Evidence map›Paper›PMID 41663846›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2026

Plasma metabolomic signatures in patients with multidrug-resistant bacterial sepsis.

Jing Wang, Gang Luo, Peng Lv, Qixiu Li, Songmei Yu, Yuwei Chen, Limei Yu, Kefeng Li

Abstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

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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

1 citing paper in PubMed.

  1. 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

8 authors.

Jing Wang *Department of Critical Care Medicine, Yantai Yuhuangding Hospital Affiliated with Medical College of Qingdao University, Yantai, Shandong, 264000, China.
Gang Luo *Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, Macau SR, China.
Peng Lv *Quality Administrative Department, Yantai Yuhuangding Hospital Affiliated with Medical College of Qingdao University, Yantai, Shandong, 264000, China.
Qixiu LiThe Second Clinical Medical College of Binzhou Medical University, Yantai, Shandong, 264000, China.
Songmei YuDepartment of Critical Care Medicine, Yantai Yuhuangding Hospital Affiliated with Medical College of Qingdao University, Yantai, Shandong, 264000, China.
Yuwei ChenDepartment of Emergency, Shandong Public Health Clinical Center, Jinan, Shandong, 250000, China.
Limei YuDepartment of Clinical Laboratory, Yantai Yuhuangding Hospital Affiliated with Medical College of Qingdao University, Yantai, Shandong, 264000, China. littlesnown@163.com.
Kefeng LiFaculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, Macau SR, China. kefengl@mpu.edu.mo.

Funding

the Joint Program between FDCT and the Department of Science and Technology of Guangdong Province FDCT-GDST, 0009/2024/AGJthe Macao Polytechnic University RP/FCSD-02/2022the National Natural Science Foundation of China 82172188The Science and Technology Development Funds (FDCT) of Macao 0033/2023/RIB2
6 · The paper itself

Abstract

BACKGROUND AND

objectiveMultidrug-resistant (MDR) bacterial infections are a leading cause of sepsis-related death. A rapid method to identify patients with MDR infections upon hospital admission is urgently needed. This study aimed to characterize the distinct plasma metabolomic signatures associated with MDR gram-positive (G+) and gram-negative (G-) sepsis and to develop predictive models for rapid, risk stratification during the initial clinical encounter.

methodsTwo independent cohorts of septic patients were recruited, with 198 subjects (117 MDR and 81 susceptible) in the discovery cohort, and 198 patients (95 MDR and 103 susceptible) in the validation cohort. Plasma metabolomic profiling was performed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Multiple machine learning algorithms were employed to identify differential metabolomic signatures and to construct and validate multi-metabolite models for the early identification of MDR bacteria.

resultsDistinct metabolomic signatures were identified for both MDR G- and G+ infections. MDR G- sepsis showed significant elevations in metabolites related to host inflammatory responses, such as histamine, alongside decreased levels of gut microbiota-derived metabolites, including cholic acid and benzoic acid, indicating profound host-microbe dysregulation. Conversely, MDR G+ sepsis was characterized by alterations in energy and amino acid metabolism, notably elevated 2-hydroxyglutarate, a marker of mitochondrial stress. An 8-metabolite model for MDR G- infection achieved excellent discrimination in both the discovery (AUROC = 0.885, 95% CI: 0.787-0.982) and validation (AUROC = 0.878, 95% CI: 0.782-0.951) cohorts. The model for MDR G+ infection demonstrated good predictive performance (AUROC = 0.763 and 0.715 in discovery and validation, respectively).

conclusionThis study identifies robust and distinct plasma metabolomic signatures that differentiate MDR from antibiotic-susceptible sepsis. These findings support the development of rapid, metabolomics-based testing using admission plasma to risk-stratify patients. This approach could guide early, stewardship-aligned antimicrobial decisions while conventional culture results are pending, potentially improving clinical outcomes.

Indexed as

Drug Resistance, Multiple, BacterialMetabolomicsSepsisAgedBiomarkersCohort StudiesFemaleHumansLiquid Chromatography-Mass SpectrometryMachine LearningMaleMetabolomeMiddle AgedTandem Mass SpectrometryBiomarkersBacterial sepsisMachine learningMetabolomic signaturesMultidrug-resistantPlasma metabolome

Identifiers

PMID41663846
PMCPMC12886213

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LicenceCC BY-NC-ND
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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.