title: Deep biomarkers of human aging: Application of deep neural networks to biomarker development.
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 R(2) = 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 R(2) = 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.
authors.affiliation: Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD 21218, USA.|Computer Technologies Lab, ITMO University, St. Petersburg 197101, Russia.
authors.forename: Evgeny
authors.initials: E
authors.lastname: Putin
authors.affiliation: Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD 21218, USA.|The Biogerontology Research Foundation, Oxford, UK.
authors.forename: Polina
authors.initials: P
authors.lastname: Mamoshina
authors.affiliation: Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD 21218, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Aliper
authors.affiliation: Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD 21218, USA.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Korzinkin
authors.affiliation: Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD 21218, USA.|School of Systems Biology, George Mason University (GMU), Fairfax, VA 22030, USA.
authors.forename: Alexey
authors.initials: A
authors.lastname: Moskalev
authors.affiliation: Invitro Laboratory, Ltd, Moscow 125047, Russia.
authors.forename: Alexey
authors.initials: A
authors.lastname: Kolosov
authors.affiliation: Invitro Laboratory, Ltd, Moscow 125047, Russia.
authors.forename: Alexander
authors.initials: A
authors.lastname: Ostrovskiy
authors.affiliation: Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
authors.forename: Charles
authors.initials: C
authors.lastname: Cantor
authors.affiliation: Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
authors.forename: Jan
authors.initials: J
authors.lastname: Vijg
authors.affiliation: Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD 21218, USA.|The Biogerontology Research Foundation, Oxford, UK.
authors.forename: Alex
authors.initials: A
authors.lastname: Zhavoronkov
mesh_terms: D000375:Aging / Q000097:blood*; D000469:Alkaline Phosphatase / Q000097:blood*; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000032:analysis*; D004906:Erythrocyte Count; D006801:Humans; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology*; D010808:Physical Examination; D012709:Serum Albumin / Q000032:analysis*; D014508:Urea / Q000097:blood*
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D012709:Serum Albumin; D014508:Urea; D000469:Alkaline Phosphatase
keywords: aging biomarkers; biomarker development; deep learning; deep neural networks; human aging; machine learning