TY - JOUR
T1 - Population specific biomarkers of human aging
T2 - A big data study using South Korean, Canadian, and Eastern European patient populations
AU - Mamoshina, Polina
AU - Kochetov, Kirill
AU - Putin, Evgeny
AU - Cortese, Franco
AU - Aliper, Alexander
AU - Lee, Won Suk
AU - Ahn, Sung Min
AU - Uhn, Lee
AU - Skjodt, Neil
AU - Kovalchuk, Olga
AU - Scheibye-Knudsen, Morten
AU - Zhavoronkov, Alex
PY - 2018
Y1 - 2018
N2 - Accurate and physiologically meaningful biomarkers for human aging are key to assessing antiaging therapies. Given ethnic differences in health, diet, lifestyle, behavior, environmental exposures, and even average rate of biological aging, it stands to reason that aging clocks trained on datasets obtained from specific ethnic populations are more likely to account for these potential confounding factors, resulting in an enhanced capacity to predict chronological age and quantify biological age. Here, we present a deep learning-based hematological aging clock modeled using the large combined dataset of Canadian, South Korean, and Eastern European population blood samples that show increased predictive accuracy in individual populations compared to population specific hematologic aging clocks. The performance of models was also evaluated on publicly available samples of the American population from the National Health and Nutrition Examination Survey (NHANES). In addition, we explored the association between age predicted by both population specific and combined hematological clocks and all-cause mortality. Overall, this study suggests (a) the population specificity of aging patterns and (b) hematologic clocks predicts all-cause mortality. The proposed models were added to the freely-available Aging.AI system expanding the range of tools for analysis of human aging.
AB - Accurate and physiologically meaningful biomarkers for human aging are key to assessing antiaging therapies. Given ethnic differences in health, diet, lifestyle, behavior, environmental exposures, and even average rate of biological aging, it stands to reason that aging clocks trained on datasets obtained from specific ethnic populations are more likely to account for these potential confounding factors, resulting in an enhanced capacity to predict chronological age and quantify biological age. Here, we present a deep learning-based hematological aging clock modeled using the large combined dataset of Canadian, South Korean, and Eastern European population blood samples that show increased predictive accuracy in individual populations compared to population specific hematologic aging clocks. The performance of models was also evaluated on publicly available samples of the American population from the National Health and Nutrition Examination Survey (NHANES). In addition, we explored the association between age predicted by both population specific and combined hematological clocks and all-cause mortality. Overall, this study suggests (a) the population specificity of aging patterns and (b) hematologic clocks predicts all-cause mortality. The proposed models were added to the freely-available Aging.AI system expanding the range of tools for analysis of human aging.
KW - Biochemistry aging clocks
KW - Biological age
KW - Deep Learning
KW - Deep Neural Networks
KW - Machine Learning
U2 - 10.1093/gerona/gly005
DO - 10.1093/gerona/gly005
M3 - Journal article
C2 - 29340580
AN - SCOPUS:85050306913
SN - 1079-5006
VL - 73
SP - 1482
EP - 1490
JO - Journals of Gerontology - Series A Biological Sciences and Medical Sciences
JF - Journals of Gerontology - Series A Biological Sciences and Medical Sciences
IS - 11
ER -
