Glycemic status and its association with retinal age gap: Insights from the UK biobank study

Chen, Ruiye; Xu, Jiawen; Zhang, Xiaoxin; Zhang, Junyao; Shang, Xianwen; Ge, Zongyuan; Wang, Wei; Zhu, Zhuoting

doi:10.1016/j.diabres.2023.110817

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Several cross-sectional studies utilising the ‘Retinal Age’ model explored the associations between certain lifestyle diseases and RAG. Central obesity [p<0.001] (Chen, Zhang, Shang, et al, 2023), higher glycemia levels [p<0.001] (Chen, Xu, Zhang, et al, 2023) and metabolic syndrome and inflammation [Odds Ratio = 1.01, 95% CI: 1.00-1.02, p= 0.0016] (Zhu, Liu, et al, 2023) were associated with higher RAGs, while a study aimed at correlating the RAG with cardiovascular health metrics, composed of 7 metrics totalling a score out of 14, determined that each 1-unit score increase in cardiovascular health was associated with a 13% decrease in calculated RAG [OR = 0.87, 95% CI: 0.85-0.90, p<0.001] (Chen, Xu, Shang, et al, 2023).…”

Section: Resultsmentioning

confidence: 99%

“…However, to date, no clinical trials have explored the utility or feasibility of the models, which is a crucial aspect for determining their clinical relevance. Furthermore, factors associated with higher RAG, including glycaemic status (Chen, Xu, Zhang, et al, 2023), central obesity (Chen, Zhang, Shang, et al, 2023) and metabolic syndrome (Zhu, Liu, et al, 2023), suggest that RAG may provide valuable insight into lifestyle habits and traits that accelerate ageing. Notably, these factors were only investigated using the ‘ Retinal Age’ model.…”

Section: Discussionmentioning

confidence: 99%

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Ageing Biomarkers Derived From Retinal Imaging: A Scoping Review

Grimbly,

Koopowitz,

Chen

et al. 2024

Preprint

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Background/Aims: The emerging concept of retinal age, a biomarker derived from retinal images, holds promise in estimating biological age. The retinal age gap (RAG) represents the difference between retinal age and chronological age which serves as an indicator of deviations from normal ageing. This scoping review aims to collate studies on retinal age to determine its potential clinical utility and to identify knowledge gaps for future research. Methods: Using the PRISMA checklist, eligible non-review, human studies were identified, selected, and appraised. Pubmed, Scopus, SciELO, PsycINFO, Google Scholar, Cochrane, CINAHL, Africa Wide EBSCO, MedRxiv, and BioRxiv databases were searched to identify literature pertaining to retinal age, the RAG, and their associations. No restrictions were imposed on publication date. Results: Thirteen articles published between 2022 and 2023 were analysed, revealing four models capable of determining biological age from retinal images. Three models, Retinal Age, EyeAge and a convolutional network-based model, achieved comparable mean absolute errors (MAE): 3.55, 3.30 and 3.97 respectively. A fourth model, RetiAGE, predicting the probability of being older than 65 years, also demonstrated strong predictive ability with respect to clinical outcomes. In the models identified, a higher predicted RAG demonstrated an association with negative occurrences, notably mortality and cardiovascular health outcomes. Conclusion: This review highlights the potential clinical application of retinal age and RAG, emphasising the need for further research to establish their generalisability for clinical use, particularly in neuropsychiatry. The identified models showcase promising accuracy in estimating biological age, suggesting its viability for evaluating health status.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Ageing Biomarkers Derived From Retinal Imaging: A Scoping Review

Grimbly,

Koopowitz,

Chen

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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Association between biological aging and diabetic retinopathy

Tang,

Luo,

Zhang

et al. 2024

Sci Rep

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The impact of aging on diabetic retinopathy (DR) remains underestimated. The current study aimed to investigate the association between biological aging and DR, in contrast to chronological age (CA). Using the National Health and Nutrition Survey data from 2005 to 2008. Biological aging was evaluated through the biological age (BA) and phenotypic age (PA), which were calculated from clinical markers. DR was identified in participants with diabetes mellitus (DM) when they exhibited one or more retinal microaneurysms or retinal blot hemorrhages under retinal imaging, with or without the presence of more severe lesions. Survey-weighted multivariable logistic regression was performed, and the regression model was further fitted using restricted cubic splines. The discriminatory capability and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Based on weighted analyses, of the 3100 participants included in this study, of which 162 had DR. In the adjusted model, BA (odds ratio [OR] = 1.12, 95% CI, 1.06–1.18) and PA (OR = 1.11, 95% CI, 1.07–1.14) were associated with DR, while CA was not significantly (OR = 1.01, 95% CI, 0.99–1.03). Narrowing the analysis to DM participants and adjusting for factors like insulin showed similar results. ROC and DCA analyses indicate that BA/PA predicted DR better than CA and offer greater clinical utility. The positive association between BA/PA and DR was consistent across subgroups despite potential interactions. Biological aging heightens DR risk, with BA/PA showing a stronger association than CA. Our findings underscored the importance of timely anti-aging interventions for preventing DR.

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Phenotypic age and biological age mediate the association between Life's Essential 8 and diabetic retinopathy among US adults

Wang,

Jin,

et al. 2023

Preprint

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Background Cardiovascular disease (CVD) is closely associated with Diabetic retinopathy (DR), and the prevalence of DR is steadily increasing. Life's Essential 8 (LE8), a recently updated assessment of cardiovascular health (CVH), was examined in this study to determine if it was related to diabetes among adults in the United States. Methods 316 DR participants and 2,314 non-DR participants were included in the analyses using data from the National Health and Nutrition Examination Survey (NHANES) database (2005–2018). LE8 scores, derived from eight individual metrics, were categorized into three groups: low (0–49), moderate (50–74), and high (75–100). Combining certain biomarker sets allowed for the measurement of both phenotypic age (PA) and biological age (BA). In order to evaluate the relationship between LE8 scores and DR, weighted logistic regression and linear analysis were used. The mediating function of PA and BA was investigated by mediation analysis. Results After adjusting for all covariates, higher LE8 scores were associated with a lower risk of DR (odds ratio [OR], 0.49; 95% confidence interval [CI], 0.26–0.93; P for trend = 0.027). A 10-score increase in LE8 score was related to a 17% decrease in the risk of DR (multivariate-adjusted OR = 0.83, 95% CI: 0.73–0.95). Mediation analysis indicated that PA and BA partially mediated 47.57% and 28.11% of the association between LE8 scores and DR, respectively. Conclusion A negative association was found between LE8 scores and the presence of DR, possibly mediated by PA and BA. As a result of these findings, encouraging adherence to optimal CVH levels may be beneficial in reducing the prevalence of DR.

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Glycemic status and its association with retinal age gap: Insights from the UK biobank study

Cited by 4 publications

References 34 publications

Ageing Biomarkers Derived From Retinal Imaging: A Scoping Review

Ageing Biomarkers Derived From Retinal Imaging: A Scoping Review

Association between biological aging and diabetic retinopathy

Phenotypic age and biological age mediate the association between Life's Essential 8 and diabetic retinopathy among US adults

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