The correlation between selected anthropometric indices and BIA - based body fat measures in nursing home women aged 80+ years

• Authors: Ryszard Zarzeczny , Agnieszka Nawrat - Szołtysik , Anna Polak , Jakub Maliszewski , Adam Kiełtyka , Beata Matyja , Magdalena Dudek , Joanna Zborowska , Adam Wajdman
• Languages of publication: EN

Abstracts

EN

The aims of the study were to assess whether commonly used anthropometric indices are associated with body fat measures obtained by Bioel ectrical Impedance Analysis (BIA) method, and to determine the best anthropometric predictor of BIA - based body fat percentage (%Fat) and visceral fat rating (VFR) in elderly women. The sample consisted of 24 women aged 80 years and older, the residents of long - term care facilities in Upper Silesia (Poland). All women were subjected to standard anthropometric measurements including the following variables: body weight, body height, waist circumference, hip circumference and neck circumference. On the basis o f these measures Body Mass Index (BMI) as well as waist - to - hip ratio (WHR) were calculated. The subjects’ %Fat and VFR were determined by body composition analyzer TANITA BC 420MA (Japan). Pearson’s correlation coefficients were used to quantify the relati onships between variables. Stepwise multiple regression analysis with backward elimination was performed to identify possible predictors. The basic characteristics of the investigated subjects were as follows (mean±SD): age - 85.5±3.7 years; body weight - 60.4±11.6 kg; body height - 150.6±7.6 cm; BMI - 26.6±4.6 kg/m 2 ; %Fat - 31.3±9.6%; VFR - 10.7±2.5. Both of BIA - based measures significantly, positively correlated with body weight, BMI and circumferences of waist, hip and neck (r values from 0.477 to 0.835) . The multiple regression analysis for %Fat revealed that the body weight was the only variable statistically significant (r 2 =0.414; p<0.001; SEE =7.503%), and for VFR the significant β coefficients were obtained for BMI and neck circumference ( 0.625±0.133 and 0.341±0.133, respectively) ( r 2 =0.754; p<0.001; SEE =1.313). Among popular anthropometric indices of body composition in the oldest old group o f women, body weight seems to be the best predictor of body fat percentage, and VFR could be predicted by BMI along with a neck circumference.

Keywords

EN

Aging; Anthropometric Indices; BMI; Bioelectrical Impedance Analysis; Body Composition; Fat Mass; Older Women

• Publisher: Institute of Physical Education and Tourism. Jan Długosz University of Czestochowa
• Journal: Physical Activity Review
• Year: 2016
• Volume: 4
• Pages: 89-99

Contributors

author

Ryszard Zarzeczny

• Institute of Physical Education, Tourism and Physiotherapy, Jan Długosz University in Częstocho wa, Poland

author

Agnieszka Nawrat - Szołtysik

• Chair of Physiotherapy Basics, The Jerzy Kukuczka Academy of Physical Education in Katowice, Poland

author

Anna Polak

• Chair of Physiotherapy Basics, The Jerzy Kukuczka Academy of Physical Education in Katowice, Poland

author

Jakub Maliszewski

• TECHNOMEX - Trade and Service Company, Gliwice, Poland

author

Adam Kiełtyka

• BetaMed Medical Center, Katowice, Poland

author

Beata Matyja

• Saint Elisabeth Nursing Home in Ruda Ś ląska, Poland

author

Magdalena Dudek

• Chair of Physiotherapy Basics, The Jerzy Kukuczka Academy of Physical Education in Katowice, Poland

author

Joanna Zborowska

• Chair of Physiotherapy Basics, The Jerzy Kukuczka Academy of Physical Education in Katowice, Poland

author

Adam Wajdman

• Chair of Physiotherapy Basics, The Jerzy Kukuczka Academy of Physical Education in Katowice, Poland

References

• 1. Mathus - Vliegen EM, Obesity Management Task Force of the European Association for the Study of Obesity: Prevalence, pathophysiology, health consequences and treatment options of obesity in the elderly: a guideline. Obesity Facts, 2012; 5(3): 460 - 83.
• 2. Batt ME, Tanji J, Börjesson M: Exercise at 65 and beyond. Sports Medicine, 2013; 43: 525 - 30.
• 3. Zamboni M, Mazzali G, Zoico E, Harris TB, Meigs JB, Di Franchesco V, Fantin F, Bissoli L, Bosello O: Health consequences of obesity in the elderly: a review of four unresolved questions. International Journal of Obesity (Lond), 2005; 2 9: 1011 – 29.
• 4. Ding J, Kritchevsky SB, Newman AB, Taaffe DR, Nicklas BJ, Visser M, Lee JS, Nevitt M, Tylavsky FA, Rubin SM, Pahor M, Harris TB; Health ABC Study: Effects of birth cohort and age on body composition in a sample of community - based elderly. The American Journal of Clinical Nutrition, 2007; 85(2): 405 – 10.
• 5. St-Onge MP, Gallagher D: Body composition changes with aging: The cause or the result of alterations in metabolic rate and macronutrient oxidation? Nutrition, 2010; 26(2): 152 - 55.
• 6. Han TS, Tajar A, Lean ME: Obesity and weight management in the elderly. British Medical Bulletin, 2011; 97: 169 – 96.
• 7. Shuster A, Patlas M, Pinthus JH, Mourtzakis M: The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analy sis. The British Journal of Radiology, 2012; 85(1009): 1 - 10.
• 8. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich - Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D’Agostino RB, O’Donnell CJ: Abdominal Visceral and Subcutaneous Adipose Tissue Co mpartments Association With Metabolic Risk Factors in the Framingham Heart Study. Circulation, 2007; 116: 39 - 48.
• 9. Lovejoy JC, Champagne CM, de Jonge L, Xie H, Smith SR: Increased visceral fat and decreased energy expenditure during the menopausal transition . International Journal of Obesity (Lond), 2008; 32(6): 949 – 58.
• 10. Wells JL, Dumbrell AC: Nutrition and aging: assessment and treatment of compromised nutritional status in frail elderly patients. Clinical Interventions in Aging, 2006; 1(1): 67 – 79.
• 11. Evans C: M alnutrition in the Elderly: A Multifactorial Failure to Thrive. The Permanente Journal, 2005; 9(3): 38 - 41.
• 12. Harris D, Haboubi N: Malnutrition screening in the elderly population. Journal of the Royal Society of Medicine, 2005; 98: 411 - 14.
• 13. Morley JE: Anorexia of aging: physiologic and pathologic. The American Journal of Clinical Nutrition, 1997; 66 (4): 760 - 73.
• 14. Kushner RF, Gudivaka R, Schoeller DA: Clinical characteristics influencing bioelectrical impedance analysis measurements. The American Journal of Clini cal Nutrition, 1996; 64: (Suppl): S423 - 27.
• 15. World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Technical Report Series, 1995; 854: 1 – 452.
• 16. de Onis M, Habicht JP: Anthr opometric reference data for international use: recommendations from a World Health Organization Expert Committee. The American Journal of Clinical Nutrition, 1996; 64: 650 – 58.
• 17. Body Composition Analyzer BC - 420MA - http://tanita.eu/media/wysiwyg/manuals/professional-body-composition-analysers/bc-420ma-instruction-manual.pdf (accessed January 27, 2016).
• 18. WHO - Health statistics and information systems. Definition of an older or elderly person - http://www.who.int/healthinfo/survey/ageingdefnolder/en/ (accessed Fe bruary 22, 2016).
• 19. Bates - Jensen BM, Alessi CA, Cadogan M, Levy - Storms L, Jorge J, Yoshii J, Al - Samarrai NR, Schnelle JF: The minimum data set bedfast quality indicator: differences among nursing homes. Nursing Research, 2004; 53: 260 – 72.
• 20. Newman AB, Cauley JA: The Epidemiology of Aging. Springer Science +Business Media. Dordrecht, 2012.
• 21. Schutz Y, Kyle UUG, Pichard C: Fat - free mass index and fat mass index percentiles in Caucasians aged 18 – 98 y. International Journal of Obesity, 2002; 26: 953 – 60.
• 22. Dias FM, Costa SO, de Freitas JP, Pinto ACR , Vigário PS , Mainenti MRM: Functional Capacity of Oldest Old Living in a Long - stay Institution in Rio De Janeiro, Brazil. Journal of Physical Therapy Science, 2014; 26: 1097 – 105.
• 23. Carlsson M, Gustafson Y, Eriksson S, Håglin L: Body composition in Swedish old people aged 65 - 99 years, living in residential care facilities. Archives of Gerontology and Geriatrics, 2009; 49(1): 98 - 107.
• 24. Reis JP, Macera CA, Araneta MR, Lindsay SP, Marshall SJ, Wingard DL: Comparison of overall obesi ty and body fat distribution in predicting risk of mortality. Obesity (Silver Spring), 2009; 17(6): 1232 - 39.
• 25. Visser M, Deeg DJH: The effect of age - related height loss on the BMI classification of older men and women. International Journal of Body Compositi on Research, 2007; 5: 35 – 40.
• 26. Visser M, Harris TB: Body composition and aging. In Newman AB, Cauley JA. (eds), The Epidemiology of Aging. Dordrecht: Springer Science +Business Media, 2012.
• 27. Newman AB, Lee JS, Visser M, Goodpaster BH, Kritchevsky SB, Tylavsky FA, Nevitt M, Harris TB: Weight change and the conservation of lean mass in old age: the Health, Aging and Body Composition Study. The American Journal of Clinical Nutrition, 2005; 82(4): 872 – 78.
• 28. Cornier MA, Després JP, Davis N, Grossniklaus DA, Klein S, Lamarche B, Lopez - Jimenez F, Rao G, St - Onge MP, Towfighi A, Poirier P: Assessing adiposity: a scientific statement from the American Heart Association. Circulation, 2011; 124(18): 1996 - 2019.
• 29. Santos Silva DA, Petroski EL, Peres MA: Is high body fat estimate d by body mass index and waist circumference a predictor of hypertension in adults? A population - based study. Nutrition Journal, 2012; 11:112 - 20.
• 30. Janssen I, Heymsfield SB, Allison DB, Kotler DP, Ross R: Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat. The American Journal of Clinical Nutrition, 2002; 75(4): 683 - 88.
• 31. Yang L, Samarasinghe YP, Kane P, Amiel SA, Aylwin SJ: Visceral adiposity is closely correlated with neck circumference and represents a significant indicator of insulin resistance in WHO grade III obesity. Clinical Endocrinology (Oxf), 2010; 73(2): 197 – 200.
• 32. Li HX, Zhang F, Zhao D, Xin Z, Guo SQ, Wang SM, Zhang JJ, Wang J, Li Y, Yang GR, Yang JK: Neck circu mference as a measure of neck fat and abdominal visceral fat in Chinese adults. BioMed Central Public Health, 2014; 14: 311 - 17.
• 33. H eim N, Snijder MB, Heymans MW, Deeg DJ, Seidell JC, Visser M: Optimal cutoff values for high - risk waist circumference in older adults based on related health outcomes. American Journal of Epidemiology, 2011; 174:479 – 89.

• Document Type: paper