15 Jul Hebrew University: AI Model Accurately Predicts Age from Examining Just Two Gene Loci

The study was done by a team of researchers at the The Hebrew University-Hadassah Medical School, led by Bracha Ochana and Daniel Nudelman, under the supervision of Prof. Tommy Kaplan, Prof. Yuval Dor and Prof. Ruth Shemer. [caption id="attachment_69520" align="alignleft" width="150"] Prof. Tommy Kaplan[/caption] MedicalResearch.com: What is the background for this study? Response: DNA methylation is a key epigenetic modification that annotates the human genome. It is established during development and cellular differentiation, and is associated with maintenance of cell type identity and control of gene expression. Nonetheless, few regions in the human genome change with age and serves as a powerful biomarker for estimating chronological and biological age. However, most current epigenetic clocks rely on average methylation at individual CpG sites using array-based data, which overlook complex regional patterns across neighboring methylation sites. This study aimed to understand how time and age are encoded at the molecular and cellular level, and to develop a highly accurate age predictor, based on regional methylation dynamics. [caption id="attachment_69521" align="alignleft" width="150"] Prof. Ruth Shemer[/caption] MedicalResearch.com: What types of cells were used in the study, ie keratinocytes, muscle cells etc? Response: The primary tissue used in this study was peripheral blood from over 300 healthy human donors (18-78 years old). To further understand how the methylation changes are associated with changes in blood cell composition, we also sorted immune cell types including neutrophils, monocytes, B cells, and T cells. For forensics applications, we also tested the clock on urine and saliva samples.     [caption id="attachment_69522" align="alignleft" width="125"] Prof. Yuval Dor[/caption] MedicalResearch.com: Does this study relate at all to telomere length?Response: Response: No, this study does not investigate or reference telomere length. It focuses entirely on DNA methylation changes at few genomics regions, each covering multiple clustered DNA methylation sites, where methylation changes are indicative of chronological age, independently of telomere biology.   MedicalResearch.com: What are the main findings? Response:  - A single-molecule analysis using DNA sequencing, revealed that age-related methylation changes often occur regionally across multiple neighboring methylation sites, either in a stochastic or in a block-like manner. - A deep neural network model, called MAgeNet, was trained on methylation patterns from two specific genomic loci (ELOVL2 and C1orf132) and was able to predict chronological age (of held-out test-set donors) at a median accuracy of 1.36 years (for individuals under 50). - These predictions are robust to sex, smoking, BMI, and biological age markers, and accurate even from as few as 50 cells or at low-depth sequencing. - Longitudinal sampling of healthy donors at the age of 32 and 42, shows that early deviations from predicted age persist over time, suggesting that as we age, methylation changes faithfully encode the passage of time.