Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E., & Heijmans, B. T. (2018). Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics & chromatin, 11(1), 1-11. PMID29848354PMC5975493doi:10.1186/s13072-018-0191-3
Li, X., Wang, J., Wang, L., Gao, Y., Feng, G., Li, G., … & Zhang, K. (2022). Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. Signal Transduction and Targeted Therapy, 7(1), 1-12. PMID35610223PMC9130224doi:10.1038/s41392-022-00964-6
Garagnani, P., Bacalini, M. G., Pirazzini, C., Gori, D., Giuliani, C., Mari, D., … & Franceschi, C. (2012). Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging cell, 11(6), 1132—1134. PMID23061750doi:10.1111/acel.12005
Manco, L., & Dias, H. C. (2022). DNA methylation analysis of ELOVL2 gene using droplet digital PCR for age estimation purposes. Forensic Science International, 333, 111206. PMID35131731doi:10.1016/j.forsciint.2022.111206
Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., … & Navlakha, S. (2018). Predicting age from the transcriptome of human dermal fibroblasts. Genome biology, 19(1), 221. doi:10.1186/s13059-018-1599-6PMC6300908PMID30567591
Shokhirev, M. N., & Johnson, A. A. (2021). Modeling the human aging transcriptome across tissues, health status, and sex. Aging cell, 20(1), e13280. PMC7811842doi:10.1111/acel.13280
Wang, F., Yang, J., Lin, H., Li, Q., Ye, Z., Lu, Q., … & Tian, G. (2020). Improved human age prediction by using gene expression profiles from multiple tissues. Frontiers in Genetics, 11. PMC7546819doi:10.3389/fgene.2020.01025
LaRocca, T. J. , Cavalier, A. N. , & Wahl, D. (2020). Repetitive elements as a transcriptomic marker of aging: Evidence in multiple datasets and models. Aging Cell, 19(7), e13167. PMID32500641PMC7412685doi:10.1111/acel.13167
Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020). Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging. Aging cell, 19(11), e13256. PMID33031577PMC7681068doi:10.1111/acel.13256
Johnson, A. A., Shokhirev, M. N., Wyss-Coray, T., & Lehallier, B. (2020). Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing research reviews, 101070. PMID32311500doi:10.1016/j.arr.2020.101070
Moaddel, R., Ubaida‐Mohien, C., Tanaka, T., Lyashkov, A., Basisty, N., Schilling, B., … & Ferrucci, L. (2021). Proteomics in aging research: A roadmap to clinical, translational research. Aging Cell, e13325. PMID33730416doi:10.1111/acel.13325
Johnson, A. A., Shokhirev, M. N., & Lehallier, B. (2021). The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets. Ageing Research Reviews, 70, 101404. PMID34242807doi:10.1016/j.arr.2021.101404
Sathyan, S., Ayers, E., Gao, T., Weiss, E. F., Milman, S., Verghese, J., & Barzilai, N. (2020). Plasma proteomic profile of age, health span, and all‐cause mortality in older adults. Aging cell, 19(11), e13250. PMID33089916PMC7681045doi:10.1111/acel.13250
Gold, L., Ayers, D., Bertino, J., Bock, C., Bock, A., Brody, E., ,,, & Zichi, D. (2010). Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010; 5(12): e15004 PMID21165148PMC3000457doi:10.1371/journal.pone.0015004
Yushkova, E., & Moskalev, A. (2023). Transposable elements and their role in aging. Ageing Research Reviews, 86, 101881. PMID36773759doi:10.1016/j.arr.2023.101881
Liu, X., Liu, Z., Wu, Z., Ren, J., Fan, Y., Sun, L., ... & Liu, G. H. (2023). Resurrection of endogenous retroviruses during aging reinforces senescence. Cell, 186(2), 287-304. PMID36610399doi:10.1016/j.cell.2022.12.017
Ndhlovu, L. C., Bendall, M. L., Dwaraka, V., Pang, A. P., Dopkins, N., Carreras, N., ... & Corley, M. J. (2024). Retro‐age: A unique epigenetic biomarker of aging captured by DNA methylation states of retroelements. Aging Cell, e14288. doi:10.1111/acel.14288
Ndhlovu, L. C., Bendall, M. L., Dwaraka, V., Pang, A. P., Dopkins, N., Carreras, N., ... & Corley, M. J. (2023). Retroelement-Age Clocks: Epigenetic Age Captured by Human Endogenous Retrovirus and LINE-1 DNA methylation states. bioRxiv. PMID38106164PMC10723416doi:10.1101/2023.12.06.570422
Li, A., Mueller, A., English, B., Arena, A., Vera, D., Kane, A. E., & Sinclair, D. A. (2022). Novel feature selection methods for construction of accurate epigenetic clocks. PLoS computational biology, 18(8), e1009938. PMID35984867PMC9432708doi:10.1371/journal.pcbi.1009938
Weidner, C. I., Lin, Q., Koch, C. M., Eisele, L., Beier, F., Ziegler, P., … & Wagner, W. (2014). Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome biology, 15(2), 1-12. PMID24490752PMC4053864doi:10.1186/gb-2014-15-2-r24
Daunay, A., Hardy, L. M., Bouyacoub, Y., Sahbatou, M., Touvier, M., Blanché, H., … & How-Kit, A. (2022). Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites. Aging, 14(19), 7718—7733. PMID36202132doi:10.18632/aging.204316
Zaguia, A., Pandey, D., Painuly, S., Pal, S. K., Garg, V. K., & Goel, N. (2022). DNA methylation biomarkers-based human age prediction using machine learning. Computational Intelligence and Neuroscience, 2022. PMID35111213PMC8803417doi:10.1155/2022/8393498
Fan, H., Xie, Q., Zhang, Z., Wang, J., Chen, X., & Qiu, P. (2021). Chronological age prediction: developmental evaluation of DNA methylation-based machine learning models. Frontiers in bioengineering and biotechnology, 9. PMID35141217PMC8819006doi:10.3389/fbioe.2021.819991
Garagnani, P., Bacalini, M. G., Pirazzini, C., Gori, D., Giuliani, C., Mari, D., … & Franceschi, C. (2012). Methylation of ELOVL 2 gene as a new epigenetic marker of age. Aging cell, 11(6), 1132—1134. PMID23061750doi:10.1111/acel.12005
Ni, X. L., Yuan, H. P., Jiao, J., Wang, Z. P., Su, H. B., Lyu, Y., … & Yang, Z. (2022). An epigenetic clock model for assessing the human biological age of healthy aging. Zhonghua yi xue za zhi, 102(2), 119—124. PMID35012300doi:10.3760/cma.j.cn112137-20210817-01862
Spólnicka, M., Pośpiech, E., Pepłońska, B., Zbieć-Piekarska, R., Makowska, Ż., Pięta, A., … & Branicki, W. (2018). DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups. International journal of legal medicine, 132(1), 1-11. PMID28725932PMC5748441doi:10.1007/s00414-017-1636-0
Jung, S. E., Lim, S. M., Hong, S. R., Lee, E. H., Shin, K. J., & Lee, H. Y. (2019). DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples. Forensic Science International: Genetics, 38, 1-8. PMID30300865doi:10.1016/j.fsigen.2018.09.010
Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E., & Heijmans, B. T. (2018). Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics & chromatin, 11(1), 1-11. PMID29848354PMC5975493doi:10.1186/s13072-018-0191-3
Li, X., Wang, J., Wang, L., Gao, Y., Feng, G., Li, G., … & Zhang, K. (2022). Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. Signal Transduction and Targeted Therapy, 7(1), 1-12. PMID35610223PMC9130224doi:10.1038/s41392-022-00964-6
Garagnani, P., Bacalini, M. G., Pirazzini, C., Gori, D., Giuliani, C., Mari, D., … & Franceschi, C. (2012). Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging cell, 11(6), 1132—1134. PMID23061750doi:10.1111/acel.12005
Manco, L., & Dias, H. C. (2022). DNA methylation analysis of ELOVL2 gene using droplet digital PCR for age estimation purposes. Forensic Science International, 333, 111206. PMID35131731doi:10.1016/j.forsciint.2022.111206
Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., … & Navlakha, S. (2018). Predicting age from the transcriptome of human dermal fibroblasts. Genome biology, 19(1), 221. doi:10.1186/s13059-018-1599-6PMC6300908PMID30567591
Shokhirev, M. N., & Johnson, A. A. (2021). Modeling the human aging transcriptome across tissues, health status, and sex. Aging cell, 20(1), e13280. PMC7811842doi:10.1111/acel.13280
Wang, F., Yang, J., Lin, H., Li, Q., Ye, Z., Lu, Q., … & Tian, G. (2020). Improved human age prediction by using gene expression profiles from multiple tissues. Frontiers in Genetics, 11. PMC7546819doi:10.3389/fgene.2020.01025
LaRocca, T. J. , Cavalier, A. N. , & Wahl, D. (2020). Repetitive elements as a transcriptomic marker of aging: Evidence in multiple datasets and models. Aging Cell, 19(7), e13167. PMID32500641PMC7412685doi:10.1111/acel.13167
Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020). Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging. Aging cell, 19(11), e13256. PMID33031577PMC7681068doi:10.1111/acel.13256
Johnson, A. A., Shokhirev, M. N., Wyss-Coray, T., & Lehallier, B. (2020). Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing research reviews, 101070. PMID32311500doi:10.1016/j.arr.2020.101070
Moaddel, R., Ubaida‐Mohien, C., Tanaka, T., Lyashkov, A., Basisty, N., Schilling, B., … & Ferrucci, L. (2021). Proteomics in aging research: A roadmap to clinical, translational research. Aging Cell, e13325. PMID33730416doi:10.1111/acel.13325
Johnson, A. A., Shokhirev, M. N., & Lehallier, B. (2021). The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets. Ageing Research Reviews, 70, 101404. PMID34242807doi:10.1016/j.arr.2021.101404
Sathyan, S., Ayers, E., Gao, T., Weiss, E. F., Milman, S., Verghese, J., & Barzilai, N. (2020). Plasma proteomic profile of age, health span, and all‐cause mortality in older adults. Aging cell, 19(11), e13250. PMID33089916PMC7681045doi:10.1111/acel.13250
Gold, L., Ayers, D., Bertino, J., Bock, C., Bock, A., Brody, E., ,,, & Zichi, D. (2010). Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010; 5(12): e15004 PMID21165148PMC3000457doi:10.1371/journal.pone.0015004
Yushkova, E., & Moskalev, A. (2023). Transposable elements and their role in aging. Ageing Research Reviews, 86, 101881. PMID36773759doi:10.1016/j.arr.2023.101881
Liu, X., Liu, Z., Wu, Z., Ren, J., Fan, Y., Sun, L., ... & Liu, G. H. (2023). Resurrection of endogenous retroviruses during aging reinforces senescence. Cell, 186(2), 287-304. PMID36610399doi:10.1016/j.cell.2022.12.017
Ndhlovu, L. C., Bendall, M. L., Dwaraka, V., Pang, A. P., Dopkins, N., Carreras, N., ... & Corley, M. J. (2023). Retroelement-Age Clocks: Epigenetic Age Captured by Human Endogenous Retrovirus and LINE-1 DNA methylation states. bioRxiv. PMID38106164PMC10723416doi:10.1101/2023.12.06.570422
Li, A., Mueller, A., English, B., Arena, A., Vera, D., Kane, A. E., & Sinclair, D. A. (2022). Novel feature selection methods for construction of accurate epigenetic clocks. PLoS computational biology, 18(8), e1009938. PMID35984867PMC9432708doi:10.1371/journal.pcbi.1009938
Weidner, C. I., Lin, Q., Koch, C. M., Eisele, L., Beier, F., Ziegler, P., … & Wagner, W. (2014). Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome biology, 15(2), 1-12. PMID24490752PMC4053864doi:10.1186/gb-2014-15-2-r24
Daunay, A., Hardy, L. M., Bouyacoub, Y., Sahbatou, M., Touvier, M., Blanché, H., … & How-Kit, A. (2022). Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites. Aging, 14(19), 7718—7733. PMID36202132doi:10.18632/aging.204316
Zaguia, A., Pandey, D., Painuly, S., Pal, S. K., Garg, V. K., & Goel, N. (2022). DNA methylation biomarkers-based human age prediction using machine learning. Computational Intelligence and Neuroscience, 2022. PMID35111213PMC8803417doi:10.1155/2022/8393498
Fan, H., Xie, Q., Zhang, Z., Wang, J., Chen, X., & Qiu, P. (2021). Chronological age prediction: developmental evaluation of DNA methylation-based machine learning models. Frontiers in bioengineering and biotechnology, 9. PMID35141217PMC8819006doi:10.3389/fbioe.2021.819991
Garagnani, P., Bacalini, M. G., Pirazzini, C., Gori, D., Giuliani, C., Mari, D., … & Franceschi, C. (2012). Methylation of ELOVL 2 gene as a new epigenetic marker of age. Aging cell, 11(6), 1132—1134. PMID23061750doi:10.1111/acel.12005
Ni, X. L., Yuan, H. P., Jiao, J., Wang, Z. P., Su, H. B., Lyu, Y., … & Yang, Z. (2022). An epigenetic clock model for assessing the human biological age of healthy aging. Zhonghua yi xue za zhi, 102(2), 119—124. PMID35012300doi:10.3760/cma.j.cn112137-20210817-01862
Spólnicka, M., Pośpiech, E., Pepłońska, B., Zbieć-Piekarska, R., Makowska, Ż., Pięta, A., … & Branicki, W. (2018). DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups. International journal of legal medicine, 132(1), 1-11. PMID28725932PMC5748441doi:10.1007/s00414-017-1636-0
Jung, S. E., Lim, S. M., Hong, S. R., Lee, E. H., Shin, K. J., & Lee, H. Y. (2019). DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples. Forensic Science International: Genetics, 38, 1-8. PMID30300865doi:10.1016/j.fsigen.2018.09.010