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Retinal age gap as a predictive biomarker of stroke risk

BACKGROUND: The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke. METHODS: A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep lear...

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Autores principales: Zhu, Zhuoting, Hu, Wenyi, Chen, Ruiye, Xiong, Ruilin, Wang, Wei, Shang, Xianwen, Chen, Yifan, Kiburg, Katerina, Shi, Danli, He, Shuang, Huang, Yu, Zhang, Xueli, Tang, Shulin, Zeng, Jieshan, Yu, Honghua, Yang, Xiaohong, He, Mingguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710167/
https://www.ncbi.nlm.nih.gov/pubmed/36447293
http://dx.doi.org/10.1186/s12916-022-02620-w
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author Zhu, Zhuoting
Hu, Wenyi
Chen, Ruiye
Xiong, Ruilin
Wang, Wei
Shang, Xianwen
Chen, Yifan
Kiburg, Katerina
Shi, Danli
He, Shuang
Huang, Yu
Zhang, Xueli
Tang, Shulin
Zeng, Jieshan
Yu, Honghua
Yang, Xiaohong
He, Mingguang
author_facet Zhu, Zhuoting
Hu, Wenyi
Chen, Ruiye
Xiong, Ruilin
Wang, Wei
Shang, Xianwen
Chen, Yifan
Kiburg, Katerina
Shi, Danli
He, Shuang
Huang, Yu
Zhang, Xueli
Tang, Shulin
Zeng, Jieshan
Yu, Honghua
Yang, Xiaohong
He, Mingguang
author_sort Zhu, Zhuoting
collection PubMed
description BACKGROUND: The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke. METHODS: A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk. RESULTS: A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00–1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37–4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511). CONCLUSIONS: We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02620-w.
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spelling pubmed-97101672022-12-01 Retinal age gap as a predictive biomarker of stroke risk Zhu, Zhuoting Hu, Wenyi Chen, Ruiye Xiong, Ruilin Wang, Wei Shang, Xianwen Chen, Yifan Kiburg, Katerina Shi, Danli He, Shuang Huang, Yu Zhang, Xueli Tang, Shulin Zeng, Jieshan Yu, Honghua Yang, Xiaohong He, Mingguang BMC Med Research Article BACKGROUND: The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke. METHODS: A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk. RESULTS: A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00–1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37–4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511). CONCLUSIONS: We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02620-w. BioMed Central 2022-11-30 /pmc/articles/PMC9710167/ /pubmed/36447293 http://dx.doi.org/10.1186/s12916-022-02620-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Zhu, Zhuoting
Hu, Wenyi
Chen, Ruiye
Xiong, Ruilin
Wang, Wei
Shang, Xianwen
Chen, Yifan
Kiburg, Katerina
Shi, Danli
He, Shuang
Huang, Yu
Zhang, Xueli
Tang, Shulin
Zeng, Jieshan
Yu, Honghua
Yang, Xiaohong
He, Mingguang
Retinal age gap as a predictive biomarker of stroke risk
title Retinal age gap as a predictive biomarker of stroke risk
title_full Retinal age gap as a predictive biomarker of stroke risk
title_fullStr Retinal age gap as a predictive biomarker of stroke risk
title_full_unstemmed Retinal age gap as a predictive biomarker of stroke risk
title_short Retinal age gap as a predictive biomarker of stroke risk
title_sort retinal age gap as a predictive biomarker of stroke risk
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710167/
https://www.ncbi.nlm.nih.gov/pubmed/36447293
http://dx.doi.org/10.1186/s12916-022-02620-w
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