10 January 2017

Comprehensive analysis of biomarkers of aging

Aging will be predicted by 26 biomarkers

Denis Strigun, Naked Science

American scientists have developed a method of systematic analysis of aging based on 26 biomarkers. The results of the study are presented in the journal Aging Cell (Paola Sebastiani et al., Biomarker signatures of aging).

One of the consequences of the increase in life expectancy in the XX century was the demographic aging of the population. At the same time, it is known that depending on environmental, hereditary and epigenetic factors, the processes of biological aging in individual individuals proceed differently. Therefore, scientists are looking for universal ways to improve the quality of life of older people.

In turn, the prevention of aging requires a single model that would help to assess the dynamics of aging of the body according to a number of criteria. Past work has shown that such criteria can be biomarkers associated with the level of physical activity, anabolism and immune response. However, none of them and all of them in a complex do not allow you to fully cover the process.

To fill in the gap, scientists from Boston University and other universities analyzed data on 4,704 people aged 30 to 110 years. The sample included, in particular, siblings (30 percent), their descendants (50 percent) and spouses (20 percent). Using the clustering algorithm, the blood samples of the subjects were studied for the presence of biomarkers associated with aging.

At the first stage, the authors identified 40, and then 26 and 19 such biomarkers. Among them: the growth of C-reactive protein (hsCRP), the growth of interleukin-6 (IL-6), the growth of the relative width of the distribution of red blood cells by volume (RDW), a decrease in albumins, an increase or decrease in total cholesterol, and others. The average aging profile was consistently repeated in about half of the participants.

However, in a number of subjects, the level of some biomarkers indicated significant deviations from the norm associated with specific diseases. For example, one of the samples was associated with dementia, while the other two, on the contrary, were associated with aging without diseases of old age or without abnormalities of the cardiovascular system characteristic of aging.

According to the authors, the proposed model is an addition to the existing assessment methods. Potentially, with its help, doctors will be able to timely identify complications associated with aging of the body, such as stroke or diabetes mellitus, and offer a person measures to prevent them. In addition, scientists note that this work highlights the benefits of big data for medicine.

Portal "Eternal youth" http://vechnayamolodost.ru  10.01.2017

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