Predicting age from the transcriptome of human dermal fibroblasts

Genome Biol. 2018 Dec 20;19(1):221. doi: 10.1186/s13059-018-1599-6.


Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.

Keywords: Aging; Biological age; Biomarker; Ensemble classifiers; Machine learning; RNA-seq; Skin fibroblasts.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Aging / metabolism*
  • Child
  • Child, Preschool
  • Fibroblasts / metabolism*
  • Gene Expression Profiling*
  • Genomics / methods
  • Humans
  • Machine Learning
  • Middle Aged
  • Progeria / metabolism
  • Transcriptome*
  • Young Adult