Evidence map›Paper›PMID 41346598›Full record

ArticleFrontiers in immunology2025

Plasma lipidomic signatures reveal age-associated patterns of septic shock risk and immune dysregulation in sepsis.

Yuhan Sun, Ke Lin, Ling Wang, Jingwen Ai, Jingjing Zhao, Ruiwen Sun, Xiaoyang Cheng, Yanmin Wan, Peng Cui, Sen Wang and 2 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Clinical lipidomics in bacterial sepsis: a systematic review of serum and plasma evaluations.Metabolomics : Official journal of the Metabolomic Society · 2026
    Pooled it
  2. Observational
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yuhan SunDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Ke LinDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Ling WangShanghai Omicsolution Co., Ltd., Shanghai, China.
Jingwen AiDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Jingjing ZhaoDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Ruiwen SunDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Xiaoyang ChengDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Yanmin WanDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Peng CuiDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Sen WangDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Jing WuDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Jialin JinDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sepsis is associated with significant lipidomic disturbances, but age-associated lipidomic patterns remain poorly characterized. Given the links between aging, immune dysfunction, and metabolic dysregulation, defining age-specific lipid profiles could improve sepsis risk assessment. This study investigates age-stratified lipidomic signatures in sepsis and identifies biomarkers for clinical severity. ​Methods: We prospectively enrolled 62 sepsis patients (21 <65 years, 41 ≥65 years) and 40 healthy controls. Plasma lipidomics was performed via untargeted LC-MS/MS, identifying 1,277 lipid species across 38 subclasses. Principal component analysis (PCA) and consensus clustering were used to assess lipidomic differences and define patient subtypes. Correlations between lipid subclasses, clinical severity (SOFA scores), and immune cell subsets were analyzed. An age-adjusted risk stratification model was developed to assess septic shock and mortality risk (AUC analysis). ​Results: Sepsis patients exhibited reduced phosphatidylcholine (PC), cholesteryl ester (CE), and lysophosphatidylcholine (LPC) levels (all P<0.05). Clustering revealed four lipidomic patterns, with Cluster 4 distinguishing two sepsis subtypes (C1, C2). Subtype C2 had higher septic shock incidence (57.1% vs. 14.8%, P = 0.0013) and downregulation of 92 lipids, 35 of which strongly correlated with SOFA scores. A risk stratification model incorporating six key lipids (LPC(19:0), PC(P-19:0), SM 32:3;2O(FA 16:3), PC(P-20:0), PC(O-18:1/20:3), CE(15:0)) and age accurately predicted septic shock (AUC: 0.87 training, 0.82 validation) and mortality risk in elderly patients. PC levels correlated with monocytes, while CE and LPC associated with complement proteins and CD8+ T cells. ​Conclusions: Our lipid-based model effectively predicts septic shock and mortality, particularly in elderly sepsis patients. Age-associated lipid alterations (PC, LPC, CE reduction) correlate with disease severity and immune dysregulation, suggesting distinct lipid-immune mechanisms in younger vs. elderly patients. These findings support lipidomics as a tool for sepsis risk stratification and personalized therapy.

Indexed as

AgingLipidomicsLipidsSepsisShock, SepticAdultAgedAge FactorsBiomarkersFemaleHumansMaleMiddle AgedProspective StudiesYoung AdultBiomarkersLipidsagingimmunitylipidomicssepsisshock

Identifiers

PMID41346598
PMCPMC12672909

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Registered trials

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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 OpenQuestion graph.