Evidence map›Paper›PMID 40548468›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Red Blood Cell Membrane Lipidomics: Potential Biomarkers Detecting Method for Plasma Volume Overload and Major Adverse Cardiovascular Events in Chronic Heart Failure Patients.

Lin Zhang, Xiangqin Ou, Jingyi Lin, Xiaofei Luo, Jiashun Zhou, Xingyue Zhou, Peihua Zhao, Li Liu, Ziran Zhao, Ying Zhou and 3 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

13 authors.

Lin ZhangMedical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.ORCID https://orcid.org/0000-0003-3064-7975
Xiangqin OuState Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
Jingyi LinMedical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Xiaofei LuoMedical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Jiashun ZhouJinghai District Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 301600, China.
Xingyue ZhouJinghai District Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 301600, China.
Peihua ZhaoJinghai District Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 301600, China.
Li LiuMedical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Ziran ZhaoMedical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Ying ZhouMedical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Guanwei FanMedical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Lifeng HanState Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
Xiumei GaoState Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.

Funding

National Key R&D Program of China 2022YFC3500300National Natural Science Foundation of China 82192914National Natural Science Foundation of China 82274436National Natural Science Foundation of China 82430121Science and Technology Project of Haihe Laboratory of Modern Chinese Medicine 22HHZYSS00002Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine ZYYCXTD-D-202207
6 · The paper itself

Abstract

Plasma volume fluctuations limit the utility of circulating lipidomics in chronic heart failure (CHF). In contrast, red blood cell (RBC) membrane lipidomics may serve as stable biomarkers that are unaffected by plasma volume changes. Two cohorts are included to investigate the association between RBC indicators and CHF. RBC membrane lipidomics is first used to characterize CHF and its impact on plasma volume overload (PVO) and major adverse cardiovascular events (MACE). The first cohort (n = 507,638) shows that the erythrocyte stress index (ESI), better than traditional RBC indicators, is associated with the prevalence of CHF with odds ratio (OR) and confidence interval (CI) of 1.57 (95% CI,1.55-1.59). ESI is also linked with in-hospital death in CHF. Another cohort (n = 1,550) indicated RBC membrane lipidomics ceramide subtype Cer 18:0;O2/16:0 and lysophosphatidylethanolamine subtype LPE 18:0 has an AUC of PVO with 0.75 and 0.61. The above two lipids are risk factors of PVO with OR of 1.62 (95% CI, 1.47-1.80) and 1.11 (95% CI, 1.06-1.16). They also are risk factors for MACE, with hazard ratios (HR) of 1.04 (95% CI, 1.01-1.07) and 1.06 (95% CI, 1.01-1.10). This research emphasizes the potential value of RBC membrane lipidomics for CHF development, prognosis, and treatment.

Indexed as

BiomarkersErythrocyte MembraneHeart FailureLipidomicsPlasma VolumeAgedChronic DiseaseCohort StudiesFemaleHumansMaleMiddle AgedBiomarkerschronic heart failureerythrocyte stress indexmajor adverse cardiovascular eventsplasma volume overloadred blood cell membrane lipidomics

Identifiers

PMID40548468
PMCPMC12412543

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.