The Levels of Oxidized Phospholipids in High-Density Lipoprotein During the Course of Sepsis and Their Prognostic Value

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Serum metabolite profiling reveals metabolic characteristics of sepsis patients using LC/MS-based metabolic profiles: a cross-sectional study

BACKGROUND

Individuals with sepsis exhibited a higher likelihood of benefiting from early initiation of specialized treatment to enhance the prognosis of the condition. The objective of this study is to identify potential biomarkers of sepsis by means of serum metabolomics.

MATERIALS AND METHODS

The screening of putative biomarkers of sepsis was conducted using serum samples from patients with sepsis and a control group of healthy individuals. The pathogenesis of sepsis was determined through the utilization of liquid chromatography-mass spectrometry-based metabolic profiles and bioinformatic techniques, which in turn provided a foundation for timely diagnosis and intervention.

RESULTS

Individuals with sepsis had significantly different metabolic characteristics compared to those with normal health. The concentrations of phosphatidylcholines (PCs), phosphatidylserine (PS), lysophosphatidylethanolamine (LysoPEs), and lysophosphatidylcholine (LysoPCs) exhibited a decrease, while the levels of creatinine, C17-Sphinganine, and PS(22:0/22:1(11Z)) demonstrated an increase in the serum of sepsis patients when compared to the control group. Additionally, ROC curves were generated to assess the discriminatory ability of the differentially expressed metabolites. The area under the ROC curve for PS (22:0/22:1(11Z)) and C17-Sphinganine were determined to be 0.976 and 0.913, respectively. These metabolites may potentially serve as diagnostic markers for sepsis. Additionally, the pathogenesis of sepsis is associated with mTOR signaling, NF-κB signaling pathway, calcium signaling, calcium transport, and tRNA charging pathway.

CONCLUSION

The identification of differential expression of these metabolites in sepsis serum samples could aid in the timely diagnosis and intervention of sepsis, as well as enhance our understanding of its pathogenesis.

Circulating protein and lipid markers of early sepsis diagnosis and prognosis: a scoping review

PURPOSE OF REVIEW

Sepsis is the extreme response to infection associated with high mortality, yet reliable biomarkers for its identification and stratification are lacking.

RECENT FINDINGS

Our scoping review of studies published from January 2017 to September 2022 that investigated circulating protein and lipid markers to inform non-COVID-19 sepsis diagnosis and prognosis identified interleukin (IL)-6, IL-8, heparin-binding protein (HBP), and angiopoietin-2 as having the most evidence. Biomarkers can be grouped according to sepsis pathobiology to inform biological data interpretation and four such physiologic processes include: immune regulation, endothelial injury and coagulopathy, cellular injury, and organ injury. Relative to proteins, the pleiotropic effects of lipid species' render their categorization more difficult. Circulating lipids are relatively less well studied in sepsis, however, low high-density lipoprotein (HDL) is associated with poor outcome.

SUMMARY

There is a lack of robust, large, and multicenter studies to support the routine use of circulating proteins and lipids for sepsis diagnosis or prognosis. Future studies will benefit from standardizing cohort design as well as analytical and reporting strategies. Incorporating biomarker dynamic changes and clinical data in statistical modeling may improve specificity for sepsis diagnosis and prognosis. To guide future clinical decisions at the bedside, point-of-care circulating biomarker quantification is needed.