Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome

Metabolomics in atrial fibrillation - A review and meta-analysis of blood, tissue and animal models

BACKGROUND

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia associated with severe cardiovascular complications. AF presents a growing global challenge, however, current treatment strategies for AF do not address the underlying pathophysiology. To advance diagnosis and treatment of AF, a deeper understanding of AF root causes is needed. Metabolomics is a fast approach to identify, quantify and analyze metabolites in a given sample, such as human serum or atrial tissue. In the past two decades, metabolomics have enabled research on metabolite biomarkers to predict AF, metabolic features of AF, and testing metabolic mechanisms of AF in animal models. Due to the field's rapid evolution, the methods of AF metabolomics studies have not always been optimal. Metabolomics research has lacked standardization and requires expertise to face methodological challenges.

PURPOSE OF THE REVIEW

We summarize and meta-analyze metabolomics research on AF in human plasma and serum, atrial tissue, and animal models. We present the current progress on metabolic biomarkers candidates, metabolic features of clinical AF, and the translation of metabolomics findings from animal to human. We additionally discuss strengths and weaknesses of the metabolomics method and highlight opportunities for future AF metabolomics research.

The Plasma Lipidomic Landscape in Patients with Sepsis due to Community-acquired Pneumonia

Rationale: The plasma lipidome has the potential to reflect many facets of the host status during severe infection. Previous work is limited to specific lipid groups or was focused on lipids as prognosticators.Objectives: To map the plasma lipidome during sepsis due to community-acquired pneumonia (CAP) and determine the disease specificity and associations with clinical features.Methods: We analyzed 1,833 lipid species across 33 classes in 169 patients admitted to the ICU with sepsis due to CAP, 51 noninfected ICU patients, and 48 outpatient controls. In a paired analysis, we reanalyzed patients still in the ICU 4 days after admission (n = 82).Measurements and Main Results: A total of 58% of plasma lipids were significantly lower in patients with CAP-attributable sepsis compared with outpatient controls (6% higher, 36% not different). We found strong lipid class-specific associations with disease severity, validated across two external cohorts, and inflammatory biomarkers, in which triacylglycerols, cholesterol esters, and lysophospholipids exhibited the strongest associations. A total of 36% of lipids increased over time, and stratification by survival revealed diverging lipid recovery, which was confirmed in an external cohort; specifically, a 10% increase in cholesterol ester levels was related to a lower odds ratio (0.84; P = 0.006) for 30-day mortality (absolute mortality, 18 of 82). Comparison with noninfected ICU patients delineated a substantial common illness response (57.5%) and a distinct lipidomic signal for patients with CAP-attributable sepsis (37%).Conclusions: Patients with sepsis due to CAP exhibit a time-dependent and partially disease-specific shift in their plasma lipidome that correlates with disease severity and systemic inflammation and is associated with higher mortality.

The shifting lipidomic landscape of blood monocytes and neutrophils during pneumonia

The lipidome of immune cells during infection has remained unexplored, although evidence of the importance of lipids in the context of immunity is mounting. In this study, we performed untargeted lipidomic analysis of blood monocytes and neutrophils from patients hospitalized for pneumonia and age- and sex-matched noninfectious control volunteers. We annotated 521 and 706 lipids in monocytes and neutrophils, respectively, which were normalized to an extensive set of internal standards per lipid class. The cellular lipidomes were profoundly altered in patients, with both common and distinct changes between the cell types. Changes involved every level of the cellular lipidome: differential lipid species, class-wide shifts, and altered saturation patterns. Overall, differential lipids were mainly less abundant in monocytes and more abundant in neutrophils from patients. One month after hospital admission, lipidomic changes were fully resolved in monocytes and partially in neutrophils. Integration of lipidomic and concurrently collected transcriptomic data highlighted altered sphingolipid metabolism in both cell types. Inhibition of ceramide and sphingosine-1-phosphate synthesis in healthy monocytes and neutrophils resulted in blunted cytokine responses upon stimulation with lipopolysaccharide. These data reveal major lipidomic remodeling in immune cells during infection, and link the cellular lipidome to immune functionality.

Healthcare data quality assessment for improving the quality of the Korea Biobank Network

Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and were divided into four dimensions: completeness, validity, accuracy, and uniqueness. Korea Biobank Network collects and manages human materials and clinical information from multiple biobanks, and is in the process of developing a common data model for data integration. The results of the data quality verification revealed an error rate of 0.74%. Furthermore, an analysis of the data from each institution was performed to examine the relationship between the institution's characteristics and error count. The results from a chi-square test indicated that there was an independent correlation between each institution and its error count. To confirm this correlation between error counts and the characteristics of each institution, a correlation analysis was conducted. The results, shown in a graph, revealed the relationship between factors that had high correlation coefficients and the error count. The findings suggest that the data quality was impacted by biases in the evaluation system, including the institution's IT environment, infrastructure, and the number of collected samples. These results highlight the need to consider the scalability of research quality when evaluating clinical epidemiological information linked to human materials in future validation studies of data quality.

Plasma oxidative lipidomics reveals signatures for sepsis-associated acute kidney injury

BACKGROUND

Oxidized lipids are essential bioactive lipid mediators generated during infection that regulate oxidative stress and the inflammatory response, but their signatures in patients with sepsis-associated acute kidney injury (SA-AKI) are poorly understood. This study analyzed the oxidative lipidomics of plasma from patients with SA-AKI to reveal the underlying biomarkers and pathophysiological mechanisms involved in sepsis.

MATERIALS

A total of 67 patients with SA-AKI and 20 age- and sex-matched healthy controls (HCs) participated in this prospective cohort study. Among the patients with SA-AKI, 14 cases had stage I-II AKI and 53 cases had stage III AKI. Oxidative lipidomic analysis of plasma samples was conducted using ultra performance liquid chromatography coupled with tandem mass spectrometric (UPLC-MS /MS) detection.

RESULTS

Among 21 kinds of differentially oxidized lipids, 5(S),12(S)-DiHETE, 5-isoPGF2VI, 5,6-DiHETrE, 11,12-EET and 9,10-DiHOME showed the best performance. The prediction model incorporating them has shown highly sensitive and specific in distinguishing different stages of SA-AKI from HCs. The annotation of Kyoto Encyclopedia of Genes and Genomes illustrated that the overall downregulation of vascular smooth muscle contraction was closely related to the pathophysiological mechanism of SA-AKI.

CONCLUSION

This study revealed alterations in the characteristic oxidized lipids in the plasma of SA-AKI patients, and these lipids had high diagnostic efficiency and potential targeted intervention value for SA-AKI.

Unraveling the Metabolic Changes in Acute Pancreatitis: A Metabolomics-Based Approach for Etiological Differentiation and Acute Biomarker Discovery

Acute pancreatitis (AP) remains a challenging medical condition, where a deeper metabolic insight could pave the way for innovative treatments. This research harnessed serum metabolomics to discern potential diagnostic markers for AP and distinguish between its biliary (BAP) and alcohol-induced (AAP) forms. Leveraging high-performance liquid chromatography coupled with mass spectrometry, the metabolic signatures of 34 AP patients were contrasted against 26 healthy participants, and then between different etiologies of AP. The results identified metabolites primarily from glycerophospholipids, glycerolipids, fatty acyls, sterol lipids, and pteridines and derivative classes, with the Human Metabolome Database aiding in classification. Notably, these metabolites differentiated AP from healthy states with high AUROC values above 0.8. Another set of metabolites revealed differences between BAP and AAP, but these results were not as marked as the former. This lipidomic analysis provides an introduction to the metabolic landscape of acute pancreatitis, revealing changes in multiple lipid classes and metabolites and identifying these metabolites. Future research could add and discover new diagnostic biomarkers and therapeutic strategies enhancing the management of acute pancreatitis.

Lipid oxidation dysregulation: an emerging player in the pathophysiology of sepsis

Sepsis is a life-threatening organ dysfunction caused by abnormal host response to infection. Millions of people are affected annually worldwide. Derangement of the inflammatory response is crucial in sepsis pathogenesis. However, metabolic, coagulation, and thermoregulatory alterations also occur in patients with sepsis. Fatty acid mobilization and oxidation changes may assume the role of a protagonist in sepsis pathogenesis. Lipid oxidation and free fatty acids (FFAs) are potentially valuable markers for sepsis diagnosis and prognosis. Herein, we discuss inflammatory and metabolic dysfunction during sepsis, focusing on fatty acid oxidation (FAO) alterations in the liver and muscle (skeletal and cardiac) and their implications in sepsis development.

Altered profile of plasma phospholipids in woman with recurrent pregnancy loss and recurrent implantation failure treated with lipid emulsion therapy

BACKGROUND

Recurrent Pregnancy Loss (RPL) and Recurrent Implantation Failure (RIF) are highly heterogeneous condition and many of the mechanisms involved still require elucidation. The aim was to analyze the lipidomic profile in plasma of women with RPL and RIF before and after receiving the Lipid Emulsion Therapy (LET) containing 10% fish oil (SMOFlipid^® 20%).

METHODS

This study included twenty-six women with RPL or RIF from immunological or inflammatory causes, with elevated natural killer cell levels and divided into a Pregnancy Loss or a Live Birth group according to the outcome. The women received intravenous LET and sample collecting was done before the first, third and fifth dose of LET in the pregnant women. Ultra-performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF MS) and multivariate statistical methods were performed to evaluate the profile of phospholipids present in the women's plasma.

RESULTS

An increase of phosphatidylcholines (PC) 40:8 and 36:5 levels with predominance of n6 polyunsaturated fatty acids (PUFA) was observed in plasma lipids of the Pregnancy Loss Group compared to Live Birth Group. We also observed an increase in the relative abundance of n3 PUFA-PC species (42:10 and 36:6) and LysoPC 15:0 with the long term use of LET.

CONCLUSION

The greater availability of n3 PUFA in plasma of the pregnant women stemming from LET use can be considered advantageous regarding the alteration of the phospholipid profile and its postulated anti-inflammatory and immunomodulatory role.

Mass-Spectrometry-Based Lipidomics Discriminates Specific Changes in Lipid Classes in Healthy and Dyslipidemic Adults

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.

Serum Predose Metabolic Profiling for Prediction of Rosuvastatin Pharmacokinetic Parameters in Healthy Volunteers

Rosuvastatin is a well-known lipid-lowering agent generally used for hypercholesterolemia treatment and coronary artery disease prevention. There is a substantial inter-individual variability in the absorption of statins usually caused by genetic polymorphisms leading to a variation in the corresponding pharmacokinetic parameters, which may affect drug therapy safety and efficacy. Therefore, the investigation of metabolic markers associated with rosuvastatin inter-individual variability is exceedingly relevant for drug therapy optimization and minimizing side effects. This work describes the application of pharmacometabolomic strategies using liquid chromatography coupled to mass spectrometry to investigate endogenous plasma metabolites capable of predicting pharmacokinetic parameters in predose samples. First, a targeted method for the determination of plasma concentration levels of rosuvastatin was validated and applied to obtain the pharmacokinetic parameters from 40 enrolled individuals; then, predose samples were analyzed using a metabolomic approach to search for associations between endogenous metabolites and the corresponding pharmacokinetic parameters. Data processing using machine learning revealed some candidates including sterols and bile acids, carboxylated metabolites, and lipids, suggesting the approach herein described as promising for personalized drug therapy.

Sepsis: deriving biological meaning and clinical applications from high-dimensional data

The pathophysiology of sepsis is multi-facetted and highly complex. As sepsis is a leading cause of global mortality that still lacks targeted therapies, increased understanding of its pathogenesis is vital for improving clinical care and outcomes. An increasing number of investigations seeks to unravel the complexity of sepsis through high-dimensional data analysis, enabled by advances in -omics technologies. Here, we summarize progress in the following major -omics fields: genomics, epigenomics, transcriptomics, proteomics, lipidomics, and microbiomics. We describe what these fields can teach us about sepsis, and highlight current trends and future challenges. Finally, we focus on multi-omics integration, and discuss the challenges in deriving biological meaning and clinical applications from these types of data.