Evaluating transcriptional alterations associated with ageing and developing age prediction models based on the human blood transcriptome

• Published in: Biogerontology (Dordrecht) Vol. 26; no. 2; p. 86
• Main Authors: Duran, Ivan, Tsurumi, Amy
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2025; Springer Nature B.V
• Subjects: Adult; Age; Aged; Aged, 80 and over; Aging; Aging - blood; Aging - genetics; Algorithms; Biomedical and Life Sciences; Blood; Cell Biology; Developmental Biology; Female; Gene expression; Gene Expression Profiling; Genes; Geriatrics/Gerontology; Humans; Life Sciences; Machine Learning; Male; Middle Aged; Ontology; Phenotypes; Population studies; Prediction models; Proteomes; Research Article; Transcriptome; Transcriptomes; Young Adult; Aging; Biomarker; Ageing; Machine learning; Prediction model; Transcriptome
• ISSN: 1389-5729; 1573-6768; 1573-6768
• DOI: 10.1007/s10522-025-10216-z

Ageing-related DNA methylome and proteome changes and machine-learned ageing clock models have been described previously; however, there is a dearth of ageing clock prediction models based on human blood transcript information. Applying various machine learning algorithms is expected to aid in the development of age prediction models. Using blood transcriptome data from healthy subjects ranging in age from 21 to 90 in the 10 K Immunomes repository, we evaluated differentially regulated transcripts, assessed enriched gene ontology, pathway and disease ontology analysis to characterize biological functions associated with the genes associated with age. Furthermore, we constructed and compared age prediction models developed by applying the Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), eXtreme Gradient Boosting (XGBoost) and Light Gradient-Boosting Machine (LightGBM) algorithms. Compared to LASSO (7 genes) and EN (9 genes) regularized regression, XGBoost (142 genes) and LightGBM (149 genes) Gradient Boosted Decision Tree methods performed better in this dataset (training set r = 0.836 (LASSO), 0.837 (EN), 1.000 (XGBoost) and 0.995 (LightGBM); test set: r = 0.883 (LASSO), 0.876 (EN), 0.931 (XGBoost) and 0.915 (LightGBM); external validation set: r = 0.535 (LASSO), 0.534 (EN), 0.591 (XGBoost) and 0.645 (LightGBM)). Blood transcriptome-based age prediction models may provide a simple method to monitor biological ageing, and provide additional molecular insight. Future studies to externally validate these models in various diverse large populations and molecular studies to elucidate the underlying mechanisms by which the gene expression levels may be related to ageing phenotypes would be advantageous.