The transcriptional landscape of age in human peripheral blood

By M. Peters, Roby Joehanes, L. Pilling, C. Schurmann, Karen N. Conneely, Joseph E. Powell, E. Reinmaa, G. Sutphin, A. Zhernakova, K. Schramm and 128 more. Nature Communications, 2015

654 citations recorded by the source corpus.

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age’ of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts. Ageing increases the risk of many diseases. Here the authors compare blood cell transcriptomes of over 14,000 individuals and identify a set of about 1,500 genes that are differently expressed with age, shedding light on transcriptional programs linked to the ageing process and age-associated diseases.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bcb0876a7c45b7d6a6ec2c0ed92c016fe0c27200

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

DNA methylation aging clocks: challenges and recommendations (2019, background): “For “Biological” clocks, another obvious area of uncertainty is that there is not one measure or “gold standard” of biological aging [6, 7, 41, 48].”
Dengue and severe dengue (2025, neutral): “Changes have been observed in the activation of neutro phils, NK and NK T cells, monocytes and macrophages, dendritic cells, as well as B cells and T cells (209).”
DNA methylation-based measures of biological age: meta-analysis predicting time to death (2016, background): “, DNA methylation), transcriptomic changes in blood [14], telomere length [15], whole-body function such as gait speed (reviewed in [16]).”
Deep biomarkers of human aging: Application of deep neural networks to biomarker development (2016, methodology): “Methylation-based markers such as epigenetic aging clocks (Horvath [6] and Hannum [7]) are currently the most accurate, while transcriptomics [8,9] and metabolomics [10] have shown to be less so.”
Metabolic Control of Longevity. (2016, background): “…upregulation of the so-called ‘‘transcriptomic age’’ (an age-associated metagene) of an individual has been associated with signs of metabolic syndrome, such as enhanced blood pressure, increased levels of circulating glucose and cholesterol, as well as high body-mass index (Peters et al., 2015).”
Measuring biological age using omics data (2022, methodology): “The first major attempt at a transcriptomic clock came from a 2015 study by Peters et al. 81 , who used standard methods to train a transcriptomic ageing clock using peripheral blood mononuclear cell gene expression data from multiple large cohorts.”