Abstract
One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r = 0.90 with R2 = 0.80 and MAE = 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r = 0.91 with R2 = 0.82 and MAE = 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. To allow for public testing and evaluate real-life performance of the predictor, we developed an online system available at http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.
Highlights
Aging is a complex process affecting all biological systems at every level of organization [1, 2]
We obtained a dataset of 62,419 anonymized blood biochemistry records, where each record consists of a person’s age, sex, and 46 standardized blood markers through a collaboration with one of the largest laboratory networks in Russia, Invitro Laboratory, Ltd
40 different deep neural networks (DNNs) were trained on 56,177 blood test samples
Summary
Aging is a complex process affecting all biological systems at every level of organization [1, 2]. One problem is that the evaluation of aging changes and possible anti-aging remedies requires a comprehensive set of robust biomarkers [4]. Several “aging clocks” able to predict human chronological age using various biomarkers have already been proposed. Telomere length is commonly used to measure senescence but has lower predictive ability of human chronological age than IgG N-glycans, immunoglobulin G glycosylated at conservative Nglycation sites [11]. Recent studies show that biomarkers of age-related pathologies could be used to evaluate senescence modifications based on the connection between age-related pathologies at the signaling pathway level [12]
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