Evidence map›Paper›PMID 40328303›Full record

ArticleProceedings. Biological sciences2025

The retinal age gap: an affordable and highly accessible biomarker for population-wide disease screening across the globe.

Christopher Nielsen, Matthias Wilms, Nils Daniel Forkert

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. 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

3 authors.

Christopher NielsenDepartment of Radiology, University of Calgary, Calgary, Alberta, Canada.ORCID 0009-0000-5206-4434
Matthias WilmsDepartment of Radiology, University of Calgary, Calgary, Alberta, Canada.
Nils Daniel ForkertDepartment of Radiology, University of Calgary, Calgary, Alberta, Canada.

Funding

Canada Research Chairs ProgramLions Sight Centre
6 · The paper itself

Abstract

Traditional biomarkers, such as those obtained from blood tests, are essential for early disease detection, improving health outcomes and reducing healthcare costs. However, they often involve invasive procedures, specialized laboratory equipment or special handling of biospecimens. The retinal age gap (RAG) has emerged as a promising new biomarker that can overcome these limitations, making it particularly suitable for disease screening in low- and middle-income countries. This study aimed to evaluate the potential of the RAG as a biomarker for broad disease screening across a vast spectrum of diseases. Fundus images were collected from 86 522 UK Biobank participants aged 40-83 (mean age: 56.2 ± 8.3 years). A deep learning model was trained to predict retinal age using 17 791 images from healthy participants. The remaining images were categorized into disease/injury groups based on clinical codes. Additionally, 8524 participants from the Brazilian Multilabel Ophthalmological Dataset (BRSET) were used for external validation. Among the 159 disease/injury groups from the 2019 Global Burden of Disease Study, 56 groups (35.2%) exhibited RAG distributions significantly different from healthy controls. Notable examples included chronic kidney disease, cardiovascular disease, blindness, vision loss and diabetes. Overall, the RAG shows great promise as a cost-effective, non-invasive biomarker for early disease screening.

Indexed as

RetinaAdultAgedAged, 80 and overBiomarkersFemaleHumansMaleMass ScreeningMiddle AgedUnited KingdomBiomarkersdeep learningmachine learningretinal age gapretinal age prediction

Identifiers

PMID40328303
PMCPMC12055285

What OpenQuestion holds

Textmetadata
Read underepoch 390

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 OpenQuestion graph.