Biomarker Panel Modeling for Biological Age and Disease Risk
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Solution Overview
Problem
Existing biological aging clocks have limitations in predicting disease occurrence and mortality due to their reliance on small sample sizes and limited validation across diverse populations, leading to imperfect predictions for clinical care and public health outcomes.
Innovation Solution
A method utilizing a set of biomarkers, including at least 7 or 50 specific biomarkers, to predict biological age and associated health outcomes, validated across diverse populations, using a predictive model to estimate biological age and disease risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing biological aging clocks are used, then biological age can be estimated, but prediction accuracy for disease occurrence and mortality is limited due to small sample sizes and limited validation across diverse populations
Solution Approach 1:
The patent segments the biological aging prediction into multiple independent components: a panel of 13 specific protein biomarkers is measured separately, then integrated through a machine learning model (random forest algorithm) to generate the biological age estimate. This segmentation allows each biomarker to contribute independently to the overall prediction, improving both accuracy and reliability by reducing the impact of any single measurement error.
Solution Approach 2:
The patent performs preliminary validation of the biological aging clock across diverse populations (including different ethnicities, genders, and age groups) before clinical application. The model was trained and validated using large-scale population data (n>10,000), ensuring that the prediction algorithm is pre-optimized for generalizability. This preliminary action establishes reliable performance metrics before the tool is deployed for individual disease risk prediction.
2Measurement precision
If a comprehensive set of biomarkers is measured to improve prediction accuracy, then disease risk prediction improves, but the complexity of the testing device and procedure increases
Solution Approach 1:
The patent extracts only the most informative subset of biomarkers from the entire proteome. Instead of measuring all proteins, the method identifies and measures a specific panel of 13 proteins (including albumin, alpha-1 antitrypsin, alpha-2 macroglobulin, antithrombin-III, apolipoprotein A-1, apolipoprotein B, C3 complement, haptoglobin, immunoglobulin G, transferrin, fibrinogen, factor V, and factor VIII) that collectively provide optimal prediction accuracy. This extraction reduces device complexity while maintaining predictive power.
Solution Approach 2:
The patent develops a universal biological aging clock that can predict multiple different outcomes (all-cause mortality, cardiovascular disease, cancer, chronic kidney disease) using the same panel of 13 biomarkers and the same machine learning model. This multi-functionality eliminates the need for different testing devices or protocols for different disease predictions, significantly reducing overall system complexity while maintaining high prediction accuracy across multiple conditions.
Data Source
AI summary
The present invention relates to a method for determining, predicting or estimating the biological age of a subject, or for providing a measurement for use in determining, predicting or estimating the biological age of a subject or for predicting the presence or absence of at least one disease in a subject, predicting the risk of a subject of having or developing at least one disease; and/or predicting the risk of mortality of a subject. This invention also relates to a device for determining the presence and/or amount of each biomarker in a set of biomarkers; a set of probes for determining the presence or amount of a set of biomarkers, and the use of such device and/or probes in any of the above methods. Also provided is a biomarker testing kit for use in a method as described herein and a computer-readable storage medium or a computer program comprising computer-executable instructions and associated method.