The DOI matches its Crossref record; 180 of the 180 references it cites were found in a registry or an index.
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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“This clock has a very low median absolute error, which is comparable to that of epigenetic clocks.”
From Multi-omic rejuvenation of human cells by maturation phase transient reprogramming (2022) · cites this paper as Meyer and Schumacher, 2021
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Recently, a novel transcription clock called BiT age clock has been defined (Meyer and Schumacher, 2021), which has been trained on binarized gene expression levels.”
From Multi-omic rejuvenation of human cells by maturation phase transient reprogramming (2022) · cites this paper as Meyer and Schumacher, 2021
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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 the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270
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Measuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570
“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…”
BackgroundBao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 402
Buckley, M. T., Sun, E., George, B. M., Liu, L. A., Schaum, N., Xu, L., Reyes, J. M., Goodell, M. A., Weissman, I. L., Wyss‐Coray, T., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00335-4 · Cited by 201
Multi-omic rejuvenation of human cells by maturation phase transient reprogramming
Gill, D., Parry, A., Santos, F., Okkenhaug, H., Todd, C. D., Hernando-Herraez, I., Stubbs, T. M., Milagre, I., & Reik, W. (2022) · eLife · doi:10.7554/elife.71624 · Cited by 185
Aging clocks based on accumulating stochastic variation
Meyer, D. H., & Schumacher, B. (2024) · Nature Aging · doi:10.1038/s43587-024-00619-x · Cited by 128
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Metadata
- 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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