BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy
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Abstract
Aging clocks dissociate biological from chronological age. The estimation of biological age is important for identifying gerontogenes and assessing environmental, nutritional, or therapeutic impacts on the aging process. Recently, methylation markers were shown to allow estimation of biological age based on age‐dependent somatic epigenetic alterations. However, DNA methylation is absent in some species such as Caenorhabditis elegans and it remains unclear whether and how the epigenetic clocks affect gene expression. Aging clocks based on transcriptomes have suffered from considerable variation in the data and relatively low accuracy. Here, we devised an approach that uses temporal scaling and binarization of C. elegans transcriptomes to define a gene set that predicts biological age with an accuracy that is close to the theoretical limit. Our model accurately predicts the longevity effects of diverse strains, treatments, and conditions. The involved genes support a role of specific transcription factors as well as innate immunity and neuronal signaling in the regulation of the aging process. We show that this binarized transcriptomic aging (BiT age) clock can also be applied to human age prediction with high accuracy. The BiT age clock could therefore find wide application in genetic, nutritional, environmental, and therapeutic interventions in the aging process.
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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 [83]
Meyer et al. demonstrated that a simple binarization and relative age scaling of transcriptomic data denoised the data and improved age prediction in Caenorhabditis elegans to the theoretical upper limit of accuracy.
“An intriguing advance came in 2021 from Meyer et al.83, who demonstrated that a simple binarization and relative age scaling of transcriptomic data denoised the data and improved age prediction in Caenorhabditis elegans to the theoretical upper limit of accuracy (as accurately as the ages of the worms were tracked in increments of 1 day).”
From Measuring biological age using omics data (2022) · cites this paper as [83]
The Meyer et al. clock could detect expected changes in biological age for long-lived daf2 mutants as well as irradiation and caloric restriction and performed well on diverse lifespan-affecting treatments in independent datasets.
“Moreover, this clock could detect expected changes in biological age for long-lived daf2 mutants as well as irradiation and caloric restriction and performed well on diverse lifespan-affecting treatments in independent datasets.”
From Measuring biological age using omics data (2022) · cites this paper as [83]
In C. elegans, it is possible to generate an accurate and biologically meaningful transcriptomic ageing clock.
“The authors showed that, in C. elegans, it is indeed possible to generate an accurate and biologically meaningful transcriptomic ageing clock.”
From Measuring biological age using omics data (2022) · cites this paper as [83]
Meyer and Schumacher developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks.
“This study developed multiple methodological innovations for training accurate and reproducible transcriptomic ageing clocks”
From Measuring biological age using omics data (2022) · cites this paper as [83]
Meyer and Schumacher developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms.
“and developed clocks with striking generalizable performance across multiple ageing treatment conditions in worms.”
From Measuring biological age using omics data (2022) · cites this paper as [83]
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Duran, I., & Tsurumi, A. (2025) · Biogerontology · doi:10.1007/s10522-025-10216-z · Cited by 3 · see citing works
Age Deceleration and Reversal Gene Patterns in Dauer Diapause
Totska, K., Barata, J. C. V. V., Sandt, W., Meyer, D. H., & Schumacher, B. (2025) · Aging Cell · doi:10.1111/acel.70253 · Cited by 3 · see citing works
Self-citationThree Tiers to biological escape velocity: The quest to outwit aging
Palmer, R. D. (2022) · Aging Medicine · doi:10.1002/agm2.12231 · Cited by 2 · see citing works
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Ferrari, E., Reichwald, K., Koch, P., Groth, M., Baumgart, M., & Cellerino, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.26.517610 · Cited by 2 · see citing works
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Varga, B., & Kerepesi, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.26.661785 · Cited by 3 · see citing works
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Kleve, G., Santa, C. D., Kristiansen, F. I. S., Tegnander, M., Buch, V., Hagemann, C. S., Helgheim, M., Engene, P., Pesonen, H., Kleveland, M. K., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1609678 · Cited by 2 · see citing works
- Authors
- Meyer, D. H., & Schumacher, B.
- Affiliations
- University of Cologne (DE)
- Corresponding author
- Schumacher, Björn
- Venue
- Aging Cell
- Publisher
- Wiley
- Published
- 2021-03
- Volume
- 20
- Issue
- 3
- Pages
- e13320-e13320
- ISSN
- 1474-9718 (ISSN-L)
- 1474-9726
- PMC
- PMC7963339
- OpenAlex
- W3133928772
- Cited by
- 165
- References
- 176
- Citations by year
- 2026: 38 · 2025: 41 · 2024: 28 · 2023: 34 · 2022: 18 · 2021: 6
- FWCI
- 7.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Aging Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- Funders
- Deutsche Krebshilfe (70112899)
- Deutsche Forschungsgemeinschaft (EXC 2030 ‐ 390661388, GRK2407, KFO 286, KFO 329, SCHU 2494/10‐1, SCHU 2494/11‐1, SCHU 2494/3‐1, SCHU 2494/7‐1, SFB 829)
- 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 › Epigenetics and DNA Methylation
- Keywords
- aging clocks, biological age estimation, transcriptome, Caenorhabditis elegans, time scaling, binarization, longevity, transcription factors, innate immunity, neuronal signaling, epigenetic aging
- MeSH terms
- Aging, Biological Clocks, Fibroblasts, Humans, Immunity, Innate, Mutation, Neurons, Stress, Physiological, Time Factors, Signal Transduction, Transcriptome
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