journal article·2025-02-19Open access

The Concordance Between the Clínica Universidad de Navarra Body Adiposity Estimator and a Bioelectrical Impedance Analysis in Assessing the Body Fat of Athletes

Marius Baranauskas, Ingrida Kupčiūnaitė, Jurgita Lieponienė, Rimantas Stukas

Applied Sciences· Volume 15· Issue 4

Abstract

An equation-derived body fat estimator, namely the Clínica Universidad de Navarra Body Adiposity Estimator (CUN-BAE), was established to assess the body fat percentage in adults. However, its efficiency compared to that of the bioelectrical impedance analysis (BIA) approach remains under-researched. This study aimed to assess the agreement between the body fat percentages measured using a BIA and estimated using the CUN-BAE in a sample of Lithuanian professional athletes. A single cross-sectional study was conducted using the BIA technique to measure and the CUN-BAE equation to calculate the body fat percentages of 323 study participants. The Bland–Altman plot system was applied to comparing both the body fat percentages estimated using the CUN-BAE equation and those obtained via the BIA approach. The average values of the body fat percentages found in the total sample of elite athletes and estimated using the BIA and CUN-BAE equaled 18.4 ± 5.3% and 18.7 ± 6.6%, respectively (ICC: 0.91; 95% confidence interval (CI): 0.88; 0.93). This study found that the CUN-BAE method overestimated the BIA’s calculation of the body fat percentages by 2.7% on average. Meanwhile, the comparison of adiposity in the athletes using the CUN-BAE equation and the BIA methods demonstrated a similar, although not identical, accuracy. The BIA method cannot be replaced by the CUN-BAE equation in routine sports medicine practice due to moderately sized limits of agreement (95% CI: −6.5; 7.1), even when the access to body fat measurement devices is limited. From a public health perspective, the outcomes derived from the CUN-BAE equation can possibly be extrapolated to females and to individuals competing in strength–power sports, as well as to populations of adults.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Body Composition Measurement Techniques · Sports Performance and Training · Thermoregulation and physiological responses

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Biomedical Sciences, State Higher Education Institution Panevėžys College, 35200 Panevėžys, Lithuania

Department of Public Health, Institute of Health Sciences, Faculty of Medicine, Vilnius University, 01513 Vilnius, Lithuania

References (53)

Show source-provided references
1. Relationship among body fat percentage, body mass index, and all-cause mortality10.7326/L16-0334 ↗
2. Current status of body composition assessment in sport: Review and position statement on behalf of the ad hoc research working group on body composition health and performance, under the auspices of the I.O.C. Medical Commission10.2165/11597140-000000000-00000 ↗
3. New frontiers of body composition in sport10.1055/a-1373-5881 ↗
4. Best practice recommendations for body composition considerations in sport to reduce health and performance risks: A critical review, original survey and expert opinion by a subgroup of the IOC consensus on Relative Energy Deficiency in Sport (REDs)10.1136/bjsports-2023-106812 ↗
5. Weight loss in combat sports: Physiological, psychological and performance effects10.1186/1550-2783-9-52 ↗
6. Higher prevalence of eating disorders among adolescent elite athletes than controls10.1249/MSS.0b013e318281a939 ↗
7. Aspects of disordered eating continuum in elite high-intensity sports10.1111/j.1600-0838.2010.01190.x ↗
8. Heymsfield, S., Lohman, T., Wang, Z., and Going, S.B. (2005). Human Body Composition, Human Kinetics.10.5040/9781492596950 ↗
9. The advantages and limitations of cross-sectional body composition analysis10.1097/SPC.0b013e32834c49eb ↗
10. Evaluation of the BOD POD for estimating percent body fat in collegiate track and field female athletes: A comparison of four methods10.1519/JSC.0b013e318185f196 ↗
11. Advanced body composition assessment: From body mass index to body composition profiling10.1136/jim-2018-000722 ↗
12. Liao, Y.-S., Li, H.-C., Lu, H.-K., Lai, C.-L., Wang, Y.-S., and Hsieh, K.-C. (2020). Comparison of bioelectrical impedance analysis and dual energy x-ray absorptiometry for total and segmental bone mineral content with a three-compartment model. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17072595 ↗
13. Dual-energy x-ray absorptiometry body composition model: Review of physical concepts
14. Body composition in athletes and sports nutrition: An examination of the bioimpedance analysis technique10.1038/ejcn.2012.165 ↗
15. Body adiposity index performance in estimating body fat in a sample of severely obese Brazilian patients10.1186/s12937-015-0119-8 ↗
16. A comparison of prediction equations for the estimation of body fat percentage in non-obese and obese older Caucasian adults in the United States10.1007/s12603-014-0017-3 ↗
17. Heyward, V.H., and Wagner, D.R. (2004). Applied Body Composition Assessment, Human Kinetics.
18. Campa, F., Toselli, S., Mazzilli, M., Gobbo, L.A., and Coratella, G. (2021). Assessment of body composition in athletes: A narrative review of available methods with special reference to quantitative and qualitative bioimpedance analysis. Nutrients, 13.10.3390/nu13051620 ↗
19. Generalized equations for predicting body density of women10.1249/00005768-198023000-00009 ↗
20. Holmes, C.J., and Racette, S.B. (2021). The utility of body composition assessment in nutrition and clinical practice: An overview of current methodology. Nutrients, 13.10.3390/nu13082493 ↗
21. Woolcott, O.O., and Bergman, R.N. (2018). Relative fat mass (RFM) as a new estimator of whole-body fat percentage—A cross-sectional study in American adult individuls. Sci. Rep., 8.10.1038/s41598-018-29362-1 ↗
22. Estimación del porcentaje de grasa corporal en función del índice de masa corporal y perímetro abdominal: Fórmula Palafolls10.1016/j.semerg.2018.04.007 ↗
23. Molina-Luque, R., Romero-Saldaña, M., Álvarez-Fernández, C., Bennasar-Veny, M., Álvarez-López, A., and Molina-Recio, G. (2019). Equation Córdoba: A simplified method for estimation of body fat (ECORE-BF). Int. J. Environ. Res. Public Health, 16.10.3390/ijerph16224529 ↗
24. Evaluation of anthropometric equations to assess body fat in adults10.1249/MSS.0000000000000213 ↗
25. Suliga, E., Ciesla, E., Gluszek-Osuch, M., Rogula, T., Gluszek, S., and Koziel, D. (2019). The usefulness of anthropometric indices to identify the risk of metabolic syndrome. Nutrients, 11.10.3390/nu11112598 ↗
26. de Moraes, A.M., Vidal-Espinoza, R., Bergamo, R.R., Gómez-Campos, R., de Lazari, E., de Campos, L.F.C.C., Sulla-Torres, J., and Cossio-Bolaños, M. (2024). Prediction of fat-free mass from body surface area in young basketball players. BMC Sports Sci. Med. Rehabil., 16.10.1186/s13102-024-00857-x ↗
27. Serafini, S., Charrier, D., Izzicupo, P., Esparza-Ros, F., Vaquero-Cristóbal, R., Petri, C., Mecherques-Carini, M., Baglietto, N., Holway, F., and Tinsley, G. (2024). Anthropometric-based predictive equations developed with multi-component models for estimating body composition in athletes. Eur. J. Appl. Physiol.10.1007/s00421-024-05672-3 ↗
28. Clinical usefulness of a new equation for estimating body fat10.2337/dc11-1334 ↗
29. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity10.1038/ijo.2011.100 ↗
30. Escala colorimétrica del porcentaje de grasa corporal según el estimador de adiposidad CUN-BAE10.1016/j.aprim.2015.09.003 ↗
31. Muntean, P., Popa, A., Miclos-Balica, M., Schick, F., Munteanu, O., Pupazan, V., Neagu, A., and Neagu, M. (2023). Learning effects in air displacement plethysmography. Life, 13.10.3390/life13061315 ↗
32. Martín, V., Dávila-Batista, V., Castilla, J., Godoy, P., Delgado-Rodríguez, M., Soldevila, N., Molina, A.J., Fernandez-Villa, T., Astray, J., and Castro, A. (2016). Comparison of body mass index (BMI) with the CUN-BAE body adiposity estimator in the prediction of hypertension and type 2 diabetes. BMC Public Health, 16.10.1186/s12889-016-2728-3 ↗
33. Bioimpedance patterns and bioelectrical impedance vector analysis (BIVA) of road cyclists10.1080/02640414.2018.1470597 ↗
34. Carrasco-Marginet, M., Castizo-Olier, J., Rodríguez-Zamora, L., Iglesias, X., Rodríguez, F.A., Chaverri, D., Brotons, D., and Irurtia, A. (2017). Bioelectrical impedance vector analysis (BIVA) for measuring the hydration status in young elite synchronized swimmers. PLoS ONE, 12.10.1371/journal.pone.0178819 ↗
35. Sample size calculation in medical studies
36. Sullivan, K.M. (2024, October 05). Open Source Statistics for Public Health. Available online: http://www.openepi.com/SampleSize/SSCohort.htm.
37. Čepulėnas, A., Saplinskas, J., and Paulauskas, R. (2011). Sporto treniruotė (Sports training). Sportininkų Gebėjimai ir jų Ugdymas (Skills and Training of Athletes), Lithuanian University of Educational Sciences Press. [1st ed.].
38. (2024, September 06). A Healthy Lifestyle—WHO Recommendations. Available online: https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations.
39. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: A systematic review and meta-analysis10.1038/ijo.2010.5 ↗
40. (2000). Quality Systems—Medical Devices—Particular Requirements for the Application of EN ISO 9002 (Revision of EN 46002:1996) (Identical to ISO 13488:1996) (Standard No. EN ISO 13488:2000).
41. Bioelectrical impedance analysis–part I: Review of principles and methods10.1016/j.clnu.2004.06.004 ↗
42. Lohman, T.G., Roche, A.F., and Martorell, R. (1988). Anthropometric Standardization Reference Manual, Human Kinetics Books.
43. Validation of InBody 770 bioelectrical impedance analysis compared to a four-compartment model criterion in young adults10.1111/cpf.12700 ↗
44. Segmental extracellular and intracellular water distribution and muscle glycogen after 72-h carbohydrate loading using spectroscopic techniques10.1152/japplphysiol.00126.2016 ↗
45. Understanding Bland Altman analysis10.11613/BM.2015.015 ↗
46. Statistical methods for assessing agreement between two methods of clinical measurement10.1016/S0140-6736(86)90837-8 ↗
47. Measuring agreement in method comparison studies10.1177/096228029900800204 ↗
48. A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines10.1186/s40798-017-0114-z ↗
49. Karastergiou, K., Smith, S.R., Greenberg, A.S., and Fried, S.K. (2012). Sex differences in human adipose tissues—The biology of pear shape. Biol. Sex Differ., 3.10.1186/2042-6410-3-13 ↗
50. Effect of energy intake on percentage body fat as measured by BIA and CUN-BAE equation formula: A comparative study of body fat measurement methods10.47191/ijmscrs/v3-i3-30 ↗
51. Agreement between equation-derived body fat estimator and bioelectrical impedance analysis for body fat measurement in middle-aged southern Indians10.14814/phy2.70095 ↗
52. Exercising your fat (metabolism) into shape: A muscle-centred view10.1007/s00125-020-05170-z ↗
53. Variations in bioelectrical impedance devices impact raw measures comparisons and subsequent prediction of body composition using recommended estimation equations10.1016/j.clnesp.2024.07.009 ↗