Evidence map›Paper›PMID 40144294›Full record

ReviewFrontiers in endocrinology2025

Non-ocular biomarkers for early diagnosis of diabetic retinopathy by non-invasive methods.

Huaixue Gou, Juping Liu

Abstract readReview
In one paragraph

Review in Frontiers in endocrinology, 2025. 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
–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

2 citing papers in PubMed.

  1. Article
  2. Review
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

2 authors.

Huaixue GouTianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
Juping LiuTianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic retinopathy (DR) is the predominant vision-threatening complication in individuals with diabetes mellitus. Timely diagnosis and intervention facilitate the prevention of diabetes-associated visual impairment. Classical imaging methods may prevent the timely detection of DR due to shortages of specialized facilities and retinal specialists, particularly in remote areas. In recent years, research on biomarkers related to DR has rapidly developed, playing an important role in risk assessment and early detection of the disease. Some ocular biomarkers from the vitreous body or aqueous humor were invasive, which hampered their application in clinical practice. Meanwhile, biomarkers based on omics were limited by their uneasily accessible use and complicated variables with a relatively low degree of reproducibility. As modern technology progresses, advanced non-ocular biomarkers of DR have established a comprehensive platform for the prompt identification of DR, independent of ophthalmic professionals or devices and accessible to non-ophthalmologists during community screenings. This review focuses on biomarkers derived from non-ocular sample sources, such as nailfold and skin, accessible through non-invasive methods, to reveal if they can be considered as an effective option for the early identification of DR by non-ophthalmologists in community screening initiatives.

Indexed as

BiomarkersDiabetic RetinopathyEarly DiagnosisHumansBiomarkersbiomarkerscommunity screeningdiabetic retinopathynailfold capillaroscopynon-invasivenon-ocularskin autofluorescence

Identifiers

PMID40144294
PMCPMC11936812

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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.