Evidence map›Paper›PMID 40842288›Full record

ArticleClinical and translational medicine2025

Multi-omics analysis revealed biomarkers for coronary atherosclerosis: Occurrence and development.

Mengxue He, Dongxue Wang, Yong-Jiang Xu, Jiachen Shi, Aiyang Liu, Xiaoxi Zhao, Yunlai Gao, Yuan He, Yu Zhang, Ru-Xing Wang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

11 authors.

Mengxue HeState Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China.
Dongxue WangDepartment of Cardiology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Yong-Jiang XuState Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China.
Jiachen ShiState Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China.
Aiyang LiuState Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China.
Xiaoxi ZhaoDepartment of Cardiology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Yunlai GaoDepartment of Cardiology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Yuan HeState Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China.
Yu ZhangState Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China.
Ru-Xing WangDepartment of Cardiology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Yuanfa LiuState Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China.ORCID 0000-0002-8259-8426

Funding

Jiangnan University Project of Jiangsu Provincial Center of Technology Innovation for Future Food BM2020023National Natural Science Foundation of China 82370342Natural Science Foundation of Jiangsu Province BK20231145Postdoctoral Fellowship Program of CPSF GZC20230985
6 · The paper itself

Abstract

backgroundCoronary atherosclerosis (CA) is a leading cause of cardiovascular diseases with the high morbidity and mortality; however, the current diagnostic methods, primarily based on symptoms, signs, lab examination and imaging, are often inadequate for detecting subclinical or early-stage CA, costly, and inaccessible in many cases. The objective of this study was to discover sensitive and specific biomarkers for the diagnosis of CA severity.

methodsWe enrolled 443 participants, including CA patients and healthy controls, from three independent cohorts: discovery, testing, and blinded validation. Multi-omics data integration during the discovery phase identified key features of atherosclerotic progression and potential biomarkers. Biomarker panels were refined using random forest models in the testing cohort, and their performance was evaluated in a blinded validation cohort to assess their ability to monitor the occurrence and development of CA.

resultsMulti-omics analysis revealed that plasma metabolites exhibited the strongest correlation with CA severity, effectively distinguished different CA stages from healthy controls. Post hoc analysis confirmed the diagnostic model's robustness, with an AUC value higher than .933 (95% CI: .828-.984, sensitivity 93.75%, and specificity 80%). In the blinded validation cohort, the biomarker panel achieved AUC values of .821-.898 for CA occurrence and .649-.849 for CA severity. Notably, 90% of these biomarkers remained significant after adjusting for comorbidities (p < .05).

conclusionsThis study identified significant metabolic changes during CA progression and established biomarker panels with potential diagnostic value for assessing CA severity. Key metabolites including cholesteryl sulphate, azelaic acid, tryptophan, arabinofuranosyluracil, TMAO, ADMA, LPC18:2, tartaric acid, L-citrulline, and L-proline, purine, sorbitol, and 2-aminoadipic acid. These findings highlight the potential of these biomarkers to improve early diagnosis and personalised management of CA.

Indexed as

BiomarkersCoronary Artery DiseaseAgedCase-Control StudiesCohort StudiesFemaleHumansMaleMetabolomicsMiddle AgedMultiomicsBiomarkersbiomarker panelcoronary atherosclerosisdiagnosticplasma metabolites

Identifiers

PMID40842288
PMCPMC12371206

What OpenQuestion holds

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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.