Yang Kai, Zhu Xiaoxuan, Li Ziyu, Lian Wei, Yan Jinxia, Ding Shasha, Wang Zhenqing, Wang Yudie, Ai Jiaqi, Guo Zhengyang, Su Binbin, Qu Jia, Lu Fan, Cui Lele, Li Ming
National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Front Physiol. 2025 Aug 26;16:1601093. doi: 10.3389/fphys.2025.1601093. eCollection 2025.
To develop a retinal age prediction model based on a foundation model using fundus images and to determine the association between gamma-glutamyl transferase (GGT) levels and the retinal age gap.
A total of 36,044 fundus images with reasonable quality from 9,752 participants in the Jidong Eye Cohort Study were included in this study. Of these images, 8,869 fundus images from 3,010 healthy individuals were used to train and validate the model based on the foundation model RETFound for age prediction using 10-fold cross-validation. A total of 4,081 fundus images from 4,081 participants who were enrolled from May to October 2023 had available GGT data, and these images were used to investigate the association between GGT levels and the retinal age gap.
The trained model in this study achieved excellent performance, with a mean absolute error (MAE) of 2.42 ± 0.08 years. The mean age of the participants in the analysis dataset was 43.7 ± 10.4 years, and 1987 (48.7%) participants were women. The multivariable βs and 95% confidence intervals (CIs) of the retinal age gap in the second, third, and fourth GGT quartiles compared with the lowest GGT quartiles were 0.42 (0.08-0.77), 0.54 (0.15-0.92), and 0.72 (0.29-1.14) (P for trend = 0.001), respectively, in the fully adjusted model (adjusted for age, sex, current smoking status, current drinking status, body mass index, hypertension, diabetes, dyslipidemia, and serum uric acid).
Increased GGT levels were significantly associated with accelerated retinal aging as quantified by the retinal age gap. Our findings indicate that elevated GGT levels may have an adverse effect on the aging process.
基于基础模型利用眼底图像开发视网膜年龄预测模型，并确定γ-谷氨酰转移酶（GGT）水平与视网膜年龄差距之间的关联。
本研究纳入了冀东眼病队列研究中9752名参与者的36044张质量合理的眼底图像。在这些图像中，来自3010名健康个体的8869张眼底图像用于基于基础模型RETFound训练和验证年龄预测模型，采用10折交叉验证。共有来自2023年5月至10月招募的4081名参与者的4081张眼底图像有可用的GGT数据，这些图像用于研究GGT水平与视网膜年龄差距之间的关联。
本研究中训练的模型表现出色，平均绝对误差（MAE）为2.42±0.08岁。分析数据集中参与者的平均年龄为43.7±10.4岁，1987名（48.7%）参与者为女性。在完全调整模型（调整年龄、性别、当前吸烟状况、当前饮酒状况、体重指数、高血压、糖尿病、血脂异常和血清尿酸）中，与最低GGT四分位数相比，第二、第三和第四GGT四分位数的视网膜年龄差距的多变量β值和95%置信区间（CI）分别为0.42（0.08 - 0.77）、0.54（0.15 - 0.92）和0.72（0.29 - 1.14）（趋势P值 = 0.001）。
GGT水平升高与视网膜年龄差距量化的视网膜衰老加速显著相关。我们的研究结果表明，GGT水平升高可能对衰老过程产生不利影响。
