Estimation of Heterogeneity in Diagnostic Parameters of Age-related Diseases

Proposal for a hierarchical, multidimensional, and multivariate approach to investigate cognitive aging

Cognitive aging is highly complex. We applied a data-driven statistical method to investigate aging from a hierarchical, multidimensional, and multivariate approach. Orthogonal partial least squares to latent structures and hierarchical models were applied for the first time in a study of cognitive aging. The association between age and a total of 316 demographic, clinical, cognitive, and neuroimaging measures was simultaneously analyzed in 460 cognitively normal individuals (35-85 years). Age showed a strong association with brain structure, especially with cortical thickness in frontal and parietal association regions. Age also showed a fairly strong association with cognition. Although a strong association of age with executive functions and processing speed was captured as expected, the association of age with visual memory was stronger. Clinical measures were less strongly associated with age. Hierarchical and correlation analyses further showed these associations in a neuroimaging-cognitive-clinical order of importance. We conclude that orthogonal partial least square and hierarchical models are a promising approach to better understand the complexity in cognitive aging.

Recognizing Degenerative Aging as a Treatable Medical Condition: Methodology and Policy

It is becoming increasingly clear that in order to accomplish healthy longevity for the population, there is an urgent need for the research and development of effective therapies against degenerative aging processes underlying major aging-related diseases, including heart disease, neurodegenerative diseases, type 2 diabetes, cancer, pulmonary obstructive diseases, as well as aging-related complications and susceptibilities of infectious communicable diseases. Yet, an important incentive for the research and development of such therapies appears to be the development of clinically applicable and scientifically grounded definitions and criteria for the multifactorial degenerative aging process (or "senility" using the existing ICD category), underlying those diseases, as well as for the safety and effectiveness of interventions against it. Such generally agreed definitions and criteria are currently absent. The devising of such criteria is important not only for the sake of their scientific value and their utility for the development of therapeutic solutions for the aging population, but also to comply with and implement major existing national and international programmatic and regulatory requirements. Some methodological suggestions and potential pitfalls for the development of such criteria are examined.

The application of information theory for the research of aging and aging-related diseases

This article reviews the application of information-theoretical analysis, employing measures of entropy and mutual information, for the study of aging and aging-related diseases. The research of aging and aging-related diseases is particularly suitable for the application of information theory methods, as aging processes and related diseases are multi-parametric, with continuous parameters coexisting alongside discrete parameters, and with the relations between the parameters being as a rule non-linear. Information theory provides unique analytical capabilities for the solution of such problems, with unique advantages over common linear biostatistics. Among the age-related diseases, information theory has been used in the study of neurodegenerative diseases (particularly using EEG time series for diagnosis and prediction), cancer (particularly for establishing individual and combined cancer biomarkers), diabetes (mainly utilizing mutual information to characterize the diseased and aging states), and heart disease (mainly for the analysis of heart rate variability). Few works have employed information theory for the analysis of general aging processes and frailty, as underlying determinants and possible early preclinical diagnostic measures for aging-related diseases. Generally, the use of information-theoretical analysis permits not only establishing the (non-linear) correlations between diagnostic or therapeutic parameters of interest, but may also provide a theoretical insight into the nature of aging and related diseases by establishing the measures of variability, adaptation, regulation or homeostasis, within a system of interest. It may be hoped that the increased use of such measures in research may considerably increase diagnostic and therapeutic capabilities and the fundamental theoretical mathematical understanding of aging and disease.

Information theoretical analysis of aging as a risk factor for heart disease

We estimate the weight of various risk factors in heart disease, and the particular weight of age as a risk factor, individually and combined with other factors. To establish the weights we use the information theoretical measure of normalized mutual information that permits determining both individual and combined correlation of diagnostic parameters with the disease status. The present information theoretical methodology takes into account the non-linear correlations between the diagnostic parameters, as well as their non-linear changes with age. Thus it may be better suited to analyze complex biological aging systems than statistical measures that only estimate linear relations. We show that individual parameters, including age, often show little correlation with heart disease. Yet in combination, the correlation improves dramatically. For diagnostic parameters specific for heart disease the increase in the correlative capacity thanks to the combination of diagnostic parameters, is less pronounced than for the less specific parameters. Age shows the highest influence on the presence of disease among the non-specific parameters and the combination of age with other diagnostic parameters substantially improves the correlation with the disease status. Hence age is considered as a primary "metamarker" of aging-related heart disease, whose addition can improve diagnostic capabilities. In the future, this methodology may contribute to the development of a system of biomarkers for the assessment of biological/physiological age, its influence on disease status, and its modifications by therapeutic interventions.