Evidence map›Paper›PMID 41356437›Full record

ArticlePeerJ2025

Identification of candidate long non-coding RNAs and mRNAs associated with heart aging in mice.

Zheng Kuai, Zheng Li, Jianguo Jia, Yongle Chen, Xiaoyi Zhang, Jianhui Zhang, Yangli Ye, Lihong Gao, Ling Li, Yu Hu

Abstract read
In one paragraph

Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Zheng KuaiDepartment of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, China.
Zheng LiClinical Science Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Jianguo JiaDepartment of Cardiology, Shanghai Institute of Cardiovascular Disease, Zhongshan Hospital, Fudan University, Shanghai, China.
Yongle ChenDepartment of Echocardiography, Shanghai Institute of Cardiovascular Diseases, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China.
Xiaoyi ZhangDepartment of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, China.
Jianhui ZhangDepartment of Cardiology, Shanghai Institute of Cardiovascular Disease, Zhongshan Hospital, Fudan University, Shanghai, China.
Yangli YeDepartment of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, China.
Lihong GaoDepartment of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, China.
Ling LiDepartment of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, China.
Yu HuDepartment of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Understanding the molecular mechanisms underlying cardiac aging may uncover novel therapeutic targets for age-related cardiovascular disease. Long non-coding RNAs (lncRNA), which regulate cell differentiation and disease progression, are emerging as promising diagnostic biomarkers and therapeutic candidates. However, their expression profiles and functional roles in the aging heart remain poorly characterized. Methods: Male C57BL/6 wild type mice aged 20 months (aged group) and 3 months (young group) underwent transthoracic echocardiography to evaluate cardiac function. Myocardial aging phenotypes were assessed using hematoxylin-eosin, Masson's trichrome, terminal deoxynucleotide transferase dUTP nick end labeling (TUNEL), and senescence-associated Results: Aged mice showed increased heart weight/body weight and heart weight/tibia length ratios. Both interventricular septum in systole and left ventricular posterior wall in diastole were elevated, while ejection fraction and fractional shortening remained unchanged. The Tei index was significantly higher, suggesting impaired myocardial performance. Histological staining revealed enlarged cardiomyocytes, increased myocardial fibrosis, enhanced apoptosis, and greater senescence-associated Conclusions: Cardiac aging is characterized by structural and functional remodeling, accompanied by transcriptional reprogramming involving both lncRNAs and mRNAs. These changes offer insights into potential molecular mechanisms and provide candidate regulatory targets for diagnosis and intervention in age-related heart disease.

Indexed as

AgingHeartMyocardiumRNA, Long NoncodingRNA, MessengerAnimalsApoptosisEchocardiographyGene Expression ProfilingMaleMiceMice, Inbred C57BLMyocytes, CardiacRNA, Long NoncodingRNA, MessengerCardiac aging phenotypeMicroarrayNaturally aging miceNon-coding RNAs

Identifiers

PMID41356437
PMCPMC12679924

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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.