Abstract
The determination of the “biological age” is one the most interesting problems in the biology of aging. The improvement of the biomarkers of aging is a very important problem. The necessity to use synthetic (i.e. holistic), rather than analytic (i.e. specific) measurements strongly contributes to a deeply complicated relationship between conventional biomedicine and a plethora of anti-aging interventions which are inferred from experimental studies of animals and observational studies of humans. Intrinsically holistic “omics” profiles, however, are subject to the “curse of dimensionality”, discussed in this chapter. It is expected that an increase in the reliability of biomarkers of aging would be achieved by concerted efforts of biostatisticians, who would successfully combine data-driven and knowledge-based approaches, and the biologists who would be instrumental in critically evaluating insights generated in silico and ensure a discernible biological rationale for the metrics of biological age.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aviv A, Valdes AM, Spector TD (2006) Human telomere biology: pitfalls of moving from the laboratory to epidemiology. Int J Epidemiol 35(6):1424–1429
Balietti M, Giuli C, Fattoretti P, Fabbietti P, Papa R, Postacchini D, Conti F (2017) Effect of a comprehensive intervention on plasma BDNF in patients with Alzheimer’s disease. J Alzheimers Dis 57:7–43
Balietti M, Giuli C, Conti F (2018) Peripheral blood brain-derived neurotrophic factor as a biomarker of Alzheimer’s disease: are there methodological biases? Mol Neurobiol 55(8):6661–6672
Bartlett JW, Frost C, Mattsson N, Skillbäck T, Blennow K, Zetterberg H, Schott JM (2012) Determining cut-points for Alzheimer’s disease biomarkers: statistical issues, methods and challenges. Biomark. Med. 6(4):391–400
Bellman RE (1957) Dynamic programming. Princeton University Press, Princeton
Böttcher MA, Dingli D, Werner B, Traulsen A (2018) Replicative cellular age distributions in compartmentalized tissues. J R Soc Interface 15:20180272
Ein-Dor L, Kela I, Getz G, Givol D, Domany E (2005) Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21(2):171–178
Ein-Dor L, Zuk O, Domany E (2006) Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci USA 103(15):5923–5928
Eisenberg DT, Salpea KD, Kuzawa CW, Hayes MG, Humphries SE, European Atherosclerosis Research Study IIG (2011) Substantial variation in qPCR measured mean blood telomere lengths in young men from eleven European countries. Am J Hum Biol 23(2):228–231
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. In: Adaptive computation and machine learning series. The MIT Press
Harries LW, Hernandez D, Henley W, Wood AR, Holly AC, Bradley-Smith RM et al (2011) Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing. Aging Cell 10(5):868–878
Juster RP, McEwen BS, Lupien SJ (2010) Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev 35:2–16
Kupershmidt I, Su QJ, Grewal A, Sundaresh S, Halperin I, Flynn J et al (2010) Ontology-based meta-analysis of global collections of high-throughput public data. PLoS ONE 5(9):e13066
Lara J, Cooper R, Nissan J, Ginty AT, Khaw K-T, Deary IJ et al (2015) A proposed panel of biomarkers of healthy ageing. BMC Med 13:222
Levada OA, Cherednichenko NV, Trailin AV, Troyan AS (2016) Plasma brain-derived neurotrophic factor as a biomarker for the main types of mild neurocognitive disorders and treatment efficacy: a preliminary study. Dis Mark 2016(4095723)
Lin Y, Damjanovic A, Metter EJ, Nguyen H, Truong T, Najarro K et al (2015) Age-associated telomere attrition of lymphocytes in vivo is co-ordinated with changes in telomerase activity, composition of lymphocyte subsets and health conditions. Clin Sci 128:367–377
Lin J, Cheon J, Brown R, Coccia M, Puterman E, Aschbacher K et al (2016) Systematic and cell-type specific telomere length changes in subsets of lymphocytes. J Immunol Res 2016(5371050)
Mather KA, Jorm AF, Parslow RA, Christensen H (2009) Is telomere length a biomarker of aging? A review. J Gerontol Biol Sci 66A:202–213
Mayer G, Heinze G, Mischak H, Hellemons ME, Heerspink HJ, Bakker SJ et al (2011) Omics-bioinformatics in the context of clinical data. Methods Mol Biol 719:479–497
McDermott JE, Wang J, Mitchell H, Webb-Robertson BJ, Hafen R, Ramey J, Rodland KD (2013) Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data. Expert Opin Med Diagn 7(1):37–51
Neshatdoust S, Saunders C, Castle SM, Vauzour D, Williams C, Butler L et al (2016) High-flavonoid intake induces cognitive improvements linked to changes in serum brain-derived neurotrophic factor: two randomised, controlled trials. Nutr Healthy Aging 4:81–93
Palmieri D, Cafueri G, Mongelli F, Pezzolo A, Pistoia V, Palombo D (2014) Telomere shortening and increased oxidative stress are restricted to venous tissue in patients with varicose veins: a merely local disease? Vasc Med 19:125–130
Ragnauth CD, Warren DT, Liu Y, McNair R, Tajsic T, Figg N et al (2010) Prelamin A acts to accelerate smooth muscle cell senescence and is a novel biomarker of human vascular aging. Circulation 121:2200–2210
Saeys Y, Inza I, Larraaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517
Simm A, Nass N, Bartling B, Hofmann B, Silber RE, Navarrete Santos A (2008) Potential biomarkers of ageing. Biol Chem 389:257–265
Southworth LK, Owen AB, Kim SK (2009) Aging mice show a decreasing correlation of gene expression within genetic modules. PLoS Genet 5:e1000776
Thomassen M, Tan Q, Eiriksdottir F, Bak M, Cold S, Kruse TA (2007) Comparison of gene sets for expression profiling: prediction of metastasis from low-malignant breast cancer. Clin Cancer Res 13(18 Pt 1):5355–5360
van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536
Venet D, Dumont JE, Detours V (2011) Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol 7(10):e1002240
Veytsman B, Wang L, Cui T, Bruskin S, Baranova A (2014) Distance-based classifiers as potential diagnostic and prediction tools for human diseases. BMC Genom 15(Suppl 12):S10
Vijg J, Kennedy BK (2016) The essence of aging. Gerontology 62(4):381–385
Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F et al (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365(9460):671–679
Xia X, Chen W, McDermott J, Han JJ (2017) Molecular and phenotypic biomarkers of aging. F1000Research 6:860
Yashin AI, Jazwinski SM (2015) Aging and health—a systems biology perspective. In: Interdisciplinary topics in gerontology, vol 40. Karger, Basel
Acknowledgements
AB is grateful for great discussions of biomarker concepts with many colleagues, with most important thoughtful contributions being made by Prof. Eytan Domany (Weizmann Institute of Science, Israel) and Prof. Alessandro Giuliani (Istituto Superiore di Sanità, Italy). AB and TC acknowledge an important contribution of Dr. Ganiraju Manyam (The UT MD Anderson Cancer, USA) who developed an initial pipeline for distance analysis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Veytsman, B., Cui, T., Baranova, A. (2019). Practical Detection of Biological Age: Why It Is not a Trivial Task. In: Moskalev, A. (eds) Biomarkers of Human Aging. Healthy Ageing and Longevity, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-24970-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-24970-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24969-4
Online ISBN: 978-3-030-24970-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)