Evidence map›Paper›PMID 39369956›Full record

ArticleJournal of advanced research2025

Serum metabolite biomarkers for the early diagnosis and monitoring of age-related macular degeneration.

Shengjie Li, Yichao Qiu, Yingzhu Li, Jianing Wu, Ning Yin, Jun Ren, Mingxi Shao, Jian Yu, Yunxiao Song, Xinghuai Sun and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 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

12 authors.

Shengjie LiDepartment of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia and Related Eye Diseases, Shanghai 200031, China; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai 200031, China. Electronic address: lishengjie6363020@163.com.
Yichao QiuDepartment of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China.
Yingzhu LiDepartment of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China.
Jianing WuDepartment of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China.
Ning YinDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China; National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai 200080, China.
Jun RenDepartment of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China.
Mingxi ShaoDepartment of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China.
Jian YuEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia and Related Eye Diseases, Shanghai 200031, China; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai 200031, China.
Yunxiao SongDepartment of Clinical Laboratory, Shanghai Xuhui Central Hospital, Fudan University, Shanghai 200031, China.
Xinghuai SunEye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia and Related Eye Diseases, Shanghai 200031, China; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai 200031, China.
Shunxiang GaoDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China; National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai 200080, China. Electronic address: shunxianggao@163.com.
Wenjun CaoDepartment of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia and Related Eye Diseases, Shanghai 200031, China; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai 200031, China. Electronic address: wgkjyk@aliyun.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAge-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide, with significant challenges for early diagnosis and treatment.

objectivesTo identify new biomarkers that are important for the early diagnosis and monitoring of the severity/progression of AMD.

methodsWe investigated the diagnostic and monitoring potential of blood metabolites in a cohort of 547 individuals (167 healthy controls, 240 individuals with other eye diseases as eye disease controls, and 140 individuals with AMD) from 2 centers over three phases: discovery phase 1, discovery phase 2, and an external validation phase. The samples were analyzed via a mass spectrometry-based, widely targeted metabolomic workflow. In discovery phases 1 and 2, we built a machine learning algorithm to predict the probability of AMD. In the external validation phase, we further confirmed the performance of the biomarker panel identified by the algorithm. We subsequently evaluated the performance of the identified biomarker panel in monitoring the progression and severity of AMD.

resultsWe developed a clinically specific three-metabolite panel (hypoxanthine, 2-furoylglycine, and 1-hexadecyl-2-azelaoyl-sn-glycero-3-phosphocholine) via five machine learning models. The random forest model effectively discriminated patients with AMD from patents in the other two groups and showed acceptable calibration (area under the curve (AUC) = 1.0; accuracy = 1.0) in both discovery phases 1 and 2. An independent validation phase confirmed the diagnostic model's efficacy (AUC = 0.962; accuracy = 0.88). The three-biomarker panel model demonstrated an AUC of 1.0 in differentiating the severity of AMD via RF machine learning, which was consistent across both the discovery and external validation phases. Additionally, the biomarker concentrations remained stable under repeated freeze-thaw cycles (P > 0.05).

conclusionsThis study reveals distinct metabolite variations in the serum of AMD patients, paving the way for the development of the first routine laboratory test for AMD.

Indexed as

BiomarkersMacular DegenerationAgedAged, 80 and overCase-Control StudiesDisease ProgressionEarly DiagnosisFemaleHumansMachine LearningMaleMetabolomicsMiddle AgedBiomarkersAge-related macular degenerationDiagnosisMachine learningWidely targeted metabolomics

Identifiers

PMID39369956
PMCPMC12302346

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.