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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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In the citing paper
“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 measuredIn 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]
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
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Measuring biological age using omics data
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“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…”
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“So far, several methods have been developed to examine age- related molecular dynamics, including clocks focusing on the epigenome, transcriptome, and immunome (Galkin et al., 2020, 2021; Horvath, 2013; Meyer & Schumacher, 2021; Sayed et al., 2021).”
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Silva, N., Rajado, A. T., Esteves, F., Brito, D. V. C., Apolónio, J. D., Roberto, V. P., Binnie, A., Araújo, I. M., Nóbrega, C., Bragança, J., et al. (2023) · Biogerontology · doi:10.1007/s10522-023-10041-2 · Cited by 51
“The Binarized Transcriptomic Aging Clock (BiT age) [(Meyer and Schumacher 2021), Table 4] is a transcriptional clock that was developed in C. elegans and validated in human fibroblasts, where it showed a high degree of accuracy in predicting BA.”
BackgroundBiological Age Predictors: The Status Quo and Future Trends
Erema, V. V., Yakovchik, A., Kashtanova, D. A., Bochkaeva, Z. V., Ivanov, M. V., Сосин, Д. В., Matkava, L. R., Yudin, V. S., Makarov, V. V., Keskinov, A. A., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232315103 · Cited by 43
“[33] Temporal scaling and binarization R2 = 0.”
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Lohman, T., Bains, G. S., Cole, S. W., Gharibvand, L., Berk, L., & Lohman, E. (2023) · Aging Cell · doi:10.1111/acel.13841 · Cited by 40
“Some of these models are designed to predict biological age (Meyer & Schumacher, 2021; Peters et al., 2015; Ren & Kuan, 2020), or more specifically, transcriptomic age (TA).”
BackgroundLongevity interventions temporally scale healthspan in Caenorhabditis elegans
Statzer, C., Reichert, P., Dual, J., & Ewald, C. Y. (2022) · iScience · doi:10.1016/j.isci.2022.103983 · Cited by 40
“Temporal scaling might also be the underlying reason why ‘‘aging clocks’’ based on transcriptional profiling work and longevity mutants’ biological age determined by the clock is younger than their chronological age (Meyer and Schumacher, 2021).”
BackgroundBiological Aging in People Living with HIV on Successful Antiretroviral Therapy: Do They Age Faster?
Akusjärvi, S. S., & Neogi, U. (2023) · Current HIV/AIDS Reports · doi:10.1007/s11904-023-00646-0 · Cited by 38
DNA repair‐deficient premature aging models display accelerated epigenetic age
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pyaging : a Python-based compendium of GPU-optimized aging clocks
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Profiling the transcriptomic age of single-cells in humans
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“In addition to epigenetic clocks, transcriptomic clocks were also developed for the estimation of age 9 – 13 .”
Smurfness‐based two‐phase model of ageing helps deconvolve the ageing transcriptional signature
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“Nevertheless, recent work has identified a ‘universal’ transcriptomic clock using C. elegans (Tarkhov et al., 2019), with the recent publication of the BiT age clock (Meyer & Schumacher, 2021), suggesting a possible conservation of critical biological age markers.”
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Ratiner, K., Abdeen, S. K., Goldenberg, K., & Elinav, E. (2022) · Microorganisms · doi:10.3390/microorganisms10030668 · Cited by 27
“[49] used a model organism (Caenorhabditis elegans) to define a gene set that predicts biological age with high accuracy.”
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Small immunological clocks identified by deep learning and gradient boosting
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BackgroundMethodSystematic mapping of organism-scale gene-regulatory networks in aging using population asynchrony
Eder, M., Martin, O. M. F., Oswal, N., Sedlackova, L., Moutinho, C., Carmen-Fabregat, A. D., Bravo, S. M., Sebé-Pedrós, A., Heyn, H., & Stroustrup, N. (2024) · Cell · doi:10.1016/j.cell.2024.05.050 · Cited by 24
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“Certainly, many more likely exist such as transcriptomic, proteomic and metabolomic clocks (135, 136) but the 3 discussed here are likely primary because they directly affect the genome either by telomere shortening, DSB, or chemical modification.”
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BackgroundImproved resilience and proteostasis mediate longevity upon DAF-2 degradation in old age
Molière, A., Park, J. Y. C., Goyala, A., Vayndorf, E. M., Zhang, B., Hsiung, K. C., Jung, Y., Kwon, S., Statzer, C., Meyer, D. H., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01232-x · Cited by 19
Self-citationLifelong behavioral screen reveals an architecture of vertebrate aging
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LipidClock: A Lipid-Based Predictor of Biological Age
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Suppression of thrombospondin-1–mediated inflammaging prolongs hematopoietic health span
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N6-Methyladenine Progressively Accumulates in Mitochondrial DNA during Aging
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“Recently, a number of age-determining biomarkers and methods have been published that can be used as an aging clock, such as Horvath’s clock and other cytosine methylation-based aging clocks [8], transcriptome-based aging clocks (such as BiT age [39]), inflammatory aging clock (such as iAge) [40], and the metabolomic…”
BackgroundCellBiAge: Improved single-cell age classification using data binarization
Yu, D., Li, M., Linghu, G., Hu, Y., Hajdarovic, K. H., Wang, A., Singh, R., & Webb, A. E. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113500 · Cited by 19
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“Data analysis allowed the setup of several “biological aging clocks”; these clocks were created in accordance with information concerning epigenomes, transcriptomes, and immunomes [10,17].”
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T-cell lymphocytes’ aging clock: telomeres, telomerase and aging
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A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age
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Multi-tissue transcriptomic aging atlas reveals predictive aging biomarkers in the killifish
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An explainable AI framework for interpretable biological agePreprint
Qiu, W., Chen, H., Kaeberlein, M. R., & Lee, S. (2022) · medRxiv · doi:10.1101/2022.10.05.22280735 · Cited by 12
“Many previous biological age clocks have been built by analysis of omics data, such as epigenetic data [16, 18, 2, 31], transcriptomic data [39, 11, 36], proteomic data [35, 47, 24], etc. Due to its flexibility, we can easily extend ENABL Age to omics data by using these data types as the input…”
MethodSelecting mRNA markers in blood for age estimation of the donor of a biological stain
Dørum, G., Hänggi, N. V., Burri, D., Marti, Y., Banemann, R., Kulstein, G., Courts, C., Gosch, A., Hadrys, T., Haas, C., et al. (2023) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2023.102976 · Cited by 13
Ageing as a two-phase process: theoretical framework
Zane, F., MacMurray, C., Guillermain, C., Cansell, C., Todd, N., & Rera, M. (2024) · Frontiers in Aging · doi:10.3389/fragi.2024.1378351 · Cited by 13
“…is “ ageing clocks ”— e.g., the mammal epigenetic clock that identi fi es 5-cytosine methylation of CpG sites (Bocklandt et al., 2011; Hannum et al., 2013; Horvath, 2013; Horvath and Raj, 2018) and transcriptomic clocks in Caenorhabditis elegans (Tarkhov et al., 2019; Meyer and Schumacher, 2021).”
BackgroundA miRNA-based epigenetic molecular clock for biological skin-age prediction
Roig-Genovés, J. V., García‐Giménez, J. L., & Mena, S. (2024) · Archives of Dermatological Research · doi:10.1007/s00403-024-03129-3 · Cited by 11
“…potential therapies and to identify optimum individualized treatments for each subject. algorithms to smallRNA-seq data can provide new insight into the epigenetic field to obtain a better understanding of the skin aging process and skin-related diseases as it is done with transcriptome [36].”
BackgroundEcker, V., Früh, M., Yang, B., Gatidis, S., & Küstner, T. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447990 · Cited by 10
“Various biomarkers for BA have emerged based on methylation [3, 4], transcriptomics [5, 6], functional assessments [7, 8], laboratory data [9, 10], or patient history [11].”
BackgroundDeepQA : A Unified Transcriptome‐Based Aging Clock Using Deep Neural Networks
Qi, H., Zhao, H., Li, E., Lu, X., Yu, N., Liu, J., & Han, J. (2025) · Aging Cell · doi:10.1111/acel.14471 · Cited by 10
“During training, the AHK loss is combined with other losses, for example, MAE to guide the learning process of the prediction model.”
InfluentialRey‐Millet, M., Pousse, M., Soithong, C., Ye, J., Méndez-Bermúdez, A., & Gilson, É. (2023) · Aging Cell · doi:10.1111/acel.13804 · Cited by 10
“Various aging clocks have been recently described: transcription (Meyer & Schumacher, 2021), methylation (Horvath, 2013), and inflammation (Sayed, 2021), providing interesting tools to assay for biological age but we are still facing a “cause or consequence” challenge.”
MethodNetwork modeling and topology of aging
Li, F., Yang, D., Wu, S., Xue, C., Sang, M., Liu, X., Che, J., Wu, J., Gragnoli, C., Griffin, C. H., et al. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.10.006 · Cited by 9
Resetting the aging clock through epigenetic reprogramming: Insights from natural products
Liu, X., Feng, J. H., Guo, M., Chen, C., Zhao, T., Sun, X., & Zhang, Y. (2025) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2025.108850 · Cited by 9
Zhang, C., Saurat, N. G., Cornacchia, D., Chung, S. Y., Sikder, T., Nemchik, A., Minotti, A. P., Studer, L., & Betel, D. (2025) · Aging Cell · doi:10.1111/acel.70075 · Cited by 14
“There have been several efforts to develop both global and cell type-specific transcriptional aging clocks (Buckley et al. 2023; Fleischer et al. 2018; Jung et al. 2023; Lu et al. 2023; Meyer and Schumacher 2021).”
From clock to clock: Therapeutic target discovery for aging and age-related diseases
Chen, J., Leung, G. H. D., Leung, H., Ustiugova, A. S., Shneyderman, A., Korzinkin, M., Gennert, D. G., Pun, F. W., Aliper, A. M., Ren, F., et al. (2025) · Ageing Research Reviews · doi:10.1016/j.arr.2025.102871 · Cited by 10
Gallrein, C., Meyer, D. H., Woitzat, Y., Ramirez-Ramirez, V., Vuong-Brender, T. T. K., Kirstein, J., & Schumacher, B. (2026) · Nature Aging · doi:10.1038/s43587-026-01067-5 · Cited by 12
“BitAge predicts already in young adult animals quantitatively how genetic, environmental or pharmacological treatments affect lifespan 10 .”
Self-citationIn vivo Pooled Screening: A Scalable Tool to Study the Complexity of Aging and Age-Related Disease
Jensen, M. B., & Marblestone, A. H. (2021) · Frontiers in Aging · doi:10.3389/fragi.2021.714926 · Cited by 9
“Both cell type and state affects viability after dissociation, so the cells that end up sequenced cannot be Frontiers in Aging | www.frontiersin.org August 2021 | Volume 2 | Article 7149266 assumed to be a random sampling of the in vivo perturbed population (Meyer and Schumacher, 2021).”
BackgroundInfluentialDoliClock: a lipid-based aging clock reveals accelerated aging in neurological disorders
Latumalea, D., Unfried, M., Barardo, D., Gruber, J., & Kennedy, B. K. (2025) · Aging · doi:10.18632/aging.206266 · Cited by 8
Transcriptomics age acceleration in prolonged treated HIV infection
Mikaeloff, F., Gelpi, M., Escós, A., Knudsen, A. D., Høgh, J., Benfield, T., de Magalhães, J. P., Nielsen, S. D., & Neogi, U. (2023) · Aging Cell · doi:10.1111/acel.13951 · Cited by 8
“We investigated four different TAE, BitAge (Meyer & Schumacher, 2021), BURNS (Shokhirev & Johnson, 2021), and RNAAgeCalc, with two different correlation methods (Ren & Kuan, 2020) using PBMC RNA sequencing.”
MethodIntegration of multi-modal datasets to estimate human aging
Ribeiro, R., Moraes, A., Moreno, M., & Ferreira, P. G. (2024) · Machine Learning · doi:10.1007/s10994-024-06588-x · Cited by 9
“The error rates obtained for gene expression are within previously reported results (Peters et al., 2015; Fleischer et al., 2018; Mamoshina et al., 2018; Xia et al., 2020; Holzscheck et al., 2021; Meyer & Schumacher, 2021).”
Hänggi, N. V., Neubauer, J., Marti, Y., Banemann, R., Kulstein, G., Courts, C., Gosch, A., Hadrys, T., Haas, C., & Dørum, G. (2025) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2025.103282 · Cited by 9
Ecker, V., Yang, B., Gatidis, S., & Küstner, T. (2026) · npj Aging · doi:10.1038/s41514-026-00377-7 · Cited by 7
Biomarkers of aging through the life course: a recent literature update
Gaylord, A., Cohen, A. A., & Kupsco, A. J. (2023) · Current Opinion in Epidemiology and Public Health · doi:10.1097/pxh.0000000000000018 · Cited by 7
Aging measures and cancer: Findings from the Health and Retirement StudyPreprint
Wang, S., Prizment, A., Moshele, P., Vivek, S., Blaes, A. H., Nelson, H. H., & Thyagarajan, B. (2023) · medRxiv · doi:10.1101/2023.09.20.23295845 · Cited by 7
Development and validation of an age prediction model using mRNA markers
Liao, M., Chen, A., Xuan, Y., Zhao, J., Liu, S., Xing, Y., Zhang, S., & Li, C. (2025) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2025.103373 · Cited by 6
The RNA-Seq data analysis shows how the ontogenesis defines aging
Salnikov, L., Goldberg, S. I., Rijhwani, H., Shi, Y., & Pinsky, E. (2023) · Frontiers in Aging · doi:10.3389/fragi.2023.1143334 · Cited by 6
“, 2022), as well as multidirectional changes in RNA production levels across individual gene groups (Santra et al., 2019; Meyer and Schumacher, 2021; Stoeger et al., 2022).”
BackgroundMicroglia Single‐Cell RNA ‐Seq Enables Robust and Applicable Markers of Biological Aging
Stanley, N., Dhawka, L., Jaikumar, S., Huang, Y., & Zannas, A. S. (2025) · Aging Cell · doi:10.1111/acel.70095 · Cited by 6
“…scRNA-seq based transcriptomic clocks derived from pseudobulk features (Buckley et al. 2023; Zakar-Polyák et al. 2024), transcriptome binarization (Meyer and Schumacher 2021), or cell-type compositions (Zhu et al. 2023; Mao et al. 2023) have been successfully applied to identify critical…”
Xia, Y., Islam, S. M. S., Li, X., Baten, A., Tan, X., & Wang, W. (2025) · Engineering · doi:10.1016/j.eng.2025.08.016 · Cited by 5
The secretome atlas of two mouse models of progeria
Quintana‐Torres, D., Valle‐Cao, A., Bousquets‐Muñoz, P., Freitas‐Rodríguez, S., Rodríguez, F. J., Lucía, A., López-Otín, C., López‐Soto, A., & Folgueras, A. R. (2023) · Aging Cell · doi:10.1111/acel.13952 · Cited by 4
“…an epigenomic clock predicted a higher age in fibroblasts from skin HGPS biopsies and two addi - tional studies assigned an older age to cell lines derived from affected patients based on the same transcriptomic datasets (Fleischer et al., 2018; Horvath et al., 2018; Meyer & Schumacher, 2021 ).”
BackgroundBrain aging is faithfully modelled in organotypic brain slices and accelerated by prions
Liu, Y., Senatore, A., Sorce, S., Nuvolone, M., Guo, J., Gumus, Z. H., & Aguzzi, A. (2022) · Communications Biology · doi:10.1038/s42003-022-03496-5 · Cited by 5
“…brain aging through genome-wide gene expression profiling, bioinformatics and machine learning, approaches widely used to measure and predict biological ages of various tissues (Jylhava, Pedersen et al. 2017, Fleischer, Schulte et al. 2018, Bulteau and Francesconi 2021, Meyer and Schumacher 2021).”
Plasma proteomic Alzheimer's risk score: Biological age clock and pseudotime trajectory
Liu, S., Huang, Y.-N., Park, T., Lee, E. H., Chaudhuri, S., Adzibolosu, N. K., Bice, P. J., Dage, J. L., Brosch, J. R., Gao, S., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70851 · Cited by 6
“18,19 23 Plasma proteomics has emerged as a valuable method for estimating biological age.”
Proteomic aging clocks in epidemiological studies: advances, applications and prospects
Xiao, H., Lau, C. E., Dehghan, A., & Robinson, O. (2026) · Nature Aging · doi:10.1038/s43587-026-01118-x · Cited by 6
“So far, aging clocks derived from various types of omics data (epigenetic 8 , transcriptomic 9 , proteomic 10 and metabolomic 11 ) have been developed and adopted for epidemiological research.”
Proteomics-based aging clocks in midlife or late-life and their associated risk of dementia
Sedaghat, S., Park, S., Walker, R. F., Wang, S., Liu, J., Hughes, T. M., Sabayan, B., Tang, W., Coresh, J., Pankow, J. S., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01096-y · Cited by 7
Pan-tissue transcriptome analysis reveals sex-dimorphic human aging
Wang, S., Dong, D., Li, X., & Wang, Z. (2024) · eLife · doi:10.7554/elife.102449.3 · Cited by 6
An snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging
Tennant, N., Pavuluri, A., Singh, G., Cortez, K., O’Connor-Giles, K., Larschan, E., & Singh, R. (2026) · Scientific Reports · doi:10.1038/s41598-026-48613-0 · Cited by 4
“Meyer and Schumacher found that simply binarizing RNA-seq data—that is, assigning expression values of either 0 or 1—significantly improved the performance of their Caenorhabditis elegans aging clock [14].”
Gene expression variation across genetically identical individuals predicts reproductive traits
Webster, A. K., Willis, J. H., Johnson, E., Sarkies, P., & Phillips, P. C. (2025) · eLife · doi:10.7554/elife.106525.3 · Cited by 4
Sun, E., Zhou, O. Y., Hauptschein, M., Rappoport, N., Xu, L., Negredo, P. N., Liu, L. A., Rando, T. A., Zou, J., & Brunet, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.16.603809 · Cited by 4
Form and function of actin impacts actin health and aging
Averbukh, M., Nelson, H. T., Wang, T. V., Lange, C. M., Alcala, A., Kim, J., Suh, D., Vega, M., Gao, S. M., Dutta, N., et al. (2026) · iScience · doi:10.1016/j.isci.2026.117256 · Cited by 3
“Consistent with actin dysfunction driving premature aging, we also observed acceleration of transcriptomic age, measured using the BiT age algorithm, 38 upon knockdown of arx-2 and unc-60 , which was not seen in actin or lev-11 knockdowns (Figure 2G).”
Niu, Z., Liu, C., Fan, Y., & Gu, L. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2533687123 · Cited by 5
“Beyond DNA methylation, researchers have developed biological age predictors using various molecular data types, including proteomics (19–21), metabolo mics (22–24), transcriptomics (25, 26), microbiome profiles (27), glycomics (28, 29), and chromatin accessibility (30).”
The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi‐Omics Integration
Liu, L., Lai, Y., Tian, C., Huang, Y., Hao, J., Zhao, Y., Chen, D., Wu, T., Zhou, D., Zheng, X., et al. (2026) · Aging Cell · doi:10.1111/acel.70579 · Cited by 3
Yang, X., Li, R., Yang, X., Zhou, Y., Liu, Y., & Han, J. J. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2518-8 · Cited by 4
“…biomarker of aging, compared with fundus imaging (Zhu et al., 2023) or biopsy-based measures such as telomere length (Vaiserman and Krasnienkov, 2020), methylome (Bell et al., 2019), proteome (Lehallier et al., 2020), transcriptome (Meyer and Schumacher, 2021) and microbiome (Galkin et al., 2020).”
BayesAge 2.0: a maximum likelihood algorithm to predict transcriptomic age
Mboning, L., Costa, E. K., Chen, J., Bouchard, L., & Pellegrini, M. A. (2025) · GeroScience · doi:10.1007/s11357-024-01499-0 · Cited by 3
Duran, I., & Tsurumi, A. (2025) · Biogerontology · doi:10.1007/s10522-025-10216-z · Cited by 3
“Transcriptome-based clock have also been developed using animal tissues, including mice (Buckley et al. 2023; Trapp et al. 2021) and C. elegans (Meyer and Schumacher 2021).”
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 4
Self-citationAging mechanisms and their relationship with the ontogenesis program: a narrative review
Salnikov, L. (2024) · Aging advances. · doi:10.4103/agingadv.agingadv-d-24-00015 · Cited by 3
Special users with special needs in autonomous vehicles: A systematic review
Mosaferchi, S., & Naddeo, A. (2025) · Work · doi:10.1177/10519815241308769 · Cited by 3
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
“We decided to develop a transcriptomic clock because orthology greatly facilitates the transfer of clocks between species [17] and we focused on the turquoise killifish Nothobranchius furzeri.”
BackgroundResultInfluentialZhang, C., Saurat, N. G., Cornacchia, D., Chung, S. Y., Sikder, T., Minotti, A. P., Studer, L., & Betel, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.03.547539 · Cited by 3
CpGeneAge: multi-omics aging clocks associated with Nf-κB signaling pathway in agingPreprint
Varga, B., & Kerepesi, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.26.661785 · Cited by 3
Three Tiers to biological escape velocity: The quest to outwit aging
Palmer, R. D. (2022) · AGING MEDICINE · doi:10.1002/agm2.12231 · Cited by 2
Salnikov, L., Goldberg, S. I., & Pinsky, E. (2023) · Medical Research Archives · doi:10.18103/mra.v12i1.4927 · Cited by 2
Multi-Omic Rejuvenation: A New Strategy for Lifespan Extension
Rybina, O. Y., & PASYUKOVA, E. G. (2024) · Molecular Biology · doi:10.1134/s0026893324700377 · Cited by 3
“Epigenetic and transcriptomic changes associated with aging are used to construct an “aging clock” that can predict biological age with some accuracy [2, 3].”
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
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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