Triacylglycerols (TAGs) and cholesterol lipoprotein levels are widely used to predict cardiovascular risk and metabolic disorders. The aim of this study is to determine how the comprehensive lipidome (individual molecular lipid species) determined by mass spectrometry is correlated to the serum whole-lipidic profile of adults with different lipidemic conditions. The study included samples from 128 adults of both sexes, and they were separated into four groups according to their lipid profile: Group I—normolipidemic (TAG < 150 mg/dL, LDL-C < 160 mg/dL and HDL-c > 40 mg/dL); Group II—isolated hypertriglyceridemia (TAG ≥ 150 mg/dL); Group III—isolated hypercholesterolemia (LDL-C ≥ 160 mg/dL) and Group IV—mixed dyslipidemia. An untargeted mass spectrometry (MS)-based approach was applied to determine the lipidomic signature of 32 healthy and 96 dyslipidemic adults. Limma linear regression was used to predict the correlation of serum TAGs and cholesterol lipoprotein levels with the abundance of the identified MS-annotated lipids found in the subgroups of subjects. Serum TAG levels of dyslipidemic adults have a positive correlation with some of the MS-annotated specific TAGs and ceramides (Cer) and a negative correlation with sphingomyelins (SMs). High-density lipoprotein-cholesterol (HDL-C) levels are positively correlated with some groups of glycerophosphocholine, while low-density lipoprotein-cholesterol (LDL-C) has a positive correlation with SMs.

Background/Objectives: This study investigates the metabolic profile of a single dose of etodolac in healthy volunteers, focusing on pharmacokinetics, clinical parameters, and metabolomic variations to identify biomarkers and pathways linked to drug response, efficacy, and safety. Methods: Thirty-seven healthy volunteers, enrolled after rigorous health assessments, received a single dose of etodolac (Flancox^® 500 mg). Pharmacokinetic profiles were determined using tandem mass spectrometry analysis, and the metabolomic profiling was conducted using baseline samples (pre-dose) and samples at maximum drug concentration (post-dose) via liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer. Network analysis was employed to interpret the data. Results: Correlations were observed between metabolomic profiles and pharmacokinetic parameters as well as clinical characteristics. Notably, metabolites derived from arachidonic acid, such as prostaglandins and leukotrienes, were linked to etodolac’s pharmacokinetics. Other metabolites involved in pathways like cholesterol biosynthesis, bile salts, riboflavin, and retinoic acid signaling were correlated with hematological and liver function parameters. These findings are consistent with the infrequent adverse events reported by participants, including hematological and biochemical changes in liver function. Conclusions: A set of metabolites was identified in possible associations between specific pathways and unusual side effects, comparing the metabolic profiles before and after doses of etodolac. Our results highlight the importance of optimizing drug therapy and minimizing adverse events by taking into account individual metabolic profile information.