{"title":"日本人视网膜年龄差距与全身性疾病之间的关系:永滨研究。","authors":"Takuro Kamei, Masahiro Miyake, Keina Sado, Kazuya Morino, Yuki Mori, Yasuharu Tabara, Fumihiko Matsuda, Hiroshi Tamura, Akitaka Tsujikawa","doi":"10.1007/s10384-025-01205-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the retinal age gap, defined as the difference between deep learning-predicted retinal age and chronological age, as a potential biomarker of systemic health in the Japanese population.</p><p><strong>Study design: </strong>Prospective cohort study.</p><p><strong>Methods: </strong>Data from the Nagahama Study, a large-scale Japanese cohort study, were used. Participants were divided into fine-tuning (n=2,261) and analysis (n=6,070) cohorts based on their visit status across the two periods. The fine-tuning cohort only included individuals without a history of systemic or cardiovascular diseases. A deep learning model, originally released in the Japan Ocular Imaging Registry, was fine-tuned using a fine-tuning cohort to predict retinal age from images. This refined model was then applied to the analysis cohort to calculate retinal age gaps. We conducted cross-sectional and longitudinal analyses to examine the association of these gaps with systemic and cardiovascular diseases.</p><p><strong>Results: </strong>The retinal age-prediction model achieved a mean absolute error of 3.00-3.42 years. Cross-sectional analysis revealed significant associations between the retinal age gap and a history of diabetes (β = 1.08, p < 0.001) and hyperlipidemia (β = -0.67, p < 0.001). Longitudinal analysis showed no significant association between the baseline retinal age gap and disease onset. However, onset of hypertension (β = 0.35, p = 0.049) and hyperlipidemia (β = 0.34, p = 0.035) showed marginal associations with an increase in retinal age gap over time.</p><p><strong>Conclusion: </strong>The retinal age gap is a promising biomarker for systemic health, particularly in relation to diabetes, hypertension, and hyperlipidemia.</p>","PeriodicalId":14563,"journal":{"name":"Japanese Journal of Ophthalmology","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Association between the retinal age gap and systemic diseases in the Japanese population: the Nagahama study.\",\"authors\":\"Takuro Kamei, Masahiro Miyake, Keina Sado, Kazuya Morino, Yuki Mori, Yasuharu Tabara, Fumihiko Matsuda, Hiroshi Tamura, Akitaka Tsujikawa\",\"doi\":\"10.1007/s10384-025-01205-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To investigate the retinal age gap, defined as the difference between deep learning-predicted retinal age and chronological age, as a potential biomarker of systemic health in the Japanese population.</p><p><strong>Study design: </strong>Prospective cohort study.</p><p><strong>Methods: </strong>Data from the Nagahama Study, a large-scale Japanese cohort study, were used. 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引用次数: 0
摘要
目的:研究视网膜年龄差距,定义为深度学习预测的视网膜年龄与实足年龄之间的差异,作为日本人群系统健康的潜在生物标志物。研究设计:前瞻性队列研究。方法:数据来自Nagahama研究,这是一项大规模的日本队列研究。参与者根据他们在两个时期的访问状态被分为微调组(n= 2261)和分析组(n= 6070)。微调队列只包括没有全身性或心血管疾病史的个体。一个深度学习模型,最初发布在日本眼成像登记处,使用微调队列进行微调,从图像中预测视网膜年龄。然后将这个改进的模型应用于分析队列来计算视网膜年龄差距。我们进行了横断面和纵向分析,以检查这些间隙与全身和心血管疾病的关系。结果:视网膜年龄预测模型的平均绝对误差为3.00 ~ 3.42年。横断面分析显示,视网膜年龄差距与糖尿病史(β = 1.08, p < 0.001)和高脂血症史(β = -0.67, p < 0.001)之间存在显著关联。纵向分析显示基线视网膜年龄差距与疾病发病之间无显著关联。然而,随着时间的推移,高血压(β = 0.35, p = 0.049)和高脂血症(β = 0.34, p = 0.035)的发病与视网膜年龄差距的增加呈边缘相关性。结论:视网膜年龄差距是一种很有前景的全身健康生物标志物,特别是与糖尿病、高血压和高脂血症有关。
Association between the retinal age gap and systemic diseases in the Japanese population: the Nagahama study.
Purpose: To investigate the retinal age gap, defined as the difference between deep learning-predicted retinal age and chronological age, as a potential biomarker of systemic health in the Japanese population.
Study design: Prospective cohort study.
Methods: Data from the Nagahama Study, a large-scale Japanese cohort study, were used. Participants were divided into fine-tuning (n=2,261) and analysis (n=6,070) cohorts based on their visit status across the two periods. The fine-tuning cohort only included individuals without a history of systemic or cardiovascular diseases. A deep learning model, originally released in the Japan Ocular Imaging Registry, was fine-tuned using a fine-tuning cohort to predict retinal age from images. This refined model was then applied to the analysis cohort to calculate retinal age gaps. We conducted cross-sectional and longitudinal analyses to examine the association of these gaps with systemic and cardiovascular diseases.
Results: The retinal age-prediction model achieved a mean absolute error of 3.00-3.42 years. Cross-sectional analysis revealed significant associations between the retinal age gap and a history of diabetes (β = 1.08, p < 0.001) and hyperlipidemia (β = -0.67, p < 0.001). Longitudinal analysis showed no significant association between the baseline retinal age gap and disease onset. However, onset of hypertension (β = 0.35, p = 0.049) and hyperlipidemia (β = 0.34, p = 0.035) showed marginal associations with an increase in retinal age gap over time.
Conclusion: The retinal age gap is a promising biomarker for systemic health, particularly in relation to diabetes, hypertension, and hyperlipidemia.
期刊介绍:
The Japanese Journal of Ophthalmology (JJO) was inaugurated in 1957 as a quarterly journal published in English by the Ophthalmology Department of the University of Tokyo, with the aim of disseminating the achievements of Japanese ophthalmologists worldwide. JJO remains the only Japanese ophthalmology journal published in English. In 1997, the Japanese Ophthalmological Society assumed the responsibility for publishing the Japanese Journal of Ophthalmology as its official English-language publication.
Currently the journal is published bimonthly and accepts papers from authors worldwide. JJO has become an international interdisciplinary forum for the publication of basic science and clinical research papers.