Deep Biomarkers of Human Aging: Application of Deep Neural Networks to Biomarker Development

Aging (Albany NY). 2016 May;8(5):1021-33. doi: 10.18632/aging.100968.

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.

Keywords: aging biomarkers; biomarker development; deep learning; deep neural networks; human aging; machine learning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aging / blood*
  • Alkaline Phosphatase / blood*
  • Biomarkers / blood
  • Blood Glucose / analysis*
  • Erythrocyte Count
  • Humans
  • Models, Biological
  • Nerve Net / physiology*
  • Physical Examination
  • Serum Albumin / analysis*
  • Urea / blood*

Substances

  • Biomarkers
  • Blood Glucose
  • Serum Albumin
  • Urea
  • Alkaline Phosphatase