Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease

Zhu, Zhuoting; Chen, Yifan; Wang, Wei; Wang, Yueye; Hu, Wenyi; Shang, Xianwen; Liao, Huan; Shi, Danli; Huang, Yu; Ha, Jason; Tan, Zachary; Kiburg, Katerina; Zhang, Xueli; Tang, Shulin; Yu, Honghua; Yang, Xiaohong; He, Mingguang

doi:10.1161/strokeaha.122.038809

Cited by 29 publications

(14 citation statements)

References 74 publications

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“…Retinal age gap was defined as the difference between retina‐predicted age and chronological age, showing the deviations from normal aging. Our group has verified that the retinal age gap could independently predict future death events and age‐related diseases events including cardiovascular diseases and Parkinson's disease 13–15 . Taken together, retinal age could reflect the overall health and thus has been considered as a reliable indicator of aging.…”

Section: Introductionmentioning

confidence: 55%

“…Our group has verified that the retinal age gap could independently predict future death events and age-related diseases events including cardiovascular diseases and Parkinson's disease. [13][14][15] Taken together, retinal age could reflect the overall health and thus has been considered as a reliable indicator of aging. As age is an independent risk factor for MetS, we hypothesized that retinal age gaps may be associated with MetS.…”

Section: Introductionmentioning

confidence: 99%

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The Association of Retinal age gap with metabolic syndrome and inflammation

Zhu

Liú

Chen

et al. 2023

Journal of Diabetes

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Background Metabolic syndrome (MetS) is a clustering of cardiometabolic components, posing tremendous burdens in the aging society. Retinal age gap has been proposed as a robust biomarker associated with mortality and Parkinson's disease. Although MetS and chronic inflammation could accelerate the aging process and increase the risk of mortality, the association of the retinal age gap with MetS and inflammation has not been examined yet. Methods Retinal age gap (retina‐predicted age minus chronological age) was calculated using a deep learning model. MetS was defined as the presence of three or more of the following: central obesity, hypertension, dyslipidemia, hypertriglyceridemia, and hyperglycemia. Inflammation index was defined as a high‐sensitivity C‐reactive protein level above 3.0 mg/L. Logistic regression models were used to examine the associations of retinal age gaps with MetS and inflammation. Results We found that retinal age gap was significantly associated with MetS and inflammation. Specifically, compared to participants with retinal age gaps in the lowest quartile, the risk of MetS was significantly increased by 10% and 14% for participants with retinal age gaps in the third and fourth quartile (odds ratio [OR]:1.10; 95% confidence interval [CI], 1.01,1.21;, p = .030; OR: 1.14, 95% CI, 1.03,1.26; p = .012, respectively). Similar trends were identified for the risk of inflammation and combined MetS and inflammation. Conclusion We found that retinal age gaps were significantly associated with MetS as well as inflammation. Given the noninvasive and cost‐effective nature and the efficacy of the retinal age gap, it has great potential to be used as a screening tool for MetS in large populations.

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Section: Introductionmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

The Association of Retinal age gap with metabolic syndrome and inflammation

Zhu

Liú

Chen

et al. 2023

Journal of Diabetes

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“…Several articles have detected, by analyzing retinal fundus images from large cohorts using deep learning algorithms, that the retinal age gap (the difference between retinal age and chronological age) is a robust biomarker for aging and closely related to the risk of mortality (Nusinovici et al, 2022;Zhu et al, 2022b). Moreover, it is associated with arterial stiffness and future cardiovascular disease events (Zhu et al, 2022a) and is able to identify individuals at high risk of developing future Parkinson's disease (Hu et al, 2022). These findings suggest that retinal measures could potentially be used as a proxy of biological aging, but further research in this area is needed.…”

Section: Discussionmentioning

confidence: 99%

Macular vessel density in the superficial plexus is not associated to cerebrospinal fluid core biomarkers for Alzheimer’s disease in individuals with mild cognitive impairment: The NORFACE cohort

et al. 2023

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BackgroundOptical coherence tomography angiography (OCT-A) is a novel method in the dementia field that allows the detection of retinal vascular changes. The comparison of OCT-A measures with established Alzheimer’s disease (AD)-related biomarkers is essential to validate the former as a marker of cerebrovascular impairment in the AD continuum. We aimed to investigate the association of macular vessel density (VD) in the superficial plexus quantified by OCT-A with the AT(N) classification based on cerebrospinal fluid (CSF) Aβ1-42, p181-tau and t-tau measurements in individuals with mild cognitive impairment (MCI).Materials and methodsClinical, demographic, ophthalmological, OCT-A and CSF core biomarkers for AD data from the Neuro-ophthalmology Research at Fundació ACE (NORFACE) project were analyzed. Differences in macular VD in four quadrants (superior, nasal, inferior, and temporal) among three AT(N) groups [Normal, Alzheimer and Suspected non-Alzheimer pathology (SNAP)] were assessed in a multivariate regression model, adjusted for age, APOE ε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, chronic obstructive pulmonary disease and smoking habit, using the Normal AT(N) group as the reference category.ResultsThe study cohort comprised 144 MCI participants: 66 Normal AT(N), 45 Alzheimer AT(N) and 33 SNAP AT(N). Regression analysis showed no significant association of the AT(N) groups with any of the regional macular VD measures (all, p > 0.16). The interaction between sex and AT(N) groups had no effect on differentiating VD. Lastly, CSF Aβ1-42, p181-tau and t-tau measures were not correlated to VD (all r < 0.13; p > 0.13).DiscussionOur study showed that macular VD measures were not associated with the AT(N) classification based on CSF biomarkers in patients with MCI, and did not differ between AD and other underlying causes of cognitive decline in our cohort.

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“…The retinal age gap, that is, the difference between the retinal age predicted by a DL model and the chronological age of a person, can predict a patient's mortality. One year increase in the retinal age gap was associated with a 2% increased risk of all‐cause mortality in a study by Zhu, Chen, et al (2022). This shows the potential of predicting age from retinal images as a screening tool for risk stratification.…”

Section: Identification Of Risk Factors Of Systemic Diseasesmentioning

confidence: 96%

Use of artificial intelligence algorithms to predict systemic diseases from retinal images

Khan

Surya

Roy

et al. 2023

WIREs Data Min & Knowl

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The rise of non‐invasive, rapid, and widely accessible quantitative high‐resolution imaging methods, such as modern retinal photography and optical coherence tomography (OCT), has significantly impacted ophthalmology. These techniques offer remarkable accuracy and resolution in assessing ocular diseases and are increasingly recognized for their potential in identifying ocular biomarkers of systemic diseases. The application of artificial intelligence (AI) has been demonstrated to have promising results in identifying age, gender, systolic blood pressure, smoking status, and assessing cardiovascular disorders from the fundus and OCT images. Although our understanding of eye–body relationships has advanced from decades of conventional statistical modeling in large population‐based studies incorporating ophthalmic assessments, the application of AI to this field is still in its early stages. In this review article, we concentrate on the areas where AI‐based investigations could expand on existing conventional analyses to produce fresh findings using retinal biomarkers of systemic diseases. Five databases—Medline, Scopus, PubMed, Google Scholar, and Web of Science were searched using terms related to ocular imaging, systemic diseases, and artificial intelligence characteristics. Our review found that AI has been employed in a wide range of clinical tests and research applications, primarily for disease prediction, finding biomarkers and risk factor identification. We envisage artificial intelligence‐based models to have significant clinical and research impacts in the future through screening for high‐risk individuals, particularly in less developed areas, and identifying new retinal biomarkers, even though technical and socioeconomic challenges remain. Further research is needed to validate these models in real‐world setting.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Prediction

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Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease

Cited by 29 publications

References 74 publications

The Association of Retinal age gap with metabolic syndrome and inflammation

The Association of Retinal age gap with metabolic syndrome and inflammation

Macular vessel density in the superficial plexus is not associated to cerebrospinal fluid core biomarkers for Alzheimer’s disease in individuals with mild cognitive impairment: The NORFACE cohort

Use of artificial intelligence algorithms to predict systemic diseases from retinal images

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