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Body composition analysis in older adults with dementia. Anthropometry and bioelectrical impedance analysis: a critical review

European Journal of Clinical Nutrition volume 68, pages 1228–1233 (2014)Cite this article

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Abstract

In clinical practice, geriatric nutritional assessment usually includes nutritional screening, a simple anthropometric assessment, measurement of various biochemical parameters, such as serum albumin, and sometimes (not always) body composition analysis (BCA). However, there is a high prevalence of undiagnosed malnutrition in patients with dementia. Several factors contribute to this situation; probably, the most notable is the methodology used to assess body composition (BC). In this regard, for BCA, techniques are needed that are noninvasive, affordable, safe, simple and that require the minimum possible collaboration by the elderly patient. Consequently, body mass index (BMI) and waist circumference (WC) are widely used as indicators of overall and central adiposity, respectively; however, there is no consensus on the cutoffs for the elderly, and changes in BC (especially muscle-mass depletion) are masked by normal values of BMI and WC. Bioimpedance analysis is a simple, cost-effective and precise method for BCA, provided that cross-validated equations are used. Its main disadvantage is that it is highly sensitive to changes in body water (overhydration or dehydration), leading to substantial errors in BC estimates. However, using Bioelectrical Impedance Vector Analysis errors are minimized, as there is no need for the subject to be normally hydrated and it does not require the use of predictive models.

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References

  1. Johnson DK, Wilkins CH, Morris JC . Accelerated weight loss may precede diagnosis in Alzheimer disease. Arch Neurol 2006; 63: 1312–1317.

    Article  Google Scholar 

  2. Miyamoto K, Higashino S, Mochizuki K, Goda T, Koyama H . Evaluation of weight loss in the community-dwelling elderly with dementia as assessed by eating behavior and mental status. Asia Pac J Clin Nutr 2011; 20: 9–13.

    PubMed  Google Scholar 

  3. Smith KL, Greenwood CE . Weight loss and nutritional considerations in Alzheimer disease. J Nutr Elder 2008; 27: 381–403.

    Article  Google Scholar 

  4. Sampson EL, Candy B, Jones L . Enteral tube feeding for older people with advanced dementia. Cochrane Database of Syst Rev 2009; 15: CD007209.

    Google Scholar 

  5. Kane RL, Shamliyan T, Talley K, Pacala J . The association between geriatric syndromes and survival. J Am Geriatr Soc 2012; 60: 896–904.

    Article  Google Scholar 

  6. Johnson CS . The association between nutritional risk and falls among frail elderly. J Nutr Health Aging 2003; 7: 247–250.

    PubMed  Google Scholar 

  7. Leaker SH . The role of nutrition in preventing pressure ulcers. Nurs Stand 2013; 28: 66–68.

    Article  Google Scholar 

  8. Harris CL, Fraser C . Malnutrition in the institutionalized elderly: the effects on wound healing. Ostomy Wound Manage 2004; 50: 54–63.

    PubMed  Google Scholar 

  9. Schaible UE, Kaufmann SH . Malnutrition and infection: complex mechanisms and global impacts. PLoS Med 2007; 4: e115.

    Article  Google Scholar 

  10. Calder PC . Feeding the immune system. Proc Nutr Soc 2013; 73: 299–309.

    Article  Google Scholar 

  11. Volkert D . Malnutrition in older adults—urgent need for action: a plea for improving the nutritional situation of older adults. Gerontology 2013; 59: 328–333.

    Article  Google Scholar 

  12. Buffa R, Floris GU, Putzu PF, Marini E . Body composition variations in ageing. Coll Antropol 2011; 35: 259–265.

    Google Scholar 

  13. Jackson AS, Janssen I, Sui X, Church TS, Blair SN . Longitudinal changes in body composition associated with healthy ageing: men, aged 20-96 years. Br J Nutr 2012; 107: 1085–1091.

    Article  CAS  Google Scholar 

  14. Milanović Z, Pantelić S, Trajković N, Sporiš G . Basic anthropometric and body composition characteristics in an elderly population: a systematic review. FU Phys Ed Sport 2011; 9: 173–182.

    Google Scholar 

  15. Chumlea WC, Baumgartner RN . Status of anthropometry and body composition data in elderly subjects. Am J Clin Nutr 1989; 50: 1158–1166.

    Article  CAS  Google Scholar 

  16. de Onis M, Habicht JP . Anthropometric reference data for international use: recommendations from a World Health Organization Expert Committee. Am J Clin Nutr l996; 64: 650–658.

    Article  Google Scholar 

  17. Chumlea WM, Sun SS . The availability of body composition reference data for the elderly. J Nutr Health Aging 2004; 8: 76–82.

    CAS  PubMed  Google Scholar 

  18. Kuczmarski MF1, Kuczmarski RJ, Najjar M . Descriptive anthropometric reference data for older Americans. J Am Diet Assoc 2000; 100: 59–66.

    Article  CAS  Google Scholar 

  19. Durnin JVGA, Womersley J . Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 Years. Br J Nutr 1974; 32: 77–97.

    Article  CAS  Google Scholar 

  20. Siri WE . Body composition from fluid spaces and density: analysis of methods. In: Brozec J, Henschel A (eds). Techniques for Measuring Body Composition. National Academy of Sciences, Natural Resources Council: Washington, DC, USA, 1961, pp 223–224.

    Google Scholar 

  21. Brozěk J, Grande F, Anderson JT, Keys A . Densitometric analysis of body composition: revision of some quantitative assumptions. Ann NY Acad Sci 1963; 110: 113–140.

    Article  Google Scholar 

  22. Baumgartner RN, Heymsfield SB, Lichtman S, Wang J, Pierson RNJ . Body composition in elderly people: effect of criterion estimates on predictive equations. Am J Clin Nutr 1991; 53: 1345–1353.

    Article  CAS  Google Scholar 

  23. Chumlea WC, Roche AF, Steinbaugh ML . Estimating stature from knee height for persons 60 to 90 years of age. J Am Geriatr Soc 1985; 33: 116–120.

    Article  CAS  Google Scholar 

  24. Ravaglia G, Forti P, Maioli F, Boschi F, Cicognani A, Gasbarrini G . Measurement of body fat in healthy elderly men: a comparison of methods. Gerontol A Biol Sci Med Sci 1999; 54: M70–M76.

    Article  CAS  Google Scholar 

  25. Brodie D, Moscrip V, Hutcheon R . Body composition measurement: a review of hydrodensitometry, anthropometry, and impedance methods. Nutrition 1998; 14: 296–310.

    Article  CAS  Google Scholar 

  26. Lean ME, Han TS, Deurenberg P . Predicting body composition by densitometry from simple anthropometric measurements. Am J Clin Nutr 1996; 63: 4–14.

    Article  CAS  Google Scholar 

  27. Fidanza F, Keys A, Anderson JT . Density of body fat in man and other mammals. J Appl Physiol 1953; 6: 252–256.

    Article  CAS  Google Scholar 

  28. Van der Ploeg GE, Gunn SM, Withers RT, Modra AC . Use of anthropometric variables to predict relative body fat determined by a four-compartment body composition model. Eur J Clin Nutr 2003; 57: 1009–1016.

    Article  CAS  Google Scholar 

  29. Kuk JL, Saunders TJ, Davidson LE, Ross R . Age-related changes in total and regional fat distribution. Ageing Res Rev 2009; 8: 339–348.

    Article  Google Scholar 

  30. Pietrobelli A, Wang Z, Formica C, Heymsfield SB . Dual-energy X-ray absorptiometry: fat estimation errors due to variation in soft tissue hydration. Am J Physiol 1998; 274: E808–E816.

    CAS  PubMed  Google Scholar 

  31. Lohman TG, Harris M, Teixeira PJ, Weiss L . Assessing body composition and changes in body composition. Another look at dual-energy X-ray absorptiometry. Ann N Y Acad Sci 2000; 904: 45–54.

    Article  CAS  Google Scholar 

  32. Wang Z, Deurenberg P, Wang W, Pietrobelli A, Baumgartner RN, Heymsfield SB . Hydration of fat-free body mass: review and critique of a classic body-composition constant. Am J Clin Nutr 1999; 69: 833–841.

    Article  CAS  Google Scholar 

  33. Ulijaszek SJ, Kerr DA . Anthropometric measurement error and the assessment of nutritional status. Br J Nutr 1999; 82: 165–177.

    Article  CAS  Google Scholar 

  34. Chumlea WM, Steinbaugh ML, Roche AF, Mukherjee D, Gopalaswamy N . Nutritional anthropometric assessment in elderly persons 65 to 90 years of age. J Nutr Elderly 1985; 4: 39–52.

    Article  Google Scholar 

  35. Williamson DF, Kahn HS, Worthman CM, Burnette JC, Russell CM . Precision of recumbent anthropometry. Am J Hum Biol 1993; 5: 159–167.

    Article  Google Scholar 

  36. Baumgartner RN, Heymsfield SB, Roche AF . Human body composition and the epidemiology of chronic disease. Obes Res 1995; 3: 73–95.

    Article  CAS  Google Scholar 

  37. Camina Martín MA, de Mateo Silleras B, Carreño Enciso L, de la Cruz Marcos S, Miján de la Torre A, Redondo del Río MP . Changes in body composition in relation to the stage of dementia in a group of institutionalized elderly. Nutr Hosp 2013; 28: 1093–1101.

    PubMed  Google Scholar 

  38. Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB . Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr 2005; 81: 555–563.

    Article  CAS  Google Scholar 

  39. Wildman RP, Gu D, Reynolds K, Duan X, He J . Appropriate body mass index and waist circumference cutoffs for categorization of overweight and central adiposity among Chinese adults. Am J Clin Nutr 2004; 80: 1129–1136.

    Article  CAS  Google Scholar 

  40. NHLBI Obesity Education Initiative Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Executive summary of the Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Arch Intern Med 1998; 158: 1855–1867.

    Article  Google Scholar 

  41. Kuk JL, Lee S, Heymsfield SB, Ross R . Waist circumference and abdominal adipose tissue distribution: influence of age and sex. Am J Clin Nutr 2005; 81: 1330–1334.

    Article  CAS  Google Scholar 

  42. Villareal DT, Apovian CM, Kushner RF, Klein S American Society for Nutrition NAASO The Obesity Society. Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, The Obesity Society. Obes Res 2005; 13: 1849–1863.

    Article  Google Scholar 

  43. Eveleth PB, Andres R, Chumlea WC, Eiben O, Ge K, Harris T et al. Uses and interpretation of anthropometry in the elderly for the assessment of physical status. Report to the Nutrition Unit of the World Health Organization: the Expert Subcommittee on the Use and Interpretation of Anthropometry in the Elderly. J Nutr Health Aging 1998; 2: 5–17.

    CAS  PubMed  Google Scholar 

  44. Bauer JM, Volkert D, Wirth R, Vellas B, Thomas D, Kondrup J et al. Diagnosing malnutrition in the elderly. Dtsch Med Wochenschr 2006; 131: 223–227.

    Article  CAS  Google Scholar 

  45. Stahn A, Terblanche E, Gunga HC . Selected applications of bioelectrical impedance analysis: body fluids, blood volume, body cell mass and fat mass. In: Preedy VR (ed). Handbook of Anthropometry: Physical Measures of Human Form in Health and Disease. Springer Science+Business Media, LLC: New York, USA, 2012, pp 415–440.

    Chapter  Google Scholar 

  46. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM et al. Composition of the ESPEN Working Group. Bioelectrical impedance analysis—part I: review of principles and methods. Clin Nutr 2004; 23: 1226–1243.

    Article  Google Scholar 

  47. Lukaski HC . Evolution of bioimpedance: a circuitous journey from estimation of physiological function to assessment of body composition and a return to clinical research. Eur J Clin Nutr 2013; 67: S2–S9.

    Article  Google Scholar 

  48. Piccoli A, Nescolarde LD, Rosell J . Análisis convencional y vectorial de impedancia en la práctica clínica. Nefrologia 2002; 23: 228–236.

    Google Scholar 

  49. Sánchez-Iglesias A, Fernández-Lucas M, Teruel JL . The electrical basis of bioimpedance. Nefrologia 2012; 32: 133–135.

    PubMed  Google Scholar 

  50. Chumlea WC, Baumgartner RN, Roche AF . Specific resistivity used to estimate fat-free mass from segmental body measures of bioelectric impedance. Am J Clin Nutr 1988; 48: 7–15.

    Article  CAS  Google Scholar 

  51. Chumlea WC, Guo SS, Kuczmarski RJ, Vellas B . Bioelectric and anthropometric assessments and reference data in the elderly. J Nutr 1993; 123: S449–S453.

    Article  Google Scholar 

  52. Mialich MS, Sicchieri JMF, Jordao Jr AA . Analysis of body composition: a critical review of the use of bioelectrical impedance analysis. Int J Clin Nutr 2014; 2: 1–20.

    Google Scholar 

  53. Hannan WJ, Cowen SJ, Fearson KCH, Plester CE, Falconer JS, Richardson RA . Evaluation of multi-frequency bioimpedance analysis for the assessment of extracellular and total body water in surgical patients. Clin Sci 1994; 86: 479–485.

    Article  CAS  Google Scholar 

  54. Brownbill RA, Ilich JZ . Measuring body composition in overweight individuals by dual energy x-ray absorptiometry. BMC Med Imaging 2005; 5: 1.

    Article  Google Scholar 

  55. Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller DA, Friedl K et al. Development of bioelectrical impedance analysis equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. Am J Clin Nutr 2003; 77: 331–340.

    Article  CAS  Google Scholar 

  56. Kyle UG, Genton L, Karsegard L, Slosman DO, Pichard C . Single prediction equation for bioelectrical impedance analysis in adults aged 20-94 years. Nutrition 2001; 17: 248.

    Article  CAS  Google Scholar 

  57. Chumlea WC, Guo SS, Kuczmarski RJ, Flegal KM, Johnson CL, Heymsfield SB et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes Relat Metab Disord 2002; 26: 1596–1609.

    Article  CAS  Google Scholar 

  58. Schutz Y1, Kyle UU, Pichard C . Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98 y. Int J Obes Relat Metab Disord 2002; 26: 953–960.

    Article  CAS  Google Scholar 

  59. Kyle UG, Genton L, Lukaski HC, Dupertuis YM, Slosman DO, Hans D et al. Comparison of fat-free mass and body fat in Swiss and American adults. Nutrition 2005; 21: 161–169.

    Article  Google Scholar 

  60. Trippo U, Koebnick C, Zunft HJ, Greil H . Bioelectrical impedance analysis for predicting body composition: what about the external validity of new regression equations? Am J Clin Nutr 2004; 79: 335–336.

    Article  CAS  Google Scholar 

  61. Genton L, Karsegard VL, Kyle UG, Hans DB, Michel JP, Pichard C . Comparison of four bioelectrical impedance analysis formulas in healthy elderly subjects. Gerontology 2001; 47: 315–323.

    Article  CAS  Google Scholar 

  62. Rösler A, Lehmann F, Krause T, Wirth R, von Renteln-Kruse W . Nutritional and hydration status in elderly subjects: clinical rating versus bioimpedance analysis. Arch Gerontol Geriatr 2010; 50: e81–e85.

    Article  Google Scholar 

  63. Nescolarde L, Núñez A, Bogónez-Franco P, Lara A, Vaillant G, Morales R, Rosell-Ferrer J . Reference values of the bioimpedance vector components in a Caribbean population. ESPEN J 2013; 8: e141–e144.

    Google Scholar 

  64. Buffa R, Floris G, Marini E . Assessment of nutritional status in free-living elderly individuals by bioelectrical impedance vector analysis. Nutrition 2009; 25: 3–5.

    Article  Google Scholar 

  65. Norman K, Smoliner C, Valentini L, Lochs H, Pirlich M . Is bioelectrical vector analysis of value in the elderly with malnutrition and impaired functionality? Nutrition 2007; 23: 564–569.

    Article  Google Scholar 

  66. Bosy-Westphal A, Danielzik S, Dörhöfer RP, Piccoli A, Müller MJ . Patterns of bioelectrical impedance vector distribution by body mass index and age: implications for body-composition analysis. Am J Clin Nutr 2005; 82: 60–68.

    Article  CAS  Google Scholar 

  67. Piccoli A, Nigrelli S, Caberlotto A, Bottazzo S, Rossi B, Pillon L . Bivariate normal values of the bioelectrical impedance vector in adult and elderly populations. Am J Clin Nutr 1995; 61: 269–270.

    Article  CAS  Google Scholar 

  68. Saragat B, Buffa R, Mereu E, Succa V, Cabras S, Mereu RM et al. Nutritional and psycho-functional status in elderly patients with Alzheimer's disease. J Nutr Health Aging 2012; 16: 231–236.

    Article  CAS  Google Scholar 

  69. Buffa R, Mereu RM, Putzu PF, Floris G, Marini E . Bioelectrical impedance vector analysis detects low body cell mass and dehydration in patients with Alzheimer's disease. J Nutr Health Aging 2010; 14: 823–827.

    Article  CAS  Google Scholar 

  70. Buffa R, Saragat B, Cabras S, Rinaldi AC, Marini E . Accuracy of specific BIVA for the assessment of body composition in the United States population. PLoS One 2013; 8: e58533.

    Article  CAS  Google Scholar 

  71. Marini E, Sergi G, Succa V, Saragat B, Sarti S, Coin A et al. Efficacy of specific bioelectrical impedance vector analysis (BIVA) for assessing body composition in the elderly. J Nutr Health Aging 2013; 17: 515–521.

    Article  CAS  Google Scholar 

  72. Norman K, Smoliner C, Kilbert A, Valentini L, Lochs H, Pirlich M . Disease-related malnutrition but not underweight by BMI is reflected by disturbed tissue properties in the bioelectrical impedance analysis. Br J Nutr 2008; 100: 590–595.

    Article  CAS  Google Scholar 

  73. Dey DK, Bosaeus I, Lissner L, Steen B . Body composition estimated by bioelectrical impedance in the Swedish elderly. Development of population-based prediction equation and reference values of fat-free mass and body fat for 70-and-75-years-olds. Eur J Clin nutria 2003; 57: 909.

    Article  CAS  Google Scholar 

  74. Roubenoff R, Baumgartner RN, Harris TB, Dallal GE, Hannan MT, Economos CD et al. Application of bioelectrical impedance analysis to elderly populations. J Gerontol Med Sci 1997; 52A: M129.

    Article  Google Scholar 

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  1. Area of Nutrition and Food Science, Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain

    M A Camina Martín, B de Mateo Silleras & M P Redondo del Río

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Camina Martín, M., de Mateo Silleras, B. & Redondo del Río, M. Body composition analysis in older adults with dementia. Anthropometry and bioelectrical impedance analysis: a critical review. Eur J Clin Nutr 68, 1228–1233 (2014). https://doi.org/10.1038/ejcn.2014.168

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