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Related Experiment Video

Updated: Jun 1, 2026

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging
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StackAge: an ensemble-based clock for precise quantification of biological age using multi-omics data.

Yingyi Jiang1,2, Lei Jia3, Yuan Fei4

  • 1College of Information Technology, Shanghai Ocean University, 999 Hucheng Ring Road, Pudong New District, Shanghai 201306, China.

Briefings in Bioinformatics
|May 31, 2026
PubMed
Summary
This summary is machine-generated.

StackAge, a novel biological aging clock using proteomic and metabolomic data, accurately predicts chronological age and improves chronic disease risk prediction. This multi-omics approach offers a powerful tool for personalized health management and disease prevention.

Keywords:
SHAP interpretabilityUK biobankbiological aging clockdisease predictionensemble learningmulti-omics integration

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Area of Science:

  • Biomarkers and Omics
  • Aging Research
  • Precision Medicine

Background:

  • Accurate biological age quantification is crucial for early chronic disease risk assessment and intervention.
  • Existing methods for biological age estimation have limitations in accuracy and scope.

Purpose of the Study:

  • To develop and validate StackAge, an ensemble-based biological aging clock integrating multi-omics data.
  • To assess StackAge's accuracy in predicting chronological age and its efficacy in enhancing chronic disease risk prediction.
  • To explore the biological pathways associated with aging and identify potential modifiable factors influencing aging trajectories.

Main Methods:

  • Integration of large-scale plasma proteomic and metabolomic profiles from 30,376 UK Biobank participants.
  • Development of an ensemble-based machine learning model (StackAge) for biological age prediction.
  • Validation of age prediction accuracy using Pearson correlation and assessment of disease risk prediction using Area Under the Curve (AUC).
  • Feature interpretation, pathway enrichment analysis, and mediation analysis to understand aging mechanisms and influencing factors.

Main Results:

  • StackAge achieved high accuracy in age prediction (Pearson r ≈ 0.93) compared to chronological age.
  • StackAge significantly improved risk prediction for 12 chronic diseases, with AUCs > 0.90 for type 2 diabetes, Alzheimer's disease, and chronic kidney disease.
  • Aging rates derived from StackAge enhanced disease prediction beyond conventional omics and demographic data.
  • Key aging biomarkers were linked to inflammation, metabolic stress, and extracellular matrix remodeling pathways.
  • Modifiable lifestyle factors were identified as potential accelerators of biological aging, increasing susceptibility to various disorders.

Conclusions:

  • StackAge provides a robust multi-omics framework for quantifying individual aging trajectories.
  • Biological age, as quantified by StackAge, is a clinically actionable indicator for precision prevention and management of age-related diseases.
  • The findings underscore the potential of multi-omics data and biological age estimation in advancing personalized healthcare.