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Indices of Total Adiposity

Evaluating CMR - Clinical Tools

Key Points

  • BMI, skinfolds, BIA, and densitometry are useful techniques for measuring total adiposity.
  • The degree to which these measures of total adiposity improve prediction of health risk beyond that of waist circumference alone is unclear.
  • These estimates of total adiposity do not provide an adequate measure of regional body fat distribution.

Indices of Total Adiposity

There are several clinical tools that can be used as indices of total and regional adiposity. These include body mass index (BMI), skinfolds, bioelectrical impedance analysis (BIA), hydrostatic weighing, and air-displacement plethysmography. These measures range from simple anthropometric tools to more complicated measures that are not normally used in clinical practice.

Indices of Total Adiposity - BMI

Excess body weight as measured by BMI can have a number of harmful health effects, such as increasing risk of cardiovascular disease, type 2 diabetes, cancer and mortality [1-8]. However, BMI’s usefulness in predicting health risk beyond that of waist circumference alone is unclear [2,3,7-11]. Although some studies have reported that BMI can help identify individuals at increased health and mortality risk beyond that of waist circumference alone [2,3,7,8], others have not [2,9,10,11].

Health-related BMI Cut-offs
BMI categories for classifying underweight, normal weight, overweight, and obesity are widely used in clinical and research settings (Table) [12, 13]. These cut-offs appear to be appropriate for many populations, but may not be appropriate for some Asian populations [8,14-16]. Specific BMI cut-offs for Asian populations are not as well established, but the suggested values for overweight range from 22 to 27 kg/m2 and 26 to 31 kg/m2 for obesity [14]. These Asian cut-offs were based on the relationship between BMI and total adiposity in Asian versus Caucasian populations and did not consider the relationship of BMI to morbidity or mortality. Asians have more body fat than Caucasians for a given BMI, and the lower proposed BMI cut-offs for Asians reflect BMI values associated with the same percent body fat indicating overweight and obesity in Caucasian populations [14]. For example, a BMI of 25 kg/m2 in Caucasian populations generally means 22% body fat in men and 35% body fat in women. In contrast, individuals from Singapore will have this degree of body fatness with a BMI of only 22 kg/m2. Some Asian populations also appears to be at increased health risk at lower BMI values compared to Caucasians. However, it is currently unclear whether ethnicity alters the association between BMI and health risk enough to warrant multiple population-specific BMI cut-offs.

Measuring BMI
BMI is calculated by dividing an individual’s weight in kilograms by their height in meters squared. When possible, the individual’s height and weight should be measured as both men and women tend to overestimate their height and underestimate their weight. The shorter a person is, the more likely they are to overestimate their height. And the heavier a person is, the more likely they are to underestimate their weight [17]. Accordingly, as many as 41% of men and 27% of women report themselves as being overweight or normal weight [17], when in fact they are obese.

Association Between BMI and Total and Visceral Adiposity
BMI is a good measure of overall adiposity and corresponds well to percent body fat in several ethnic groups, with standard errors of 3 to 5% after controlling for age [18]. The amount of body fat for a given BMI varies with age, gender, and race. For example, for a given BMI, white men aged 60 to 79 tend to have 4 to 5% more body fat than white men aged 20 to 39 [18]. Similarly, for a given age and BMI, women tend to have 12% more body fat than men [18], and Indonesians have approximately 5% more body fat than Caucasians [19]. Although less often acknowledged, BMI is also linked to visceral fat, but is generally considered a slightly weaker predictor of visceral adiposity than waist circumference [20-26]. Nevertheless, BMI and waist circumference both predict visceral fat independently [20], which may partly explain their independent contribution to health risk [2,3,7,8]. Clearly, BMI and waist circumference are both useful predictors of adiposity and health risk. However, waist circumference is generally considered the strongest predictor of both.

Table:
BMI categories for classifying normal weight, overweight and obesity in Caucasians

Indices of Total Adiposity - Skinfolds

Skinfold measures are commonly used to assess total and regional subcutaneous adiposity. Skinfold measures are useful for estimating total adiposity, but are unable to measure visceral fat directly. Skinfold calipers (Figure 1) are used to measure the thickness of a double layer of folded skin and fat at common landmarks, such as the biceps, triceps, chest, subscapular, iliac crest, abdomen, anterior thigh, and medial calf [27-30]. These skinfold thicknesses can be entered into one of several equations [31,32] that use various skinfold combinations to estimate total body fat with accuracies ranging from moderate to good (Standard Error of Estimate (SEE): 3 to 11%) [28]. However, many of the common skinfold equations tend to overestimate body fat in lean individuals and underestimate body fat in obese individuals [33,34]. Furthermore, it requires a fair amount of expertise to accurately and reliably measure skinfold thicknesses, as they are subject to higher inter- and intra-observer error than circumferences [35]. It is also unclear whether skinfold measures offer any clear advantage over simpler measures such as BMI and waist circumference in predicting health risk [36-39].

Figure 1:
Example of a skinfold caliper and of a skinfold measurement (biceps)

Indices of Total Adiposity – BIA

BIA is a simple, inexpensive technique that uses the body’s ability to conduct a mild electrical current to indirectly estimate fat-free mass or percent body fat (Figure 2) [29,30,40]. Body conductivity is proportional to total body water and fat-free mass, but can be affected by many factors such as hydration, temperature, distribution of fluid within the intra- and extra-cellular compartments, the cross-sectional area of the limbs, and body length [29,30,40]. Depending on the model and equation, BIA has been reported to both overestimate and underestimate fat mass [41], while generally underestimating body fat in obese individuals [29]. Nevertheless, it appears that BIA is a fairly accurate predictor of fat and fat-free mass (SEE = 2 to 3 kg) [29], with smaller estimate errors compared to BMI and other anthropometric measures [2]. However, one of the major limitations of BIA is that most equations lose accuracy when they are applied to other populations that differ with regards to age, race, gender, or adiposity, and few if any equations appear useful across heterogeneous populations [29]. In addition, studies are divided as to whether BIA is better than BMI in predicting traditional cardiovascular disease risk factors [43-45].

Figure 2:
Example of a bioelectrical impedance analysis (BIA) segmental scale also showing an individual on the scale

Indices of Total Adiposity – Densitometry (Hydrostatic Weighing and Air Displacement Plethysmography)

Densitometry estimates body composition based on body density, which is the ratio of body mass to volume. Because fat is less dense than lean tissue, the lower the body density, the higher the percent body fat. Historically, body density was estimated using hydrostatic weighing and was once considered the gold standard measure for body composition. Hydrostatic weighing entails weighing an individual while they are submerged after a maximal exhalation. The major source of error in measuring body density is associated with the subject’s ability to maximally exhale when the measure is taken [46]. In addition, hydrostatic weighing is labour intensive and requires high subject compliance, making this measure impractical for some diseased populations, the elderly, and young children [29]. More recently, the advent of air displacement plethysmography—commonly known as the BodPod®—has offered an alternative method for assessing body density that requires much less effort by users and patients [47].

The body fat estimates of both air displacement plethysmography and hydrostatic weighing are similarly repeatable (coefficient of variation <5%). In theory, air displacement plethysmography and hydrostatic weighing should provide the same estimates of body fat, as they are based on the same principles of densitometry. However, studies that have compared air displacement plethysmography and hydrostatic weighing report that their body fat estimates vary considerably (~9-16%). However, there does not appear to be any systematic differences in their measures, with studies reporting that air displacement plethysmography can under or overestimate percent body fat compared to hydrostatic weighing [47]. As such, measures by air displacement plethysmography and hydrostatic weighing are not interchangeable and more research will be needed to understand where the discrepancies between the measures lie. However, from a clinical standpoint, it is interesting to note that percent body fat as measured by air displacement plethysmography is reported to be no better than BMI or waist circumference in predicting health risk [48].

There are several techniques available to quantify total and regional adiposity. These range from relatively simple measures such as BMI, skinfolds, and BIA to more complicated measures such as densitometry. Currently, there is little evidence to suggest that these measures improve prediction of health risk beyond that of waist circumference alone.

References

  1. Folsom AR, Kushi LH, Anderson KE, et al. Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women’s Health Study. Arch Intern Med 2000; 160: 2117-28.

    PubMed ID: 10904454

  2. Ho SC, Chen YM, Woo JL, et al. Association between simple anthropometric indices and cardiovascular risk factors. Int J Obes Relat Metab Disord 2001; 25: 1689-97.

    PubMed ID: 11753592

  3. Bigaard J, Tjonneland A, Thomsen BL, et al. Waist circumference, BMI, smoking, and mortality in middle-aged men and women. Obes Res 2003; 11: 895-903.

    PubMed ID: 12855760

  4. Allison DB, Zhu SK, Plankey M, et al. Differential associations of body mass index and adiposity with all-cause mortality among men in the first and second National Health and Nutrition Examination Surveys (NHANES I and NHANES II) follow-up studies. Int J Obes Relat Metab Disord 2002; 26: 410-6.

    PubMed ID: 11896498

  5. Calle EE, Thun MJ, Petrelli JM, et al. Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med 1999; 341: 1097-105.

    PubMed ID: 10511607

  6. GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 2017; 377:13-27.

    PubMed ID: 28604169

  7. Farin HM, Abbasi F and Reaven GM. Body mass index and waist circumference both contribute to differences in insulin-mediated glucose disposal in nondiabetic adults. Am J Clin Nutr 2006; 83: 47-51.

    PubMed ID: 16400048

  8. Wildman RP, Gu D, Reynolds K, et al. Are waist circumference and body mass index independently associated with cardiovascular disease risk in Chinese adults? Am J Clin Nutr 2005; 82: 1195-202.

    PubMed ID: 16332651

  9. Janssen I, Katzmarzyk PT and Ross R. Body mass index, waist circumference, and health risk: evidence in support of current National Institutes of Health guidelines. Arch Intern Med 2002; 162: 2074-9.

    PubMed ID: 12374515

  10. Karter AJ, D’Agostino RB, Jr., Mayer-Davis EJ, et al. Abdominal obesity predicts declining insulin sensitivity in non-obese normoglycaemics: the Insulin Resistance Atherosclerosis Study (IRAS). Diabetes Obes Metab 2005; 7: 230-8.

    PubMed ID: 15811139

  11. Janssen I, Katzmarzyk PT and Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr 2004; 79: 379-84.

    PubMed ID: 14985210

  12. Health Canada. (2003) Canadian Guidelines for Body Weight Classification in Adults – Quick Reference Tool for Professionals. Cat. No: H49-179/2003-1E

    PubMed ID:

  13. Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation 2004; 25 (suppl 2), S102-38.

    PubMed ID: 24222017

  14. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157-63.

    PubMed ID: 14726171

  15. Deurenberg-Yap M and Deurenberg P. Is a re-evaluation of WHO body mass index cut-off values needed? The case of Asians in Singapore. Nutr Rev 2003; 61: S80-7.

    PubMed ID: 12828197

  16. Dalton M, Cameron AJ, Zimmet PZ, et al. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med 2003; 254: 555-63.

    PubMed ID: 14641796

  17. Spencer EA, Appleby PN, Davey GK, et al. Validity of self-reported height and weight in 4808 EPIC-Oxford participants. Public Health Nutr 2002; 5: 561-5.

    PubMed ID: 12186665

  18. Gallagher D, Heymsfield SB, Heo M, et al. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000; 72: 694-701.

    PubMed ID: 10966886

  19. Gurrici S, Hartriyanti Y, Hautvast JG, et al. Relationship between body fat and body mass index: differences between Indonesians and Dutch Caucasians. Eur J Clin Nutr 1998; 52: 779-83.

    PubMed ID: 9846588

  20. Janssen I, Heymsfield SB, Allison DB, et al. Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr 2002; 75: 683-8.

    PubMed ID: 11916754

  21. Després JP, Prud’homme D, Pouliot MC, et al. Estimation of deep abdominal adipose-tissue accumulation from simple anthropometric measurements in men. Am J Clin Nutr 1991; 54: 471-7.

    PubMed ID: 1877502

  22. Han TS, McNeill G, Seidell JC, et al. Predicting intra-abdominal fatness from anthropometric measures: the influence of stature. Int J Obes Relat Metab Disord 1997; 21: 587-93.

    PubMed ID: 9226490

  23. Ross R, Shaw KD, Rissanen J, et al. Sex differences in lean and adipose tissue distribution by magnetic resonance imaging: anthropometric relationships. Am J Clin Nutr 1994; 59: 1277-85.

    PubMed ID: 8198051

  24. Ross R, Leger L, Morris D, et al. Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol 1992; 72: 787-95.

    PubMed ID: 1559959

  25. Seidell JC, Bjorntorp P, Sjostrom L, et al. Regional distribution of muscle and fat mass in men–new insight into the risk of abdominal obesity using computed tomography. Int J Obes 1989; 13: 289-303.

    PubMed ID: 2767882

  26. van der Kooy K, Leenen R, Seidell JC, et al. Waist-hip ratio is a poor predictor of changes in visceral fat. Am J Clin Nutr 1993; 57: 327-33.

    PubMed ID: 8438766

  27. Lohman TG, Roche AF, Martorell R. (Eds.) (1988). Anthropometric Standardization Manual. Human Kinetics. Champaign, IL. 177 p.

    PubMed ID:

  28. Wang J, Thornton JC, Kolesnik S, et al. Anthropometry in body composition. An overview. Ann N Y Acad Sci 2000; 904: 317-26.

    PubMed ID: 10865763

  29. Heymsfield SB, Lohman TG, Wang Z, Going SB. (Eds.) (2005). Human Body Composition. Human Kinetics. 536 p.

    PubMed ID:

  30. Bray GA, Bouchard C, James WPT. (Eds.)(1998). Handbook of Obesity. Marcel Dekker, Inc., New York. 1012 p.

    PubMed ID:

  31. Pollock ML, Hickman T, Kendrick Z, et al. Prediction of body density in young and middle-aged men. J Appl Physiol 1976; 40: 300-4.

    PubMed ID: 931840

  32. van der Ploeg GE, Gunn SM, Withers RT, et al. Use of anthropometric variables to predict relative body fat determined by a four-compartment body composition model. Eur J Clin Nutr 2003; 57: 1009-16.

    PubMed ID: 12879096

  33. Peterson MJ, Czerwinski SA and Siervogel RM. Development and validation of skinfold-thickness prediction equations with a 4-compartment model. Am J Clin Nutr 2003; 77: 1186-91.

    PubMed ID: 12716670

  34. Fogelholm GM, Sievanen HT, van Marken Lichtenbelt WD, et al. Assessment of fat-mass loss during weight reduction in obese women. Metabolism 1997; 46: 968-75.

    PubMed ID: 9258284

  35. Mueller WH and Malina RM. Relative reliability of circumferences and skinfolds as measures of body fat distribution. Am J Phys Anthropol 1987; 72: 437-9.

    PubMed ID: 3605318

  36. Steinberger J, Jacobs DR, Jr, Raatz S, et al. Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents. Int J Obes 2005; 29: 1346-52.

    PubMed ID: 16044176

  37. Kim J, Meade T and Haines A. Skinfold thickness, body mass index, and fatal coronary heart disease: 30 year follow up of the Northwick Park heart study. J Epidemiol Community Health 2006; 60: 275-9.

    PubMed ID: 16476761

  38. Yarnell JW, Patterson CC, Thomas HF, et al. Central obesity: predictive value of skinfold measurements for subsequent ischaemic heart disease at 14 years follow-up in the Caerphilly Study. Int J Obes Relat Metab Disord 2001; 25: 1546-9.

    PubMed ID: 11673779

  39. Kalmijn S, Curb JD, Rodriguez BL, et al. The association of body weight and anthropometry with mortality in elderly men: the Honolulu Heart Program. Int J Obes Relat Metab Disord 1999; 23: 395-402.

    PubMed ID: 10340818

  40. Ellis KJ, Bell SJ, Chertow GM, et al. Bioelectrical impedance methods in clinical research: a follow-up to the NIH Technology Assessment Conference. Nutrition 1999; 15: 874-80.

    PubMed ID: 10575664

  41. Fogelholm M and van Marken Lichtenbelt W. Comparison of body composition methods: a literature analysis. Eur J Clin Nutr 1997; 51: 495-503.

    PubMed ID: 11248873

  42. Baumgartner R, Chumlea W and Roche A. Bioelectric impedance phase angle and body composition. Am J Clin Nutr 1988; 48: 16-23.

    PubMed ID: 3389323

  43. Willett K, Jiang R, Lenart E, et al. Comparison of bioelectrical impedance and BMI in predicting obesity-related medical conditions. Obesity (Silver Spring) 2006; 14: 480-90.

    PubMed ID: 16648620

  44. Nagaya T, Yoshida H, Takahashi H, et al. Body mass index (weight/height2) or percentage body fat by bioelectrical impedance analysis: which variable better reflects serum lipid profile? Int J Obes Relat Metab Disord 1999; 23: 771-4.

    PubMed ID: 10454113

  45. Kobayashi J, Murano S, Kawamura I, et al. The relationship of percent body fat by bioelectrical impedance analysis with blood pressure, and glucose and lipid parameters. J Atheroscler Thromb 2006; 13: 221-6.

    PubMed ID: 17146149

  46. Akers R and Buskirk ER. An underwater weighing system utilizing “force cube” transducers. J Appl Physiol 1969; 26: 649-52.

    PubMed ID: 5781620

  47. Fields DA, Goran MI and McCrory MA. Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am J Clin Nutr 2002; 75: 453-67.

    PubMed ID: 11864850

  48. Bosy-Westphal A, Geisler C, Onur S, et al. Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors. Int J Obes 2005; 30: 475-83.

    PubMed ID: 16261188
Reference 1 CLOSECLOSE

Folsom AR, Kushi LH, Anderson KE, et al. Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women’s Health Study. Arch Intern Med 2000; 160: 2117-28.

PubMed ID: 10904454
Reference 2 CLOSECLOSE

Ho SC, Chen YM, Woo JL, et al. Association between simple anthropometric indices and cardiovascular risk factors. Int J Obes Relat Metab Disord 2001; 25: 1689-97.

PubMed ID: 11753592
Reference 3 CLOSECLOSE

Bigaard J, Tjonneland A, Thomsen BL, et al. Waist circumference, BMI, smoking, and mortality in middle-aged men and women. Obes Res 2003; 11: 895-903.

PubMed ID: 12855760
Reference 4 CLOSECLOSE

Allison DB, Zhu SK, Plankey M, et al. Differential associations of body mass index and adiposity with all-cause mortality among men in the first and second National Health and Nutrition Examination Surveys (NHANES I and NHANES II) follow-up studies. Int J Obes Relat Metab Disord 2002; 26: 410-6.

PubMed ID: 11896498
Reference 5 CLOSECLOSE

Calle EE, Thun MJ, Petrelli JM, et al. Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med 1999; 341: 1097-105.

PubMed ID: 10511607
Reference 6 CLOSECLOSE

GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 2017; 377:13-27.

PubMed ID: 28604169
Reference 7 CLOSECLOSE

Farin HM, Abbasi F and Reaven GM. Body mass index and waist circumference both contribute to differences in insulin-mediated glucose disposal in nondiabetic adults. Am J Clin Nutr 2006; 83: 47-51.

PubMed ID: 16400048
Reference 8 CLOSECLOSE

Wildman RP, Gu D, Reynolds K, et al. Are waist circumference and body mass index independently associated with cardiovascular disease risk in Chinese adults? Am J Clin Nutr 2005; 82: 1195-202.

PubMed ID: 16332651
Reference 9 CLOSECLOSE

Janssen I, Katzmarzyk PT and Ross R. Body mass index, waist circumference, and health risk: evidence in support of current National Institutes of Health guidelines. Arch Intern Med 2002; 162: 2074-9.

PubMed ID: 12374515
Reference 10 CLOSECLOSE

Karter AJ, D’Agostino RB, Jr., Mayer-Davis EJ, et al. Abdominal obesity predicts declining insulin sensitivity in non-obese normoglycaemics: the Insulin Resistance Atherosclerosis Study (IRAS). Diabetes Obes Metab 2005; 7: 230-8.

PubMed ID: 15811139
Reference 11 CLOSECLOSE

Janssen I, Katzmarzyk PT and Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr 2004; 79: 379-84.

PubMed ID: 14985210
Reference 12 CLOSECLOSE

Health Canada. (2003) Canadian Guidelines for Body Weight Classification in Adults – Quick Reference Tool for Professionals. Cat. No: H49-179/2003-1E

PubMed ID:
Reference 13 CLOSECLOSE

Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation 2004; 25 (suppl 2), S102-38.

PubMed ID: 24222017
Reference 14 CLOSECLOSE

WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157-63.

PubMed ID: 14726171
Reference 15 CLOSECLOSE

Deurenberg-Yap M and Deurenberg P. Is a re-evaluation of WHO body mass index cut-off values needed? The case of Asians in Singapore. Nutr Rev 2003; 61: S80-7.

PubMed ID: 12828197
Reference 16 CLOSECLOSE

Dalton M, Cameron AJ, Zimmet PZ, et al. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med 2003; 254: 555-63.

PubMed ID: 14641796
Reference 17 CLOSECLOSE

Spencer EA, Appleby PN, Davey GK, et al. Validity of self-reported height and weight in 4808 EPIC-Oxford participants. Public Health Nutr 2002; 5: 561-5.

PubMed ID: 12186665
Reference 18 CLOSECLOSE

Gallagher D, Heymsfield SB, Heo M, et al. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000; 72: 694-701.

PubMed ID: 10966886
Reference 19 CLOSECLOSE

Gurrici S, Hartriyanti Y, Hautvast JG, et al. Relationship between body fat and body mass index: differences between Indonesians and Dutch Caucasians. Eur J Clin Nutr 1998; 52: 779-83.

PubMed ID: 9846588
Reference 20 CLOSECLOSE

Janssen I, Heymsfield SB, Allison DB, et al. Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr 2002; 75: 683-8.

PubMed ID: 11916754
Reference 21 CLOSECLOSE

Després JP, Prud’homme D, Pouliot MC, et al. Estimation of deep abdominal adipose-tissue accumulation from simple anthropometric measurements in men. Am J Clin Nutr 1991; 54: 471-7.

PubMed ID: 1877502
Reference 22 CLOSECLOSE

Han TS, McNeill G, Seidell JC, et al. Predicting intra-abdominal fatness from anthropometric measures: the influence of stature. Int J Obes Relat Metab Disord 1997; 21: 587-93.

PubMed ID: 9226490
Reference 23 CLOSECLOSE

Ross R, Shaw KD, Rissanen J, et al. Sex differences in lean and adipose tissue distribution by magnetic resonance imaging: anthropometric relationships. Am J Clin Nutr 1994; 59: 1277-85.

PubMed ID: 8198051
Reference 24 CLOSECLOSE

Ross R, Leger L, Morris D, et al. Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol 1992; 72: 787-95.

PubMed ID: 1559959
Reference 25 CLOSECLOSE

Seidell JC, Bjorntorp P, Sjostrom L, et al. Regional distribution of muscle and fat mass in men–new insight into the risk of abdominal obesity using computed tomography. Int J Obes 1989; 13: 289-303.

PubMed ID: 2767882
Reference 26 CLOSECLOSE

van der Kooy K, Leenen R, Seidell JC, et al. Waist-hip ratio is a poor predictor of changes in visceral fat. Am J Clin Nutr 1993; 57: 327-33.

PubMed ID: 8438766
Reference 27 CLOSECLOSE

Lohman TG, Roche AF, Martorell R. (Eds.) (1988). Anthropometric Standardization Manual. Human Kinetics. Champaign, IL. 177 p.

PubMed ID:
Reference 28 CLOSECLOSE

Wang J, Thornton JC, Kolesnik S, et al. Anthropometry in body composition. An overview. Ann N Y Acad Sci 2000; 904: 317-26.

PubMed ID: 10865763
Reference 29 CLOSECLOSE

Heymsfield SB, Lohman TG, Wang Z, Going SB. (Eds.) (2005). Human Body Composition. Human Kinetics. 536 p.

PubMed ID:
Reference 30 CLOSECLOSE

Bray GA, Bouchard C, James WPT. (Eds.)(1998). Handbook of Obesity. Marcel Dekker, Inc., New York. 1012 p.

PubMed ID:
Reference 31 CLOSECLOSE

Pollock ML, Hickman T, Kendrick Z, et al. Prediction of body density in young and middle-aged men. J Appl Physiol 1976; 40: 300-4.

PubMed ID: 931840
Reference 32 CLOSECLOSE

van der Ploeg GE, Gunn SM, Withers RT, et al. Use of anthropometric variables to predict relative body fat determined by a four-compartment body composition model. Eur J Clin Nutr 2003; 57: 1009-16.

PubMed ID: 12879096
Reference 33 CLOSECLOSE

Peterson MJ, Czerwinski SA and Siervogel RM. Development and validation of skinfold-thickness prediction equations with a 4-compartment model. Am J Clin Nutr 2003; 77: 1186-91.

PubMed ID: 12716670
Reference 34 CLOSECLOSE

Fogelholm GM, Sievanen HT, van Marken Lichtenbelt WD, et al. Assessment of fat-mass loss during weight reduction in obese women. Metabolism 1997; 46: 968-75.

PubMed ID: 9258284
Reference 35 CLOSECLOSE

Mueller WH and Malina RM. Relative reliability of circumferences and skinfolds as measures of body fat distribution. Am J Phys Anthropol 1987; 72: 437-9.

PubMed ID: 3605318
Reference 36 CLOSECLOSE

Steinberger J, Jacobs DR, Jr, Raatz S, et al. Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents. Int J Obes 2005; 29: 1346-52.

PubMed ID: 16044176
Reference 37 CLOSECLOSE

Kim J, Meade T and Haines A. Skinfold thickness, body mass index, and fatal coronary heart disease: 30 year follow up of the Northwick Park heart study. J Epidemiol Community Health 2006; 60: 275-9.

PubMed ID: 16476761
Reference 38 CLOSECLOSE

Yarnell JW, Patterson CC, Thomas HF, et al. Central obesity: predictive value of skinfold measurements for subsequent ischaemic heart disease at 14 years follow-up in the Caerphilly Study. Int J Obes Relat Metab Disord 2001; 25: 1546-9.

PubMed ID: 11673779
Reference 39 CLOSECLOSE

Kalmijn S, Curb JD, Rodriguez BL, et al. The association of body weight and anthropometry with mortality in elderly men: the Honolulu Heart Program. Int J Obes Relat Metab Disord 1999; 23: 395-402.

PubMed ID: 10340818
Reference 40 CLOSECLOSE

Ellis KJ, Bell SJ, Chertow GM, et al. Bioelectrical impedance methods in clinical research: a follow-up to the NIH Technology Assessment Conference. Nutrition 1999; 15: 874-80.

PubMed ID: 10575664
Reference 41 CLOSECLOSE

Fogelholm M and van Marken Lichtenbelt W. Comparison of body composition methods: a literature analysis. Eur J Clin Nutr 1997; 51: 495-503.

PubMed ID: 11248873
Reference 42 CLOSECLOSE

Baumgartner R, Chumlea W and Roche A. Bioelectric impedance phase angle and body composition. Am J Clin Nutr 1988; 48: 16-23.

PubMed ID: 3389323
Reference 43 CLOSECLOSE

Willett K, Jiang R, Lenart E, et al. Comparison of bioelectrical impedance and BMI in predicting obesity-related medical conditions. Obesity (Silver Spring) 2006; 14: 480-90.

PubMed ID: 16648620
Reference 44 CLOSECLOSE

Nagaya T, Yoshida H, Takahashi H, et al. Body mass index (weight/height2) or percentage body fat by bioelectrical impedance analysis: which variable better reflects serum lipid profile? Int J Obes Relat Metab Disord 1999; 23: 771-4.

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