Predicting age from the transcriptome of human dermal fibroblasts
Open accessAbstract
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.
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An elastic net-based clock derived from binarized transcriptomic data improved chronological age prediction to an r2 of 0.92 and mean error of 6.63 years and could detect accelerated ageing in progeria samples.
“Additionally, they demonstrated, using human fibroblast data from Fleischer et al.82, that an elastic net-based clock derived from binarized transcriptomic data improved chronological age prediction to an r2 of 0.92 and mean error of 6.63 years and could detect accelerated ageing in progeria samples.”
From Measuring biological age using omics data (2022) · cites this paper as [82]
The Fleischer et al. model was not evaluated on an independent test dataset.
“However, the model was not evaluated on an independent test dataset, so it remains to be seen whether the method was a true improvement.”
From Measuring biological age using omics data (2022) · cites this paper as [82]
The Fleischer et al. method outperformed standard penalized linear regression-based clocks at chronological age prediction and uniquely detected accelerated ageing in progeria samples.
“The method outperformed standard penalized linear regression-based clocks at chronological age prediction and uniquely detected accelerated ageing in progeria samples, suggesting that methods designed to be robust to transcriptional noise improve performance.”
From Measuring biological age using omics data (2022) · cites this paper as [82]
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- Authors
- Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., Huang, L., Hetzer, M. W., & Navlakha, S.
- Affiliations
- Salk Institute for Biological Studies (US)
- Corresponding author
- Hetzer, Martin W.
- Navlakha, Saket
- ORCID
- Venue
- Genome Biology
- Published
- 2018-12
- Volume
- 19
- Issue
- 1
- Pages
- 221-221
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC6300908
- OpenAlex
- W2905232298
- Cited by
- 232
- References
- 26
- Citations by year
- 2026: 25 · 2025: 34 · 2024: 36 · 2023: 43 · 2022: 27 · 2021: 33 · 2020: 23 · 2019: 10 · 2018: 1
- FWCI
- 6.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R01-NS096786, 1R01DC017695)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- age prediction, aging biomarkers, transcriptome, human dermal fibroblasts, RNA-seq, ensemble learning, Hutchinson-Gilford progeria syndrome, accelerated aging, age-related diseases
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Aged, 80 and over, Aging, Child, Child, Preschool, Fibroblasts, Humans, Middle Aged, Progeria, Gene Expression Profiling, Genomics, Young Adult, Transcriptome
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