ArticleGeroScience2026
Modifiable risk factors associated with increased retinal age gap in an Australian population.
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.
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.
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.
Who cites it
1 citing paper in PubMed.
- Clinical determinants of retinal age gap estimated from fundus photographs in glaucoma patients.Scientific reports · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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
Identifiers
41790169What OpenQuestion holds
Registered trials
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.