26 October 2015

-Omics and aging: from biomarkers to systems biology (1)

Translated by Evgenia Ryabtseva

ResumeAge is the most important risk factor for many diseases, including neurodegenerative diseases, coronary heart disease, type 2 diabetes and cancer.

Due to the increase in life expectancy and low birth rates in developed countries, there is an increase in the incidence of age-related diseases. Therefore, understanding the relationship between diseases and aging, as well as making the idea of healthy aging a reality, are the main goals of medical research. Over the past decades, there has been a significant increase in the volume of biological data due to the introduction of highly efficient technologies that allow the evaluation of thousands of variables, including (epi)genetic and metabolic. The most common and, to date, successful approach to analyzing these data is the so-called reductionist (simplistic) approach. It involves a separate check of each variable for associations with an indicator of interest to the researcher, such as age or age-related disease. However, most of the observed diversity of characteristics of phenotypes remains unexplained; in addition, there is no complete understanding of their most complex complexes. 

Phenotype is a set of external and internal signs acquired as a result of individual development of the organism. The phenotype is formed on the basis of a genotype mediated by environmental factors. 

The purpose of systems biology is to integrate data obtained during various experiments to provide an understanding of the system as a whole instead of a detailed study of individual factors. This allows us to look deeper into the mechanisms underlying complex features that appear as a result of the combined impact of several interacting changes in the biological system. This review examines the latest advances in the use of so-called "-omics" technologies for the identification of biomarkers of aging. After that, the existing approaches of systems biology are analyzed, ensuring the integration of various types of data, and the need for further developments in this area in order to improve the quality of epidemiological studies is explained.

IntroductionAging is often described as a progressive accumulation of changes occurring over time, leading to loss of physiological viability and fertility, increased susceptibility to diseases and, ultimately, death (Harman, 1988, 2001; Kirkwood & Austad, 2000; Vijg & Suh, 2005; López-Otín et al., 2013).

Despite considerable efforts and the development of many theories, the underlying aging process is largely unclear today (Kirkwood & Austad, 2000; Weinert & Timiras, 2003; Rattan, 2006).

Experts distinguish between chronological and biological age. Chronological age is defined as the life expectancy of a person in absolute numbers. Biological age, on the contrary, is a broader concept that takes into account the physical and mental health of a person, which allows taking into account the individual characteristics of the aging process. The task of most studies devoted to the study of aging is to search for associations between chronological age and clinical, as well as molecular characteristics of the phenotype (Warming et al., 2002). 

However, in a number of studies, certain characteristics of the phenotype, such as the function of external respiration, hand strength or bone mineral density, were used as intermediate indicators, based on which it is possible to calculate the required data necessary to study molecular changes in the biological aging process (Jackson et al., 2003; Bell et al., 2012; Levine, 2013). Scientists also studied the causes of longevity and slowing biological aging by comparing centenarians with younger representatives of the control group (Biagi et al., 2012; Sebastiani et al., 2012).

Over the past two decades, life expectancy in the UK has increased by 5.3 years for men and 4.7 years for women. According to experts, this trend will continue over the next 20 years (Oeppen & Vaupel, 2002; Office for National Statistics, 2014). Against the background of an increase in average life expectancy, a sharp increase in the incidence of age–related diseases is expected (in 2000 and 2012, 700,000 and 800,000 people suffered from senile dementia, respectively - Alzheimer's Society, 2014), which will have a strong impact on health care costs. Therefore, a more complete understanding of the mechanisms of aging and its impact on diseases is a long-term goal of public health and a topical topic of modern medical research.

The technologies used by "-omics" are valuable tools for studying aging at the molecular level. Until now, a simplistic approach to data analysis and individual testing of measured variables for the presence of an associative relationship with age have been actively used in this area. Hundreds of epigenetic mutations, gene expression levels, and metabolite concentrations associated with chronological and/or biological age have been identified during such studies (see below for details). Even though these results have clarified many aspects of aging as a complex phenotype, the underlying mechanisms of these associations and the role of interactions between different biological objects are still unclear in many cases. In contrast to simplistic approaches, the goal of systems biology is to analyze all components of a biological process, taking into account their interactions and their inherent hierarchical structure (Ideker et al., 2001; Barabási & Oltvai, 2004). With the availability of an increasing amount of data obtained using high-performance methods, systems biology has given rise to many new methods and ensured their successful application to issues of age and age phenotypes (as described below).

This article provides a brief overview of the current state in the field of "-omic" technologies and their application in the study of aging. In addition, a number of problems of the simplistic approach are outlined and the possibilities of overcoming them with the help of systems biology are discussed. The article describes statistical methods used in systems biology, as well as current and promising areas of their application in the field of aging research for the transition from biomarkers of aging to a more holistic understanding of the aging process.

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