Evidence map›Paper›PMID 41645160›Full record

ArticleLipids in health and disease2026

Metabolic phenotyping of sepsis in large multicenter cohorts: identification of a reproducible high-risk subgroup.

Yining Zhang, Guoxiang Liu, Zhaoming Shang, Xianxian Yu, Huailong Shang, Xiao Cui, Jiameng Chen, Jiawei Ye, Jiyuan Zhang, Yidan Zhai and 6 more

Abstract readMulticenter Study
In one paragraph

Article in Lipids in health and disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

16 authors.

Yining Zhang *Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Guoxiang Liu *Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. lgx549lgx@163.com.
Zhaoming Shang *Department of Emergency, Huashan Hospital, Fudan University, Shanghai, China.
Xianxian Yu *Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Huailong Shang *Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiao CuiDepartment of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiameng ChenDepartment of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiawei YeDepartment of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiyuan ZhangDepartment of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yidan ZhaiThe First Clinical College, Wuhan University, Wuhan, Hubei, China.
Junwei QianDepartment of Emergency, Huashan Hospital, Fudan University, Shanghai, China.
Chaoping MaDepartment of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wenjie LiuDepartment of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Mingquan ChenDepartment of Emergency, Huashan Hospital, Fudan University, Shanghai, China. mingquanchen@fudan.edu.cn.
Bing ZhaoDepartment of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. zhaobing124@163.com.
Chengjin GaoDepartment of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. gaochengjin@xinhuamed.com.cn.

Funding

Researching Project of Shanghai Municipal Health Commission 2023ZDFC0106
6 · The paper itself

Abstract

backgroundSepsis represents a profound metabolic crisis, yet the prognostic significance of glycolipid biomarkers remains unclear. Most prior studies examined single metabolic indicators, overlooking their combined effects on organ dysfunction and survival. This study aimed to identify glycolipid-related biomarkers predicting outcomes in sepsis and to define a reproducible metabolic phenotype linked to multi-organ dysfunction and mortality.

methodsWe conducted a multicenter retrospective cohort study of 2,970 adults with sepsis admitted to three tertiary hospitals in China (2015–2023), with external validation using the international MIMIC-IV database (2008–2019). A comprehensive panel of glycolipid markers was evaluated through univariable Cox and LASSO regression, supported by tree-based machine learning and logistic regression for sepsis-associated acute kidney injury. Low-density lipoprotein cholesterol (LDL-C) and the triglyceride–glucose (TyG) index emerged as the most consistent prognostic biomarkers and were used to define metabolic phenotypes.

resultsAmong 2,970 patients (median age 68 years [IQR 60–80]; 62.5% male), LDL-C and TyG independently predicted mortality across statistical and machine-learning analyses. Patients with a high TyG–low LDL phenotype exhibited the most severe multi-organ dysfunction—renal, hepatic, coagulative, and inflammatory—and the highest mortality risk (HR 1.31, 95% CI 1.04–1.65). Validation in MIMIC-IV confirmed the reproducibility of this phenotype across populations and healthcare systems.

conclusionsIntegrating LDL-C and TyG identifies a reproducible metabolic phenotype marking sepsis patients at high risk of multi-organ failure and death, offering a simple framework for early risk stratification and personalized management in critical care.

Indexed as

SepsisAgedAged, 80 and overBiomarkersBlood GlucoseChinaCholesterol, LDLFemaleHumansMaleMiddle AgedMultiple Organ FailurePhenotypePrognosisRetrospective StudiesRisk FactorsBiomarkersBlood GlucoseCholesterol, LDLTriglyceridesGlycolipid metabolismLow-density lipoprotein cholesterolMetabolic phenotypeSepsisTriglyceride–glucose index

Identifiers

PMID41645160
PMCPMC12918294

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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.