PDF(Pubmed)
Abstract：
Biological age, distinct from an individual\'s chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals\' chronological age. Our retinal aging clocking, \'eyeAge\', predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank, respectively). Additionally, eyeAge was independent of blood marker-based measures of biological age, maintaining an all-cause mortality hazard ratio of 1.026 even when adjusted for phenotypic age. The individual-specific nature of eyeAge was reinforced via multiple GWAS hits in the UK Biobank cohort. The top GWAS locus was further validated via knockdown of the fly homolog, Alk, which slowed age-related decline in vision in flies. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.
摘要：
生物年龄,与个人的实际年龄不同，已经通过预测老化时钟进行了广泛的研究。然而,这些时钟在短时间尺度上精度有限。在这里，我们在EyePACS数据集中的眼底图像上训练了深度学习模型，以预测个体的实际年龄。我们的视网膜老化时钟，\'eyeAge\',预测的实际年龄比其他老化时钟更准确(来自EyePACS和UKBiobank的质量过滤数据的平均绝对误差为2.86和3.30年，分别)。此外,眼睛年龄独立于基于血液标志物的生物年龄测量，即使根据表型年龄进行了调整，全因死亡率风险比仍保持在1.026。在UKBiobank队列中，通过多次GWAS命中增强了eyeAge的个体特异性。通过击倒果蝇同源物进一步验证了顶部的GWAS基因座，Alk,减缓了与年龄相关的苍蝇视力下降。这项研究证明了视网膜衰老时钟在研究衰老和与年龄有关的疾病以及在非常短的时间尺度上定量测量衰老方面的潜在用途。为快速和可操作地评估老年保护疗法开辟了道路。
