The Association of Retinal age gap with metabolic syndrome and inflammation

Abstract

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.

Ageing Biomarkers Derived From Retinal Imaging: A Scoping Review

“…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).…”

“…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.…”

“…Several cross-sectional studies utilising the 'Retinal Age' model explored the associations between certain lifestyle diseases and RAG. Central obesity [p<0.001] (Chen,, higher glycemia levels [p<0.001] (Chen, Xu, 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].The 'EyeAge'The Abreu 'convolutional network-based model' determined, from a case-control study, that in diabetic patients from the Retisalud programme of the Canary Islands, a higher RAG was associated with more severe, progressive diabetic retinopathy [p<0.001] (Abreu-Gonzalez et al, 2023), echoing the findings of the 'Retinal Age' model in diabetic patients(Chen, Zhang, Hu, et al, 2023).The 'RetiAGE' model also used the UK Biobank cohort to assess its clinical performance. In contrast to 'Retinal Age' and the 'convolutional network-based model,' which utilised calculated RAG for outcome assessment, the developers of the 'RetiAGE' biological age marker directly investigated its association with different age-related outcomes.…”

Metabolic Syndrome and the Aging Retina: A Systematic Review

“…Similar tendencies were found for the risk of inflammation as well as the combined risk of metS and inflammation. 13 In the course of our investigations into the links between retinal age gaps, MetS, and inflammation, we have been able to make use of some very valuable suggestions that were provided to us by earlier research that had been conducted. First, alterations in the structural makeup of the retina were found in patients who suffered from both MetS and inflammation.…”

“…Why was a particular study chosen to be included in the synthesis of the available literature? RESULT Zhu, et al (2023) 13 showed the retinal age gap was found to be substantially linked with MetS and inflammation. Specifically, compared to participants with retinal age gaps in the lowest quartile, the risk of MetS increased by 10% and 14% for those with retinal age gaps in the third and fourth quartiles, respectively (odds ratio [OR] = 1.10; 95% confidence interval [CI] = 1.01,1.21;, p = 0.030; OR = 1.14, 95% CI = 1.03,1.26; p = 0.012, respectively).…”