Differential analysis of lipoprotein and glycoprotein profiles in bacterial infections and COVID-19 using proton nuclear magnetic resonance and machine learning.

Background: We scrutinized variations in the proton nuclear magnetic resonance (1H NMR) lipoprotein and glycoprotein profiles among hospitalized individuals with infectious diseases. Methods: We obtained sera from 124 patients with COVID-19, 50 patients with catheter-related bacterial infections, and 50 healthy volunteers. Results were interpreted using machine learning. Results: COVID-19 patients had bigger and more abundant VLDL particles than the control group and higher VLDL-cholesterol and VLDL-triglyceride concentrations. Patients with bacterial infections showed similar trends, but differences often did not reach statistical significance. Both types of patients showed lower LDL-cholesterol concentrations than the controls. LDL were larger, and the number of particles was lower than that of the healthy individuals. HDL particles had decreased cholesterol and increased triglycerides. Small particles were reduced. Glycoproteins were increased in both groups of patients. All these alterations were more pronounced in COVID-19 patients than those with bacterial infections. The diagnostic accuracy of these profiles exceeded 90 % when distinguishing between healthy individuals and patients, and 85 % when differentiating between the two patient groups. Conclusion: Our findings highlight the potential of 1H NMR analysis for lipoproteins and glycoproteins as infection biomarkers. Additionally, they reveal differences between viral and bacterial infections, shedding light on an area with promising clinical significance.

Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy.

Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases and poor prognosis in advanced stages. This study explored shifts in circulating metabolite levels in NSCLC patients versus healthy controls and examined the effects of conventionally fractionated radiation therapy (CFRT) and stereotactic body radiation therapy (SBRT). We enrolled 91 NSCLC patients (38 CFRT and 53 SBRT) and 40 healthy controls. Plasma metabolite levels were assessed using semi-targeted metabolomics, revealing 32 elevated and 18 reduced metabolites in patients. Key discriminatory metabolites included ethylmalonic acid, maltose, 3-phosphoglyceric acid, taurine, glutamic acid, glycocolic acid, and d-arabinose, with a combined Receiver Operating Characteristics curve indicating perfect discrimination between patients and controls. CFRT and SBRT affected different metabolites, but both changes suggested a partial normalization of energy and amino acid metabolism pathways. In conclusion, metabolomics identified distinct metabolic signatures in NSCLC patients with potential as diagnostic biomarkers. The differing metabolic responses to CFRT and SBRT reflect their unique therapeutic impacts, underscoring the utility of this technique in enhancing NSCLC diagnosis and treatment monitoring.

Serum Levels of Arachidonic Acid, Interleukin-6, and C-Reactive Protein as Potential Indicators of Pulmonary Viral Infections: Comparative Analysis of Influenza A, Respiratory Syncytial Virus Infection, and COVID-19.

Acute respiratory tract infections, including influenza A (FluA), respiratory syncytial virus (RSV) infection, and COVID-19, can aggravate to levels requiring hospitalization, increasing morbidity and mortality. Identifying biomarkers for an accurate diagnosis and prognosis of these infections is a clinical need. We performed a cross-sectional study aimed to investigate the changes in circulating levels of arachidonic acid, interleukin 6 (IL-6), and C-reactive protein (CRP) in patients with FluA, RSV, or COVID-19, and to analyze the potential of these parameters as diagnosis or prognosis biomarkers. We analyzed serum samples from 172 FluA, 80 RSV, and 217 COVID-19 patients, and 104 healthy volunteers. Individuals with lung viral diseases showed reduced arachidonic acid concentrations compared to healthy people, with these differences being most pronounced in the order COVID-19 > RSV > FluA. Conversely, IL-6 and CRP levels were elevated across diseases, with IL-6 emerging as the most promising diagnostic biomarker, with areas under the curve (AUC) of the receiver operating characteristics plot higher than 0.85 and surpassing arachidonic acid and CRP. Moreover, IL-6 displayed notable efficacy in distinguishing between FluA patients who survived and those who did not (AUC = 0.80). These findings may provide useful tools for diagnosing and monitoring the severity of acute viral respiratory tract infections, ultimately improving patient outcomes.

Metabolomics and triple-negative breast cancer: A systematic review.

Triple-negative breast cancer stands out as the most aggressive subtype of breast malignancy and is characterized by an unfavourable prognosis. Objective: This systematic review summarizes the insights gleaned from metabolomic analyses of individuals afflicted with this cancer variant. The overarching goal was to delineate the molecular alterations associated with triple-negative breast cancer, pinpointing potential therapeutic targets and novel biomarkers. Methods: We systematically searched for evidence using the PubMed database and followed the PRISMA and STARLITE guidelines. The search parameters were delimited to articles published within the last 13 years. Results: From an initial pool of 148 scrutinized articles, 17 studies involving 1686 participants were deemed eligible for inclusion. The current body of research shows a paucity of studies, and the available evidence presents conflicting outcomes. Notwithstanding, Pathway Enrichment Analysis identified the urea and glucose-alanine cycles as the most affected metabolic pathways, followed by arginine, proline, and aspartate metabolism. Conclusion: Future investigations need to focus on elucidating which of those metabolites and/or pathways might be reliable candidates for novel therapeutic interventions or reliable biomarkers for diagnosis and prognosis of this subtype of breast cancer.

Metabolic adaptations in severe obesity: Insights from circulating oxylipins before and after weight loss.

BACKGROUND: The relationship between lipid mediators and severe obesity remains unclear. Our study investigates the impact of severe obesity on plasma concentrations of oxylipins and fatty acids and explores the consequences of weight loss. METHODS: In the clinical trial identifier NCT05554224 study, 116 patients with severe obesity and 63 overweight/obese healthy controls matched for age and sex (≈2:1) provided plasma. To assess the effect of surgically induced weight loss, we requested paired plasma samples from 44 patients undergoing laparoscopic sleeve gastrectomy one year after the procedure. Oxylipins were measured using ultra-high-pressure liquid chromatography coupled to a triple quadrupole mass spectrometer via semi-targeted lipidomics. Cytokines and markers of interorgan crosstalk were measured using enzyme-linked immunosorbent assays. RESULTS: We observed significantly elevated levels of circulating fatty acids and oxylipins in patients with severe obesity compared to their metabolically healthier overweight/obese counterparts. Our findings indicated that sex and liver disease were not confounding factors, but we observed weak correlations in plasma with circulating adipokines, suggesting the influence of adipose tissue. Importantly, while weight loss restored the balance in circulating fatty acids, it did not fully normalize the oxylipin profile. Before surgery, oxylipins derived from lipoxygenase activity, such as 12-HETE, 11-HDoHE, 14-HDoHE, and 12-HEPE, were predominant. However, one year following laparoscopic sleeve gastrectomy, we observed a complex shift in the oxylipin profile, favoring species from the cyclooxygenase pathway, particularly proinflammatory prostanoids like TXB2, PGE2, PGD2, and 12-HHTrE. This transformation appears to be linked to a reduction in adiposity, underscoring the role of lipid turnover in the development of metabolic disorders associated with severe obesity. CONCLUSIONS: Despite the reduction in fatty acid levels associated with weight loss, the oxylipin profile shifts towards a predominance of more proinflammatory species. These observations underscore the significance of seeking mechanistic approaches to address severe obesity and emphasize the importance of closely monitoring the metabolic adaptations after weight loss.

Serum Arylesterase, Paraoxonase, and Lactonase Activities and Paraoxonase-1 Concentrations in Morbidly Obese Patients and Their Relationship with Non-Alcoholic Steatohepatitis.

Paraoxonase-1 (PON1) is an antioxidant enzyme associated with high-density lipoproteins (HDL). Reduced serum PON1 activity is found in diseases marked by oxidative stress and inflammation, but its role in obesity remains unclear. This study investigated PON1 activities and concentrations in morbidly obese individuals and explored the impacts of the genetic polymorphism PON1 rs662 and non-alcoholic fatty liver disease on enzymatic properties. We recruited 1349 morbidly obese patients undergoing bariatric surgery and 823 non-obese volunteers. PON1-related variables, including arylesterase, paraoxonase, and lactonase activities and PON1 concentrations, were examined. Our results showed that morbidly obese individuals exhibited higher PON1 concentrations but lower enzymatic activities than non-obese individuals. We observed inverse associations of arylesterase and paraoxonase activities with waist circumference (rho = -0.24, p < 0.001, and rho = -0.30, p < 0.001, respectively) and body mass index (rho = -0.15, p = 0.001, and rho = -0.23, p < 0.001), as well as direct associations of arylesterase, paraoxonase, and lactonase activities with HDL cholesterol (rho = 0.11, p = 0.005, rho = 0.20, p < 0.001, and rho = 0.20, p < 0.001). No significant differences were observed regarding metabolic syndrome, type 2 diabetes mellitus, hypertension, dyslipidemia, rs662 polymorphism allele frequencies, or the diagnosis of non-alcoholic steatohepatitis. Nevertheless, correlations were found between certain PON1-related variables, steatosis, and ballooning. In conclusion, changes in PON1-related variables in morbidly obese patients are dependent on the disease itself and HDL levels. The relationships between these variables and specific liver histological changes raise intriguing questions for consideration in future studies.

Paraoxonase-1: How a xenobiotic detoxifying enzyme has become an actor in the pathophysiology of infectious diseases and cancer.

Both infectious and non-infectious diseases can share common molecular mechanisms, including oxidative stress and inflammation. External factors, such as bacterial or viral infections, excessive calorie intake, inadequate nutrients, or environmental factors, can cause metabolic disorders, resulting in an imbalance between free radical production and natural antioxidant systems. These factors may lead to the production of free radicals that can oxidize lipids, proteins, and nucleic acids, causing metabolic alterations that influence the pathogenesis of the disease. The relationship between oxidation and inflammation is crucial, as they both contribute to the development of cellular pathology. Paraoxonase 1 (PON1) is a vital enzyme in regulating these processes. PON1 is an enzyme that is bound to high-density lipoproteins and protects the organism against oxidative stress and toxic substances. It breaks down lipid peroxides in lipoproteins and cells, enhances the protection of high-density lipoproteins against different infectious agents, and is a critical component of the innate immune system. Impaired PON1 function can affect cellular homeostasis pathways and cause metabolically driven chronic inflammatory states. Therefore, understanding these relationships can help to improve treatments and identify new therapeutic targets. This review also examines the advantages and disadvantages of measuring serum PON1 levels in clinical settings, providing insight into the potential clinical use of this enzyme.

Retrospective Analysis of Vaccination Status and Predominant Viral Variants in Patients Hospitalized with COVID-19 in Reus, Spain.

SARS-CoV-2 infection in already-vaccinated individuals is still possible and may require hospitalization. The aim of the present study was to evaluate the clinical evolution of patients with COVID-19 admitted to a public hospital. The outcomes were assessed in relation to the predominant viral variant and the vaccination status. This retrospective study was performed on 1295 COVID-19-positive patients who attended a 352-bed university hospital between 2021 and 2022. Clinical variables and vaccination status were recorded. Of the patients, 799 had not been vaccinated (NV, 61.7%), 449 were partially vaccinated (PV, 34.7%), and 47 were completely vaccinated (CV, 3.6%). The mean age of the CV patients was significantly higher than that of PV and NV. Additionally, they had higher percentages of chronic diseases. The outcomes depended on age but not on vaccination status. There were 209 patients admitted during the Omicron-infection period, of whom 70 (33.5%) were NV, 135 (64.6%) were PV, and 4 (1.9%) were CV. In conclusion, correct vaccination greatly reduces the risk of acquiring severe COVID-19. Partial vaccination does not guarantee protection of the population. This highlights the need for continuous vaccination promotion with all recommended doses, while also investigating alternative treatments for those patients who do not respond to the vaccines.

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19.

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.

Gradient Boosting Machine Identified Predictive Variables for Breast Cancer Patients Pre- and Post-Radiotherapy: Preliminary Results of an 8-Year Follow-Up Study.

Radiotherapy (RT) is part of the standard treatment of breast cancer (BC) because of its effects on relapse reduction and survival. However, response to treatment is highly variable, and some patients may develop disease progression (DP), a second primary cancer, or may succumb to the disease. Antioxidant systems and inflammatory processes are associated with the onset and development of BC and play a role in resistance to treatment. Here, we report our investigation into the clinical evolution of BC patients, and the impact of RT on the circulating levels of the antioxidant enzyme paraoxonase-1 (PON1), cytokines, and other standard biochemical and hematological variables. Gradient Boosting Machine (GBM) algorithm was used to identify predictive variables. This was a retrospective study in 237 patients with BC. Blood samples were obtained pre- and post-RT, with samples of healthy women used as control subjects. Results showed that 24 patients had DP eight years post-RT, and eight patients developed a second primary tumor. The algorithm identified interleukin-4 and total lymphocyte counts as the most relevant indices discriminating between BC patients and control subjects, while neutrophils, total leukocytes, eosinophils, very low-density lipoprotein cholesterol, and PON1 activity were potential predictors of fatal outcome.

Combining Dietary Intervention with Metformin Treatment Enhances Non-Alcoholic Steatohepatitis Remission in Mice Fed a High-Fat High-Sucrose Diet.

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are serious health concerns for which lifestyle interventions are the only effective first-line treatment. Dietary interventions are effective in body weight reduction, but not in improving insulin sensitivity and hepatic lipid mobilization. Conversely, metformin increases insulin sensitivity and promotes the inhibition of de novo hepatic lipogenesis. In this study, we evaluated the metformin effectiveness in NASH prevention and treatment, when combined with dietary intervention in male mice fed a high-fat high-sucrose diet (HFHSD). Eighty 5-week-old C57BL/6J male mice were fed a chow or HFHSD diet and sacrificed at 20 or 40 weeks. The HFHSD-fed mice developed NASH after 20 weeks. Lipoprotein and lipidomic analyses showed that the changes associated with diet were not prevented by metformin administration. HFHSD-fed mice subject to dietary intervention combined with metformin showed a 19.6% body weight reduction compared to 9.8% in those mice subjected to dietary intervention alone. Lower hepatic steatosis scores were induced. We conclude that metformin should not be considered a preventive option for NAFLD, but it is effective in the treatment of this disorder when combined with dietary intervention.

Machine learning and semi-targeted lipidomics identify distinct serum lipid signatures in hospitalized COVID-19-positive and COVID-19-negative patients.

BACKGROUND: Lipids are involved in the interaction between viral infection and the host metabolic and immunological responses. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, as well as with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases. METHODS: We analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. A semi-targeted lipidomics analysis was performed using liquid chromatography coupled to mass spectrometry. Two-hundred and eighty-three lipid species were identified and quantified. Results were interpreted by machine learning tools. RESULTS: We identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients who had other causes of inflammation. Conversely, lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids were the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients. CONCLUSION: This study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, and which differentiate them from the healthy population. The most notable alterations were observed in oxylipins, while alterations in bile acids and glycerophospholipis best distinguished between COVID-19-positive and COVID-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.