Estimating biological age by hematological blood parameters
Abstract
Abstract. For the estimation of the biological age (BA) of people based on hematological parameters of the clinical blood test there were used MLR and Deep Neural Networks. In the archive of the Institute of Gerontology NAMS of Ukraine there were selected people aged from 20 up to 90 years (440 men and 504 women), who had all hematological parameters within normal limits. When using the MLR method, the multiple correlation coefficients (R) have low values for both men (0.37) and women (0.38). The use of Deep Neural Networks has given good results. The values of the correlation coefficients between BA and chronological age were 0.92 for men and 0.79 for women. The average absolute error in determining BA was 3.68 years for the men and 6.55 years for the women. The developed method for assessing hematological age can be used in clinical practice to identify people with the risk of developing hematological pathology, as well as in population researches.
References
Murabito, J. M.; Zhao, Q.; Larson, M. G.; Rong, J.; Lin, H. et al. Measures of biologic age in a community sample predict mortality and age-related disease: the framingham offspring study. J Gerontol Ser A Biol Sci Med Sci 2018, 73, 757–762. doi: 10.1093/gerona/glx144
Jia, L.; Zhang, W.; Chen, X. Common methods of biological age estimation. Clin Interv Aging 2017, 12, 759–772. doi: 10.2147/CIA.S134921.
Mamoshina, P.; Kochetov, K.; Putin, E.; Cortese, F.; Aliper, A.et al. Population specific biomarkers of human aging: a big data study using South Korean, Canadian and Eastern European patient populations. J Gerontol Ser A 2018, 1, 1–9. doi: 10.1093/gerona/gly005
Mamoshina, P.; Vieira, A.; Putin, E.; Zhavoronkov, A. Applications of deep learning in biomedicine. Mol Pharm 2016, 13, 1445–1454. doi: 10.1021/acs.molpharmaceut.5b00982
Sebastiani, P.; Thyagarajan, B.; Sun, F.; Schupf, N.; Newman et al. Biomarker signatures of aging. Aging Cell 2017, 16, 329–338. doi: 10.1111/acel.12557
Putin, E.; Mamoshina, P.; Aliper, A.; Korzinkin, M.; Moskalev, A. Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging (Albany NY) 2016, 8, 1021–1033. doi: 10.18632/aging.100968
Caballero, F.F.; Soulis, G.; Engchuan, W.; Sanchez-Niubo, A.; Arndt, H. et al. Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project. Sci Rep 2017, 7, 439-455. doi: 10.1038/srep43955.7
Ching, T.; Himmelstein, D. S.; Beaulieu-Jones, B. K.; Kalinin, A. A.; Do B. T. et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018, 15. doi: 10.1098/rsif.2017.0387
Cole, J. H.; Poudel, R. P. K.; Tsagkrasoulis, D.; Caan, M. W. A. et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 2017, 163, 115–124. doi: 10.1016/j.neuroimage.2017.07.059
Korkushko, O. V.; Pisaruk, A. V.; Chyzhova,V. P. Estimation of human metabolic age using regression and neural network analysis. Zaporozhye medical journal 2021, 23, 60-64. doi:10.14739/2310-1210.2021.1.224883
