Evidence map›Paper›PMID 39102240›Full record

ArticleTranslational vision science & technology2024

Identification of the Metabolic Signature of Aging Retina.

Wan Mu, Xiaoyan Han, Ming Tong, Shuai Ben, Mudi Yao, Ya Zhao, Jiao Xia, Ling Ren, Chang Huang, Duo Li and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2024. 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.

  1. Article
  2. Article
  3. 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.

Wan MuEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Xiaoyan HanEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Ming TongEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Shuai BenDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Mudi YaoDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ya ZhaoDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiao XiaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ling RenEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Chang HuangEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Duo LiThe Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China.
Xiumiao LiThe Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China.
Qin JiangThe Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China.
Biao YanDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aims to explore the metabolic signature of aging retina and identify the potential metabolic biomarkers for the diagnosis of retinal aging. Methods: Retinal samples were collected from both young (two months) and aging (14 months) mice to conduct an unbiased metabolic profiling. Liquid chromatography-tandem mass spectrometry analysis was conducted to screen for the metabolic biomarkers and altered signaling pathways associated with retinal aging. Results: We identified 166 metabolites differentially expressed between young and aged retinas using a threshold of orthogonal projection to latent structures discriminant analysis variable importance in projection >1 and P < 0.05. These metabolites were significantly enriched in several metabolic pathways, including purine metabolism, citrate cycle, phenylalanine, tyrosine and tryptophan biosynthesis, glycerophospholipid metabolism, and alanine, aspartate and glutamate metabolism. Among these significantly enriched pathways, glycerophospholipid metabolites emerged as promising candidates for retinal aging biomarkers. We assessed the potential of these metabolites as biomarkers through an analysis of their sensitivity and specificity, determined by the area under the receiver-operating characteristic (ROC) curves. Notably, the metabolites like PC (15:0/22:6), PC (17:0/14:1), LPC (P-16:0), PE (16:0/20:4), and PS (17:0/16:1) demonstrated superior performance in sensitivity, specificity, and accuracy in predicting retinal aging. Conclusions: This study sheds light on the molecular mechanisms underlying retinal aging by identifying distinct metabolic profiles and pathways. These findings provide a valuable foundation for developing future clinical applications in diagnosing, identifying, and treating age-related retinal degeneration. Translational Relevance: This study sheds light on novel metabolic profiles and biomarkers in aging retinas, potentially paving the way for targeted interventions in preventing, diagnosing, and treating age-related retinal degeneration and other retinal diseases.

Indexed as

AgingBiomarkersMice, Inbred C57BLRetinaTandem Mass SpectrometryAnimalsChromatography, LiquidMetabolic Networks and PathwaysMetabolomeMetabolomicsMiceROC CurveBiomarkers

Identifiers

PMID39102240
PMCPMC11309042

What Socratic holds

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LicenceCC BY-NC-ND
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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.