Metabolome-Based Age Prediction Services
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Metabolome-Based Age Prediction Services


Two individuals of the same age may have different biological aging states. Capturing this difference may constitute a powerful age predictor. Metabolomics can be used to characterize the differences in changes with aging and has also been applied to predict and characterize common age-related diseases such as cancer, diabetes, Parkinson's disease, etc. Thus, the metabolome can be a powerful tool for age prediction and aging assessment. CD BioSciences provides metabolome-based age prediction research services, including characterizing the metabolome and constructing predictive models of biological age.

Our Metabolome-Based Age Prediction Services

The metabolome can characterize all small molecules of biological systems and changes with age. Therefore, we provide research services aimed at identifying metabolomic features associated with biological aging to construct biological age prediction models.

We measure metabolomic data to identify metabolomic features associated with biological age, including some relevant metabolites such as amino acids, fatty acids, acylcarnitine, etc. These predictors are used to develop a metabolomic-based age prediction model, which has important implications for the analysis of aging mechanisms. Our specific service process is shown in the figure below.

Our Metabolome-Based Age Prediction Services

Metabolomics data analysis

  • Quality control. We provide reliable data results for the quantification and characterization of sample metabolites.
  • Differential metabolite screening. We perform a univariate and multivariate analysis of all identified metabolites.
  • Metabolic pathway analysis of differential metabolites. We also conduct functional annotation analysis, clustering analysis, and enrichment analysis of differential metabolites.
  • Biomarker discovery. Here, we carry out our metabolite differential correlation analysis and biomarker ROC analysis.

Biological age model construction

We calculate biological age using our screened and determined metabolomic parameters via basic equations. That is, age prediction equation models are constructed by linear regression estimating the linear relationship between each parameter and the actual age. We reduce model bias and overfitting to accurately assess the calculated biological age.

Clients We Serve

We emphasize that every industry may have different applications and requirements. Therefore, we tailor our products and services to the specific needs of our customers. Our services are geared toward industries that include but are not limited to the following.

  • Biotechnology industry
  • Biomedical industry
  • Other related research institutions

Why Choose CD BioSciences?

  • The aging metabolomics platform integrates metabolomic testing and age prediction model building in a single solution for rapid and accurate screening and establishment of predictive metabolic factors.
  • We provide additional insights containing selected unique variant metabolomics data from various biological populations that can be used to confirm and validate predictors.
  • High-throughput metabolomics-based approaches allow us to continuously discover new and highly specific biomarkers for future use as tools to diagnose aging and age-related diseases and monitor the effectiveness of treatments.

Based on metabolomics techniques, we perform qualitative and quantitative analyses of all metabolites in a sample under general or specific conditions to discover and identify different metabolites with predictive potential, capturing such age-related differential metabolomics measurements constituting predictive models.

CD BioSciences, as one of the professional aging research technology service providers, provides a one-stop metabolomic age prediction service based on our rich service experience and advanced technology platform. If you are interested in our services, please feel free to contact us or make an online inquiry.


  1. Johnson L C, et al. The plasma metabolome as a predictor of biological aging in humans. GeroScience, 2019, 41 (6): 895-906.

All of our services and products are intended for preclinical research use only and cannot be used to diagnose, treat or manage patients.