Body fat mass assessment and obesity classification: a review of the available methods for adiposity estimation

Main Article Content

Gabriele Castelnuovo
Begoña de Cuevillas
Santiago Navas-Carretero
J Alfredo Martinez


obesity, body composition, fat mass, anthropometry


Obesity is a growing public health problem, which often leads to severe comorbidities that can reduce quality of life and living expectancy. Overweight is caused by a greater food intake compared to the energy expenditure, which involves an excessive deposition of body fat. The distribution of adipose tissue also varies depending on sex, whereas men usually show android-type obesity, or visceral adiposity, women exhibit more commonly a deposition of fat involving the gynoid gluteo-femoral or subcutaneous type. Overweight and obesity are accompanied by a series of clinical manifestations, being the most common hyperglycemia, hypertriglyceridemia and high blood pressure, which may depend on body fat distribution. Consequently, not only promoting initiatives to adopt a healthy lifestyle based on recommended dietary models and an active living is necessary, but also having reliable techniques for body fat determination. Besides the Body Mass Index (BMI), whose limits on the correct quantification of body fat are known, nowadays diverse approaches for fat measurement are available. In addition, the assessment of body fat could be achieved also through complex methods such as Bioelectric Impedance Analysis (BIA), Dual-Energy X-Ray Absorptiometry (DXA) and Total Body Electrical Conductivity (TOBEC), which may be complemented by approaches to categorize/differentiate obese individuals through classification systems and scores. Indeed, adequate measurement of fat is required for obesity characterization and for management purposes as reported in this review.

Abstract 1427 | PDF Downloads 639


1. World Health Organization. Obesity and overweight. 2018. Available at:
2. Arroyo-Johnson C, Mincey KD. Obesity epidemiology trends by race/ethnicity, gender, and education: National Health Interview Survey, 1997–2012. Gastroenterol Clin North Am 2016; 45(4): 571.
3. Salmasi L, Celidoni M. Investigating the poverty-obesity paradox in Europe. Econ Hum Biol 2017; 26: 70–85.
4. Rosenheck R. Fast food consumption and increased caloric intake: A systematic review of a trajectory towards weight gain and obesity risk. Obes Rev 2008; 9(6): 535–47.
5. Goedecke JH, Micklesfield LK. The Effect of Exercise on Obesity, Body Fat Distribution and Risk for Type 2 Diabetes. Diabetes Phys Act 2014; 60: 82–93.
6. McAllister EJ, Dhurandhar NV, Keith SW, et al. Ten putative contributors to the obesity epidemic. Crit Rev Food Sci Nutr 2009; 49(10): 868–913.
7. Kaur Y, de Souza RJ, Gibson WT, Meyre D. A systematic review of genetic syndromes with obesity. Obes Rev 2017; 18(6): 603–34.
8. Verhaegen AA, Van Gaal LF. Drug-induced obesity and its metabolic consequences: a review with a focus on mechanisms and possible therapeutic options. J Endocrinol Invest 2017; 40(11): 1165–74.
9. Labib M. The investigation and management of obesity. J Clin Pathol 2003; 56(1): 17–25.
10. Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Mamun A Al, Bonneux L. Obesity in Adulthood and Its Consequences for Life Expectancy: A Life-Table Analysis. Ann Intern Med 2003; 138(1): 24.
11. Cawley J, Meyerhoefer C. The medical care costs of obesity: An instrumental variables approach. J Health Econ 2012; 31(1): 219–30.
12. Billingsley HE, Carbone S, Lavie CJ. Dietary Fats and Chronic Noncommunicable Diseases. Nutrients 2018; 10(10).
13. Khaodhiar L, McCowen KC, Blackburn GL. Obesity and its comorbid conditions. Clin Cornerstone 1999; 2(3): 17–31.
14. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, Obesity, and Mortality from Cancer in a Prospectively Studied Cohort of U.S. Adults. N Engl J Med 2003; 348(17): 1625–38.
15. Freisling H, Arnold M, Soerjomataram I, et al. Comparison of general obesity and measures of body fat distribution in older adults in relation to cancer risk: meta-analysis of individual participant data of seven prospective cohorts in Europe. Br J Cancer 2017; 116(11): 1486–97.
16. Lee SY, Gallagher D. Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care 2008; 11(5): 566–72.
17. Samsell L, Regier M, Walton C, Cottrell L. Importance of Android/Gynoid Fat Ratio in Predicting Metabolic and Cardiovascular Disease Risk in Normal Weight as well as Overweight and Obese Children. J Obes 2014; 2014: 1–7.
18. Abdelaal M, le Roux CW, Docherty NG. Morbidity and mortality associated with obesity. Ann Transl Med 2017; 5(7): 161–161.
19. Forbes GB. Body Fat Content Influences the Body Composition Response to Nutrition and Exercise. Ann N Y Acad Sci 2006; 904(1): 359–65.
20. de Cuevillas B, Alvarez-Alvarez I, Cuervo M, Fernández-Montero A, Navas-Carretero S, Martínez JA. Definition of Nutritionally qualitative categorizing (proto)nutritypes and a pilot quantitative nutrimeter for mirroring nutritional wellbeing based on a quality of life health related questionnaire. Nutr Hosp 2019; ([Impress]).
21. Zhang H, Yu C, Guan Q, et al. Viscus fat area contributes to the Framingham 10-year general cardiovascular disease risk in patients with type 2 diabetes mellitus. Life Sci 2019; 220: 69–75.
22. García-García ML, Martín-Lorenzo JG, Lirón-Ruiz R, Torralba-Martínez JA, García-López JA, Aguayo-Albasini JL. Failure of the Obesity Surgery Mortality Risk Score (OS-MRS) to Predict Postoperative Complications After Bariatric Surgery. A Single-Center Series and Systematic Review. Obes Surg 2017; 27(6): 1423–9.
23. Cronin P, Ryan F, Coughlan M. Undertaking a literature review: a step-by-step approach. Br J Nurs 2008; 17(1): 38–43.
24. Gasparyan AY, Ayvazyan L, Blackmore H, Kitas GD. Writing a narrative biomedical review: considerations for authors, peer reviewers, and editors. Rheumatol Int 2011; 31(11): 1409–17.
25. Agarwal N, Dewan P. Writing a Review Article: Making Sense of the Jumble. Indian Pediatr 2016; 53(8): 715–20.
26. Apovian CM. Obesity: definition, comorbidities, causes, and burden. Am J Manag Care 2016; 22(7 Suppl): s176-85.
27. Webb VL, Wadden TA. Intensive Lifestyle Intervention for Obesity: Principles, Practices, and Results. Gastroenterology 2017; 152(7): 1752–64.
28. Saunders KH, Umashanker D, Igel LI, Kumar RB, Aronne LJ. Obesity Pharmacotherapy. Med Clin North Am 2018; 102(1): 135–48.
29. Kahan S. Overweight and obesity management strategies. Am J Manag Care 2016; 22(7 Suppl): s186-96.
30. Kamadjeu RM, Edwards R, Atanga JS, Kiawi EC, Unwin N, Mbanya J-C. Anthropometry measures and prevalence of obesity in the urban adult population of Cameroon: an update from the Cameroon Burden of Diabetes Baseline Survey. BMC Public Health 2006; 6: 228.
31. Duren DL, Sherwood RJ, Czerwinski SA, et al. Body Composition Methods: Comparisons and Interpretation. J Diabetes Sci and Technol 2008; 2(6): 1139-46.
32. Kamimura MA, Avesani CM, Cendoroglo M, Canziani MEF, Draibe SA, Cuppari L. Comparison of skinfold thicknesses and bioelectrical impedance analysis with dual-energy X-ray absorptiometry for the assessment of body fat in patients on long-term haemodialysis therapy. Nephrol Dial Transplant 2003; 18(1): 101–5.
33. Ruiz De Eguilaz MH, Martínez De Morentín B, Pérez-Diez S, Navas-Carretero S, Martínez JA. Estudio comparativo de medidas de composición corporal por absorciometría dual de rayos X, bioimpedancia y pliegues cutańeos en mujeres. An la Real Acad Nac Farm 2010; 76(2): 209–22.
34. Jackson AS, Pollock ML. Practical assessment of body composition. Phys Sportsmed 1985; 13(5): 76–90.
35. Siri WE. Body composition from fluid spaces and density: analysis of methods. 1961. Nutrition 9(5): 480–91.
36. Brožek J. Body composition: Models and estimation equations. Am J Phys Anthropol 1966; 24(2): 239–46.
37. Visser M, Heuvel E Van Den, Deurenberg P. Prediction equations for the estimation of body composition in the elderly using anthropometric data. Br J Nutr 1994; 71(6): 823–33.
38. Bergman RN, Stefanovski D, Buchanan TA, et al. A Better Index of Body Adiposity. Obesity 2011; 19(5): 1083–9.
39. McDougall A, Bredlau C, Mueller M, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity 2013; 21(11): 2264–71.
40. Bernhard AB, Scabim VM, Serafim MP, Gadducci AV, Santo MA, de Cleva R. Modified body adiposity index for body fat estimation in severe obesity. J Hum Nutr Diet 2017; 30(2): 177–84.
41. Deurenberg P, Weststrate JA, Seidell JC. Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr 1991; 65(2): 105–14.
42. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000; 72(3): 694–701.
43. Gomez-Ambrosi J, Silva C, Catalan V, et al. Clinical Usefulness of a New Equation for Estimating Body Fat. Diabetes Care 2012; 35(2): 383–8.
44. Kagawa M, Byrne NM, Hills AP. Comparison of body fat estimation using waist:height ratio using different ‘waist’ measurements in Australian adults. Br J Nutr 2008; 100(5): 1135–41.
45. Kanellakis S, Skoufas E, Khudokonenko V, et al. Development and validation of two equations based on anthropometry, estimating body fat for the Greek adult population. Obesity 2017; 25(2): 408–16.
46. Jackson AS, Stanforth PR, Gagnon J, et al. The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study. Int J Obes Relat Metab Disord 2002; 26(6): 789–96.
47. 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(01): 77–97.
48. Leahy S, O’Neill C, Sohun R, Toomey C, Jakeman P. Generalised equations for the prediction of percentage body fat by anthropometry in adult men and women aged 18–81 years. Br J Nutr 2013; 109(4): 678–85.
49. Jackson AS, Pollock ML. Generalized equations for predicting body density of men. Br J Nutr 1978; 40(3): 497–504.
50. Jackson AS, Pollock ML, Ward A. Generalized equations for predicting body density of women. Med Sci Sports Exerc 1980; 12(3): 175–81.
51. Lean ME, Han TS, Deurenberg P. Predicting body composition by densitometry from simple anthropometric measurements. Am J Clin Nutr 1996; 63(1): 4–14.
52. Plank LD. Dual-energy X-ray absorptiometry and body composition. Curr Opin Clin Nutr Metab Care 2005; 8(3): 305–9.
53. Hoffe S, Saeed N, Shridhar R, Chuong M, Almhanna K, Meredith K. CT-based assessment of visceral adiposity and outcomes for esophageal adenocarcinoma. J Gastrointest Oncol 2017; 8(5): 833–41.
54. Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis--part I: review of principles and methods. Clin Nutr 2004; 23(5): 1226–43.
55. Presta E, Wang J, Harrison GG, Björntorp P, Harker WH, Van Itallie TB. Measurement of total body electrical conductivity: a new method for estimation of body composition. Am J Clin Nutr 1983; 37(5): 735–9.
56. Casanova Román M. Técnicas de valoración del estado nutricional. Vox Paedriatr 2003; 11.1: 26-35.
57. Pérez S, Parra MD, Martínez de Morentin BE, Rodríguez CM, Martínez JA. Evaluación de la variabilidad intraindividual de la medida de composición corporal mediante bioimpedancia en voluntarias sanas y su relación con el índice de masa corporal y el pliegue tricipital. Enferm Clin 2005; 15(6): 343–7.
58. Brooke-Wavell K, Jones PR, Norgan NG, Hardman AE. Evaluation of near infra-red interactance for assessment of subcutaneous and total body fat. Eur J Clin Nutr 1995; 49(1): 57–65.
59. Bielemann RM, Gonzalez MC, Barbosa-Silva TG, et al. Estimation of body fat in adults using a portable A-mode ultrasound. Nutrition 2016; 32(4): 441–6.
60. Fields DA, Goran MI, McCrory MA. Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am J Clin Nutr 2002; 75(3): 453–67.
61. Clark RR, Kuta JM, Sullivan JC. Prediction of percent body fat in adult males using dual energy x-ray absorptiometry, skinfolds, and hydrostatic weighing. Med Sci Sports Exerc 1993; 25(4): 528–35.
62. 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.
63. Cordes C, Franz D, Hauner H, et al. MR-based assessment of body fat distribution and characteristics. Eur J Radiol 2016; 85(8): 1512–8.
64. Wong WW, Cochran WJ, Klish WJ, et al. Body fat in normal adults estimated by oxygen-18- and deuterium-dilution and by anthropometry: a comparison. Eur J Clin Nutr 1988; 42(3): 233–42.
65. Bruce A, Andersson M, Arvidsson B, Isaksson B. Body composition. Prediction of normal body potassium, body water and body fat in adults on the basis of body height, body weight and age. Scand J Clin Lab Invest 1980; 40(5): 461–73.
66. Van Loan MD, Boileau RA, Slaughter MH, et al. Association of bioelectrical resistance with estimates of fat-free mass determined by densitometry and hydrometry. Am J Hum Biol 1990; 2(3): 219–26.
67. Davies JS, Bell W, Evans W, Villis RJ, Scanlon MF. Body composition derived from whole body counting of potassium in growth hormone-deficient adults: a possible low intracellular potassium concentration. J Clin Endocrinol Metab 1996; 81(5): 1720–3.
68. Forbes GB, Bruining GJ. Urinary creatinine excretion and lean body mass. Am J Clin Nutr 1976; 29(12): 1359–66.
69. Cohn SH, Vaswani AN, Yasumura S, Yuen K, Ellis KJ. Improved models for determination of body fat by in vivo neutron activation. Am J Clin Nutr 1984; 40(2): 255–9.
70. Perl W, Lesser GT, Steele JM. The kinetics of distribution of the fat-soluble inert gas cyclopropane in the body. Biophys J 1960; 1: 111–35.
71. Seidell JC, Flegal KM. Assessing obesity: classification and epidemiology. Vol. 53, Britijh M.dical Bulletin. 1997.
72. Antonopoulos AS, Oikonomou EK, Antoniades C, Tousoulis D. From the BMI paradox to the obesity paradox: the obesity-mortality association in coronary heart disease. Obes Rev 2016; 17(10): 989–1000.
73. Marković-Jovanović SR, Stolić RV, Jovanović AN. The reliability of body mass index in the diagnosis of obesity and metabolic risk in children. J Pediatr Endocrinol Metab 2015; 28(5–6): 515–23.
74. Chiquete E, Ruiz-Sandoval JL, Ochoa-Guzmán A, et al. The Quételet index revisited in children and adults. Endocrinol y Nutr 2014; 61(2): 87–92.
75. Frankenfield DC, Rowe WA, Cooney RN, Smith JS, Becker D. Limits of body mass index to detect obesity and predict body composition. Nutrition 2001; 17(1): 26–30.
76. World Health Organization. Body mass index - BMI. 2019. Available at:
77. Ricci MA, De Vuono S, Scavizzi M, Gentili A, Lupattelli G. Facing Morbid Obesity. Angiology 2016; 67(4): 391–7.
78. Sharma AM, Kushner RF. A proposed clinical staging system for obesity. Int J Obes 2009; 33(3): 289–95.
79. Martínez-Urbistondo D, Martínez JA. Utilidad del cuestionario «Edmonton Obesity Staging System» para el desarrollo de la nutrición médica de precisión. Rev Clin Esp 2017; 217(2): 97–8.
80. Lloyd-Jones DM, Wilson PW, Larson MG, et al. Framingham risk score and prediction of lifetime risk for coronary heart disease. Am J Cardiol 2004; 94(1): 20–4.
81. DeMaria EJ, Portenier D, Wolfe L. Obesity surgery mortality risk score: proposal for a clinically useful score to predict mortality risk in patients undergoing gastric bypass. Surg Obes Relat Dis 2007; 3(2): 134–40.
82. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. World Health Organ Tech Rep Ser. Who 2004; 1–253.
83. Carranza Leon BG, Jensen MD, Hartman JJ, Jensen TB. Self-Measured vs Professionally Measured Waist Circumference. Ann Fam Med 2016; 14(3): 262–6.
84. Hingorjo MR, Qureshi MA, Mehdi A. Neck circumference as a useful marker of obesity: a comparison with body mass index and waist circumference. J Pak Med Assoc 2012; 62(1): 36–40.
85. Rådholm K, Tengblad A, Dahlén E, et al. The impact of using sagittal abdominal diameter to predict major cardiovascular events in European patients with type 2 diabetes. Nutr Metab Cardiovasc Dis 2017; 27(5): 418–22.
86. Zamboni M, Turcato E, Armellini F, et al. Sagittal abdominal diameter as a practical predictor of visceral fat. Int J Obes 1998; 22(7): 655–60.
87. Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr 2005; 56(5): 303–7.
88. Croft JB, Keenan NL, Sheridan DP, Wheeler FC, Speers MA. Waist-to-hip ratio in a biracial population: measurement, implications, and cautions for using guidelines to define high risk for cardiovascular disease. J Am Diet Assoc 1995; 95(1): 60–4.
89. Valdez R. A simple model-based index of abdominal adiposity. J Clin Epidemiol 1991; 44(9): 955–6.
90. Ehrampoush E, Arasteh P, Homayounfar R, et al. New anthropometric indices or old ones: Which is the better predictor of body fat? Diabetes Metab Syndr Clin Res Rev 2017; 11(4): 257–63.