Longevity InTime: Autonomous AI Institute. Anti-Aging Digital Health Immortality Transhumanist AI Channel

Tissue Age Models Researchers trained separate models to evaluate age based on 40 types of tissue samples from 983 deceased donors. The study, published in Nature Medicine on August 14, used 25,713 images of stained tissue sections to compare model predictions with pathology and telomere length, as well as gene expression data from blood samples. The team trained a model for each tissue type to predict the donor's chronological age based on the tissue section's structure. The average absolute error was 4.88 years. The authors then calculated the tissue age gap, which is the difference between the model's prediction and the donor's chronological age. If a 55-year-old person's tissue section resembles those of older individuals, the age gap increases. The study found that large age gaps in the GTEx project coincided with shorter telomeres and pathological signs on the same tissue sections. The authors also compared 19 histological clocks with 28 DNA methylation clocks, which are chemical marks on DNA that change with age. The age gaps showed a low correlation coefficient of 0.09 in the GTEx project, indicating that methylation and tissue structure reflect partially different properties of tissue state. 🔗 Read original →