidation was studied. PAF-AH activity, measured before oxidation, was much higher in LDL3 subfraction (28.4 +/- 8.6 nmol/mg per min) comparing to LDL2 (14.1 +/- 5.8 nmol/mg per min), and to LDL1, 8.7 +/- 3.7 nmol/mg per min. During oxidation, the enzyme activity was continuously decreased and this phenomenon was more pronounced in LDL1. PC hydrolysis was studied measuring the lyso-PC production expressed as lyso-PC/Sph molar ratio. Before oxidation, the lyso-PC/Sph molar ratio, did not differ significantly among the LDL subfractions, whereas, 4 h after the onset of oxidation, it was significantly higher in LDL2 and LDL3 subfractions (0.42 +/- 0.12 and 0.45 +/- 0.10, respectively), comparing to LDL1 (0.29 +/- 0.06). Our results show that the distribution of PAF-AH activity in LDL subfractions is heterogeneous (mainly distributed in LDL2 and LDL3 subfractions) and it is positively correlated with higher lyso-PC production in those subfractions during oxidation. The contribution of this phenomenon to the enhanced susceptibility to oxidation as well as to the higher atherogenicity of the dense LDL subfractions is under investigation.

A PAF aggregating activity corresponding to 427 +/- 91, 668 +/- 111 and 1319 +/- 217 pg/mg protein was detected when LDL was preincubated at pH 3.5 or with 4 mM PMSF or both for 30 min (treatments that inactivate PAF-AH) and then oxidized with 20 microM Cu2+ at 37 degrees C for 24 h. This molecule was characterized as PAF by its chromatographic behavior on TLC and other established methods and was further characterized as 1-O-hexadecyl-2-acetyl-sn-glycero-3-phosphocholine (C16: PAF) by its retention time on reverse phase HPLC and by fast atom bombardment-mass spectroscopy. Native LDL incubated under non oxidizing conditions, even when PAF-AH has been inactivated, or oxidized in the absence of PAF-AH inactivating agents or after pretreatment with 0.5 mM pBPB, does not produce detectable amounts of PAF. The kinetics of PAF formation in relation to PAF-AH activity, show that the apparent rate of PAF formation as well as its total amount depends on both the existence of oxidative conditions and the remaining PAF-AH activity the first hours following the onset of oxidation. Peroxidation of the phosphatidylcholine (PC) content of native LDL produces PAF-like aggregating activity much lower than that produced when intact LDL is oxidized and is not inhibited by BN 52021 as effectively as PAF produced by LDL peroxidation. Our results provide evidence that C16: PAF is formed during LDL peroxidation when PAF-AH has been inactivated and it does not result as a product of peroxidation of the LDL-PC content.