Evidence map›Paper›PMID 38184583›Full record

ArticleCardiovascular diabetology2024

Characterizing the metabolic divide: distinctive metabolites differentiating CAD-T2DM from CAD patients.

Yingjian Liu, Ju-E Liu, Huafeng He, Min Qin, Heping Lei, Jinxiu Meng, Chen Liu, Xiaoping Chen, Wenwei Luo, Shilong Zhong

Open access · goldAbstract read
In one paragraph

Article in Cardiovascular diabetology, 2024. 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
1.9field-weighted citation impact, top 16% of its field
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, 8 citations in OpenAlex.

  1. Article
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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

10 authors at 3 institutions in 1 country.

Yingjian Liu *School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
Ju-E Liu *Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
Huafeng He *School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
Min QinSchool of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
Heping LeiGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
Jinxiu MengGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
Chen LiuDepartment of Cardiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Xiaoping ChenDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.
Wenwei LuoDepartment of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China. luowenwei@gdph.org.cn.
Shilong ZhongSchool of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China. shz2020@qq.com.
Guangdong Academy of Medical Sciences · CNCentral South University · CNSun Yat-sen University · CN

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515011133Key-Area Research and Development Program of Guangdong Province, China 2019B020229003National Key Research and Development Program of China 2017YFC0909301National Natural Science Foundation of China 82003746National Natural Science Foundation of China 82274016Science and Technology Program of Guangzhou 2023A04J0542Science and Technology Program of Guangzhou 2023B03J1251
6 · The paper itself

Abstract

objectiveTo delineate the metabolomic differences in plasma samples between patients with coronary artery disease (CAD) and those with concomitant CAD and type 2 diabetes mellitus (T2DM), and to pinpoint distinctive metabolites indicative of T2DM risk.

methodPlasma samples from CAD and CAD-T2DM patients across three centers underwent comprehensive metabolomic and lipidomic analyses. Multivariate logistic regression was employed to discern the relationship between the identified metabolites and T2DM risk. Characteristic metabolites' metabolic impacts were further probed through hepatocyte cellular experiments. Subsequent transcriptomic analyses elucidated the potential target sites explaining the metabolic actions of these metabolites.

resultsMetabolomic analysis revealed 192 and 95 significantly altered profiles in the discovery (FDR < 0.05) and validation (P < 0.05) cohorts, respectively, that were associated with T2DM risk in univariate logistic regression. Further multivariate regression analyses identified 22 characteristic metabolites consistently associated with T2DM risk in both cohorts. Notably, pipecolinic acid and L-pipecolic acid, lysine derivatives, exhibited negative association with CAD-T2DM and influenced cellular glucose metabolism in hepatocytes. Transcriptomic insights shed light on potential metabolic action sites of these metabolites.

conclusionsThis research underscores the metabolic disparities between CAD and CAD-T2DM patients, spotlighting the protective attributes of pipecolinic acid and L-pipecolic acid. The comprehensive metabolomic and transcriptomic findings provide novel insights into the mechanism research, prophylaxis and treatment of comorbidity of CAD and T2DM.

Indexed as

Coronary Artery DiseaseDiabetes Mellitus, Type 2Gene Expression ProfilingHepatocytesHumansMetabolomicsCoronary artery diseaseGlucose metabolismLipidomicsMetabolomicsType 2 diabetes mellitus

Identifiers

PMID38184583
PMCPMC10771670
OpenAlexW4390640288

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Registered trials

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