Evidence map›Paper›PMID 41790169›Full record

ArticleGeroScience2026

Modifiable risk factors associated with increased retinal age gap in an Australian population.

Yuqing Lu, Ruiye Chen, Samantha Sze-Yee Lee, Gareth Lingham, Wenyi Hu, Michael Hunter, David A Mackey, Zhuoting Zhu

Abstract read
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In one paragraph

Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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

8 authors.

Yuqing LuCentre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.ORCID http://orcid.org/0009-0006-8230-6338
Ruiye ChenCentre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.ORCID http://orcid.org/0000-0003-3128-0394
Samantha Sze-Yee LeeCentre for Ophthalmology and Visual Science (Incorporating the Lions Eye Institute), University of Western Australia, Perth, WA, Australia.ORCID http://orcid.org/0000-0001-6635-1098
Gareth LinghamCentre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.ORCID http://orcid.org/0000-0002-8957-0733
Wenyi HuCentre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.ORCID http://orcid.org/0000-0001-7366-3874
Michael HunterSchool of Population and Global Health, University of Western Australia, Perth, WA, Australia. Michael.Hunter@health.wa.gov.au.ORCID http://orcid.org/0000-0001-6704-4815
David A MackeyCentre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia. david.mackey@lei.org.au.ORCID http://orcid.org/0000-0001-7914-4709
Zhuoting ZhuCentre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia. lisa.zhu@unimelb.edu.au.ORCID http://orcid.org/0000-0002-9897-1192

Funding

National Health and Medical Research Council 2010072National Health and Medical Research Council APP1175405
6 · The paper itself

Abstract

Retinal age gap (RAG)-the difference between retina-predicted age and chronological age-indicates biological ageing that has been linked to the risk of mortality and chronic disease. We aimed to identify risk factors associated with higher RAG in an Australian population. This cross-sectional study included 5107 participants from the Busselton Healthy Ageing Study (BHAS), a Western Australian community-based cohort. Retinal age was estimated using a validated deep-learning model applied to fundus photographs. Multivariable linear regression models were employed to examine associations between sociodemographic, lifestyle, and clinical factors and the RAG. RCS analysis was performed to investigate potential non-linear relationships and determine threshold effects between each risk factor and the RAG. A total of 4798 BHAS participants had available retinal images for RAG estimation, with a mean age of 58.0 years (SD = 5.8). Fifty-five percent were women, and around 1.5% identified as non-Caucasians. After adjusting for age, sex, and ethnicity, systolic blood pressure (SBP) (β = 0.033, 95% confidence interval [CI]: 0.025-0.042, p < 0.001), diastolic blood pressure (β = 0.052, 95% CI: 0.038-0.066, p < 0.001), body mass index (BMI) (β = 0.065, 95% CI: 0.039-0.092, p < 0.001), alcohol consumption (β = 0.146, 95% CI: 0.078-0.215, p < 0.001), and glycated haemoglobin (HbA1c) (β = 0.461, 95% CI: 0.224-0.698, p < 0.001) were significantly, positively associated with RAG. This indicates the potential clinical use of RAG to facilitate epidemiological investigation, risk stratification, healthy ageing promotion, and reducing age-related disease burden.

Indexed as

Anti-agingAustralian populationRetinal ageRisk factors

Identifiers

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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.