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Open Access 01-12-2024 | Research

Associations between 47 anthropometric markers derived from a body scanner and relative fat-free mass in a population-based study

Authors: Maximilian Dietzmann, Dörte Radke, Marcello RP Markus, Mats Wiese, Henry Völzke, Stephan B. Felix, Marcus Dörr, Martin Bahls, Till Ittermann

Published in: BMC Public Health | Issue 1/2024

Abstract

Background

Low relative fat free mass (FFM) is associated with a greater risk of chronic diseases and mortality. Unfortunately, FFM is currently not being measured regularly to allow for individuals therapy.

Objective

One reason why FFM is not being used may be related to additional equipment and resources, thus we aimed to identify easily accessible anthropometric markers related with FFM.

Materials and methods

We analyzed data of 1,593 individuals (784 women; 49.2%, age range 28–88 years) enrolled in the population-based Study of Health in Pomerania (SHIP-TREND 1). Forty-seven anthropometric markers were derived from a 3D optical body-scanner. FFM was assessed by bioelectrical impedance analysis (FFM_BIA) or air displacement plethysmography (FFM_ADP). In sex-stratified linear regression models, FFM was regressed on anthropometric measurements adjusted for body height and age. Anthropometric markers were ranked according to the coefficient of determination (R²) derived from these regression models.

Results

Circumferences of high hip, belly, middle hip, waist and high waist showed the strongest inverse associations with FFM. These relations were stronger in females than in males. Associations of anthropometric markers with FFM_APD were greater compared to FFM_BIA.

Conclusion

Anthropometric measures were more strongly associated with FFM_ADP compared to FFM_BIA. Anthropometric markers like circumferences of the high or middle hip, belly or waist may be appropriate surrogates for FFM to aid in individualized therapy. Given that the identified markers are representative of visceral adipose tissue, the connection between whole body strength as surrogate for FFM and fat mass should be explored in more detail.

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Kohler A, King R, Bahls M, Gross S, Steveling A, Gartner S, Schipf S, Glaser S, Volzke H, Felix SB, et al. Cardiopulmonary fitness is strongly associated with body cell mass and fat-free mass: the study of Health in Pomerania (SHIP). Scand J Med Sci Sports. 2018;28(6):1628–35.PubMedCrossRef

Imboden MT, Kaminsky LA, Peterman JE, Hutzler HL, Whaley MH, Fleenor BS, Harber MP. Cardiorespiratory Fitness Normalized to Fat-Free Mass and Mortality Risk. Med Sci Sports Exerc. 2020;52(7):1532–7.PubMedCrossRef

Abramowitz MK, Hall CB, Amodu A, Sharma D, Androga L, Hawkins M. Muscle mass, BMI, and mortality among adults in the United States: a population-based cohort study. PLoS ONE. 2018;13(4):e0194697.PubMedPubMedCentralCrossRef

Lagace JC, Marcotte-Chenard A, Paquin J, Tremblay D, Brochu M, Dionne IJ. Increased odds of having the metabolic syndrome with greater fat-free mass: counterintuitive results from the National Health and Nutrition Examination Survey database. J Cachexia Sarcopenia Muscle. 2022;13(1):377–85.PubMedCrossRef

Kawakami R, Tanisawa K, Ito T, Usui C, Miyachi M, Torii S, Midorikawa T, Ishii K, Muraoka I, Suzuki K, et al. Fat-Free Mass Index as a surrogate marker of appendicular skeletal muscle Mass Index for low muscle Mass Screening in Sarcopenia. J Am Med Dir Assoc. 2022;23(12):1955–61. e1953.PubMedCrossRef

Cederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, Baptista G, Barazzoni R, Blaauw R, Coats A, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1–9.PubMedCrossRef

Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (Sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002;50(5):889–96.PubMedCrossRef

Rolland Y, Czerwinski S, Van Abellan G, Morley JE, Cesari M, Onder G, Woo J, Baumgartner R, Pillard F, Boirie Y, et al. Sarcopenia: its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Health Aging. 2008;12(7):433–50.PubMedPubMedCentralCrossRef

Ittermann T, Werner N, Lieb W, Merz B, Nothlings U, Kluttig A, Tiller D, Greiser KH, Vogt S, Thorand B, et al. Changes in fat mass and fat-free-mass are associated with incident hypertension in four population-based studies from Germany. Int J Cardiol. 2019;274:372–7.PubMedCrossRef

10.

Lemos T, Gallagher D. Current body composition measurement techniques. Curr Opin Endocrinol Diabetes Obes. 2017;24(5):310–4.PubMedPubMedCentralCrossRef

11.

Ellis KJ. Human body composition: in vivo methods. Physiol Rev. 2000;80(2):649–80.PubMedCrossRef

12.

Sergi G, De Rui M, Stubbs B, Veronese N, Manzato E. Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res. 2017;29(4):591–7.PubMedCrossRef

13.

Chabin X, Taghli-Lamallem O, Mulliez A, Bordachar P, Jean F, Futier E, Massoullie G, Andonache M, Souteyrand G, Ploux S, et al. Bioimpedance analysis is safe in patients with implanted cardiac electronic devices. Clin Nutr. 2019;38(2):806–11.PubMedCrossRef

14.

Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol (1985). 2000;89(2):465–71.PubMedCrossRef

15.

Chien MY, Huang TY, Wu YT. Prevalence of Sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc. 2008;56(9):1710–5.PubMedCrossRef

16.

Achamrah N, Colange G, Delay J, Rimbert A, Folope V, Petit A, Grigioni S, Dechelotte P, Coeffier M. Comparison of body composition assessment by DXA and BIA according to the body mass index: a retrospective study on 3655 measures. PLoS ONE. 2018;13(7):e0200465.PubMedPubMedCentralCrossRef

17.

Dempster P, Aitkens S. A new air displacement method for the determination of human body composition. Med Sci Sports Exerc. 1995;27(12):1692–7.PubMedCrossRef

18.

McCrory MA, Gomez TD, Bernauer EM, Mole PA. Evaluation of a new air displacement plethysmograph for measuring human body composition. Med Sci Sports Exerc. 1995;27(12):1686–91.PubMedCrossRef

19.

Tucker LA, Lecheminant JD, Bailey BW. Test-retest reliability of the Bod Pod: the effect of multiple assessments. Percept Mot Skills. 2014;118(2):563–70.PubMedCrossRef

20.

de Castro JAC, de Lima LRA, Silva DAS. Accuracy of octa-polar bioelectrical impedance analysis for the assessment of total and appendicular body composition in children and adolescents with HIV: comparison with dual energy X-ray absorptiometry and air displacement plethysmography. J Hum Nutr Diet. 2018;31(2):276–85.PubMedCrossRef

21.

Wingfield HL, Smith-Ryan AE, Woessner MN, Melvin MN, Fultz SN, Graff RM. Body composition assessment in overweight women: validation of air displacement plethysmography. Clin Physiol Funct Imaging. 2014;34(1):72–6.PubMedCrossRef

22.

Koepke N, Zwahlen M, Wells JC, Bender N, Henneberg M, Ruhli FJ, Staub K. Comparison of 3D laser-based photonic scans and manual anthropometric measurements of body size and shape in a validation study of 123 young Swiss men. PeerJ. 2017;5:e2980.PubMedPubMedCentralCrossRef

23.

Sager R, Gusewell S, Ruhli F, Bender N, Staub K. Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men. PLoS ONE. 2020;15(6):e0234552.PubMedPubMedCentralCrossRef

24.

Nishida C, Ko GT, Kumanyika S. Body fat distribution and noncommunicable diseases in populations: overview of the 2008 WHO Expert Consultation on Waist Circumference and Waist-Hip ratio. Eur J Clin Nutr. 2010;64(1):2–5.PubMedCrossRef

25.

Takai Y, Nakatani M, Aoki T, Komori D, Oyamada K, Murata K, Fujita E, Akamine T, Urita Y, Yamamoto M, et al. Body shape indices are predictors for estimating fat-free mass in male athletes. PLoS ONE. 2018;13(1):e0189836.PubMedPubMedCentralCrossRef

26.

Wagner DR, Castaneda F, Bohman B, Sterr W. Comparison of a 2D iPad application and 3D body scanner to air displacement plethysmography for measurement of body fat percentage. J Hum Nutr Diet. 2019;32(6):781–8.PubMedCrossRef

27.

Rumbo-Rodriguez L, Sanchez-SanSegundo M, Ferrer-Cascales R, Garcia-D’Urso N, Hurtado-Sanchez JA, Zaragoza-Marti A. Comparison of body scanner and manual anthropometric measurements of body shape: a systematic review. Int J Environ Res Public Health 2021, 18(12).

28.

Volzke H, Schossow J, Schmidt CO, Jurgens C, Richter A, Werner A, Werner N, Radke D, Teumer A, Ittermann T et al. Cohort Profile Update: the study of Health in Pomerania (SHIP). Int J Epidemiol 2022.

29.

Bretschneider T, Koop U, Schreiner V, Wenck H, Jaspers S. Validation of the body scanner as a measuring tool for a rapid quantification of body shape. Skin Res Technol. 2009;15(3):364–9.PubMedCrossRef

30.

Kohler A, Filges B, Volzke H, Felix SB, Ewert R, Stubbe B, Markus MRP, Gross S, Dorr M, Ittermann T, et al. Body surface scan anthropometrics are related to cardiorespiratory fitness in the general population. Sci Rep. 2022;12(1):22185.PubMedPubMedCentralCrossRef

31.

Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gomez JM, Heitmann BL, Kent-Smith L, Melchior JC, Pirlich M, et al. Bioelectrical impedance analysis–part I: review of principles and methods. Clin Nutr. 2004;23(5):1226–43.PubMedCrossRef

32.

GmbH D-I. Das B.I.A.-Kompendium 3. Ausgabe. In. Darmstadt; 2005.

33.

Völzke H, Alte D, Schmidt CO, Radke D, Lorbeer R, Friedrich N, Aumann N, Lau K, Piontek M, Born G, et al. Cohort profile: the study of health in Pomerania. Int J Epidemiol. 2011;40(2):294–307.PubMedCrossRef

34.

Lew J, Sanghavi M, Ayers CR, McGuire DK, Omland T, Atzler D, Gore MO, Neeland I, Berry JD, Khera A, et al. Sex-based differences in Cardiometabolic Biomarkers. Circulation. 2017;135(6):544–55.PubMedPubMedCentralCrossRef

35.

Veilleux A, Tchernof A. Sex Differences in Body Fat Distribution. In: Adipose Tissue Biology edn. Edited by Symonds ME. New York, NY: Springer New York; 2012: 123–166.

36.

Bredella MA. Sex Differences in Body Composition. In: Sex and Gender Factors Affecting Metabolic Homeostasis, Diabetes and Obesity edn. Edited by Mauvais-Jarvis F. Cham: Springer International Publishing; 2017: 9–27.

37.

Schonlau M, Zou RJ. The random forest algorithm for statistical learning. Stata J. 2020;20:3–29.CrossRef

38.

Filges B, Bahls M, Radke D, Groß S, Ewert R, Stubbe B, Markus M, Felix S, Völzke H, Dörr M et al. Body surface scan anthropometrics are associated with grip strength in the general population. Nutr Metab Cardiovasc Dis 2024.

39.

Compher C, Cederholm T, Correia M, Gonzalez MC, Higashiguch T, Shi HP, Bischoff SC, Boirie Y, Carrasco F, Cruz-Jentoft A, et al. Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition diagnosis of malnutrition. JPEN J Parenter Enter Nutr. 2022;46(6):1232–42.CrossRef

40.

Repp KD, Radke D, Ittermann T, Albers M, Markus MRP, de Santa Helena ET, Friedrich N, Bulow R, Volzke H. The site of waist measurement impacts the estimation of visceral fat: results from three-dimensional photonic body scanning. Br J Nutr 2021:1–32.

41.

ISO. ISO 7250-1:2017 - Basic human body measurements for technical design. In. Switzerland; 2017.

42.

Ng BK, Hinton BJ, Fan B, Kanaya AM, Shepherd JA. Clinical anthropometrics and body composition from 3D whole-body surface scans. Eur J Clin Nutr. 2016;70(11):1265–70.PubMedPubMedCentralCrossRef

43.

Bennett JP, Liu YE, Quon BK, Kelly NN, Wong MC, Kennedy SF, Chow DC, Garber AK, Weiss EJ, Heymsfield SB, et al. Assessment of clinical measures of total and regional body composition from a commercial 3-dimensional optical body scanner. Clin Nutr. 2022;41(1):211–8.PubMedCrossRef

44.

Wong Vega M, Srivaths PR. Air Displacement Plethysmography Versus Bioelectrical Impedance to Determine Body Composition in Pediatric Hemodialysis patients. J Ren Nutr. 2017;27(6):439–44.PubMedCrossRef

45.

Wingo BC, Barry VG, Ellis AC, Gower BA. Comparison of segmental body composition estimated by bioelectrical impedance analysis and dual-energy X-ray absorptiometry. Clin Nutr ESPEN. 2018;28:141–7.PubMedPubMedCentralCrossRef

46.

King S, Wilson J, Kotsimbos T, Bailey M, Nyulasi I. Body composition assessment in adults with cystic fibrosis: comparison of dual-energy X-ray absorptiometry with skinfolds and bioelectrical impedance analysis. Nutrition. 2005;21(11–12):1087–94.PubMedCrossRef

47.

Day K, Kwok A, Evans A, Mata F, Verdejo-Garcia A, Hart K, Ward LC, Truby H. Comparison of a Bioelectrical Impedance Device against the Reference Method Dual Energy X-Ray Absorptiometry and Anthropometry for the Evaluation of Body Composition in Adults. Nutrients 2018, 10(10).

48.

Nunez FJ, Munguia-Izquierdo D, Petri C, Suarez-Arrones L. Field methods to Estimate Fat-free Mass in International Soccer players. Int J Sports Med. 2019;40(10):619–24.PubMedCrossRef

49.

Huang AC, Chen YY, Chuang CL, Chiang LM, Lu HK, Lin HC, Chen KT, Hsiao AC, Hsieh KC. Cross-mode bioelectrical impedance analysis in a standing position for estimating fat-free mass validated against dual-energy x-ray absorptiometry. Nutr Res. 2015;35(11):982–9.PubMedCrossRef

50.

Roubenoff R. Sarcopenia and its implications for the elderly. Eur J Clin Nutr. 2000;54(Suppl 3):S40–47.PubMedCrossRef

51.

Seidell JC, Hautvast JG, Deurenberg P. Overweight: fat distribution and health risks. Epidemiological observations. A review. Infusionstherapie. 1989;16(6):276–81.PubMed

52.

Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gomez J, Lilienthal Heitmann B, Kent-Smith L, Melchior JC, Pirlich M, et al. Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr. 2004;23(6):1430–53.PubMedCrossRef

53.

Demirci MS, Demirci C, Ozdogan O, Kircelli F, Akcicek F, Basci A, Ok E, Ozkahya M. Relations between malnutrition-inflammation-atherosclerosis and volume status. The usefulness of bioimpedance analysis in peritoneal dialysis patients. Nephrol Dial Transpl. 2011;26(5):1708–16.CrossRef

54.

Ulijaszek SJ, Kerr DA. Anthropometric measurement error and the assessment of nutritional status. Br J Nutr. 1999;82(3):165–77.PubMedCrossRef

55.

Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. 1982;36(5):936–42.PubMedCrossRef

Title: Associations between 47 anthropometric markers derived from a body scanner and relative fat-free mass in a population-based study
Authors: Maximilian Dietzmann
Dörte Radke
Marcello RP Markus
Mats Wiese
Henry Völzke
Stephan B. Felix
Marcus Dörr
Martin Bahls
Till Ittermann
Publication date: 01-12-2024
Publisher: BioMed Central
Published in: BMC Public Health / Issue 1/2024
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-024-18611-w

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Associations between 47 anthropometric markers derived from a body scanner and relative fat-free mass in a population-based study

Abstract

Background

Objective

Materials and methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Objective

Materials and methods

Results

Conclusion

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