Age Prediction Based on Proteome


Biological aging is a growing challenge for today's world, which poses many problems, and we need to develop therapies capable of delaying aging and treating age-related diseases, then the development of anti-aging interventions can be tested and accelerated by constructing aging clocks.

Many different aging clocks have emerged, including epigenomics, transcriptomics and proteomics, which are capable of predicting biological age and providing information about the health of the organism. To help optimize the aging clock and identify targeted therapies for anti-aging, CD BioSciences provides correlation analysis services between proteomics and aging studies of targeted biological samples to discover proteins that change significantly with age and to construct age prediction models.

Proteomics Predictor Discovery

In our service, it is possible to search the data to identify a large number of common proteins that change with age, and we can also identify a small number of specific proteins that are proposed as potential predictors of age.

Methods Descriptions
Literature Search All proteins that change significantly with age are sorted out by searching proteomics studies on aging and age-related diseases in public resource databases.

Protein Measurement We use high-throughput proteomics methods to obtain proteomic data from study organisms and identify proteins associated with actual age through linear regression processing of the data and sequencing support.

Our Services

We help build the most accurate proteomic biological age predictor, using state-of-the-art technology and powerful algorithms to view the biological age of your submitted samples.

Our proteomics-based age prediction services — CD BioSciences
  • Protein Data Collection. We can collect protein data through two strategies, mainly including literature search and sample protein measurement, to comprehensively and efficiently build the data base for prediction models.
  • Statistical Analysis. Age prediction uses statistical analytics to characterize proteomic changes in younger and older age, with specific protein changes selected as predictors.
  • Predictor Construction. We compile and train with the collected and prepared dataset to generate the predictor.
  • Age Predictor Assessment. Predictors can be tested using representative data sets as well as detecting whether application-specific results are consistent with previous ones.

Statistical Analysis Workflow

We carry out bioinformatic statistical analysis of the aggregated protein data, with the following workflow.

  • Data Quality Control - Transform the protein data to exclude outliers and control for data quality.
  • Age-Related Protein Identification – We adjust the parameters by running linear regression to find proteins that correlate with actual age.
  • Unrelated Protein Deletion – We study which proteins are independent of each other and age by reverse linear regression and remove that factor from the model.
  • Age-Related Disease Association Analysis - We can identify proteins among the identified proteins that can serve as biomarkers of age-related diseases by running regressions adjusted for age and related disease correlations.

Why Choose Us?

Given the global trend of aging, there is an escalating interest in predicting biological age. We need to find some reliable biomarkers to predict longevity, health and death. However, we face many challenges in identifying biomarkers, including data access and processing, huge population variation, technical issues, etc.

As the leading technology platform for aging research, CD BioSciences' unique age prediction technology can predict age from any sample that provides proteomic data, extending age prediction methods based on epigenetic age. We provide robust technical support and data resources, and you can access detailed services on how to use proteomic age prediction here.

We are confident that we can complete your aging proteomic study and provide accurate and valid predictive models. If you would like more information, please feel free to contact us.


  1. Aaj A, et al. Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing Research Reviews, 2020, 60.

Our services are for research use only and not for any clinical use.

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We are a comprehensive technology platform company integrating aging DNA methylation, telomere, transcriptome, proteome, and metabolome research.

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