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Peer-reviewedCC BY 4.0Frontiers in Immunology

Plasma metabolomic profiling identifies candidate biomarkers associated with sepsis diagnosis and mortality prediction

  • Yanyan Zhu1,2,3,4,5,6
  • Haocheng Wang7
  • Sanjun Yi1,2,3,4,5,6
  • Fuping You7
  • Pingsen Zhao1,2,3,4,5,6
7 affiliations
  1. 1Department of Laboratory Medicine, Yuebei People’s Hospital Affiliated to Shantou University Medical College
  2. 2Laboratory for Diagnosis of Clinical Microbiology and Infection, Yuebei People’s Hospital Affiliated to Shantou University Medical College
  3. 3Research Center for Interdisciplinary and High-quality Innovative Development in Laboratory Medicine
  4. 4Shaoguan Municipal Quality Control Center for Laboratory Medicine, Yuebei People’s Hospital Affiliated to Shantou University Medical College
  5. 5Shaoguan Municipal Quality Control Center for Surveillance of Bacterial Resistance
  6. 6Shaoguan Engineering Research Center for Research and Development of Molecular and Cellular Technology in Rapid Diagnosis of Infectious Diseases and Cancer
  7. 7Institute of Systems Biomedicine, Department of Immunology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, National Health Commission (NHC) Key Laboratory of Medical Immunology, Peking University Health Science Center
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Abstract

Introduction: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection; its early diagnosis and mortality prediction remain challenging, and conventional markers (CRP, PCT) have limited sensitivity and specificity. We aimed to characterize the plasma metabolome of sepsis and identify candidate metabolic biomarkers for diagnosis and 30-day mortality prediction.

Methods: In this retrospective, single-center study, plasma from 30 healthy volunteers, 107 sepsis/septic shock patients and 19 non-septic ICU patients was analyzed by non-targeted LC-MS/MS and stratified into discovery (n=86) and replication (n=70) cohorts. Differential metabolites were defined by VIP > 1 and p < 0.05, with Benjamini–Hochberg q-values reported. Diagnostic and prognostic performance was assessed by ROC analysis and compared with CRP, PCT and the SOFA score; survival was analyzed by Kaplan–Meier with the log-rank test. A longitudinal subset was sampled on days 1, 3 and 5.

Results: Multivariate analyses identified nine significantly dysregulated metabolites in sepsis patients versus both controls and non-septic ICU patients. Three—3-methoxyanthranilate, docosahexaenoic acid and N-acetyl-beta-alanine—showed particular significance, with alterations in amino-acid, carbohydrate and lipid metabolism, and were elevated in non-survivors. Longitudinal analysis revealed distinctive temporal patterns for 3-methoxyanthranilate and N-acetyl-beta-alanine. The combined panel showed diagnostic and prognostic performance that compared favorably with CRP, PCT and the SOFA score for sepsis differentiation and 30-day mortality.

Discussion: 3-methoxyanthranilate, docosahexaenoic acid and N-acetyl-beta-alanine are candidate metabolic biomarkers for sepsis diagnosis and prognosis. Given the modest, single-center sample, these exploratory, hypothesis-generating findings require confirmation in larger, multi-center cohorts and targeted metabolomic studies.

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Cite

BibTeXRIS
BibTeX
@article{zhu2026plasma,
  title = {{Plasma metabolomic profiling identifies candidate biomarkers associated with sepsis diagnosis and mortality prediction}},
  author = {Yanyan Zhu and Haocheng Wang and Sanjun Yi and Fuping You and Pingsen Zhao},
  journal = {Frontiers in Immunology},
  year = {2026},
  month = oct,
  doi = {10.3389/fimmu.2026.1931808},
  url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1931808/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Plasma metabolomic profiling identifies candidate biomarkers associated with sepsis diagnosis and mortality prediction
AU  - Yanyan Zhu
AU  - Haocheng Wang
AU  - Sanjun Yi
AU  - Fuping You
AU  - Pingsen Zhao
PY  - 2026
DA  - 2026/10/14
JO  - Frontiers in Immunology
DO  - 10.3389/fimmu.2026.1931808
UR  - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1931808/full
AB  - Introduction: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection; its early diagnosis and mortality prediction remain challenging, and conventional markers (CRP, PCT) have limited sensitivity and specificity. We aimed to characterize the plasma metabolome of sepsis and identify candidate metabolic biomarkers for diagnosis and 30-day mortality prediction.  Methods: In this retrospective, single-center study, plasma from 30 healthy volunteers, 107 sepsis/septic shock patients and 19 non-septic ICU patients was analyzed by non-targeted LC-MS/MS and stratified into discovery (n=86) and replication (n=70) cohorts. Differential metabolites were defined by VIP > 1 and p < 0.05, with Benjamini–Hochberg q-values reported. Diagnostic and prognostic performance was assessed by ROC analysis and compared with CRP, PCT and the SOFA score; survival was analyzed by Kaplan–Meier with the log-rank test. A longitudinal subset was sampled on days 1, 3 and 5.  Results: Multivariate analyses identified nine significantly dysregulated metabolites in sepsis patients versus both controls and non-septic ICU patients. Three—3-methoxyanthranilate, docosahexaenoic acid and N-acetyl-beta-alanine—showed particular significance, with alterations in amino-acid, carbohydrate and lipid metabolism, and were elevated in non-survivors. Longitudinal analysis revealed distinctive temporal patterns for 3-methoxyanthranilate and N-acetyl-beta-alanine. The combined panel showed diagnostic and prognostic performance that compared favorably with CRP, PCT and the SOFA score for sepsis differentiation and 30-day mortality.  Discussion: 3-methoxyanthranilate, docosahexaenoic acid and N-acetyl-beta-alanine are candidate metabolic biomarkers for sepsis diagnosis and prognosis. Given the modest, single-center sample, these exploratory, hypothesis-generating findings require confirmation in larger, multi-center cohorts and targeted metabolomic studies.
SN  - 1664-3224
ER  -