Correlation between body composition data obtained by DXA and skinfold predictive protocols in sprinters

Correlação entre dados de composição corporal obtidos pelo DXA e protocolos preditivos de dobras cutâneas em velocistas

Abstract

The aim of this study was to describe the correlation between body composition data obtained through DXA and through skinfolds strategy, with some of their respective formulas, in sprinters. The sample consisted of 15 male sprinters (23.81 years ± 3.11; 70.06 Kg ± 4.38; and 179.13 CM ± 5.16) all high performance runners of speed and barriers events (100m, 200m, 400m, 110m with barriers and 400m with barriers). The athletes were submitted to DXA evaluation procedure and to skinfolds collection (triceps, biceps, subscapular, supra iliac, abdominal, medial thigh and calf) and the results were calculated through four distinct equations: Slaughter, Faulkner, Lázari and Boileau. The respective DXA correlations (0.60; 0.81; 0.23 and 0.48) and the equations predicted by skinfold strategy were calculated using Pearson correlation. Among the equations used, Faulkner's was the one presenting highest correlation value when compared to DXA protocol, although all of them aimed to estimate values for BF%.

Key words:
Absorptiometry photon; Anthropometry; Body composition; Sports

Resumo

O objetivo deste estudo foi descrever a correlação entre dados de composição corporal obtidos através de DEXA, e pela estratégia de dobras cutâneas, com algumas de suas respectivas fórmulas, em velocistas. A amostra foi composta por 15 velocistas do sexo masculino (23,81 anos ± 3,11; 70,06 Kg ± 4,38; e 179,13 cm ± 5,16) todos corredores de alto desempenho das provas de velocidade e barreiras (100m, 200m, 400m, 110m com barreiras e 400m com barreiras). Os atletas foram submetidos ao procedimento de avaliação do DEXA e a coleta de dobras cutâneas (tricipital, bicipital, subescapular, supra ilíaca, abdominal, coxa medial e panturrilha) e os resultados calculados através de quatros distintas equações Slaughter, Faulkner, Lázari e Boileau. As respectivas correlações (0,60; 0,81; 0,23 e 0,48) de DEXA e as equações previstas pela estratégia de dobras cutâneas foram calculadas através da correlação de Pearson. Dentre as equações utilizadas, a de Faulkner foi a que apresentou maior valor de correlação quando comparada ao protocolo do DEXA, apesar de todas terem por objetivo estimar valores para o %G.

Palavras-chave:
Absorciometria de fóton; Antropometria; Composição corporal; Esportes

INTRODUCTION

The maximum speed performance in athletics is a multifactorial phenomenon. It requires an interrelation of some determining factors, such as strength development rate, muscle power, maximum dynamic strength and anaerobic power¹.

Changes in body composition during athletes preparation period for the most varied modalities are part of a variables set that must be taken into account during physical abilities preparation and development^2,3. Some studies have addressed the body composition variables, regarding lean and fat mass compartments, and their possible relations with sports performance.

For such, dual-energy X-ray absorptiometry (DXA), considered the gold standard for assessing body composition, is a fast and non-invasive method. It is based on 3 components indirect measurement: mineral bone density, fat-free mass and fat mass^2,4.

DXA collected results may also be combined with anthropometric parameters for development of more data on body composition⁵.

Reference tables, with DXA values differentiated by sports modalities and sex, may be a good measure to help understanding athletes specific needs and comparing evaluation methods⁵.

Among the most used methods for measuring body composition, skinfolds stands out. For having a lower cost compared to DXA, coaches involved with most varied sports modalities have been using it. In this sense, several studies show athletes morphological data obtained via this method^6-8.

A longitudinal study with female sprinter athletes found a correlation between performance improvements in 100-meter dash and fat mass percentage reduction³. Another study with 98 professional sprinters pointed out that athletes who achieved the best 100-meter dash marks had higher lean mass and lower fat mass amounts².

Based on these considerations, this study sought to describe the correlation between DXA obtained body composition data, considered gold standard for this purpose, and skinfolds measuring strategy, with some of their respective formulas, in sprinters.

METHOD

Sample

The sample (Table 1) consisted of 15 male sprinter athletes, with an average of 23.81 ± 3.11 years for age, 70.06 ± 4.38 kilograms for weight and 179.13 ± 5.16 centimeters for height, all high-performance runners of speed and barriers events (100m, 200m, 400m, 110m with barriers and 400m with barriers). The individuals come from an athletics club in Campinas (SP) and have as best results average (Personal Best – PB) 91.9 ± 1.56% in relation to World Records, in official competitions in their respective races.

Table 1
Descriptive statistical values in mean and standard deviation for sample characterization through the variables age, experience, weight, height, fat mass, lean mass and body mass index (BMI).

	Mean	Standard Deviation
Age (years)	23.81	3.21
Experience (years)	7.73	3.02
Weight (kg)	70.06	4.53
Height (cm)	179.13	5.35
Fat mass (kg)	7.86	1.52
Lean mass (kg)	59.34	3.57
BMI	21.85	1.45

Of the studied group, 12 athletes (80% of the sample) have already been part of national teams, representing Brazil in international competitions; besides, 4 of them took part in Rio 2016 Olympic Games, 3 of these reaching finals.

All athletes in the study were duly registered in Brazilian Athletics Confederation, participating in national and international competitions. The selection was intentionally made (non-probabilistic). All individuals participated in systematic training for at least three years and performed their practices five to six times a week.

Anthropometry assessment procedures

Both DXA and skinfolds evaluations were conducted in the morning, always at rest. For skinfolds evaluation, we used the suggestions described by Ross and MarfellL-Jones⁹ and by Harrison et al.¹⁰. The skinfolds (mm) measured were: triceps, biceps, subscapular, supra iliac, abdominal, medial thigh and calf. Then results were calculated in four distinct protocols (Table 2): Boileau et al.¹¹, Faulkner¹², Lázari¹³ and Slaughter et al.¹⁴.

Table 2
Predictive equations description by skinfolds method, population intended in its original article, folds quantity and author.

Equation	Population	skinfolds Quantity	Author (res)
BF% = 5.783 + 0.153 (TR + SB + SI + AB)	Swimming, Water Polo and Triathlon	4	Faulkner12
BF% = 0.735 (TR + PA) + 1	Black and white children	2	Slaughter et al.14
BF% =1.35 (TR+SE) - 0.012 (TR + SE)2 - 4.4	Young male	2	Boileau et al.11
FM = -5051.087 + 134.581 * weight + 117.540 * Σ7D + 44.846 * C.P	Athletics	7	Lázari13

For body composition analysis by the above method, a scientific adipometer of the Harpenden brand was used, all data were collected by the same evaluator, and three measurements were made for each of the folds. A fractionation of two components was carried out: fat mass (FM) and muscle mass (MM).

For body composition analyzed through DXA, an iDXA model equipment was used (GE Health care Lunar, Madison, WI, USA) with fan beam detectors, enCore^tm 2011 software, version 13.6. The whole body (including head) was measured, the body composition was analyzed and three components were determined: lean mass (LM), bone mass (BM), fat mass (FM).

The procedure consisted in lying the subjects on the scanning platform at supine position, with arms and legs extended (in pronated position). The ankles were secured with a velcro strap to ensure standard positioning. The subjects were instructed not to use jewelry or any other metal accessory that prevented analysis.

All protocols and evaluations carried out by the study were duly submitted to analysis of the Research Ethics Committee of UNICAMP – Campinas Campus, and obtained its approval through the opinion 1,511,796.

Statistical analysis

For data statistical treatment, the softwares Biostat (version 5.3) and Microsoft Excel 365 were used. Descriptive statistical data analysis was performed, correlations were established through Pearson correlation test and linear regression analysis (Simple Linear Regression). Through these data it was possible to develop a linear equation. For significance index, a value of p<0.05 was established.

RESULTS

Table 3 shows the values referring to each athlete BF% and the differences found in their values (%) by changing the protocols used for calculation.

Table 3
Descriptive statistical values (Minimum, Maximum, Mean and Standard Deviation) for the different prediction methods and equations: DXA, Slaugther, Faulkner, Lázari and Boileau.

	Minimum	Maximum	Mean	Standard Deviation
DXA	7.90	14.29	11.20	1.91
Slaughter	5.85	10.11	7.33	1.24
Faulkner	8.67	11.15	9.99	0.76
Lázari	8.63	10.51	9.42	0.59
Boileau	7.34	13.25	10.71	1.78

Table 4 shows the correlation results obtained between BF% values collected through DXA and other protocols. The results indicate a strong correlation between the data presented by Faulkner protocol and DXA, unlike other protocols. Figure 1 displays the graphs indicating the dispersion of each protocol calculated together with the DXA. By simple linear regression calculus, it was possible to generate a linear equation and its respective R² (Table 5).

Table 4
Values for correlation strength and p value corresponding to the predictive equations of Salughter, Faulkner, Lázari and Boileau.

Protocol	Pearson Correlation (r)	Value (p)
Slaughter	0.60	0.0170
Faulkner	0.81	0.0002
Lázari	0.23	0.4079
Boileau	0.48	0.0667

Figure 1
Scatter plots with indication of maximum, mean and minimum value of predictive equations of Slaughter, Faulkner, Lázari and Boileau.

Table 5
Determination of Linear Equation and respective R² for predictive equations of Slaughter, Faulkner, Lazari and Boileau from DXA.

Protocol	Linear Equation	R2
Slaughter	y = 0.9247x + 4.4240	0.364
Faulkner	y = 2.0468x - 9.2547	0.664
Lázari	y = 7468x + 4.1636	0.053
Boileau	y = 0.518x + 5.6478	0.235

DISCUSSION

This study sought to describe the correlation between data obtained through DXA and other fat percentage evaluation protocols by skinfold strategy in sprinters.

The results found bring to athletics practice and training more reliability regarding elite athletes body composition analysis. The findings were able to show a strong correlation between data obtained through DXA and Faulkner's predictive equation. The other equations used in this study could have their results interpreted as: moderate correlation (Boileau and Slaughter) and weak correlation (Lázari).

According to a study by Martins et al.¹⁵ countless researches have been comparing body composition methods such as anthropometry, bioimpedance and DXA. The results found in this study corroborate those presented by Deutz et al.¹⁶, which compared gymnastic athletes and runners body composition values through DXA and Jackson's equation anthropometry. In both, a strong correlation was found between the adopted methods, and also that DXA values were higher in relation to fold method. These convergences show evidence of a greater proportionality between some equations and DXA values, upon a given population.

This effect can also be described in the study by Moreira et al.¹⁷, since when comparing the BF% values found in Taekwondo athletes, through DXA and six distinct prediction equations, it was possible to verify a greater correlation of one above the others. This same behavior is also evidenced in the study of Lozano-Berges et al.¹⁸, which also compared six different folds to DXA, in teenage football players.

It is important to note that certain equations were developed for specific populations, which would explain the differences found between the protocols used. In this study, we noted that the equation closest to DXA results in terms of proportionality, Faulkner's, is not meant for athletics, the modality tested by us. It is important saying there are divergences in what is understood as the origin and author of such equation. According to Pires Neto and Glaner¹⁹, the formula up to then attributed to Faulkner, in reality, would have been created from the adaptation of two equations developed by Yuhasz. However, this strong correlation is understandable, given that both samples fit within the profile determined by the formula, that is, well-trained young men.

Once we had an equation showing a strong correlation with the values obtained by an evaluation considered gold standard, it was also possible, through a linear equation, to correct its values in order to reproduce DXA values, being R2 the determination coefficient whose function is to explain the results obtained. Thus, we interpret that such data is able to explain 66% of the results, while other data and variables would be needed to explain the remaining 34%.

We suggest for the next study a greater variables range to be analyzed from the statistical scope, as well as a greater number of protocols to be compared. Greater sampling could bring more comprehensive results. However, we believe that athletes with expressive results at national and international level, such as those in this study, are sufficient to obtain relevant results for sport science.

CONCLUSION

Although all the formulas in our study aim to estimate fat percentage, we could identify that, among the equations used, Faulkner's was the one showing the highest correlation value when compared to DXA protocol. This fact indicates proportionality with the gold standard method, in order to ascertain the body composition and the aforementioned formula.

Absolute mean values of fat percentage were also observed as higher than DXA, regarding anthropometry. Finally, new studies are needed, including exploring new protocols, formulas and sampling in order to provide a greater understanding of the body composition from different populations and modalities.

ACKNOWLEDGEMENTS

The authors thank Espaço da Escrita – Pró-Reitoria de Pesquisa – UNICAMP - for the language services provided.

• How to cite this articleLázari E, Moraes AM, Alcântara RA, Oliveira RL, Gazzaneo RM. Correlation between body composition data obtained by DXA and skinfold predictive protocols in sprinters. Rev Bras Cineantropom Desempenho Hum 2022, 24:e83828. DOI: http://doi.org/10.1590/1980-0037.2022v24e83828

• Funding

  This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This study was funded by the authors.

REFERENCES

• ¹
  Loturco I, Kobal R, Kitamura K, Fernandes V, Moura N, Siqueira F, et al. Predictive factors of elite sprint performance: Influences of muscle mechanical properties and functional parameters. J Strength Cond Res 2019;33(4):974-86. http://dx.doi.org/10.1519/JSC.0000000000002196 PMid:30913203.
  » http://dx.doi.org/10.1519/JSC.0000000000002196
• ²
  Barbieri D, Zaccagni L, Babic V, Rakovac M, Misigoj-Durakovic M, Gualdi-Russo E. Body composition and size in sprint athletes. J Sports Med Phys Fitness 2017;57(9):1142-6. http://dx.doi.org/10.23736/S0022-4707.17.06925-0 PMid:28085130.
  » http://dx.doi.org/10.23736/S0022-4707.17.06925-0
• ³
  Abe T, Kawamoto K, Dankel SJ, Bell ZW, Spitz RW, Wong V, et al. Longitudinal associations between changes in body composition and changes in sprint performance in elite female sprinters. Eur J Sport Sci 2020;20(1):100-5. http://dx.doi.org/10.1080/17461391.2019.1612950 PMid:31109250.
  » http://dx.doi.org/10.1080/17461391.2019.1612950
• ⁴
  IAAF: International Association of Athletics Federations [Internet]. 2020 [cited 2020 Apr 24]. Available from: https://www.worldathletics.org/records/by-category/world-records
  » https://www.worldathletics.org/records/by-category/world-records
• ⁵
  Kraemer WJ, Fleck SJ, Deschenes MR. Fisiologia do exercício: teoria & prática. Rio de Janeiro: Guanabara Koogan; 2015.
• ⁶
  Almeida TA, Soares EA. Perfil dietético antropométrico de atletas adolescentes de voleibol. Rev Bras Med Esporte 2017;4(9):191-7.
• ⁷
  Paiva Neto A, César MC. Avaliação da composição corporal de atletas de basquetebol do sexo masculino participantes da liga nacional 2003. Rev Bras Cineantropom Desempenho Hum 2005;7(1):35-44.
• ⁸
  Prado WL, Botero JP, Guerra RLF, Rodrigues CL, Cuvello LC, Damaso AR. Perfil antropométrico e ingestão de macronutrientes em atletas profissionais brasileiros de futebol, de acordo com suas posições. Rev Bras Med Esporte 2006;2(12):61-5. http://dx.doi.org/10.1590/S1517-86922006000200001
  » http://dx.doi.org/10.1590/S1517-86922006000200001
• ⁹
  Ross WD, Marfell-Jones MT. Kinanthropometry. In: MacDougall JD, Wenger HA, Green HJ, editors. Physiological testing of the high-performance athlete. 2nd ed. Champaign: Human Kinetics; 1991. p. 233-306.
• ¹⁰
  Harrison GG, Buskirk ER, Carter JEL, Johnston FE, Lohman TG, Pollock ML, Skinfold thicknesses and measurement technique. In: Lohman TG, Roche AF, Martorell R, editors. Anthropometric standardization reference manual. Champaign: Human Kinetics; 1988. p. 55-70.
• ¹¹
  Boileau RA, Lohman TG, Slaughter MH. Exercise and body composition in children and youth. Scand. J Sports Sci 1985;7:17-27.
• ¹²
  Faulkner JA. Physiology of swimming and diving. In: Falls H., editor. Exercise physiology. Baltimore: Academic Press; 1968. p. 415-446.
• ¹³
  Lázari EC. Acompanhamento da potência muscular, antropometria e performance em velocistas de alto nível, [Tese de Doutorado em Educação Física, na área de Biodinâmica do Movimento e Esporte]. Campinas: Universidade Estadual de Campinas; 2017.
• ¹⁴
  Slaughter MH, Lohman RA, Boileau CA, Horswill RJ, Stillman RJ, Van Loan MD, et al. Skinfold equations for estimation of body fatness in children and youth. Hum Biol 1988;60(5):709-23. PMid:3224965.
• ¹⁵
  Martins NC, Alves FD, Sehl P, Schneider CD, Souza GC. Comparação entre métodos de avaliação da composição corporal em atletas: uma revisão sistemática. Rev Bras Nutr Esportiva 2019;82(13):912-22.
• ¹⁶
  Deutz RC, Benardot D, Martin DE, Cody MM. Relationship between energy deficits and body composition in elite female gymnasts and runners. Med Sci Sports Exerc 2000;32(3):659-68. http://dx.doi.org/10.1097/00005768-200003000-00017 PMid:10731010.
  » http://dx.doi.org/10.1097/00005768-200003000-00017
• ¹⁷
  Moreira PVS, Silva AM, Crozara LF, Veloso AP, Vieira F. Análise de equações preditivas da gordura corporal em jovens atletas de “taekwondo”. Rev Bras Educ Fís Esporte 2012;3(26):391-9. http://dx.doi.org/10.1590/S1807-55092012000300005
  » http://dx.doi.org/10.1590/S1807-55092012000300005
• ¹⁸
  Lozano-Berges G, Matute-Llorente A, Gòmez-Bruton A, González-Agüero A, Vicente-Rodríguez G, Casajùs JA. Accurate prediction equation to assess body fat in male and female adolescent football player. Int J Sport Nutr Exerc Metab 2019;29(3):297-302. http://dx.doi.org/10.1123/ijsnem.2018-0099 PMid:30160545.
  » http://dx.doi.org/10.1123/ijsnem.2018-0099
• ¹⁹
  Pires Neto CS, Glaner NF. “Equação de Faulkner” para predizer a gordura corporal: o fim de um mito. Rev Bras Cineantropom Desempenho Hum 2007;9(2):207-13.

Publication Dates

• Publication in this collection
  04 Feb 2022
• Date of issue
  2022

History

• Received
  09 Sept 2021
• Accepted
  10 Nov 2021