RU2795721C1 - Method for predicting premature aging in young and middle-aged men associated with polymorbid cardiovascular pathology - Google Patents

Abstract

FIELD: medicine; gerontology.

SUBSTANCE: invention can be used to predict premature aging (PS) in young and middle-aged men associated with polymorbid cardiovascular pathology. The fibroblast growth factor value is assessed. The concentration of fibroblast growth factor 21 in blood serum is determined by enzyme immunoassay in ng/l, then the concentration of p53 protein in blood serum is determined by enzyme-linked immunosorbent assay in U/ml; then a determination of the concentration of CRP in the blood serum in mg/l is produced; then the concentration is determined β-endorphin in blood serum by enzyme immunoassay in ng/ml; then, the concentration of 6-SOMT day and 6-SOMT night in the urine is determined by enzyme immunoassay in ng/ml. Daily urine is collected not later than the third day from the time of hospitalization, and the samples collected from 11.00 p.m. to 7.00 a.m. are taken as one, the total volume of urine in the samples is measured, the urine is shaken and 5.0 ml is taken from the middle of the container into an Eppendorf-type test tube. All nightly sampling is carried out with illumination of no more than 30 lux, and patients are preliminarily adapted to the sleep/wake pattern — waking up at 07.00 a.m., falling asleep at 11.00 p.m., and prior to hospitalization, patients are excluded from the diet of foods rich in L-tryptophan. The average sleep duration of patients in groups is at least 7 hours. In the preanalytical period, urine tubes are centrifuged at 1,500 rpm for 15 minutes. Samples are then frozen and stored at -80°C up to the time of analysis. Then, each marker is encoded according to the formula z=ay+b, where y is the value of a particular marker in units of measurement, z is its potential code in relative units, a and b are coefficients, the values of which are taken from Table 4. Then, a private function d_i is calculated for each of the six markers according to the formula: d_i =exp(-exp(-z)), where d_i is a private function for the i-th marker, z is a potential code in relative units i-th marker. Then, the PS probability is calculated using the generalized Harrington function for all six markers according to the formula:

, where d_1-6 is a private function of each marker, D is the probability of PS. If the value of the function is less than or equal to 0.2, an unlikely risk of developing body PS in young and middle-aged men is predicted according to the line of markers. When the value of the function is from 0.2 to 0.37 inclusive, a low risk of developing body PS in young and middle-aged men is predicted according to the line of markers. If the value of the function is from 0.37 to 0.63 inclusive, the average risk of developing PS of the organism in young and middle-aged men is predicted according to the line of markers. If the value of the function is from 0.63 to 0.8 inclusive, a high risk of developing body PS in young and middle-aged men is predicted according to the line of markers.

EFFECT: method provides the possibility of more informative and accurate prediction of PS in young and middle-aged men associated with polymorbid cardiovascular pathology, by taking into account such additional parameters as the concentration of p53 protein, CRP, β-endorphin in blood serum and the concentration of day and night fractions of 6-SOMT.

1 cl, 1 dwg, 4 tbl, 4 ex

Description

The invention relates to medicine, namely gerontology, and can be used to predict the risk of premature aging (PS) associated with polymorbid cardiovascular pathology (PSSP) in young and middle-aged men.

In recent decades, we have faced new challenges that fully affect a person, be it climate change, urbanization, chronic stress, infectious and non-communicable diseases, but the global aging of the population is decisive [8, 14].

According to modern concepts of most evolutionary biologists, aging is an age-dependent decrease in physiological functions leading to the death of an organism [13, 24, 25, 45].

Aging can proceed according to the natural (physiological) type, when a person has a natural development of involutional changes in all organs and systems that correspond to the biological, adaptive and regulatory capabilities of this human population, retarded (slowed down), as well as premature (pathological) type [17, 21 ].

Retarded aging is characterized by a slower development of age-related changes leading to an increase in life expectancy and the phenomenon of longevity than in the entire population [12, 18]. PS, on the contrary, is accompanied by an acceleration of the average rate of aging characteristic of this population, as well as by the early development of age-associated diseases [3, 16, 26, 47].

The diagnostic criteria for PS include BV of the body, its subjective and objective manifestations. Subjective manifestations are nonspecific and can be regarded in favor of PS only with laboratory and instrumental exclusion of the underlying disease. The subjective manifestations of PS include fatigue, emotional lability, sleep disturbance, decreased performance, memory, and reproductive ability [9, 23].

A dynamic study of biological age (BV) makes it possible to objectively evaluate the effectiveness of preventive measures aimed at increasing life expectancy and preventing PS [5, 6, 7-10]. The calendar (passport) age (CA) of a person compared to his BA is not an unambiguous criterion for assessing his ability to work [19]. Morphofunctional changes in organs and tissues that occur with age in each person have their own individual differences. These features make it possible, by comparing BV, to assess the functional state of people of the same CV, comparing the rates of their aging [6]. If BV is ahead of CV, aging develops prematurely [4].

In gerontology, endogenous and exogenous factors are distinguished that affect the rate of aging. Endogenous factors include heredity, metabolic and immune disorders. Exogenous factors are mainly due to occupational hazards (chemicals, ionizing and non-ionizing treatments, carcinogens, electromagnetic radiation, overheating), environmental conditions, bad habits (smoking, alcohol abuse), sedentary lifestyle (physical inactivity), overeating and chronic inflammation [1, 16, 20, 46]. The causes of PS are diverse and multifactorial, but the financial well-being and social and living conditions of each individual person play an important role in its development [15].

The emerging dramatic circumstances associated with the global aging of the population, on the one hand, and the increase in the number of age-associated diseases, on the other hand, determined the need for early detection of PS in young and middle-aged people.

In a number of independent studies, data were obtained indicating a close relationship between age indicators - biological age, the results of laboratory and instrumental assessment of the metabolic status, cardiovascular system, psychophysiological state, as well as factors affecting the induction of apoptosis and PS, primarily - 6-hydroxymelatonin sulfate (6-COMT), p53 protein, β-endorphin, fibroblast growth factor 21 (FGF 21) and C-reactive protein (CRP).

Melatonin (N-acetyl-5-methoxytryptamine) is a neuroendocrine hormone [30] whose synthesis and secretion are regulated by circadian rhythms and light intensity [44]. It has antitumor, anti-inflammatory, cytoprotective functions. Several studies have confirmed the protective role of melatonin (MT) in atherosclerosis [49], myocardial infarction [38], and stroke [41]. The antiatherogenic effect of MT is due to a reduction in the number and area of atheromatous plaques in vessels as a result of modulation of signaling pathways of mitogen-activated protein kinase (MAPK) [23]. Also, in the model of genetically modified mice, inhibition of MT methylation of endothelial nitric oxide synthase made it possible to reduce blood pressure (BP) [33].

A number of clinical and experimental studies indicate a common pathogenesis of disturbances in circadian regulation of MT and hormonal and metabolic homeostasis of lipid and carbohydrate metabolism associated with activation of pro-inflammatory signaling pathways, obesity, insulin resistance (IR) and, as a consequence, with CVD, type 2 diabetes mellitus (DM) and PS.

Fibroblast growth factor 21 belongs to the fibroblast growth factor superfamily, which is involved in the regulation of energy homeostasis, glucose and lipid metabolism, and tissue sensitivity to insulin. A positive correlation has been described between the level of FGF 21 and inflammation markers (IL-6, CRP, fibrinogen), insulin, and the IR index -HOMA-IR [27, 28, 39].

The mechanism underlying FGF21 expression in white adipose tissue is not fully understood, however, in humans, this process is accompanied by an increase in plasma glucose and insulin levels. These results demonstrate the pleiotropic role of FGF21 in maintaining metabolic homeostasis, as well as its tissue and nutritional specific regulation [26, 42].

Thus, in patients with type 2 diabetes, elevated levels of circulating fibroblast activation proteins are observed compared with patients without diabetes, which suggests a direct inhibitory effect of IR on FGF21 activity [26, 34].

Thus, the results of the studies made it possible to establish resistance induced by obesity and type 2 DM to the pleiotropic effects of FGF21, which affect systemic insulin sensitivity, metabolic homeostasis, and its geroprotective properties in these diseases.

The opioid peptides β-endorphin and β-lipotropin are responsible for adaptation, regulation of consumption and use of incoming energy, supplying a person with positive emotions in response to a successfully performed action, thus representing the brain's mediated reward system. Endorphins are key mediators of hedonic balance and emotional response to food selection and intake [31]. Most people with obesity do not have eating disorders, but the risk of its manifestation increases with changes in body weight [32].

The mechanisms of aging are closely related to increased levels of oxidative damage to DNA, proteins, and lipids as a result of unbalanced pro-oxidant and antioxidant activities. The results of numerous studies indicate that oxidative stress (OS) is the main physiological factor of aging, the key coordinator of which is the p53 protein [35]. Transcription factor 53, which regulates the cell cycle, when activated, plays an important role associated with stopping the cell cycle and DNA replication, initiating apoptosis, aging, and cell differentiation in response to various damages to the genetic apparatus, including OC [37, 43, 50] .

Despite numerous studies, no theory has yet been able to fully explain the mechanism of aging. The theories of aging presented in this review seem to be the most relevant for the present period of time, but with some objections, since each of them partially contributes to the general explanation of the entire aging process. The main direction of future research will be the integration of various theories for a scientifically based understanding of the mechanism of aging and the study of individual differences in the rate of aging in humans, depending on the risk factors influencing it.

The results obtained in a number of studies made it possible to study the pathophysiological mechanisms of PS in young and middle-aged patients with SSSP, as well as to construct a probabilistic scheme for the pathogenesis of PS in this pathology. In addition, we have developed a method for predicting the risk of PS in PRSP.

There is a known method for predicting the risk of premature aging (PS) of young and middle-aged men, which consists in determining the actual biological age (FBV) taking into account the measured parameters of the subject - systolic blood pressure, breath holding time on inspiration, and the duration of static balancing on one leg, and due biological age (DBV), examined according to the formula: DBV=0.629⋅KB+18.56, where DBV is due biological age, CV is calendar age. Next, the difference between the obtained FBV and FBV values is determined, which is used to predict the risk of premature aging (PS) in young and middle-aged men, in which the fibroblast growth factor 21 is additionally determined, followed by the calculation of the FBV of the subject according to the formula:

FBV=15.948+0.746⋅1n (FGF 21)+0.301⋅SBP-0.193⋅ZDV-0.152⋅SB,

where FBV - actual biological age, years; FGF 21 - the value of fibroblast growth factor 21, ng/l; GARDEN - the value of systolic blood pressure, mm Hg; ZDV - the value of the breath holding time on inspiration, s; SB is the value of the duration of static balancing on one leg, s, and if the value of the difference between the obtained values (FBV and DBV) <3, a low risk of premature aging of young and middle-aged men is predicted, which corresponds to the first risk group of body PS, with a value of (FBV- WBV) 3-9 predict the average risk of PS in young and middle-aged men, which corresponds to the second risk group for PS of the organism; with a value of (FBV-WBV)> 9, a high risk of PS in young and middle-aged men is predicted, which corresponds to the third risk group for PS of the organism .

The disadvantage of this method is the incomplete accounting of laboratory markers that affect the biological age, a significant number of factors that affect and distort the measurement data of SBP by the Korotkov method.

The invention is based on the task of creating a method for predicting premature aging in young and middle-aged men associated with polymorbid cardiovascular pathology, which would be more informative and accurate by taking into account such additional parameters as the concentration of p53 protein, CRP, β-endorphin in serum and the concentration of day and night fractions of 6-COMT.

The proposed method is carried out as follows:

To determine the actual biological age, the following indicators are studied:

1. Determination of the concentration of fibroblast growth factor 21 (FGF-21) in blood serum is carried out by enzyme-linked immunosorbent assay (ELISA) using VCM Diagnostics SK00145-01 kits (VCM Diagnostics, USA), in ng/l.

2. Determination of p53 protein concentration in blood serum is carried out by enzyme-linked immunosorbent assay using BMS eBioscience BMS256 kits (BenderMedSystems, Austria) in U/ml.

3. Determination of the concentration of CRP in blood serum is carried out using a biochemical analyzer Bekman Coulter AU 680 (USA) using original reagents in mg/l.

4. Determination of the concentration of β-endorphin in the blood serum is carried out by ELISA using VCM Diagnostics kits (Germany) in ng/ml.

5. Determination of the concentration of 6-COMT day and 6-COMT night in urine was carried out by ELISA using BüHLMANNEK-M6S kits (BüHLMANN, Switzerland) in ng/ml. Daily urine is collected no later than the third day from the moment of hospitalization. Samples collected from 23.00 to 07.00 are taken as one, due to the risk of disturbing the rhythm of the patients' sleep, or the absence of the urge to urinate. The total volume of urine in the samples is measured. Urine is shaken and 5.0 ml are taken from the middle of the container into an Eppendorf tube. All night sampling should be carried out with illumination not exceeding 30 lux. Patients must first be adapted to the sleep / wakefulness scheme - awakening at 07:00, falling asleep at 23:00, before hospitalization, patients should exclude foods rich in L-tryptophan from the diet: red caviar, black caviar, squid, horse mackerel, cheese, dried apricots, peanuts, almonds, cashews, pine nuts, sunflower seeds, pistachios, soybeans, peas, beans, chocolate, beer [29]. The average sleep duration of patients in groups should be 7-8 hours. During the preanalytical period, urine tubes were centrifuged at 1500 rpm for 15 minutes. Freeze samples and store at -80°C until analysis.

7. Each marker is coded according to the formula z=ay+b, where y is the value of a particular marker (unit), z is its potential code (arb. unit), and and b are coefficients, the values of which must be taken from table 4.

8. The private function d _i is calculated for each of the six markers according to the formula: d _i =exp(-exp(-z)), where d _i is the private function for the i -th marker, z is the potential code (conventional unit ) of the i -th marker.

, где d_1-6 - функция каждого маркера, D -вероятность ПС.9. The probability of PS is calculated using the generalized Harrington desirability function for all six markers according to the formula:

, where d _1-6 is a function of each marker, D is the probability of PS.

10. If the value of the function is ≤0.2, an unlikely risk of developing PS of the body in young and middle-aged men is predicted by the line of markers, with ≤0.37, but >0.2, a low risk of developing PS of the body in young and middle-aged men is predicted by the line markers, at ≤0.63, but >0.37 predict the average risk of developing PS of the body in young and middle-aged men according to the line of markers and at ≤0.8, but >0.63, a high risk of developing PS of the body in young and middle-aged men is predicted. middle age on line markers.

The invention is illustrated in FIG. 1, which shows a scheme for assessing the probability of PS of an organism by a line of markers.

The assessment of the risk of PS of the organism according to the line of markers provides for the presence of a certain metric of the studied phenomenon (event) and its meter. In the context of such consideration of the problem, the role of a metric is assigned to a generalized indicator, and the role of a meter should be performed by a special scale. The introduction of a generalized indicator, in turn, provides for the scaling of markers.

The qualitative assessment "very good - good - satisfactory - bad - very bad" exhaustively reflects the diversity of the researcher's ideas about the level of the trait, and the Harrington preference scale best adapts them to a numerical form, however, the quality "very good" is not necessarily equivalent to the level "very high » - everything depends on the problem being solved (see Table 1).

In the context of each task to be solved, it is necessary to establish an adequate connection between the event (phenomenon) being evaluated, the actual values (levels) of the attribute, qualitative representations and marks on the scale.

The Harrington scale corresponds to a sigmoid-type function, the so-called Harrington desirability function: d=exp(-exp(-z)), where d is the mark on the scale; z is the coded value of the feature, that is, the value on a conditional scale.

The interval of possible values "y" of the marker must correspond to the interval of values "z" in such a way that among all ordered pairs of the form (y; z), two pairs provide a relevant binding to the above thresholds on the scale of d values. The probability of PS (d), equal to 0.2 and 0.8, if necessary, can also be considered as reference or control (see preference scale) points.

In this case, the corresponding values of z are calculated through the inverse function of d according to the following formula: z=-ln(-ln(d))

For two points with known coordinates, the equation of the corresponding straight line is written:

.This equation can always be converted to the form: z=ay+b, where

.

The above equation establishes a functional relationship between all possible values of a particular marker (y, units) and their potential codes (z, arb. units) used for the evaluation task. It defines an individual scaling rule for each individual marker.

Thus, the actual value "y" of the marker obtained by the laboratory method is assigned the code "z" according to a previously developed rule, then the quantitative mark "d" on the Harrington preference scale is calculated and the corresponding qualitative assessment of the event is given according to the following scheme:

, где D - обобщенная функция; d_1-6 - частная функция для каждого маркера.In general, the probability of an organism's PS according to the line of markers is estimated using the generalized Harrington desirability function for this method:

, where D is a generalized function; d _1-6 is a private function for each marker.

Such a metric is the most sensitive to changes in private functions d _1-6 , therefore, it is most convenient for the researcher when solving evaluation problems. The generalized desirability function D is an abstract construction, therefore, its characteristics such as adequacy and efficiency require clarification. Due to the fact that there is a functional relationship between the variables embedded in the model (that is, they are equivalent), the model is adequate by definition. Its efficiency is not lower than the efficiency of any indicator used in it [2, 11]. This circumstance eliminates the need to develop special criteria for assessing the quality of this model.

The aging process of an organism is a derivative function of many variables. We are faced with the problem of determining the individual BV for each individual - a parameter that gives us a more accurate idea of its true life expectancy against the background of various risk factors, including existing background diseases. In this regard, the probabilistic assessment of aging and PS of the body according to a number of objective and sufficiently studied signs (markers) looks more rational. It is this conceptual approach to assessing the aging of the body that is the basis of this method.

When selecting reference points, we proceeded from the following obvious limitations and assumptions:

- the value of d=0.37 on the scale of Harrington's preferences should be linked to the value of the marker, from which the countdown of undesirable (clinically disturbing) variants of the development of PS of the body can begin;

- the upper limit of the empirical norm of the marker should correspond to an insignificant (negligible) probability of the body's PS, that is, the value d≤0.20 on the Harrington preference scale;

- to the value of d=0.63 on the scale of Harrington's preferences, the value of the marker should be tied, from which the countdown of critical variants of the development of PS of the body can begin.

Correspondences between the empirically identified minimum and maximum values of markers and their marks on the Harrington preference scale and the calculation of coefficients a and b are presented in Table 2.

Thus, we have selected reference points for constructing the corresponding marker scaling rules (see Table 3).

Then the scaling rules for each marker were developed as a linear function z=ay+b:

The developed formulas contain two constants each (a and b), the calculation of which leads to fatal computational errors. Despite the fact that this condition violates the theoretical accuracy of formula representations, this greatly simplifies their form. Thus, for the convenience of using the rules, they were transformed by performing the indicated mathematical operations. The final result was rounded up to the third or fifth decimal place (see Table 4).

When assessing the probability of PS of the body of a patient with PSSP, it is necessary to calculate all private d _1-6 , integrate the obtained values into a generalized indicator D, and then interpret the result according to the following scheme for assessing the probability of PS of the body using a line of markers (see Fig. 1):

We give examples of the specific implementation of the proposed method:

Example 1. Subject A., 42 years old.

z(p53)=0.1255*5.6-0.601;

z(6-COMT day)=-0.00139*873+1.407;

z(6-COMT night)=-0.00089*1505+1.643;

z(FGF 21)=-0.0149*47+0.9438;

z(RBR)=0.2241*4.1-0.566;

z(β-endorphin)=-0.000358*2548+1.2744

Thus, in the subject A., 42 years old, according to the method claimed by us, the average risk of premature aging is predicted. When re-measuring these indicators 8.5 months after the complex treatment and lifestyle correction in this patient, the following results were obtained:

z(p53)=0.1255*1.95-0.601;

z(6-COMT day)=-0.00139*1231+1.407;

z(6-COMT night)=-0.00089*1952+1.643;

z(FGF 21)=-0.0149*52+0.9438;

z(RBR)=0.2241*1.8-0.566;

z(β-endorphin)=-0.000358*3774+1.2744

Thus, in the subject A., 42 years old, according to the method claimed by us, a low risk of premature aging is predicted.

Example 2. Subject V., 44 years old.

z(p53)=0.1255*2.2-0.601;

z(6-COMT day)=-0.00139*1107+1.407;

z(6-COMT night)=-0.00089*2194+1.643;

z(FGF 21)=-0.0149*42+0.9438;

z(RBR)=0.2241*2.1-0.566;

z(β-endorphin)=-0.000358*4438+1.2744

Thus, in the subject V., 44 years old, according to the method claimed by us, a low risk of premature aging is predicted. When these indicators were re-measured 7 months after lifestyle correction, the following results were obtained in this patient:

z(p53)=0.1255*1.4-0.601;

z(6-COMT day)=-0.00139*1431+1.407;

z(6-COMT night)=-0.00089*2798+1.643;

z(FGF 21)=-0.0149*31+0.9438;

z(RBR)=0.2241*0.7-0.566;

z(β-endorphin)=-0.000358*4371+1.2744

Thus, in the subject V., 44 years old according to the method claimed by us, premature aging is unlikely.

Example 3. Subject C, 39 years old.

z(p53)=0.1255*12-0.601;

z(6-COMT day)=-0.00139*417+1.407;

z(6-COMT night)=-0.00089*1034+1.643;

z(FGF 21)=-0.0149*112+0.9438;

z(RBR)=0.2241*8.3-0.566;

z(β-endorphin)=-0.000358*1401+1.2744

Thus, in the subject C, 39 years old, according to the method claimed by us, a high risk of premature aging is predicted. When these indicators were re-measured 11 months after the complex treatment and lifestyle correction, the following results were obtained in this patient:

z(p53)=0.1255*4.2-0.601;

z(6-COMT day)=-0.00139*890+1.407;

z(6-COMT night)=-0.00089*1722+1.643;

z(FGF 21)=-0.0149*75+0.9438;

z(RBR)=0.2241*3.7-0.566;

z(β-endorphin)=-0.000358*3291+1.2744

Thus, in the subject S., 39 years old, according to the method claimed by us, an average risk of premature aging is predicted.

Example 4. Subject D., 32 years old.

z(p53)=0.1255*1.1-0.601;

z(6-COMT day)=-0.00139*1420+1.407;

z(6-COMT night)=-0.00089*2472+1.643;

z(FGF21)=-0.0149*19+0.9438;

z(RBR)=0.2241*0.4-0.566;

z(β-endorphin)=-0.000358*4841+1.2744

Thus, the subject D., 32 years old according to the method claimed by us, premature aging is unlikely. When these indicators were re-measured after 9.5 months and the previous lifestyle was observed, the following results were obtained in this patient:

z(p53)=0.1255*1.12-0.601;

z(6-COMT day)=-0.00139*1398+1.407;

z(6-COMT night)=-0.00089*2578+1.643;

z(FGF 21)=-0.0149*21+0.9438;

z(RBR)=0.2241*0.2-0.566;

z(β-endorphin)=-0.000358*4702+1.2744

Thus, the subject D., 32 years old according to the method claimed by us, premature aging is unlikely.

Bibliography:

1. Abrashitova A.T. The role of endogenous and exogenous factors in the development of premature aging of workers in a gas production enterprise / A.T. Abrashitova, T.N. Panova, I.A. Belolapenko // Kuban. scientific honey. vestn. - 2011. - T. 127, No. 4. - S. 10-12.

2. Adler Yu.P. Planning an experiment in the search for optimal conditions / Yu.P. Adler, E.V. Markova, Yu.V. Granovsky. - Moscow: Nauka, 1976. - 279 p.

3. Anisimov V.N. Molecular and physiological mechanisms of aging / V.N. Anisimov. - St. Petersburg: Nauka, 2003. - 468 p.

4. Belozerova L. M. Physical performance and biological age of men / L. M. Belozerova // Wedge, gerontology. - 2008. - V. 14, No. 5. - S. 21-24.

5. Voitenko V.P. Zdorovye zdorovye (introduction to sanology) / V.P. Voitenko. - Kyiv: Health, 1991. - 246 p.

6. Voitenko V.P. Systemic mechanisms of development and aging / V.P. Voitenko, A.M. Polyukhov. - Leningrad: Nauka, 1986. - 184 p.

7. Voitenko, V.P. Mortality factors and life expectancy / V.P. Voitenko. - Kyiv: Health. - 1987. - 144 p.

8. Denisevich M.N. Life expectancy in Russia: World threats to national security. / M.N. Denisevich // Creative, manager. - 2016. - No. 3. - S. 47-57.

9. Dilman V.M. Large biological clock / V.M. Dilman // Moscow: Knowledge, 1986. - 256 p.

10. Izmerov N.F. Modern medical and demographic situation in Russia / N.F. Izmerov, G.I. Tikhonova, T.P. Yakovleva // Occupational Medicine and Industry. ecology. - 2005. - No. 5. - S. 1-8.

11. Kartashova T.M. Issues of optimization in the development of formulations and technology for the production of new polymeric materials / T.M. Kartashov. - Moscow: MKhTI im. Mendeleev, 1969. - 198 p.

12. Kendal M.V. Aging as a social problem of society / M.V. Kendal, L.A. Koisman, V.P. Nazarova // Vestn. Priamur. state un-ta im. Sholom Aleichem. - 2018. - V. 3, No. 32. - S. 96-103.

13. Lyashok V.Yu. Raising the retirement age: Positive effects and probable risks / V.Yu. Lyashok, T.M. Maleva, Yu.M. Gorlin // Economy, politics. - 2018. - V. 13, No. 1. - S. 148-179.

14. Novoselova E.N. Aging of the population of global cities (On the example of Moscow) / E. N. Novoselova // Vestn. Moscow university Ser. Sociology and political science. - 2015. - No. 4. - S. 150-168.

15. Partsernyak S.A. Premature aging, polymorbidity and integrative medicine: direction of decisions and actions / S.A. Parcernyak; ed. S.A. Saiganova; Ministry of Health Ros. Federation, FGBOU VO North-West. state honey. un-t im. I.I. Mechnikov. - St. Petersburg: Publishing house of SZGMU im. I.I. Mechnikova, 2018. - 332 p.

16. Pristrom M.S. Aging is physiological and premature. Modern view of the problem. / M.S. Pristrom, S.L. Pristrom, I.I. Semenenkov // Med. news. - 2015. - No. 2. - S. 36-45.

17. Usmanova S.V. The concept of gerontology. Classification of age groups: methodical manual /S. V. Usmanov. - Irkutsk: IrGUPS MK ZhT, 2017. - 30 p.

18. Frolkis V.V. Aging and increase in life expectancy / V.V. Frolkis. - Leningrad: Nauka, 1988. - 239 p. 19. Chebotarev, D.F. Biological (functional) age of a person / D.F. Chebotarev, A. Ya. Mints. - Moscow: Medicine, 1978. - 372 p.

20. Chernysheva E.N. Influence of exogenous and endogenous factors on the development of premature aging in patients with metabolic syndrome / E.N. Chernysheva, T.N. Panova, T.A. Esaulova // Kuban. scientific honey. vestn. - 2013. - V. 140, No. 5. - S. 193-196.

21. Anstey, K.J. Measuring human functional age: a review of empirical findings / K.J. Anstey, S.R. Lord, G.A. Smith // Exp. Aging Res. - 1996. - Vol. 22, No. 3.-P. 245-266.

22. Cheng X. Melatonin alleviates myosin light chain kinase expression and activity via the mitogen-activated protein kinase pathway during atherosclerosis in rabbits / X. Cheng, Y. Wan, Y. Xu [et al] // Mol. Med. Rep. - 2015. - Vol. 11, #1. - p. 99-104.

23. Converso D. Aging and work ability: The moderating role of job and personal resources / D. Converso, I. Sottimano, G. Guidetti [et al.] // Front. Psychol. - 2017. - Vol. 8, No. 2262.

24. Flatt T. Integrating evolutionary and molecular genetics of aging / T. Flatt, P.S. Schmidt // Biochim. Biophys. acta. - 2009. - Vol. 1790, No. 10. - P. 951-962.

25. Flatt T.L. Still pondering an age-old question / T.L. Flatt, D. E. Promislow // Science. - 2007. - Vol. 318 (5854). - P. 1255-1256.

26 Fraga M.F. Genetic and epigenetic regulation of aging / M.F. Fraga // Curr. Opin. Immunol. - 2009. - Vol. 21, no. 4. - P. 446-453.

27. Gallego-Escuredo J.M. Opposite alterations in FGF21 and FGF 19 levels and disturbed expression of the receptor machinery for endocrine FGFs in obese patients / J.M. Gallego-Escuredo, J. Gómez-Ambrosi, V. Catalan [et al.] // Int. J. Obes. (Lond). - 2015. - Vol. 39, no. 1. - P. 121-129.

28. Gómez-Ambrosia, J. FGF19 and FGF21 serum concentrations in human obesity and type 2 diabetes behave differently after diet- or surgically-induced weight loss / J. Gómez-Ambrosia, J.M. Gallego-Escuredo, V. Catalana [et al] // Clin. Nutr. - 2017. - Vol. 36, no. 3. - P. 861-868.

29. Gonster J.M. Tryptophan metabolism and related pathways in psychoneuroimmunology: the impact of nutrition and lifestyle / J.M. Gostner, S.B. Geisler, M. Stonig [et al] // Neuropsychobiology. - 2020. - Vol. 79, no. 1. - P. 89-99.

30. Hardeland, R. Melatonin / R. Hardeland, S.R. Pandi Perumal, D.P. Cardinali // Int. J Biochem. cell. Biol. - 2006. - Vol. 38, no. 3. - P. 313-316.

31. Hasan T.F. Anorexia nervosa: a unified neurological perspective / T.F. Hasan, H. Hasan // Int. J. Med. sci. - 2011. - Vol. 8, No. 8. - P. 679-703.

32 Hill A.J. Obesity and eating disorders / A.J. Hill // Obes. Rev. - 2007. - Vol. 8, #1. - P. 151-155.

33. Hribal M.L. Regulation of insulin-like growth factor-dependent myoblast differentiation by FOXO forkhead transcription factors / M.L. Hribal, J. Nakae, T. Kitamura [et al.] // J. Cell Biol. - 2003. - Vol. 162, no. 4. - P. 535-541.

34 Lewis, J.E. Going back to the biology of FGF21: New Insights / J.E. Lewis, F. J. P. Ebling, R. J. Samms [et al.] // Trends Endocrinol. Metab. - 2019. - Vol. 30, no. 8. - P. 491-504.

35. Liu, D. p53, Oxidative stress, and aging / D. Liu, Y. Xu, Antioxid. redox. signal. - 2011. - Vol. 15, #6. - P. 1669-1678.

36. Lundsgaard A.M. Circulating FGF21 in humans is potently induced by short term overfeeding of carbohydrates / A.M. Lundsgaard, A.M. Fritzen, K.A. Sjøberg [et al.] // Mol. Metab. - 2017. - Vol. 6, no. 1. - P. 22-29.

37. Mansur C.P. The regulation and function of the p53 tumor suppressor / C.P. Mansur // Adv. Dermatol. - 1997. - Vol. 13. - P. 121-166.

38. Mao Z.J. Melatonin against myocardial ischemia-reperfusion injury: A meta-analysis and mechanism insight from animal studies / Z. Mao, H. Lin, F. Xiao [et al] // Oxidative Med. cell. Longev. - 2020. - Vol. 2020. - P. 1-11.

39. Marchelek-Myśliwiec M. Chronic kidney disease is associated with increased plasma levels of fibroblast growth factors 19 and 21 / M. Marchelek-Myśliwiec, V. Dziedziejko, M. Nowosiad-Magda [et al] // Physiology. - 2019. - Vol. 44. - P. 1207-1218.

40. Monteleone P. New frontiers in endocrinology of eating disorders / P. Monteleone // Curr. Top Behavior. neurosci. - 2011. - Vol. 6. - P. 189-208.

41. Sadanandan N. Melatonin - a potent therapeutic for stroke and stroke-related dementia / N. Sadanandan, B. Cozene, J. Cho [et al] // Antioxidants. - 2020. - Vol. 9, no. 8. - P. 672.

42. Samms R.J. FGF21 is an insulin-dependent postprandial hormone in adult humans / R.J. Samms, J.E. Lewis, L. Norton [et al.] // J. Clin. Endocrinol. Metab. - 2017. - Vol. 102, no. 10. - P. 3806-3813.

43. Sarfstein R. Tumor suppressor p53 regulates insulin receptor (INSR) gene expression via direct binding to the INSR promoter / R. Sarfstein, H. Werner // Oncotarget. - 2020. - Vol. 11, #25. - P. 2424-2437.

44. Skene D.J. Human circadian rhythms: physiological and therapeutic relevance of light and melatonin / D.J. Skene, J. Arendt // Ann. Clin. Biochem. - 2006. - Vol. 43, Pt. 5. - P. 344-353.

45. Tatar M. Senescence / M. Tatar // Evolutionary Ecology - Concepts and Case Studies / eds. C.W. Fox, D.A. Roff, D.J. Fairbairn. - New York: Oxford University Press, 2001. - P. 128-141.

46. Wang Z. Aging and aging-related diseases / Z. Wang. - Singapore: Springer Nature, 2018. - 294 p.

47. Xia X. Molecular and phenotypic biomarkers of aging / X. Xia, W. Chen, J. McDermott, J. J. Han // F1000Res. - 2017. - No. 6. - P. 860.

48. Yeomans, M.R. Opioid peptides and the control of human ingestive behavior / M.R. Yeomans, R.W. Gray // Neurosci. Biobehav. Rev. - 2002. - Vol. 26. - P. 713-728.

49. Zhang R. Potential role of melatonin as an adjuvant for atherosclerotic carotid arterial stenosis / R. Zhang, L. Ni, X. Di [et al] // Molecules. - 2021. - Vol. 26, no. 4. - P. 811.

50. Zhu D. BMP4 mediates oxidative stress-induced retinal pigment epithelial cell senescence and is overexpressed in age-related macular degeneration / D. Zhu, J. Wu, C. Spee [et al.] // J. Biol. Chem. - 2009. - Vol. 284, no. 14. - P. 9529-9539.

Claims (1)

A method for predicting premature aging (PS) in young and middle-aged men associated with polymorbid cardiovascular pathology, including an assessment of the value of fibroblast growth factor, characterized in that the concentration of fibroblast growth factor 21 in blood serum is determined by enzyme immunoassay in ng/l , then determine the concentration of p53 protein in the blood serum by enzyme-linked immunosorbent assay in U/ml; then produce a determination of the concentration of CRP in the blood serum in mg/l; then produce a determination of the concentration of β-endorphin in the blood serum by enzyme immunoassay in ng/ml; then, the concentration of 6-COMT day and COMT night in the urine is determined by enzyme immunoassay in ng / ml, while the daily urine is collected no later than the third day from the moment of hospitalization, and the samples collected from 23.00 to 7.00 are taken as one, the total volume is measured urine in samples, urine is shaken and 5.0 ml is taken from the middle of the container into an Eppendorf-type test tube, while all nightly sampling is carried out with illumination of no more than 30 lux, and patients are preliminarily adapted to the sleep / wakefulness scheme - awakening at 07.00, falling asleep at 23.00 and before hospitalization, patients are excluded from the diet of foods rich in L-tryptophan; while the average duration of sleep of patients in groups is at least 7 hours; in the preanalytical period, urine tubes are centrifuged at 1500 rpm for 15 minutes, then the samples are frozen and stored at a temperature of -80°C until the analysis; then each marker is encoded according to the formula z=ay+b, where y is the value of a particular marker in units. meas., z - its potential code in arb. units, a and b - coefficients, the values of which are taken from table 4; then a private function d _i is calculated for each of the six markers according to the formula: d _i =exp(-exp(-z)), where d _i is a private function for the i-th marker, z is a potential code in arb. units i-th marker; then, the PS probability is calculated using the generalized Harrington function for all six markers according to the formula:

, where d _1-6 is a private function of each marker, D is the probability of PS; with a function value of less than or equal to 0.2, an unlikely risk of developing body PS in young and middle-aged men is predicted by the line of markers, with a function from 0.2 to 0.37 inclusive, a low risk of developing body PS in young and middle-aged men is predicted by the line markers, with a function value from 0.37 to 0.63 inclusive, the average risk of developing body PS in young and middle-aged men is predicted by the line of markers, and with a function value from 0.63 to 0.8 inclusive, a high risk of developing body PS in men is predicted young and middle age according to the line of markers.

Images