Trial reportBMC anesthesiology2023
Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.
Trial report in BMC anesthesiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Predictive enrichment using biomarkers in studies of critically-ill patients with sepsis: a systematic review.Critical care (London, England) · 2025Pooled it
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Pathophysiology-guided biomarkers and therapeutics for precision trauma medicine in polytrauma with musculoskeletal injuries.Military Medical Research · 2026Review
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- Plasma metabolomics analysis in murine prerenal azotemia reveals changes in energy substrates, amino acid metabolism, and uremic toxins.American journal of physiology. Renal physiology · 2026Article
- Artificial intelligence-driven cluster analysis for identifying clinical phenotypes in suspected sepsis patients in the emergency department.BMC emergency medicine · 2026Article
- Blood Transfusion Practices in Rural Hospitals: Indications, Challenges, and Practical Solutions.Cureus · 2026Review
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Metabolomics analysis identifies differential metabolites and potential diagnostic biomarkers among pediatric sepsis subtypes.PloS one · 2026Article
- Peripheral Blood Mononuclear Cells in Sepsis: Immune Trajectories, Monocyte Dysfunction, and Translational Biomarkers.Journal of inflammation research · 2026Review
- Integrated serum metabolomics reveals severity-associated exploratory metabolic signatures in burn patients.Frontiers in physiology · 2026Article
- Identification of age-specific urinary metabolic biomarkers in Wilson disease using machine learning: a comparative study of ensemble tree models.Open medicine (Warsaw, Poland) · 2026Article
- The role of artificial intelligence in burn assessment, complication diagnosis, and outcome prediction: a narrative review.Burns & trauma · 2026Review
- Article
- Metabolomics for the Diagnosis of Secondary Infections in Critically Ill Patients With COVID-19.Critical care explorations · 2025Article
- Risk factors of ICU-acquired weakness in sepsis patients.Pakistan journal of medical sciences · 2025Article
- From cardiac injury to omics signatures: a narrative review on biomarkers in septic cardiomyopathy.Clinical and experimental medicine · 2025Review
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.Diagnostics (Basel, Switzerland) · 2025Review
- Serum metabolic profiling in diabetic kidney disease patients using ultra-high performance liquid chromatography-tandem mass spectrometry.Diabetology & metabolic syndrome · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis.
methodsSeptic patients and healthy individuals were enrolled to investigate metabolic changes using non-targeted liquid chromatography-high-resolution mass spectrometry metabolomics. Machine learning algorithms were subsequently employed to identify key differentially expressed metabolites (DEMs). Prognostic-related DEMs were then identified using univariate and multivariate Cox regression analyses. The septic rat model was established to verify the effect of phenylalanine metabolism-related gene MAOA on survival and mean arterial pressure after sepsis.
resultsA total of 532 DEMs were identified between healthy control and septic patients using metabolomics. The main pathways affected by these DEMs were amino acid biosynthesis, phenylalanine metabolism, tyrosine metabolism, glycine, serine and threonine metabolism, and arginine and proline metabolism. To identify sepsis diagnosis-related biomarkers, support vector machine (SVM) and random forest (RF) algorithms were employed, leading to the identification of four biomarkers. Additionally, analysis of transcriptome data from sepsis patients in the GEO database revealed a significant up-regulation of the phenylalanine metabolism-related gene MAOA in sepsis. Further investigation showed that inhibition of MAOA using the inhibitor RS-8359 reduced phenylalanine levels and improved mean arterial pressure and survival rate in septic rats. Finally, using univariate and multivariate cox regression analysis, six DEMs were identified as prognostic markers for sepsis.
conclusionsThis study employed metabolomics and machine learning algorithms to identify differential metabolites that are associated with the diagnosis and prognosis of sepsis patients. Unraveling the relationship between metabolic characteristics and sepsis provides new insights into the underlying biological mechanisms, which could potentially assist in the diagnosis and treatment of sepsis.
trial registrationThis human study was approved by the Ethics Committee of the Research Institute of Surgery (2021-179) and was registered by the Chinese Clinical Trial Registry (Date: 09/12/2021, ChiCTR2200055772).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.