Study of Criteria of Cardiovascular System Desynchronization in Locomotive Drivers’ Brigade in Russia
M. V. Dementyev^{2}, S.M Chibisov^{1}, M. V. Abramova^{1}, S.D. Beeharry^{1},
M.L. Blagonravov^{1}, S.P. Syatkin^{1}, E.V. Neborak^{1}, I.P. Smirnova^{1}, G.I. Myandina^{1}, V.I. Kuznetsov^{1}
^{1}RUDN University, Russia
^{2}South Ural State Hospital, Chelyabinsk, Russia
*Corresponding Author E-mail: russia@prescopus.com
ABSTRACT:
To prevent disease it is not enough to eliminate only modifiable factors but it is also necessary to identify patients at a preclinical stage, when physiological parameters are still within normal limits, but there is an excess stress on the regulation systems. In this study, we aim to examine the parameters of the cardiovascular system(CVS) from their physiological limits to diagnose desynchronization at an early stage.
KEYWORDS: Desynchronosis, shift work, desynchronization, driver, prophylaxis.
INTRODUCTION:
Since the introduction of antibiotic therapy in routine clinical practice an epidemic of non-communicable diseases also started. Based on its rapid growth is the ubiquity of unhealthy lifestyles, demographic aging, urbanization, and violations of the environmental sphere. Despite all the advances of modern medicine, many non-communicable diseases couldn’t be completely cured, but the words of Hippocrates: "Prevention is better than cure," which is sometimes true.
However, to prevent disease it is not enough to eliminate only modifiable factors but it is also necessary to identify patients at a preclinical stage, when physiological parameters are still within normal limits, but there is an excess stress on the regulation systems.
Violations in the consistency of the functional activity of physiological systems often attract attention as the pace of life and its conditions in modern society are often at odds with the speed of the adaptive processes [11, 12].
To predict pathological changes it is more appropriate to study the temporary organization as deviations arising at this level, preceded by information, energy, metabolic and structural abnormalities [1,2,3,4, and 5]. In our work[6], the idea of desynchronosis was justified as the typical pathological process that likely is a temporary structure of stress.
Thus, identifying the desynchronization of certain physiological functions, we will be able to identify patients at risk and to conduct preventive measures at an early stage.
To identify desynchronization apart from estimating the parameters of specific rhythms (which requires specialized software) a simpler method can be used to ensure the identification of consistency and/or the degree of mismatch of physiological functions - correlation and regression analysis, can be easily achieved with a standard package of Microsoft Excel [8,13].
The internal de-synchronization is one of the essential manifestations of the general adaptation syndrome [7].
It is important to distinguish between two fundamentally different concepts: desynchronization as a process and desynchronosis as a state. Desynchronization – a process whereby there is a loss of the mutual coincidence in the oscillation frequencies of different oscillators, resulting in earlier co-frequency and its phases are no longer the same – an integral part of adaptation and is not necessarily associated with the pathology. Desynchronosis on the other hand- is a pathological condition of the body when there is a disturbance in rhythm coordination of the various components of the chronometer [7].
In industrialized countries, about 20 % of the population have shift work regimes, which inevitably leads to the development of desynchronization [15,16], disrupting the circadian clock, and subsequently leads to desynchronosis. This study aims to diagnose the desynchronization of the cardiovascular system in the earliest stages in healthy people with shift work regime.
SUBJECTS AND METHODS:
The computing resources today makes complex correlation and regression analysis of the observed phenomena easily accessible with the use of applications like Microsoft Excel, included in the standard set of Microsoft Office. Quantification of desynchronization was done by calculating the strength and direction of the connection between the parameters of the CVS such as Syst BP-Dias BP, Syst BP-HR and Dias BP-HR.
Sample size calculation:
Recruitment of subjects to study the early desynchronization we selected a group of locomotive drivers which included 85 subjects who have been subjected to daily monitoring of blood pressure and heart rate. Totally we had 200 available locomotive drivers. In common100 locomotive drivers were screened.
Criteria of selection:
Were the subjects randomly selected based on computer generated random numbers. From the available sample we selected especially locomotive drivers with regard to age, so that they were of different ages randing from 29 to 65 ages and the number investigated persons in each age category was about the same. The male sex, the age and the absence of somatic pathology was the selection criteria for locomotive drivers and for control group.
Criteria of desynchronization:
Increase of the correlation coefficients and decrease of negative and unreliable regression coefficients. Identifying desynchronosis each individual locomotive driver we used the program FORM created by G.S. Katinas, which checked the data with daily blood pressure monitoring [19].
Criteria of exclusion:
10 locomotive drivers were excluded due to the detection of Coronary heart disease and 5 drivers were dismissed.
Approval from ethic committee:
Official permission of the ethic committee was obtained. Also received approval from the leadership of the Russian Railways on the possibility of data processing.
The control group included healthy students and staff of the Peoples' Friendship University of Russia (51 subjects), who were also subjected to daily monitoring of blood pressure and heart rate. The age and sex were matched with investigated volunteers.
Statistical analysis:
In the early stage, the temporary data received during measurements were sorted and cleared of statistical irrelevant extremes and artifacts. Their sources in registration of such measurements could be caused by casual and random movement of the patient’s hand during the inflation and deflation of air cuff and also could include technical errors during monitoring, typographical errors when inserting and formatting data.
There are a few variants in the sample which differ from the average value by more than 3 standard deviations. They are excluded from the selected sample. In the presence of trends, in particular, the periodic fluctuations and in the blind application of said sample, many variants cannot be taken into consideration [9]. At the same time, due to the presence of expressed circadian rhythm the greatest values of height of wave rate and the lowest in terms of depth of its decline can be spaced from the daily average value of more than three standard deviations, and they are in such a standard approach and may be wrongly excluded. In such instances, analysis of the differences of successive values has been proposed [10], but the specific implementation of the program was not disclosed.
The exclusion algorithm, regardless of trends was developed in Microsoft Office Excel [10]. Checking of the exclusion program has been carried out on the data of pre-trip inspections of drivers.
With the help of Microsoft Excel application between the rows of observations SystBP-DiasBP, SystBP-HR and DiasBP-HR were calculated in paired correlation and linear regression, for each subject we took into account the relevant correlation coefficients (r), the regression coefficient (b), its standard error (SE) and probability of significance level (P) - the absence of significant regression dependence by comparing a series of observations. Based on the results of individual analysis, variation series were compiled for each group, which was compared in turn with each other.
On the basis of the initial coefficient correlation and regression paired hemodynamic parameters, to assess the consistency of the conjugated secondary physiological contours calculated regression coefficients and correlation coefficients, which we called conjugation. Mutual agreement of all the indicators change is also reflected in the positive and statistically significant, moreover, the high secondary regression coefficients between them.
RESULTS:
The first sign of de-synchronization of cardiovascular system are changes in the interfaced physiological contours: increase in correlation coefficient and decrease in regression coefficient.
The regression coefficient indicates the existence of interrelated changes in the compared indicators. If in a specific examinee the correlation value is positive, it means that with the increase of one value the other also another increases. The greater the regression coefficient (b), the more significant the increase in values of the compared indicator in relation to the other which is conditionally accepted as the independent. A negative regression coefficient means that with an increase of the "independent" indicator the value of the "dependent" indicator decreases.
Correlation coefficients, unlike regression coefficients, does not express the dependence (in statistical sense) of one indicator from the other, but the relation strength between indicators, degree of dispersion of studied values around the regression line. In functional terms, they can be regarded as the strength of interrelation between indicators.
As the circadian frequency is peculiar in all the studied indicators of the organism condition, a violation in the rhythm could be the cornerstone of inconsistency in the interrelated functions of the organism. It could be the consequence of a change in the duration of fluctuations, as well as in their amplitude and in the acrophase (changes in levels do not affect the value of regression and correlation coefficients). Changes in amplitude could alter, first of all, the regression coefficient value; change in acrophase would lead to considerable changes in correlation and regression coefficients.
The regression and correlation relations in SystBP-Dias BP Significant regression coefficients in all the studied groups were positive. The greatest values were observed in the drivers’ group (Table 2).
The greatest dispersion was revealed in drivers’ group, minimum – in the control group, i.e. in drivers’ group the regression coefficients varied independently that, apparently, showed different degree of individuality for adaptation to the shift system working schedule.
Table 1.Statistical regression characteristics in control group
Parameters |
DBPvsSBP |
HRvsSBP |
HRvsDBP |
Dispersion |
0,505 |
0,795 |
1,009 |
Minimum |
0,261 |
0,006 |
0,022 |
Maximum |
0,766 |
0,801 |
1,031 |
Average |
0,502 |
0,369 |
0,503 |
St. error |
0,016 |
0,027 |
0,031 |
St. deviation |
0,116 |
0,194 |
0,220 |
DBPvsSBP – DiasBP and SystBP, HRvsSBP – HR and SystBP, HRvsDBP – HR and Dias BP
The maximum average value of the correlation coefficients is observed in the drivers’ group (see Table 4), which, apparently, was a manifestation of a decrease in flexibility of the physiological functions, and the strengthening of conditions for chronic de-synchronization. In healthy people, moderately high correlation coefficient between DiasBP and SystBP with small dispersion of values within the group (Table 2) was observed.
Table 2.Statistical correlation characteristics in control group
Parameters |
DBPvsSBP |
HRvsSBP |
HRvsDBP |
Dispersion |
0,403 |
0,617 |
0,792 |
Minimum |
0,434 |
0,010 |
0,020 |
Maximum |
0,837 |
0,627 |
0,813 |
Average |
0,652 |
0,365 |
0,406 |
St. error |
0,015 |
0,022 |
0,023 |
St. deviation |
0,105 |
0,157 |
0,168 |
DBPvsSBP – DiasBP and SystBP, HRvsSBP – HR and SystBP, HRvsDBP – HR and Dias BP
The regression and correlation relations inHR and SystBP. Only in the control group, all regression coefficients were positive. As a result of shift system working schedule, regression coefficients in some drivers were negative, which not only showa decrease in coherence, but also reflect the divergence of their work on their physiological functions.
The minimum values were observed in the drivers’ group and the maximum values also belonged to that group. The greatest average values were also seen in the drivers’ group (Table 3).
Table 3.Statistical regression characteristics in group of locomotive drivers
Parameters |
DBPvsSBP |
HRvsSBP |
HRvsDBP |
Dispersion |
1,110 |
1,533 |
2,081 |
Minimum |
0,319 |
-0,287 |
-0,475 |
Maximum |
1,429 |
1,246 |
1,606 |
Average |
0,712 |
0,459 |
0,476 |
St. error |
0,020 |
0,027 |
0,033 |
St. deviation |
0,180 |
0,246 |
0,307 |
DBPvsSBP – DiasBP and SystBP, HRvsSBP – HR and SystBP, HRvsDBP – HR and Dias BP
The maximum average value of correlation coefficients in the drivers’ group was as follows (Table 4).
Table 4.Statistical correlation characteristics in group of locomotive drivers
Parameters |
DBPvsSBP |
HRvsSBP |
HRvsDBP |
Dispersion |
0,403 |
0,617 |
0,792 |
Minimum |
0,434 |
0,010 |
0,020 |
Maximum |
0,837 |
0,627 |
0,813 |
Average |
0,652 |
0,365 |
0,406 |
St. error |
0,015 |
0,022 |
0,023 |
St. deviation |
0,105 |
0,157 |
0,168 |
DBPvsSBP – DiasBP and SystBP, HRvsSBP – HR and SystBP, HRvsDBP – HR and Dias BP
The regression and correlation relations inHR and DiasBP. Only in the control group all regression coefficients were found to be positive. The greatest average values were seen in the control group.
The emergence of negative regression coefficients in a few drivers reflects the divergence oftheir work on their physiological functions (Table 3).
The greatest average value of the correlation coefficient was seen in the drivers’ group that once again confirms strong correlations, decrease in systemic plasticity in relation to a long influence of the de-synchronizing factors (Table 4).
Secondary coefficients of regression (association coefficient) in the drivers’ group. Association of regression coefficients in the drivers’ group were statistically significant for the DiasBPtoSystBP and HR to SystBP contours; for the HR to SystBP and HRto DiasBp; the greatest values were seen in the HR to SystBP and HR to DiasBP (see figure 1).
On the x and y axis,the coefficients of regression is shown. The grey background corresponds to a lack of the statistical significance.
Secondary coefficients of regression (association coefficient) in the control group.
In the control group only we found that the value of all association coefficients of the studied contours were statistically significant, the greatest coherence was noted in the SystBP and DiasBP – the SystBP and HR, the smallest coherence was in HRandSystBP – HRand Dias BP (see fig. 2).
A B C
Fig.1.Regression coefficients inlocomotive drivers’ group
А – HRtoSystBP&DiasBPtoSystBP, B – HRto DiasBP&DiasBPtoSystBP, ; C – HRto DiasBP& HRtoSystBP.
A B C
Fig. 2. Secondary regression coefficients (association of contours)
А – HRtoSystBP&DiasBPtoSystBP, B – HRto Dias BP &DiasBPtoSystBP, ; C – HRto Dias BP& HRtoSystBP
On the x and y axis, the coefficients of regression is shown. The grey background corresponds to a lack of the statistical significance.
DISCUSSION:
Most studies for the diagnosis of desynchronosis is proposed to calculate the parameters of rhythm (period, amplitude, acrophase) [17, 18] with subsequent determination of their shift relative to the confidence limits, which can be traded signal changes in the norm (chronodesms). However, studies based on representative samples to create a reliable database of chronodesms different biological rhythms taking into account age and gender have not been conducted. In addition the time series should be equidistant and this significantly narrows (decreases) the scope. In the work by Baevsky R. M. (2000) [2] daily monitoring of blood pressure was conducted by astronauts in microgravity on Earth and during space flight. One of the objectives of the study was to identify the relationship between changes in blood pressure and heart rate. Noteworthy that this work took into account only the absolute values of correlation coefficients without taking into account the reliability of the probability of the null hypothesis. This study also did not include a comparison group.
Using a combination of correlation and regression analysis we can detect desynchronization at a very early stage that we demonstrated on the example of locomotive drivers.
Thus, even for clinically healthy drivers long influence of shift system working schedule doesn't last without any sign.
The first sign of de-synchronization of cardiovascular system are changes in the relations of the heart rate and the blood pressure, in particular the strengthening of communications between the interfaced physiological contours (increased in correlation coefficient) in combination with gradual decrease in coherence of their work (decrease in regression coefficient).
CONCLUSION:
The use of these complex estimations, as shown in our study, allows to diagnose the de-synchronization at an early stage, even before development of desynchronosis, therefore promotes prevention of a number of diseases initiated by the shift system working schedule.
REFERENCES:
1. Baevsky R. M. Temporal organization of functions and adaptive capacity of the organism // Theoretical and applied aspects of the temporal organization of biological systems. Moscow: Nauka, 1976;88-95.
2. Baevsky R. M., Funtova I. I., Kush J. Daily dynamics of arterial pressure of humans in microgravity // Vestn. Arrhythmology. 2002; 26: 61-66.
3. Katinas G. S., Moiseeva N. I. Biological rhythms and their adaptive dynamics // Environmental human physiology. Leningrad: Nauka, 1980; 468-528 (Amanual of physiology).
4. Katinas G. S. Martynikhin A. V. Adaptation operators to shift work // Actual problems of ecological chronobiology. Yekaterinburg, 1994; 93-94.
5. Komarov F. I., Zakharov L. V., Lisovsky V. A. Circadian rhythm of physiological functions in healthy and sick person. Leningrad: Medicina. 1966.
6. Chibisov S. M., Ovchinnikova L. K., Breus T. K. Biological rhythms of the heart and “externals tress”: monograph. Moscow. 1998; 250 p.
7. Chibisov S. M., Katinas G. S., Ragulskaya M. V. Biorhythms and space: monitoring of cosmobiospheric communications. Moscow, Publishing house “Capital Print”, 2013; 442 p.
8. Alexina L. A., Dementyev M. V., Katinas G. S. andothers. Methods of complexcorrelation and regression analyses of the functional state of the body systems // Scientific notes of St-Petersburg medical University. 2011.
9. Katinas G. S. Methods of observations series analysis // Chronobiology and chrono medicine (ed. By Rapoport S.I., Frolov V. A., Khetagurova L. G.) Moscow: Medical Inform Agency, 2012; 206-251.
10. Muravyov P. A., Solovyov M. N., Dementyev M. V. Cleaning the time series of emissions. 12^{th} international Congress // “Health and education in the XXI century – Innovative technology, modernization, quality, accessibility and safety of medicines in the health system of modern Russia”. December 7-10, 2011, Moscow, Russian Peoples’ Friendship University; 150-151.
11. Stepanova S. I. Biorhythmic aspects of adaptation problem. Moscow: Nauka, 1986; 241 p.
12. Aghajanian N. A., Baevsky R.M., Berseneva A. P. Problems of adaptation and teaching about health. Moscow: Publishing house of PFU, 2006; 284 p.
13. Baevsky R.M. Prediction of states on the verge of norm and pathology. Moscow: Medicina, 1979; 298 p.
14. Cornelissen G. Automatic detection or multiple outliers in physiologic time series notably temperature // Ann. Rev. Chronopharmacology. Vol. 1.Biological rhythms and medications. Proc. First Montroux Conf. of Chronopharmacology. 1984; 157-161.
15. Shift-work research: Where do we stand, where should we go»? Review article / Kantermann T., Juda M., Vetter C., Roenneberg T. // Sleep and Biological Rhythms. 2010; 8: 95-105.
16. Young M.E., Bray M.S. Potential role for peripheral circadian clock dyssynchrony in the pathogenesis of cardiovascular dysfunction. // Sleep Med. 2007; 8(6): 656-667.
17. Halberg F., Engeli M., Hamburger C., Hillman D. Spectral resolution of low-frequency, small-amplitude rhythms in excreted 17- ketosteroid: probable androgen induced circaseptan desychronization / // Acta Endocrinol. 1965. Vol. 103 (Suppl). P. 5-54.
18. Halberg F. Organisms as circadian systems; temporal analysis of their physiologic and pathologic responses, including injury and death // Symp. “Med Aspects Stress Milit Climate. Washington. 1965. P. 1-36.
19. Katinas G.S., Dementyev M.V., Halberg F., Grambsch P., Sorokin A.V. Evaluating the form of nonsinusoidal variations // World Heart J. 2011. Vol. 3(2). P. 135-149.
Received on 26.11.2016 Modified on 14.12.2016
Accepted on 06.01.2017 © RJPT All right reserved
Research J. Pharm. and Tech. 2017; 10(3): 891-895.
DOI: 10.5958/0974-360X.2017.00166.4