The global impact of SARS-CoV-2, which causes COVID-19, remains significant, being intensified by the emergence of variants. Comprehensive metabolomic studies aimed to elucidate the distinctive metabolic footprint of the virus. For critically ill patients with COVID-19 in the intensive care unit (ICU), longitudinal monitoring based on their prognosis is crucial to optimize treatment outcomes. This study retrospectively investigated the temporal changes in the metabolomic profiles of patients admitted to the ICU with COVID-19, who were categorized into three prognostic groups: healthy discharged (HD), polyneuropathic syndrome (PS), and Exitus. In total, 32 serum samples collected in April 2020 at regular intervals (four samples per patient) and stored at −80°C, were analyzed using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Significant (p < 0.05) prognostic changes in creatine and tyrosine levels were revealed by two-way analysis of variance (ANOVA) and ANOVA–simultaneous component analysis (ASCA). Furthermore, supervised random forest analysis demonstrated excellent group prediction with a 21.9% out-of-bag error rate based on prognosis. Specifically, creatine levels were highest in the PS group, whereas tyrosine levels were highest in the Exitus group. However, no metabolite displayed significant changes over time. In addition, metabolic pathway analysis using the Kyoto Encyclopedia of Genes and Genomes database indicated that the most significantly impacted pathway (p < 0.05) across different prognostic groups was “phenylalanine, tyrosine and tryptophan biosynthesis.” This preliminary study emphasizes the need for time-series analysis of samples from unvaccinated patients with varying prognoses, providing valuable insights into the metabolic impact of COVID-19.

Background/Objectives: Temporal lobe epilepsy (TLE) often develops following an initial brain injury, where specific triggers lead to epileptogenesis—a process transforming a healthy brain into one prone to spontaneous, recurrent seizures. Although electroencephalography (EEG) remains the primary diagnostic tool for epilepsy, it cannot predict the risk of epilepsy after brain injury. This limitation highlights the need for biomarkers, particularly those measurable in peripheral samples, to assess epilepsy risk. This study investigated urinary metabolites in a rat model of TLE to identify biomarkers that track epileptogenesis progression across the acute, latent, and chronic phases and elucidate the underlying mechanisms. Methods: Status epilepticus (SE) was induced in rats using repeated intraperitoneal injections of lithium chloride–pilocarpine hydrochloride. Urine samples were collected 48 h, 1 week, and 6 weeks after SE induction. Nuclear magnetic resonance spectrometry was used for metabolomic analysis, and statistical evaluations were performed using MetaboAnalyst 6.0. Differences between epileptic and control groups were represented using the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Volcano plot analysis identified key metabolic changes, applying a fold-change threshold of 1.5 and a p-value < 0.05. Results: The acute phase exhibited elevated levels of acetic acid, dihydrothymine, thymol, and trimethylamine, whereas glycolysis and tricarboxylic acid cycle metabolites, including pyruvic and citric acids, were reduced. Both the acute and latent phases showed decreased theobromine, taurine, and allantoin levels, with elevated 1-methylhistidine in the latent phase. The chronic phase exhibited reductions in pimelic acid, tiglylglycine, D-lactose, and xanthurenic acid levels. Conclusions: These findings highlight stage-specific urinary metabolic changes in TLE, suggesting distinct metabolites as biomarkers for epileptogenesis and offering insights into the mechanisms underlying SE progression.

Although the impact of COVID-19, caused by SARS-CoV-2, may appear to have diminished in recent years, the emergence of new variants still continues to cause significant global health and economic challenges. While numerous metabolomic studies have explored serum-based alterations linked to the infection, investigations utilizing urine as a biological matrix remain notably limited. This gap is especially significant given the potential advantages of urine, a non-invasive and easily obtainable biofluid, in clinical settings. In the context of patients in intensive care units (ICUs), temporal monitoring through such non-invasive samples may offer a practical and effective approach for tracking disease progression and tailoring therapeutic interventions. This study retrospectively explored the longitudinal metabolomic alterations in COVID-19 patients admitted to the ICU, stratified into three prognostic outcome groups: healthy discharged (HD), polyneuropathic syndrome (PS), and Exitus. A total of 32 urine samples, collected at four distinct time points per patient during April 2020 and preserved at −80 °C, were analyzed by proton nuclear magnetic resonance (¹H-NMR) spectroscopy for comprehensive metabolic profiling. Statistical evaluation using two-way ANOVA and ANOVA–Simultaneous Component Analysis (ASCA) identified significant prognostic variations (p < 0.05) in the levels of taurine, 3-hydroxyvaleric acid and formic acid. Complementary supervised classification via random forest modeling yielded moderate predictive performance with out-of-bag error rate of 40.6% based on prognostic categories. Particularly, taurine, 3-hydroxyvaleric acid and formic acid levels were highest in the PS group. However, no significant temporal changes were observed for any metabolite in analyses. Additionally, metabolic pathway analysis conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database highlighted the “taurine and hypotaurine metabolism” pathway as the most significantly affected (p < 0.05) across prognostic classifications. Harnessing urinary metabolomics, as indicated in our preliminary study, could offer valuable insights into the dynamic metabolic responses of ICU patients, thereby facilitating more personalized and responsive critical care strategies in COVID-19 patients.