Correlation between lipid metabolism and competitive success rates in elite cross-country skiing

Associate Professor, PhD A.S. Bakhareva1
Professor, PhD V.I. Zalyapin1
Dr.Biol., Professor A.P. Isaev1
A.S. Ushakov1
1South Ural State University (National Research University), Chelyabinsk

 

Keywords: cross-country skiers, triglyceride, lipoproteins, cholesterol, hypoxic training, anaerobic glycolysis.

 

Introduction. The analysis of modern scientific literature has revealed that the functional and physico-chemical state of plasma lipids is an important aspect in the assessment of athletes’ adaptive abilities [1, 8]. Some scientists emphasize that it is lipoproteins that transport metabolites, hormones, vitamins, and support cholesterol homeostasis [9]. The research data demonstrate that a high level of physical fitness of athletes is associated with an increase in the lipoprotein lipase (LPL) activity in the muscle vessels [11]. In turn, the LPL activity increases against the background of increased blood flow and enhanced activating effect of such hormones as cortisol and growth hormone (and possibly, cytokines), which levels depend on the nature of muscular activity during various training periods [7].     

Objective of the study was to find correlations between the lipid metabolism and competitive success rates in the elite cross-country skiing sport.

Methods and structure of the study. Sampled for the study purposes were the 18-25 year-old elite male cross-country skiers (n=17) with a wide range of FIS-points

[1]{C}

(from 4.53 to 200). FIS-points in distance racing (DI) and sprint (S) were scored by the athletes in the 2017/2018 season. The lipid metabolism in the sample was tested by a quantitative biochemical analysis of the blood serum

[2]{C}

. The object of in vitro studies was heparinized blood collected from the cubital vein in the morning on an empty stomach. The contents of total cholesterol and triglycerides in the blood serum were determined using the "Pronto" analyzer (Italy)

[3]{C}

. The low-density , very-low-density and high-density lipoproteins were studied using the "Microtech 648 PC" device ("Interlab", Italy) and "Lipoproteins" software. The statistical data processing was made by means of a discriminative analysis [12]; rank correlation analysis and Kruskal-Wallis nonparametric analysis of variance by ranks (Kruskal-Wallis one way analysis of variance) [6]. The study was carried out in the pre-season of the training cycle – in June.

Results and discussion. The comparative analysis of the levels of physical fitness in the athletes was made by producing an integrated rate (hereinafter IR) based on the data from the FIS database on the athletes’ performance in the high-ranking competitions. All the athletes on the list were ranked by each parameter in such a way that the athlete with the best result was given the highest rating, after which these ranks were averaged and scaled to the range from 0 to 1.

 

Table 1. Clustering of cross-country skiers by IR

IR

Cluster

Cluster characteristics{C}

[4]{C}

0.50 – 1.0

I

Top-ranking athletes

0.33 – 0.49

II

High-ranking athletes

0.20 – 0.32

III

Middle-ranking athletes

0.00 – 0.19

IV

Low-ranking athletes

 

Clustering of the experimental data enabled to divide all participants of the study (17 subjects) into four groups (clusters) by the individual IR values attributed to each athlete versus their competitive success rates (Table 1). The IR value, calculated through the correlation analysis, accumulated the information contained in the studied parameters on the competitive success rate and achievements of the athletes, as evidenced by Table 2.

 

Table 2. Paired Spearman's rank correlation coefficients 

Indicator

FIS-points DI

Rank DI

FIS-points S

Rank S

IR

-0.914

-0.914

-0.865

-0.813

 

Table 3. General dynamics of lipid metabolism values in different groups in pre-season

Group

TG

(mmol/l)

LDLP

(mmol/l)

VLDLP (mmol/l)

HDLP (mmol/l)

TC (mmol/l)

Mean values

I

1.5411

2.2756

0.2922

1.1067

5.0956

II

1.1033

2.3133

0.3100

1.1100

5.3467

III

0.9500

2.4400

0.3375

1.3225

5.3700

IV

2.2700

2.3200

0.2600

1.1500

5.0900

Total

1.3676

2.3235

0.3041

1.1606

5.2041

Standard deviations

I

0.45173

0.02351

0.02108

0.01500

0.86734

II

0.19502

0.02517

0.01000

0.00000

1.12117

III

0.30757

0.18493

0.04856

0.24717

1.24748

IV

-

-

-

-

-

Total

0.49904

0.10712

0.03411

0.14228

0.91825

 

The grouping of the mean lipid metabolism values (Table 3) shows that in the pre-season of the training cycle the TG rate in Group I was higher by 28.36% than in Group II (p>0.05) and by 38.35% than in Group III (p<0.05), but lower by 47.29% (p<0.05) as compared to the low-ranking cross-country skiers. At the same time, it was found that the TG rate in Group IV exceeded the normal limit (0.55-1.85 mmol/l). The analysis of the LDLP rate revealed that in Group I it was lower by 1.65% than in Group II (p>0.05), by 7.22% than in Group III (p<0.05), and by 1.95% (p>0.05) as compared to the cross-country skiers of Group IV. In addition, the LDLP rates in Groups I, II, and IV were below the normal limit, while in Group III - higher (2.35-2.43 mmol/l). The VLDLP, HDLP and TC rates in the groups of cross-country skiers were within the normal limits and did not have any statistically significant differences. Therefore, the cross-country skiers with the lower LDLP rate in the pre-season demonstrated high results (Groups I-II). According to literary sources, LDLP serve as a marker of the current functional state of the body of athletes and the degree of its nonspecific resistance [2]. The decreased LDLP rates in the pre-season indicate hypoxia caused by the activation of anaerobic glycolysis of ATP resynthesis in the athletes’ muscle fibers [4]. As a result, the LDLP rate falls, which increases cholesterol transport to the peripheral tissues when forming membranes, especially myocytes, and hormones (adrenaline, noradrenaline), as well as steroidogenic processes.

It was found that the results demonstrated by the cross-country skiers, whose LDLP rates in the pre-season were above the normal limit, were at the middle-ranking level (Group III). These study results are consistent with the findings of the authors, according to which excessive LDLP leads to the formation of atherosclerotic plaques and, consequently, luminal occlusion [3]. In addition, the high LDLP rate in Group III simultaneously testify to an excess of polyunsaturated fatty acids, which are precursors of eicosanoids - inflammatory mediators [10].

Based on the research findings, the lowest results in Group IV can be explained by the fact that the athletes’ TG rate was above the normal limit. The authors also believe that lower TG oxidation rates in the pre-season are associated with the training effects of hypoxic nature. Due to which there is a decrease in the lipid mechanisms of energy supply. However, it was found that with increasing concentration of free fatty acids there is also an increase in the plateau of the cardiac action potential. This provides conditions for the development of arrhythmias [5].

Conclusion. It is only under hypoxia that the intracellular processes that provide a basis for diagnosing the effectiveness of functional adaptation of the bodily systems of cross-country skiers are activated. In prospect, the established relationship can be used to predict the individual competitive success in the regular season based on the dynamics of lipid metabolism in the pre-season.

 

References

  1. Vasilenko V.S., Semenova E.S., Semenova Y.B. Lipidy krovi u sportsmenov v zavisimosti ot napravlennosti trenirovochnogo protsessa [Blood lipids in athletes in view of training process direction]. Pediatr, 2017, vol. 8, no. 2, pp. 10-14. DOI: 10.17816/PED8210-14

  2. Vikulov A.D., Margazin V.A., Kaunina D.V. Lipoproteiny nizkoy plotnosti i fizicheskaya rabotosposobnost sportsmenov-plovtsov [Low density lipoproteins and physical work capacity of swimmers]. Lechebnaya fizicheskaya kultura i sportivnaya meditsina, 2014, no. 1 (121), pp. 10-15.

  3. Harris W.S., Schacky C. Von. The Omega-3 Index: A new risk factor from death for coronary artery disease? Pev. Med. 2004;39:212–220.  doi:10.1016/j.ypmed.2004.02.030

  4. Isaev A.P., Erlikh VV., Romanov Y.N., Bakhareva A.S. Adaptation of athletes to middle-altitude conditions via the intensive development of local-regional muscular endurance and strength motor capability, stretching, and relaxation. Journal of Physical Education and Sport. 2016;16(4):1219–1229. doi:10.7752/jpes.2016.04194

  5. Jenkins N.D.M.,  Buckner S.L., Cochrane K.C., Bergstrom H.C.,  Goldsmith J.A., Weir J.P., Housh T.J., Cramer J.T. CLA Supplementation and Aerobic Exercise Lower Blood Triacylglycerol, but Have No Effect on Peak Oxygen Uptake or Cardiorespiratory Fatigue Thresholds. Lipids. 2014;49(9):871–880. doi:10.1007/s11745-014-3929-0

  6. Kruskal W.H., Wallis W.A. Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association. 1952:47(260):583–621.

  7. Maleki B.H., Tartibian B. Long-term low-to-intensive cycling training: impact on semen parameters and seminal cytokines. Clinical Journal of Sport Medicine. 2015;25(6):535–540. doi: 10.1097/JSM.0000000000000122

  8. Mann S., Beedie C., Jimenez A. Differential effects of aerobic exercise, resistance training and combined exercise modalities on cholesterol and the lipid profile: review, synthesis and recommendations. Sports Medicine. 2014;44 (2):211–221. doi:10.1007/s40279-013-0110-5

  9. Nakagawa Y., Hattori M. Intramyocellular lipids of muscle type in athletes of different sport disciplines. Open Access J Sports Med. 2017; 8: 161–166. doi:  10.2147/OAJSM.S139801

  10. Tartibian B., Maleki B.H., Abbasi A. Omega-3 fatty acids supplementation attenuates inflammatory markers after eccentric exercise in untrained Men. Clinical Journal of Sport Medicine. 2011;21(2):131–137doi: 10.1097/JSM.0b013e31820f8c2f

  11. Wagganer J.D., Robison C.E., Ackerman T.A., Davis P.G. Effects of exercise accumulation on plasma lipids and lipoproteins. Applied Physiology, Nutrition, and Metabolism. 2015;40(5): 441–447.  doi: 10.1139/apnm-2014-0321

  12. William R. Klecka. Discriminant Analysis (Quantitative Applications in the Social Sciences) Seventh Printing, Sage Publication, Inc.,1986.

 

Corresponding author: bakharevaas@susu.ru

 

Abstract

The study analyzes a correlation between the lipid metabolism and competitive success rates in the elite cross-country skiing sport. A statistical data processing toolkit was applied to analyze the FIS-point data versus the lipid metabolism rates to produce an integrated rate indicative of the individual competitive success. We classified the sample into four clusters by the IR versus the competitive success rates. It was found that the first-cluster competitive success rate can be achieved and maintained conditional on the training process giving a high priority to the hypoxic trainings to modify the muscular fibers; with the hypoxic effects controlled by the blood triglyceride rates that must be kept at around 1.5411 mmol/l.

Objective of the study was to find correlations between the lipid metabolism (with triglyceride TG, low-density lipoproteins LDLP, very-low-density lipoproteins VLDLP, high-density lipoproteins HDLP, and the total cholesterol TC rates) and competitive success rates in the elite cross-country skiing sport. Sampled for the study purposes were the 18-25 year-old elite male cross-country skiers (n=17) with a wide range of FIS-points, and with the lipid metabolism in the sample tested by a qualitative biochemical analysis of the blood serum.

 


{C}

[1]{C}

Universal evaluation of the result demonstared by the athlete in the FIS calendar race, relative to the winner of this race. It depends on the loss to the leader; the type of race; the penalty in the race itself.

{C}

[2]{C}

All subjects consented to blood sampling, so the procedure used complied with the Declaration of Helsinki.

{C}

[3]{C}

All subjects consented to blood sampling, so the procedure used complied with the Declaration of Helsinki.

[4]{C}

   The ranking list was built based on the competitive success of the examined athletes and is conditional rather than absolute.