Genomic determinants of biological age estimated by deep learning applied to retinal images

TY - JOUR

T1 - Genomic determinants of biological age estimated by deep learning applied to retinal images

AU - Huang, Yu

AU - Syed, Mohammad Ghouse

AU - Chen, Ruiye

AU - Li, Cong

AU - Shang, Xianwen

AU - Wang, Wei

AU - Zhang, Xueli

AU - Zhang, Xiayin

AU - Tang, Shulin

AU - Liu, Jing

AU - Liu, Shunming

AU - Srinivasan, Sundar

AU - Hu, Yijun

AU - Mookiah, Muthu Rama Krishnan

AU - Wang, Huan

AU - Trucco, Emanuele

AU - Yu, Honghua

AU - Palmer, Colin

AU - Zhu, Zhuoting

AU - Doney, Alexander S F

AU - He, Mingguang

N1 - © 2025. The Author(s).

PY - 2025/4

Y1 - 2025/4

N2 - With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between chronological age and predicted retinal age has been established previously to predict the age-related disease. In this study, we performed discovery genome-wide association analysis (GWAS) on the RAG using the 31,271 UK Biobank participants and replicated our findings in 8034 GoDARTS participants. The genetic correlation between RAGs predicted from the two cohorts was 0.67 (P = 0.021). After meta-analysis, we found 13 RAG loci which might be related to retinal vessel density and other aging processes. The SNP-wide heritability (h2) of RAG was 0.15. Meanwhile, by performing Mendelian randomization analysis, we found that glycated hemoglobin, inflammation hemocytes, and anemia might be associated with accelerated retinal aging. Our study explored the biological implications and molecular-level mechanism of RAG, which might enable causal inference of the aging process as well as provide potential pharmaceutical intervention targets for further treatment.

AB - With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between chronological age and predicted retinal age has been established previously to predict the age-related disease. In this study, we performed discovery genome-wide association analysis (GWAS) on the RAG using the 31,271 UK Biobank participants and replicated our findings in 8034 GoDARTS participants. The genetic correlation between RAGs predicted from the two cohorts was 0.67 (P = 0.021). After meta-analysis, we found 13 RAG loci which might be related to retinal vessel density and other aging processes. The SNP-wide heritability (h2) of RAG was 0.15. Meanwhile, by performing Mendelian randomization analysis, we found that glycated hemoglobin, inflammation hemocytes, and anemia might be associated with accelerated retinal aging. Our study explored the biological implications and molecular-level mechanism of RAG, which might enable causal inference of the aging process as well as provide potential pharmaceutical intervention targets for further treatment.

KW - Retinal age

KW - Biological age

KW - Genomewide association analysis

KW - Mendelian randomization

KW - Genome-wide association analysis

UR - https://www.scopus.com/pages/publications/85217204873

U2 - 10.1007/s11357-024-01481-w

DO - 10.1007/s11357-024-01481-w

M3 - Article

C2 - 39775603

SN - 2509-2723

VL - 47

SP - 2613

EP - 2629

JO - GeroScience

JF - GeroScience

IS - 2

M1 - e1001779

ER -