Evaluating CMR

Imaging Techniques

Computed Tomography (CT)

Key Points

  • CT is a criterion method for assessing intra-abdominal fat.
  • CT can also be used to measure ectopic fat in the liver and skeletal muscle.
  • High cost and radiation exposure currently limit the routine use of this tool for assessing body composition and related health risk in clinical practice.

Determining Total Adiposity with Computed Tomography

3-D reconstruction of the thigh and abdomen using multiple computed tomography (CT) images Figure 1: 3-D reconstruction of the thigh and abdomen using multiple computed tomography (CT) images

3-D reconstruction of the thigh and abdomen using multiple computed tomography (CT) images Figure 1: 3-D reconstruction of the thigh and abdomen using multiple computed tomography (CT) images

There are a number of imaging techniques for determining total and regional body composition. Computed tomography (CT) and magnetic resonance imaging (MRI) provide cross-sectional images that can be used to determine total adiposity and are one of the most accurate tools available for directly quantifying body composition at a tissue level. As such, CT and MRI (discussed further in Magnetic Resonance Imaging) are often considered the criterion measures for assessing intra-abdominal (visceral) fat and skeletal muscle in vivo. Other imaging techniques—such as dual energy x-ray absorptiometry (DEXA) and ultrasound—are also useful clinical imaging techniques for assessing total and intra-abdominal adiposity. The strengths and weaknesses of DEXA and ultrasonography are addressed in another section (see Others).

CT uses ionizing radiation and differences in tissue x-ray attenuation to produce cross-sectional images of the body (1, 2). X-ray attenuation depends mainly on matter density and is commonly expressed in Hounsfield units (HU). Lower density tissues such as fat have lower HU ratings than higher density tissues such as muscle or bone. Cross-sectional CT images are composed of many pixels, each with a HU value reflecting the molecular composition of the tissue. Although CT is easier to use than MRI, radiation exposure makes it unsuitable for multiple-image whole body tissue quantification and for use in children and premenopausal women (Figure 1).

Determining Tissue Area or Mass Using CT

CT images are normally analyzed using one of two methods: 1) the perimeter of the tissue is traced manually (3) and the area within the perimeter is calculated by multiplying the number of pixels in the region of interest by their known area, or 2) image segmentation algorithms are used to identify all pixels within a selected range of intensities (i.e., HU) believed to be representative of a specific tissue (4). The latter method is more common when assessing CT images. 

Because of the radiation involved with CT, multiple CT images are generally not acquired. However, if multiple images are acquired, tissue volumes are calculated with the same methods used for MRI images (5-8). They can then be converted to mass units by multiplying the volume by the assumed density values for that tissue (9, 10).  

Measuring Skeletal Muscle Mass Using CT

Example of a non-segmented and segmented computed tomography (CT) image at the mid-thigh and abdomen (L4-L5) Figure 2: Example of a non-segmented and segmented computed tomography (CT) image at the mid-thigh and abdomen (L4-L5)

Example of a non-segmented and segmented computed tomography (CT) image at the mid-thigh and abdomen (L4-L5) Figure 2: Example of a non-segmented and segmented computed tomography (CT) image at the mid-thigh and abdomen (L4-L5)

CT is one of the gold standard techniques used for in vivo quantification of skeletal muscle mass (Figure 2). Muscle mass and changes to it are related to muscle strength (11-13), and accurately determining skeletal muscle mass is particularly important in elderly populations, who are at increased risk of sarcopenia and functional impairment due to low muscle mass. Measures of skeletal muscle by a single CT image have been validated using cadaver measures and show a high level of agreement (R2=0.94, standard error of estimate=9.5%), with a coefficient of variation of approximately 2% (14). When compared to cadaver values, CT error improved to approximately 1% when volume measures were acquired from multiple images. However, as CT involves radiation exposure, a single image at the mid-thigh is commonly used as a proxy measure of whole body skeletal muscle in both men and women (R2=0.77 to 0.79) (15).

Measuring Intra-abdominal Fat Using MRI and CT

Contribution of intra-abdominal fat to total body fat in men and women Figure 3: Contribution of intra-abdominal fat to total body fat in men and women

Contribution of intra-abdominal fat to total body fat in men and women Figure 3: Contribution of intra-abdominal fat to total body fat in men and women

MRI and CT are the only in vivo methods available to directly and accurately quantify intra-abdominal fat. Intra-abdominal fat is the fat located within the abdominal muscle wall that surrounds the organs (or viscera). On average, it accounts for only 12% and 5% of total body fat in men and women, respectively (Figure 3). As with skeletal muscle, measuring intra-abdominal fat with multiple images is costly, labour intensive, and in the case of CT, involves substantial radiation exposure. Consequently, intra-abdominal fat is normally assessed using a single MRI or CT image at L4-L5. However, it is important to note that intra-abdominal fat values obtained through CT are not necessarily comparable to those obtained through MRI (16, 17).

There is a growing literature on the importance of intra-abdominal fat as a strong predictor of dyslipidemia (18-24), glucose tolerance (25, 26), insulin resistance (26), and incidence of hypertension (20-22, 27), cardiovascular disease (28), and type 2 diabetes (29). Even within a given BMI category, men and women with greater amounts of intra-abdominal fat are more likely to be at increased risk of developing the metabolic syndrome (30), disturbances in glucose tolerance (31, 32), and insulin resistance (31, 33). Further, prospective studies have indicated that intra-abdominal fat predicts future hypertension independent of age, BMI, weekly energy expenditure, systolic blood pressure, and glucose tolerance (27). Similarly, intra-abdominal fat is a significant predictor of future type 2 diabetes independent of age, BMI, glucose intolerance, and family history (29).  It is not surprising, then, that intra-abdominal fat has also recently been linked to increased risk for all-cause mortality (34).

Although excess intra-abdominal fat is increasingly recognized as a health hazard, there is little consensus as to optimal cut-offs for identifying individuals with excess intra-abdominal fat and therefore at increased health risk. Suggested cut-offs range from 100 to 130 cm2. Regardless of optimal cut-off values, an increase in intra-abdominal fat clearly worsens the metabolic profile, even among normal weight individuals (35). Consequently, individuals should try to minimize intra-abdominal adiposity and avoid increases in intra-abdominal fat regardless of cut-off values. Moreover, because it is not possible to routinely measure intra-abdominal fat using MRI and CT, individuals should focus on routinely measuring waist circumference, which is the best surrogate measure currently available.

Measuring Ectopic Fat Using CT

Measuring liver fat by computed tomography (CT): normal vs. fatty liver Figure 4: Measuring liver fat by computed tomography (CT): normal vs. fatty liver

Measuring liver fat by computed tomography (CT): normal vs. fatty liver Figure 4: Measuring liver fat by computed tomography (CT): normal vs. fatty liver

CT can also be used to assess ectopic fat in the muscle and liver (36-39). As measured by CT, liver fat infiltration is calculated by determining the attenuation values for each voxel within a region of interest in the liver. CT attenuation values depend on the molecular composition of the tissues within each voxel. Fat has a lower density than water and protein, and liver fat infiltration is reflected by a lower liver density and thus lower attenuation values (37). However, normal and fatty liver values overlap (40). As such, it has been suggested that liver density (CTL) should be expressed relative to the spleen attenuation value (CTS), which is not infiltrated with fat (Figure 4) (40). In normal individuals, liver and spleen attenuation values have a constant relationship. The liver, however, is a denser organ and therefore has a higher attenuation value. A liver-to-spleen attenuation (CTL/CTS) ratio of less than one therefore indicates fatty infiltration (37, 40). Normally, the liver and spleen mean attenuation values are based on two or three regions of interest within the liver and spleen. However, due to the small area of interest and subjectivity involved in determining the regions of interest, the whole liver and spleen surface areas should be used to determine respective mean attenuation values (Figure 4) (41). Attenuation values within the liver and spleen are fairly homogeneous throughout, and using the whole surface area can slightly reduce the inter-observer coefficient of variation from 5.1% (42) to 2.9% (41). However, it is difficult to obtain a CT image that contains both liver and spleen. Not only does the spleen’s vertical position vary relative to the liver, both organs also vary in terms of their position within the abdominal cavity. As a multi-image approach is not feasible because of excess radiation exposure (43), a single axial image at the T12-L1 inter-vertebral space may be the best landmark for assessing both liver and spleen attenuation, given that liver and spleen can be identified at that level in approximately 90% of the men and women studied (41).

Measuring skeletal muscle lipid content by computed tomography (CT) in lean and obese men Figure 5: Measuring skeletal muscle lipid content by computed tomography (CT) in lean and obese men

Measuring skeletal muscle lipid content by computed tomography (CT) in lean and obese men Figure 5: Measuring skeletal muscle lipid content by computed tomography (CT) in lean and obese men

The average HU or mean attenuation value of adipose tissue-free skeletal muscle voxels can also be used as an index of skeletal muscle lipid content (Figure 5) (39). As with the liver, the lower the skeletal muscle mean attenuation value or the greater the number of low-density skeletal muscle voxels (e.g., 0-30 HU), the higher the skeletal muscle lipid content. However, unlike the liver, fat is stored both inside and outside the muscle cell. As such, CT muscle attenuation values reflect both intra-myocellular (IMCL) and extra-myocellular (EMCL) lipid content. Although similar, they are not analogous to intra-myocellular lipid values obtained through skeletal muscle biopsy or proton magnetic resonance spectroscopy.

In summary, CT is one of the criterion methods for measuring intra-abdominal fat and skeletal muscle mass. It can also be used to assess lipid infiltration in tissues such as muscle and the liver. However, CT is expensive and involves radiation exposure, which limits the routine use of this tool for assessing body composition and predicting obesity-related health risk in clinical practice.

References

  1. Heymsfield SB, Lohman TG, Wang Z, et al. Human Body Composition. 2005.
  2. Bray GA, Bouchard C and James WPT. Handbook of Obesity. 1998.
  3. Abate N, Burns D, Peshock RM, et al. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. J Lipid Res 1994; 35: 1490-6.
  4. Mourier A, Gautier JF, De Kerviler E, et al. Mobilization of visceral adipose tissue related to the improvement in insulin sensitivity in response to physical training in NIDDM. Effects of branched-chain amino acid supplements. Diabetes Care 1997; 20: 385-91.
  5. Kvist H, Sjostrom L and Tylen U. Adipose tissue volume determinations in women by computed tomography: technical considerations. Int J Obes 1986; 10: 53-67.
  6. Ross R, Rissanen J, Pedwell H, et al. Influence of diet and exercise on skeletal muscle and visceral adipose tissue in men. J Appl Physiol 1996; 81: 2445-55.
  7. Ross R. Magnetic resonance imaging provides new insights into the characterization of adipose and lean tissue distribution. Can J Physiol Pharmacol 1996; 74: 778-85.
  8. Shen W, Wang Z, Tang H, et al. Volume estimates by imaging methods: model comparisons with visible women as the reference. Obes Res 2003; 11: 217-25.
  9. Snyder WS, Cooke MJ, Manssett ES, et al. Report of the Task Group on Reference Man. 1975.
  10. Gallagher D, Belmonte D, Deurenberg P, et al. Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass. Am J Physiol 1998; 275: E249-58.
  11. Hughes VA, Frontera WR, Wood M, et al. Longitudinal muscle strength changes in older adults: influence of muscle mass, physical activity, and health. J Gerontol A Biol Sci Med Sci 2001; 56: B209-17.
  12. Newman AB, Haggerty CL, Goodpaster B, et al. Strength and muscle quality in a well-functioning cohort of older adults: the Health, Aging and Body Composition Study. J Am Geriatr Soc 2003; 51: 323-30.
  13. Visser M, Goodpaster BH, Kritchevsky SB, et al. Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci 2005; 60: 324-33.
  14. Mitsiopoulos N, Baumgartner RN, Heymsfield SB, et al. Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol 1998; 85: 115-22.
  15. Lee SJ, Janssen I, Heymsfield SB, et al. Relation between whole-body and regional measures of human skeletal muscle. Am J Clin Nutr 2004; 80: 1215-21.
  16. Seidell JC, Bakker CJ and van der Kooy K. Imaging techniques for measuring adipose-tissue distribution--a comparison between computed tomography and 1.5-T magnetic resonance. Am J Clin Nutr 1990; 51: 953-7.
  17. Ohsuzu F, Kosuda S, Takayama E, et al. Imaging techniques for measuring adipose-tissue distribution in the abdomen: a comparison between computed tomography and 1.5-tesla magnetic resonance spin-echo imaging. Radiat Med 1998; 16: 99-107.
  18. Rennie K, McCarthy N, Yazdgerdi S, et al. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int. J. Epidemiol. 2003; 32: 600-6.
  19. Ekelund U, Griffin SJ, Wareham NJ, et al. Physical Activity and Metabolic Risk in Individuals With a Family History of Type 2 Diabetes. Diabetes Care 2007; 30: 337-42.
  20. Carroll S, Cooke CB, Butterly, et al. Metabolic clustering, physical activity and fitness in nonsmoking, middle-aged men. Med Sci Sports Exerc 2000; 32: 2079-86.
  21. Lakka TA, Laaksonen DE, Lakka HM, et al. Sedentary lifestyle, poor cardiorespiratory fitness, and the metabolic syndrome. Med Sci Sports Exerc 2003; 35: 1279-86.
  22. Thune I, Njolstad I, Lochen M-L, et al. Physical Activity Improves the Metabolic Risk Profiles in Men and Women: The Tromso Study. Arch Intern Med 1998; 158: 1633-40.
  23. Lemieux I, Pascot A, Lamarche B, et al. Is the gender difference in LDL size explained by the metabolic complications of visceral obesity? Eur J Clin Invest 2002; 32: 909-17.
  24. Kanaley JA, Sames C, Swisher L, et al. Abdominal fat distribution in pre- and postmenopausal women: The impact of physical activity, age, and menopausal status. Metabolism 2001; 50: 976-82.
  25. Lemieux S, Prud'homme D, Nadeau A, et al. Seven-year changes in body fat and visceral adipose tissue in women. Association with indexes of plasma glucose-insulin homeostasis. Diabetes Care 1996; 19: 983-91.
  26. Brochu M, Starling RD, Tchernof A, et al. Visceral adipose tissue is an independent correlate of glucose disposal in older obese postmenopausal women. J Clin Endocrinol Metab 2000; 85: 2378-84.
  27. Hayashi T, Boyko EJ, Leonetti DL, et al. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med 2004; 140: 992-1000.
  28. Fujimoto WY, Bergstrom RW, Boyko EJ, et al. Visceral adiposity and incident coronary heart disease in Japanese-American men. The 10-year follow-up results of the Seattle Japanese-American Community Diabetes Study. Diabetes Care 1999; 22: 1808-12.
  29. Boyko EJ, Fujimoto WY, Leonetti DL, et al. Visceral adiposity and risk of type 2 diabetes: a prospective study among Japanese Americans. Diabetes Care 2000; 23: 465-71.
  30. Goodpaster BH, Krishnaswami S, Harris TB, et al. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med 2005; 165: 777-83.
  31. Ross R, Aru J, Freeman J, et al. Abdominal adiposity and insulin resistance in obese men. Am J Physiol Endocrinol Metab 2002; 282: E657-63.
  32. Pouliot MC, Després JP, Nadeau A, et al. Visceral obesity in men. Associations with glucose tolerance, plasma insulin, and lipoprotein levels. Diabetes 1992; 41: 826-34.
  33. Ross R, Freeman J, Hudson R, et al. Abdominal obesity, muscle composition, and insulin resistance in premenopausal women. J Clin Endocrinol Metab 2002; 87: 5044-51.
  34. Kuk JL, Katzmarzyk PT, Nichaman MZ, et al. Visceral Fat Is an Independent Predictor of All-cause Mortality in Men. Obes Res 2006; 14: 336-41.
  35. Kuk JL, Nichaman MZ, Church TS, et al. Liver fat is not a marker of metabolic risk in lean premenopausal women. Metabolism 2004; 53: 1066-71.
  36. Banerji MA, Buckley MC, Chaiken RL, et al. Liver fat, serum triglycerides and visceral adipose tissue in insulin-sensitive and insulin-resistant black men with NIDDM. Int J Obes Relat Metab Disord 1995; 19: 846-50.
  37. Ricci C, Longo R, Gioulis E, et al. Noninvasive in vivo quantitative assessment of fat content in human liver. J Hepatol 1997; 27: 108-13.
  38. Goto T, Onuma T, Takebe K, et al. The influence of fatty liver on insulin clearance and insulin resistance in non-diabetic Japanese subjects. Int J Obes Relat Metab Disord 1995; 19: 841-5.
  39. Goodpaster BH, Kelley DE, Thaete FL, et al. Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol 2000; 89: 104-10.
  40. Piekarski J, Goldberg HI, Royal SA, et al. Difference between liver and spleen CT numbers in the normal adult: its usefulness in predicting the presence of diffuse liver disease. Radiology 1980; 137: 727-9.
  41. Davidson LE, Kuk JL, Church TS, et al. Protocol for Measurement of Liver Fat by Computed Tomography. J Appl Physiol 2005.
  42. Nguyen-Duy TB, Nichaman MZ, Church TS, et al. Visceral fat and liver fat are independent predictors of metabolic risk factors in men. Am J Physiol Endocrinol Metab 2003; 284: E1065-71.
  43. Joy D, Thava VR and Scott BB. Diagnosis of fatty liver disease: is biopsy necessary? Eur J Gastroenterol Hepatol 2003; 15: 539-43.
Reference
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1. Heymsfield SB, Lohman TG, Wang Z, et al. Human Body Composition. 2005.
2. Bray GA, Bouchard C and James WPT. Handbook of Obesity. 1998.
3. Abate N, Burns D, Peshock RM, et al. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. J Lipid Res 1994; 35: 1490-6.
4. Mourier A, Gautier JF, De Kerviler E, et al. Mobilization of visceral adipose tissue related to the improvement in insulin sensitivity in response to physical training in NIDDM. Effects of branched-chain amino acid supplements. Diabetes Care 1997; 20: 385-91.
5. Kvist H, Sjostrom L and Tylen U. Adipose tissue volume determinations in women by computed tomography: technical considerations. Int J Obes 1986; 10: 53-67.
6. Ross R, Rissanen J, Pedwell H, et al. Influence of diet and exercise on skeletal muscle and visceral adipose tissue in men. J Appl Physiol 1996; 81: 2445-55.
7. Ross R. Magnetic resonance imaging provides new insights into the characterization of adipose and lean tissue distribution. Can J Physiol Pharmacol 1996; 74: 778-85.
8. Shen W, Wang Z, Tang H, et al. Volume estimates by imaging methods: model comparisons with visible women as the reference. Obes Res 2003; 11: 217-25.
9. Snyder WS, Cooke MJ, Manssett ES, et al. Report of the Task Group on Reference Man. 1975.
10. Gallagher D, Belmonte D, Deurenberg P, et al. Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass. Am J Physiol 1998; 275: E249-58.
11. Hughes VA, Frontera WR, Wood M, et al. Longitudinal muscle strength changes in older adults: influence of muscle mass, physical activity, and health. J Gerontol A Biol Sci Med Sci 2001; 56: B209-17.
12. Newman AB, Haggerty CL, Goodpaster B, et al. Strength and muscle quality in a well-functioning cohort of older adults: the Health, Aging and Body Composition Study. J Am Geriatr Soc 2003; 51: 323-30.
13. Visser M, Goodpaster BH, Kritchevsky SB, et al. Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci 2005; 60: 324-33.
14. Mitsiopoulos N, Baumgartner RN, Heymsfield SB, et al. Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol 1998; 85: 115-22.
15. Lee SJ, Janssen I, Heymsfield SB, et al. Relation between whole-body and regional measures of human skeletal muscle. Am J Clin Nutr 2004; 80: 1215-21.
16. Seidell JC, Bakker CJ and van der Kooy K. Imaging techniques for measuring adipose-tissue distribution--a comparison between computed tomography and 1.5-T magnetic resonance. Am J Clin Nutr 1990; 51: 953-7.
17. Ohsuzu F, Kosuda S, Takayama E, et al. Imaging techniques for measuring adipose-tissue distribution in the abdomen: a comparison between computed tomography and 1.5-tesla magnetic resonance spin-echo imaging. Radiat Med 1998; 16: 99-107.
18. Rennie K, McCarthy N, Yazdgerdi S, et al. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int. J. Epidemiol. 2003; 32: 600-6.
19. Ekelund U, Griffin SJ, Wareham NJ, et al. Physical Activity and Metabolic Risk in Individuals With a Family History of Type 2 Diabetes. Diabetes Care 2007; 30: 337-42.
20. Carroll S, Cooke CB, Butterly, et al. Metabolic clustering, physical activity and fitness in nonsmoking, middle-aged men. Med Sci Sports Exerc 2000; 32: 2079-86.
21. Lakka TA, Laaksonen DE, Lakka HM, et al. Sedentary lifestyle, poor cardiorespiratory fitness, and the metabolic syndrome. Med Sci Sports Exerc 2003; 35: 1279-86.
22. Thune I, Njolstad I, Lochen M-L, et al. Physical Activity Improves the Metabolic Risk Profiles in Men and Women: The Tromso Study. Arch Intern Med 1998; 158: 1633-40.
23. Lemieux I, Pascot A, Lamarche B, et al. Is the gender difference in LDL size explained by the metabolic complications of visceral obesity? Eur J Clin Invest 2002; 32: 909-17.
24. Kanaley JA, Sames C, Swisher L, et al. Abdominal fat distribution in pre- and postmenopausal women: The impact of physical activity, age, and menopausal status. Metabolism 2001; 50: 976-82.
25. Lemieux S, Prud'homme D, Nadeau A, et al. Seven-year changes in body fat and visceral adipose tissue in women. Association with indexes of plasma glucose-insulin homeostasis. Diabetes Care 1996; 19: 983-91.
26. Brochu M, Starling RD, Tchernof A, et al. Visceral adipose tissue is an independent correlate of glucose disposal in older obese postmenopausal women. J Clin Endocrinol Metab 2000; 85: 2378-84.
27. Hayashi T, Boyko EJ, Leonetti DL, et al. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med 2004; 140: 992-1000.
28. Fujimoto WY, Bergstrom RW, Boyko EJ, et al. Visceral adiposity and incident coronary heart disease in Japanese-American men. The 10-year follow-up results of the Seattle Japanese-American Community Diabetes Study. Diabetes Care 1999; 22: 1808-12.
29. Boyko EJ, Fujimoto WY, Leonetti DL, et al. Visceral adiposity and risk of type 2 diabetes: a prospective study among Japanese Americans. Diabetes Care 2000; 23: 465-71.
30. Goodpaster BH, Krishnaswami S, Harris TB, et al. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med 2005; 165: 777-83.
31. Ross R, Aru J, Freeman J, et al. Abdominal adiposity and insulin resistance in obese men. Am J Physiol Endocrinol Metab 2002; 282: E657-63.
32. Pouliot MC, Després JP, Nadeau A, et al. Visceral obesity in men. Associations with glucose tolerance, plasma insulin, and lipoprotein levels. Diabetes 1992; 41: 826-34.
33. Ross R, Freeman J, Hudson R, et al. Abdominal obesity, muscle composition, and insulin resistance in premenopausal women. J Clin Endocrinol Metab 2002; 87: 5044-51.
34. Kuk JL, Katzmarzyk PT, Nichaman MZ, et al. Visceral Fat Is an Independent Predictor of All-cause Mortality in Men. Obes Res 2006; 14: 336-41.
35. Kuk JL, Nichaman MZ, Church TS, et al. Liver fat is not a marker of metabolic risk in lean premenopausal women. Metabolism 2004; 53: 1066-71.
36. Banerji MA, Buckley MC, Chaiken RL, et al. Liver fat, serum triglycerides and visceral adipose tissue in insulin-sensitive and insulin-resistant black men with NIDDM. Int J Obes Relat Metab Disord 1995; 19: 846-50.
37. Ricci C, Longo R, Gioulis E, et al. Noninvasive in vivo quantitative assessment of fat content in human liver. J Hepatol 1997; 27: 108-13.
38. Goto T, Onuma T, Takebe K, et al. The influence of fatty liver on insulin clearance and insulin resistance in non-diabetic Japanese subjects. Int J Obes Relat Metab Disord 1995; 19: 841-5.
39. Goodpaster BH, Kelley DE, Thaete FL, et al. Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol 2000; 89: 104-10.
40. Piekarski J, Goldberg HI, Royal SA, et al. Difference between liver and spleen CT numbers in the normal adult: its usefulness in predicting the presence of diffuse liver disease. Radiology 1980; 137: 727-9.
41. Davidson LE, Kuk JL, Church TS, et al. Protocol for Measurement of Liver Fat by Computed Tomography. J Appl Physiol 2005.
42. Nguyen-Duy TB, Nichaman MZ, Church TS, et al. Visceral fat and liver fat are independent predictors of metabolic risk factors in men. Am J Physiol Endocrinol Metab 2003; 284: E1065-71.
43. Joy D, Thava VR and Scott BB. Diagnosis of fatty liver disease: is biopsy necessary? Eur J Gastroenterol Hepatol 2003; 15: 539-43.