Evidence map›Paper›PMID 42027872›Full record

ReviewFrontiers in immunology2026

Interaction networks of macrophage glycolysis and inflammation in sepsis: mechanisms and therapeutic potential.

Peiyao Luo, Hao Liu, Ting Zhang, Wenfang He

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Peiyao LuoDepartment of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Hao LiuDepartment of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Ting ZhangDepartment of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Wenfang HeDepartment of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a severe threat to human health with high mortality rates, but so far its pathogenesis is unclear and lacks effective therapeutic drugs. Macrophages function as one of the most important innate immune cells and play an integral role in the sepsis inflammatory process. Recently, studies have shown that its immune function is associated with the Warburg effect. The Warburg effect refers to the preferential metabolism of glucose to lactate by cells through aerobic glycolysis even with abundant oxygen. It has shown that increasing aerobic glycolysis promotes M1 polarization of macrophages to facilitate inflammation, whereas decreasing aerobic glycolysis can lead to M2 polarization and alleviated inflammation. Interestingly, it was demonstrated that not only does glycolysis affect inflammation, but inflammation in sepsis in turn affects glycolysis. Currently, there is no comprehensive review regarding this issue. Therefore, our review focuses on the mechanisms of the interaction between inflammation and macrophage glycolysis in sepsis. We will address both how inflammatory molecules affect the process of glycolysis in septic macrophages and how glycolytic enzymes and related metabolites contribute to inflammation. We also discuss the potential in targeting glycolysis for the treatment of sepsis. We hope to bring a new perspective to clinical practice.

Indexed as

GlycolysisInflammationMacrophagesSepsisAnimalsHumansinflammation moleculesmacrophagesmetabolismsepsisthe Warburg effect

Identifiers

PMID42027872
PMCPMC13100870

What Socratic holds

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Registered trials

None linked

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 Socratic graph.