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.

本文回顾了信息理论分析的应用，采用熵和互信息度量方法，用于研究衰老和与衰老相关的疾病。衰老和与衰老有关的疾病的研究特别适合于信息论方法的应用，因为衰老过程和相关疾病是多参数的，连续参数与离散参数共存，并且这些参数之间的关系通常是非线性的。信息论为解决此类问题提供了独特的分析能力，与普通的线性生物统计学相比具有独特的优势。在与年龄有关的疾病中，信息论已用于神经退行性疾病的研究（尤其是使用EEG时间序列进行诊断和预测），癌症（尤其是用于建立单独的和组合的癌症生物标记物），糖尿病（主要利用相互信息来表征疾病和衰老状态）和心脏病（主要用于分析心率变异性）。很少有工作采用信息论来分析一般的衰老过程和衰弱，作为衰老相关疾病的基本决定因素和可能的早期临床前诊断措施。通常，信息理论分析的使用不仅允许在目标诊断或治疗参数之间建立（非线性）相关性，而且还可以通过建立可变性度量来提供对衰老和相关疾病性质的理论见解，感兴趣的系统内的适应，调节或体内平衡。