ArticleAging2016
Deep biomarkers of human aging: Application of deep neural networks to biomarker development.
Article in Aging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
158 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Change in the Rate of Biological Aging in Response to Caloric Restriction: CALERIE Biobank Analysis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2017Trial
- A novel clinical biomarker-based Physiology Healthy Aging Index and risk of all-cause and cause-specific mortality: A 20-year prospective cohort study.GeroScience · 2026Article
- Hematopoietic stem cell aging: a review of transcriptional and multi-omics insights and potential paths for AI integration.Experimental & molecular medicine · 2026Review
- Cross-sectional evaluation of cardiovascular biological age using point-of-care ultrasound.European heart journal. Digital health · 2026Article
- Article
- Blood biomarkers and breed genetics of aging in pet dogs.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence Applications in Gastric Cancer Surgery: Bridging Early Diagnosis and Responsible Precision Medicine.Journal of clinical medicine · 2026Review
- Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations.Scientific reports · 2026Article
- Machine learning assessment of retinal blood flow links metabolic dysfunction and accelerated microvascular aging.Scientific reports · 2026Article
- Automated generation of personalized trajectories of aging phenotypes with DyViA-GAN.Frontiers in aging · 2026Article
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Mechanistic redundancy and hierarchy of aging mechanisms: implications for strategies to extend healthspan and biomarker integration.Frontiers in aging · 2026Review
- A sex-adjusted 7-biomarker clinical aging clock for translational preventative medicine.Scientific reports · 2025Article
- Patterns of Organ-Specific Proteomic Aging in Relation to Lifestyle, Diseases, and Mortality.Aging cell · 2025Article
- Leveraging Artificial Intelligence and Modulation of Oxidative Stressors to Enhance Healthspan and Radical Longevity.Biomolecules · 2025Review
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- Association between the retinal age gap and systemic diseases in the Japanese population: the Nagahama study.Japanese journal of ophthalmology · 2025Article
- Age Estimation From Blood Test Results Using a Random Forest Model.Journal of clinical laboratory analysis · 2025Article
- A DEEP NEURAL NETWORK TWO-PART MODEL AND FEATURE IMPORTANCE TEST FOR SEMI-CONTINUOUS DATA.The annals of applied statistics · 2025Article
98 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.