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Peer-reviewedCC BYScientific data

A longitudinal plasma metabolomics dataset of sepsis-associated liver injury across multiple clinical time points.

  • Huan Liu1,2,3
  • Jing Yang1
  • Dong Leng4
  • Yihui Wang1
  • Enqiang Mao1
  • Erzhen Chen1,2,3
  • Ying Chen5,6,7
  • Zhitao Yang8,9,10
10 affiliations
  1. 1Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
  2. 2Shanghai Institute of Aviation Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
  3. 3Emergency & Critical Care Medicine Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
  4. 4College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China.
  5. 5Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. [email protected].
  6. 6Shanghai Institute of Aviation Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. [email protected].
  7. 7Emergency & Critical Care Medicine Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. [email protected].
  8. 8Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. [email protected].
  9. 9Shanghai Institute of Aviation Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. [email protected].
  10. 10Emergency & Critical Care Medicine Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. [email protected].
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Abstract

Sepsis-associated liver injury is a severe complication of sepsis characterized by substantial metabolic alterations, yet longitudinal metabolomics resources for this condition remain limited. Here, we present an LC-MS-based longitudinal metabolomics dataset comprising 88 blood samples collected from healthy controls and sepsis patients with or without liver injury across multiple ICU-associated clinical time points. Comprehensive metabolomic profiling generated 18,247 high-quality metabolic features and 2,894 annotated metabolites, providing a resource for investigating temporal metabolite patterns with annotations related to nucleotide, lipid, and bile acid metabolism across clinical groups and sampling time points. Exploratory analytical approaches, including temporal clustering, weighted correlation network analysis, and regularized regression-based feature selection, were applied to facilitate characterization of longitudinal metabolic patterns within the dataset. This dataset provides a reusable resource for future studies on longitudinal metabolic dynamics, biomarker validation, integrative multi-omics analyses, and methodological development in sepsis and critical care research.

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Cite

BibTeXRIS
BibTeX
@article{liu2026longitudinal,
  title = {{A longitudinal plasma metabolomics dataset of sepsis-associated liver injury across multiple clinical time points.}},
  author = {Huan Liu and Jing Yang and Dong Leng and Yihui Wang and Enqiang Mao and Erzhen Chen and Ying Chen and Zhitao Yang},
  journal = {Scientific data},
  year = {2026},
  month = aug,
  doi = {10.1038/s41597-026-08148-5},
  url = {https://europepmc.org/article/MED/42919828},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A longitudinal plasma metabolomics dataset of sepsis-associated liver injury across multiple clinical time points.
AU  - Huan Liu
AU  - Jing Yang
AU  - Dong Leng
AU  - Yihui Wang
AU  - Enqiang Mao
AU  - Erzhen Chen
AU  - Ying Chen
AU  - Zhitao Yang
PY  - 2026
DA  - 2026/08/24
JO  - Scientific data
DO  - 10.1038/s41597-026-08148-5
UR  - https://europepmc.org/article/MED/42919828
AB  - Sepsis-associated liver injury is a severe complication of sepsis characterized by substantial metabolic alterations, yet longitudinal metabolomics resources for this condition remain limited. Here, we present an LC-MS-based longitudinal metabolomics dataset comprising 88 blood samples collected from healthy controls and sepsis patients with or without liver injury across multiple ICU-associated clinical time points. Comprehensive metabolomic profiling generated 18,247 high-quality metabolic features and 2,894 annotated metabolites, providing a resource for investigating temporal metabolite patterns with annotations related to nucleotide, lipid, and bile acid metabolism across clinical groups and sampling time points. Exploratory analytical approaches, including temporal clustering, weighted correlation network analysis, and regularized regression-based feature selection, were applied to facilitate characterization of longitudinal metabolic patterns within the dataset. This dataset provides a reusable resource for future studies on longitudinal metabolic dynamics, biomarker validation, integrative multi-omics analyses, and methodological development in sepsis and critical care research.
SN  - 2052-4463
ER  -