Project Summary

Expert SW system for indication of physiological age, for early diagnosis of aging-related ill health and personalization of aging-related and healthspan extending treatments

(Quantified Longevity Guide - QLG)
Clinical data to pilot the model are needed

Executive Summary:

Product: We will produce, for the first time, both practically applicable and at the same time sophisticated expert SW system for indication of physiological age, for early diagnosis of aging-related ill health and personalization of aging-related and healthspan extending treatments – “The Quantified Longevity Guide – QLG”. The system will facilitate early detection and corresponding preventive early treatment of major aging-related diseases (such as cancer, Alzheimer’s disease, heart disease and diabetes), based on the assessment of physiological age.

Methodological advantage: The use of Information Theory

The main advantage of the proposed system is in terms of methodology: Providing an integrated approach that will take into consideration the non-linear interrelation of a multitude of parameters – biomarkers and intervention factors, using information theoretical measures rather than linear statistical measures.[i]

The proposed Expert SW System will use the methodology of information theory that will uniquely allow the selection of the most beneficial and economical individual factors and factors’ combinations for combating chronic age-related diseases and increasing the health span, indicating physiological age as a cost-effective method of preclinical diagnosis for a variety of chronic age-related diseases.

Endpoint product capabilities

Basic computational software technologies, algorithms, tools and platforms will be developed for the following purposes that may serve physicians, biomedical researchers and general health-conscious consumers:
1) Incorporating information-theoretical methods with statistical methods for common use by biomedical researchers, treating physicians and health planners.
2) Improving interoperability of different model systems by their description in the common terms of information theory.
3) Developing a tool to estimate mutual applicability of different model systems based on their mutual information.
4) Determining combined positive and adverse effects of drugs and treatments interactions on health span and disease status, based on combined parameter influences.
5) Developing a tool for selecting the most informative diagnostic parameters and their combinations.
6) Developing a tool for predicting the efficacy of a new treatment based on the degree of similarity of model systems and time series methodology.
7) Providing a combined metrics for health status for the short and long term, based on the degree of system stability, using information theory.
8)  Providing a combined metrics for the effects of various treatments or their combinations on the health status, for the short and long term, based on the degree of system stability.

References


[i] Blokh D and Stambler I., 2014. Estimation of Heterogeneity in Diagnostic Parameters of Age-related Diseases. Aging and Disease, 5, 218-225.  http://www.aginganddisease.org/EN/10.14336/AD.2014.0500218 
Blokh D and Stambler I., 2015. Information theoretical analysis of aging as a risk factor for heart disease. Aging and Disease, 6, 196-207. http://www.aginganddisease.org/EN/10.14336/AD.2014.0623
Blokh D and Stambler I, 2015. Applying information theory analysis for the solution of biomedical data processing problems. American Journal of Bioinformatics, 3 (1), 17-29. http://thescipub.com/abstract/10.3844/ajbsp.2014.17.29  
Blokh D and Stambler I, 2016. The application of information theory for the research of aging and aging-related diseases. Progress in Neurobiology, doi:10.1016/j.pneurobio.2016.03.005, http://www.sciencedirect.com/science/article/pii/S0301008215300599