HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) All Versions of this Article: M300508-JLR200v1 45/5/941    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Tanoli, T. Articles by Schonfeld, G. Search for Related Content PubMed PubMed Citation Articles by Tanoli, T. Articles by Schonfeld, G. Social Bookmarking                      What's this?

Journal of Lipid Research, Vol. 45, 941-947, May 2004
Copyright © 2004 by American Society for Biochemistry and Molecular Biology

Fatty liver in familial hypobetalipoproteinemia

Tariq Tanoli, Pin Yue, Dmitriy Yablonskiy and Gustav Schonfeld¹

Departments of Internal Medicine and Radiology, Washington University School of Medicine, St. Louis, MO

Published, JLR Papers in Press, February 16, 2004. DOI 10.1194/jlr.M300508-JLR200

¹ To whom correspondence should be addressed. e-mail: gschonfe{at}wustl.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Fatty liver is frequent in the apolipoprotein B (apoB)-defective genetic form of familial hypobetalipoproteinemia (FHBL), but interindividual variability in liver fat is large. To explain this, we assessed the roles of metabolic factors in 32 affected family members with apoB-defective FHBL and 33 related and unrelated normolipidemic controls matched for age, sex, and indices of adiposity. Two hour, 75 g oral glucose tests, with measurements of plasma glucose and insulin levels, body mass index, and waist-hip ratios were obtained. Abdominal subcutaneous, intraperitoneal (IPAT), and retroperitoneal adipose tissue masses were quantified by MR imaging, and hepatic fat was quantified by MR spectroscopy. Mean ± SD liver fat percentage values of FHBL and controls were 14.8 ± 12.0 and 5.2 ± 5.9, respectively (P = 0.001). Means for these measures of obesity and insulin action were similar in the two groups. Important determinants of liver fat percentage were FHBL-affected status, IPAT, and area under the curve (AUC) insulin in both groups, but the strongest predictors were IPAT in FHBL (partial R² = 0.55, P < 0.0002) and AUC insulin in controls (partial R² = 0.59, P = 0.0001). Regression of liver fat percentage on IPAT fat was significantly greater for FHBL than for controls (P < 0.001).

In summary, because apoB-defective FHBL imparts heightened susceptibility to liver triglyceride accumulation, increasing IPAT and insulin resistance exert greater liver fat-increasing effects in FHBL.

Supplementary key words abdominal adipose tissue • insulin resistance • apolipoprotein B

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Nonalcoholic fatty liver [NAFL] is highly prevalent in human populations. It may develop into nonalcoholic steatohepatitis and in some cases into cirrhosis requiring liver transplantation (1–5). The overwhelming majority of NAFL cases are associated with obesity, dyslipidemia, hypertension, insulin resistance, type 2 diabetes mellitus, and atherosclerotic cardiovascular disease (6–9). This constellation defines the metabolic syndrome (10, 11). Several mouse models have been engineered that result in fatty liver: mouse overexpressors of genes specifying enzymes or transcription factors of the fatty acid synthetic pathway (12), knockouts of genes of the hepatic fatty acid oxidation pathways (13), and genes that regulate the development of adipose tissue (14). Some of these mice exhibit aspects of the metabolic syndrome, such as insulin resistance.

In humans, one among many causes of fatty liver is naturally occurring familial hypobetalipoproteinemia (FHBL) (15–22). It is defined by <5th percentile plasma levels of LDL-cholesterol and/or total apolipoprotein B (apoB), segregating in families as an autosomal dominant trait (23, 24). Three genetic subclasses of FHBL have been identified to date: 1) mutations of the apoB gene (APOB) that lead to dysfunctional export of hepatic triglycerides via the VLDL export system and to fatty liver in humans (25–28) and mouse models (29, 30); 2) FHBL linked to a susceptibility locus on chromosome 3p21 (31, 32); and 3) FHBL linked to neither of the above (P. Yue, M. R. Averna, and G. Schonfeld, unpublished observations). We have reported that the mean liver triglyceride content in apoB-impaired FHBL subjects (group 1 above) is 5-fold that of controls (33), but liver fat content in FHBL subjects (as well as in the controls we studied) varies greatly among individuals. In seeking sources of variation, we determined indices of adiposity such as the waist-hip ratio and the body mass index (BMI) and indices of insulin action such as area under the curve (AUC) for glucose and insulin during oral glucose tolerance tests in both FHBL and control subjects. Mean values for these parameters did not differ in the two groups, suggesting that higher levels of liver fat in FHBL subjects were not attributable to more adiposity or insulin resistance in FHBL subjects. However, both groups of indices were strongly correlated with liver fat in both FHBL subjects and controls (33). Since that report, we have expanded the number of FHBL subjects and controls and added direct measurements of abdominal adipose tissue by MR imaging. Abdominal fat (34) has been segmented anatomically into subcutaneous (SAT), intraperitoneal (IPAT), and retroperitoneal abdominal adipose tissue (RPAT). The various anatomic sites exhibit quantitatively differing physiological activities, such as basal and stimulated rates of lipid and carbohydrate metabolism (35). We now report on the roles of abdominal adiposity and insulin sensitivity in liver fat contents of subjects with the apoB-defective form of FHBL and matched controls. Lean FHBL subjects and lean controls tended to have similar amounts of liver fat. However, the slope of the regression lines of liver fat on intra-abdominal fat (i.e., IPAT) diverged with increasing amounts of liver fat. The line for FHBL subjects was significantly steeper than the line for controls. This suggests that FHBL subjects are more susceptible to developing fatty livers at any given amount of abdominal adipose tissue than are controls.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study subjects, protocols, and routine chemistries
The Washington University Human Studies Committee approved our protocols and informed consent procedures. No subjects were acutely ill or taking any medications known to affect lipid metabolism. Recently, we reported on the liver fat contents of 22 FHBL subjects with a variety of APOB truncation mutations and 16 normolipidemic controls matched for gender, BMI, and age (33). Since then, we have studied an additional 10 FHBL subjects and 17 controls. Thus, the total number of subjects is 32 FHBL subjects and 33 controls. The specific apoB defects of FHBL subjects and the citations to their original descriptions are provided in the legend to Table 1. The controls consist of relatives and unrelated volunteers matched for age, gender, and indices of obesity. No upper "normal" limit on the liver fat content of our control group was set because the amount of liver fat was continuously distributed. Thus, the control group included subjects who probably would have been classified as having fatty livers had a cutoff point been applied (a frequently used cutoff point is liver fat = 5%) (36).

Quantitation of abdominal fat masses by magnetic resonance imaging
Subjects were scanned using a 1.5 T Siemens Magnetom Vision scanner (Siemens, Erlanger, Germany). Axial MRI scans of the abdomen were obtained using a body coil. Gradient echo sequences were used with a repetition time of 160 ms and an echo time of 2.7 ms. Seventeen contiguous slices of 1.0 cm thickness were obtained from the diaphragmatic surface of the liver in the caudal direction. The duration of acquisition was 19 s. All images were acquired on a 116 x 256 matrix within a 33.8 x 45.0 cm² field of view. During image acquisition, subjects were asked to hold their breath in full inspiration. The Analyze 3.1 image-analysis software program (Biomedical Imaging Resource) was used for the quantification of adipose tissue volume. Eight contiguous slices (with the first slice from the top of the right kidney; in deep inspiration, these slices usually correspond to vertebra levels L1, L2, and L3) were used for the quantification of SAT and intra-abdominal adipose tissue (IAAT). IAAT was divided into RPAT and IPAT compartments using anatomical structures as markers, such as pancreas, ascending and descending colon, inferior vena cava, and aorta. Analyze software allows segmentation of the images into various compartments using threshold values and knowledge of anatomy. We used this feature for the segmentation of images into SAT, RPAT, and IPAT. Different threshold values were assigned to each compartment. The total number of pixels in eight slices was calculated for each compartment. The number of pixels was converted to volume. Average volume per slice was calculated. From this average volume, the average mass of adipose tissue (kilograms) per slice was derived [assuming that adipose tissue is composed of 84.67% fat, 12.67% water, and 2.66% proteins and that the density of adipose tissue is 0.9196 kg/l (34, 39)]. Thus, the mass of fat per slice = the average volume of adipose tissue in liters x 0.846 x 0.9196.

Magnetic resonance spectroscopy
As reported above, a 1.5 T Siemens Magneton Vision scanner was used with a body radio frequency (RF) coil as a transmitter and a small flex coil as a receiver. A localized volume MR technique based on a double-spin echo PRESS sequence (40) without water suppression was used. Accurate voxel localization was achieved using specially designed numerically optimized RF pulses (41). Each individual signal acquisition occurred over 512 ms with a repetition period of 2 s. Ten signal averages were obtained over a 20 s period. Both the anatomical images and the spectroscopic data were obtained while subjects held their breath.

For accurate quantification of low-intensity fat signal in the presence of the strong signal from water (dynamic range problem), a digital low-pass Savitszky-Golay filter with bandwidth of 30 Hz centered at water resonance frequency was applied to model the strong water time domain signal. Bayesian probability theory was used for further data analysis of digitally separated water and fat signals (Bayesian programs were written by Dr. G. Larry Bretthorst). Data were analyzed by modeling the water and fat signals each as an exponentially decaying sinusoid.

Three 2 x 2 x 2 cm voxels were examined in each subject. The coefficient of variation of replicate values of the triplicate determinations for three voxels was 1.5% (n = 31 MRS examinations). Two data sets with spin echo times of 23 and 53 ms were obtained from each voxel and used to evaluate the spin density for fat and water contributions. The MRS liver fat percentage was reported as the spin density of the aliphatic ¹H signal divided by the sum of the spin densities of aliphatic plus water ¹H signals.

We have reported on the comparability of chemical measurements of liver triglycerides and liver fat measured by MRS (33). The regression of weight percentage liver triglyceride (chemical) on MRS liver fat percentage was y = 0.807x (R² = 0.986).

Statistical analysis
All statistical analyses were done using SAS (SAS Institute, Cary, NC). Results shown are means ± SD. Pearson correlation coefficients were used as appropriate. Log transformation of liver fat percentage was used because it was not normally distributed. The Chi-square test was used to compare sex differences between FHBL and control groups, and the Kruskal-Wallis test was used to compare age differences. The PROC GLM was used to compare means of groups with levels of 0.01. Multivariate stepwise regression was used to determine the independent sources of liver fat variation among individuals, with MRS liver fat percentage or log (liver fat percentage) as the dependent variable. The values for liver fat percentage are presented as measured, without adjustments for covariates (e.g., age, BMI, etc.).

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study subjects
Assignment of FHBL-affected status in all subjects is based on genetic analysis of APOB. The clinical characteristics of the subjects are given in Table 1. As expected, there were clear-cut differences between affected and unaffected subjects in liver fat percentage and plasma levels of total and LDL-cholesterol and apoB, but other characteristics, such as age, gender distribution, body weight, BMI, and waist-hip ratio were similar. Liver fat percentage was significantly correlated with serum alanine aminotransferase and alanine aminotransferase-aspartate aminotransferase ratio in FHBL subjects (r = 0.558 and 0.580, respectively, both P < 0.001) and less so in controls (r = 0.339, P = 0.057 and r = 0.419, P = 0.017, respectively). Liver fat percentage also tended to increase with age (liver fat vs. age r = 0.366, P = 0.051 in FHBL subjects and r = 0.324, P = 0.099 in controls).

SAT, IPAT, and RPAT: correlation with each other and with liver fat percentage
Mean values of SAT, RPAT, and IPAT were comparable to those reported by others (34, 39), and the mean values were similar in the FHBL and control groups (Table 2).

View larger version (11K): [in this window] [in a new window]   Fig. 1. Regression of liver fat percentage on intraperitoneal adipose tissue (IPAT) in human subjects with apolipoprotein B-defective familial hypobetalipoproteinemia (FHBL) and matched controls. The slopes of the regression lines are significantly different (P = 0.0001).

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
The associations between obesity and fatty liver and between diabetes and fatty liver are well documented (10). However, few reports have directly examined the quantitative relationship between liver fat and abdominal fat, and the results are not consistent. For example, Doron et al. (42) reported a correlation of liver fat to subcutaneous fat but not to intra-abdominal fat. Seppala-Lindroos et al. (7) reported no significant correlation between liver fat and "visceral fat volume" in normal men, and Tiikkainen et al. (43) reported no correlation between liver fat and abdominal fat in obese women. On the other hand, Nguyen-Duy et al. (44) found visceral and liver fat to be correlated. To our knowledge, no reports exist on simultaneous measurements of liver and abdominal fat in the same subjects with FHBL.

Taking all FHBL subjects reported by us previously (33) and in this article, mean liver fat percentage values measured by MRS were 3-fold those of controls. We had reported a 5-fold difference previously (33). The smaller multiple is probably attributable to the inclusion of controls with liver fat values ranging up to 29%. As mentioned above, because liver fat percentage is a continuous variable in both groups of subjects, we chose not to apply any arbitrary cutoff points to set a "normal" value. Rather, we present all of the data.

There was great interindividual variability with respect to liver fat in both the FHBL and control groups. The variability was not attributable to the method used to quantify liver fat, because similar values were obtained on repeated determinations of the same pixel, in three different 2 x 2 x 2 cm pixels examined in any given liver, and over time in any given study subject (33), i.e., intraindividual variability was small. Furthermore, we attempted to minimize other sources of variability by requesting that patients not drink alcohol or change their diets before the tests.

Nevertheless, interindividual variability in liver fat contents persisted. To assess some of the potential sources of this variability, we measured indices of insulin action, indices of generalized and abdominal adiposity, and abdominal fat masses directly by MRI. Then, we performed univariate and multivariate statistical analyses. In both FHBL subjects and controls, the correlation between liver fat and indices of adiposity was strongest for IPAT. If abdominal adipose tissue can indeed affect liver fat contents, the mechanisms are not clear, but there are two possibilities: 1) the flux of fatty acids from adipose to liver would provide more precursors; and 2) cytokines/hormones (e.g., adipsin, leptin, tumor necrosis factor-) secreted from adipose could affect the liver (45). There were also significant correlations between indices of insulin action and liver fat, and it is known that chronic exposure of liver to high levels of circulating insulin results in increased hepatic lipogenesis (46, 47).

Considering FHBL and control groups together, the important factors determining liver fat were FHBL-affected status, IPAT, and AUC insulin. On stepwise regression analysis, IPAT accounted for 55% of the variation in liver fat in FHBL subjects, and apoB accounted for 19% (R² for the model was 0.94); HOMA index and AUC glucose each accounted for <10% of the variation. In controls, on the other hand, AUC insulin accounted for 50% of the variation, HOMA index for 13%, and IPAT for only 8% (R² for the model was 0.71). Furthermore, the regression line of liver fat on IPAT, although positive in both groups, was significantly steeper in FHBL subjects than in controls. Thus, although intra-abdominal fat was an important determinant of liver fat in both groups, it was more important in FHBL subjects than in controls. Conversely, indices of insulin action were more important in controls. This shows that the apoB mutations alter the metabolic relationships between abdominal fat depots and liver fat.

Why is IPAT a stronger predictor of liver fat percentage in FHBL subjects than in controls? IPAT (and RPAT), by means of some unknown agent, seems to be an important actor in setting the load of hepatic triglycerides available for export in both FHBL subjects and controls. If the hepatic triglyceride-increasing activity of IPAT depends on its size, IPAT of increasing sizes would present similarly increasing loads of precursors or stimulants of triglyceride for hepatic export in FHBL subjects and controls. But the VLDL-exporting system of livers is impaired only in FHBL subjects relative to controls. At lower loads of transportable hepatic triglyceride (i.e., smaller masses of IPAT), even an impaired system may be adequate, and hepatic triglyceride accumulation would be similar in FHBL subjects and controls. But as hepatic triglyceride loads increase (i.e., as IPAT masses increase), the capacity of the impaired VLDL system would become increasingly limiting to export in FHBL subjects. This would lead to selectively greater amounts of triglyceride accumulation in FHBL subjects, as we observed. The accumulation occurred despite the attempts of FHBL livers to compensate by diminishing fatty acid synthesis (48) and secreting more VLDL particles (suggested by the positive relationship of liver fat percentage to plasma apoB levels on multivariate analysis). Livers of controls, with presumably fully functional VLDL systems, responded to increased lipid loads by more efficiently exporting hepatic VLDL-triglyceride. Thus, IPAT would appear to be a stronger correlate of liver fat percentage in FHBL subjects, overriding the importance of indices of insulin action, which is more discernible in controls.

Another potential source of interindividual variation may have been the "genetic background" of our study subjects. Some of the correlates of liver fat, such as adiposity (49) and other features of the metabolic syndrome, appear to have genetic components in humans (50–53) and mice (54, 55). The amount of liver fat itself appears to be at least in part genetically determined. In a survey of 10 inbred mouse strains, liver triglyceride contents varied severalfold between the different strains (X. Lin et al., unpublished observations). The identities of the genes contributing to this variation remain to be determined.

In summary, the accumulation of fat in liver of subjects with the apoB-defective form of FHBL is determined by several factors, including the genetic APOB defects per se, which render these subjects particularly susceptible to the effects of adiposity and insulin resistance.

	ACKNOWLEDGMENTS

 
Funding for this study was provided by Grant R01 HL-59515 from the National Institutes of Health and by a gift from the Alan and Edith Wolf Charitable Foundation. The authors appreciate the input of several resources: the General Clinical Research Center is funded by National Institutes of Health Grant 5MO1 RR-00036, the Diabetes Research and Training Center by Grant 5P60 DK-20579, the Clinical Nutrition Research Unit by Grant DK-56341, and the Digestive Disease Research Center by Grant 1P30 KD-52574. The authors are grateful to our study subjects and for the productive interactions of Jackie Dudley, RN, and Sherry Banez-Mueth, RN, with them. The authors thank Patrick Doyle and Mary Lou Rheinheimer for preparation of the manuscript.

Manuscript received December 11, 2003 and in revised form January 14, 2004.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

1. Angulo, P. 2002. Nonalcoholic fatty liver disease. N. Engl. J. Med. 346: 1221–1231.[Free Full Text]

2. Teli, M. R., O. F. James, A. D. Burt, M. K. Bennett, and C. P. Day. 1995. The natural history of nonalcoholic fatty liver: a follow-up study. Hepatology. 22: 1714–1719.[CrossRef][Medline]

3. Matteoni, C. A., Z. M. Younossi, T. Gramlich, N. Boparai, Y. C. Liu, and A. J. McCullough. 1999. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity. Gastroenterology. 116: 1413–1419.[CrossRef][Medline]

4. Marchesini, G., M. Brizi, G. Bianchi, S. Tomassetti, E. Bugianesi, M. Lenzi, A. J. McCullough, S. Natale, G. Forlani, and N. Melchionda. 2001. Nonalcoholic fatty liver disease: a feature of the metabolic syndrome. Diabetes. 50: 1844–1850.[Abstract/Free Full Text]

5. Powell, E. E., W. G. Cooksley, R. Hanson, J. Searle, J. W. Halliday, and L. W. Powell. 1990. The natural history of nonalcoholic steatohepatitis: a follow-up study of forty-two patients for up to 21 years. Hepatology. 11: 74–80.[Medline]

6. Ikai, E., M. Ishizaki, Y. Suzuki, M. Ishida, Y. Noborizaka, and Y. Yamada. 1995. Association between hepatic steatosis, insulin resistance and hyperinsulinaemia as related to hypertension in alcohol consumers and obese people. J. Hum. Hypertens. 9: 101–105.[Medline]

7. Seppala-Lindroos, A., S. Vehkavaara, A. M. Hakkinen, T. Goto, J. Westerbacka, A. Sovijarvi, J. Halavaara, and H. Yki-Jarvinen. 2002. Fat accumulation in the liver is associated with defects in insulin suppression of glucose production and serum free fatty acids independent of obesity in normal men. J. Clin. Endocrinol. Metab. 87: 3023–3028.[Abstract/Free Full Text]

8. Zavaroni, I., S. Mazza, E. Dall'Aglio, P. Gasparini, M. Passeri, and G. M. Reaven. 1992. Prevalence of hyperinsulinaemia in patients with high blood pressure. J. Intern. Med. 231: 235–240.[Medline]

9. Marchesini, G., M. Brizi, A. M. Morselli-Labate, G. Bianchi, E. Bugianesi, A. J. McCullough, G. Forlani, and N. Melchionda. 1999. Association of nonalcoholic fatty liver disease with insulin resistance. Am. J. Med. 107: 450–455.[CrossRef][Medline]

10. Lakka, H. M., D. E. Laaksonen, T. A. Lakka, L. K. Niskanen, E. Kumpusalo, J. Tuomilehto, and J. T. Salonen. 2002. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. J. Am. Med. Assoc. 288: 2709–2716.[Abstract/Free Full Text]

11. Reaven, G. M. 1988. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 37: 1595–1607.[Abstract]

12. Shimano, H., J. D. Horton, R. E. Hammer, I. Shimomura, M. S. Brown, and J. L. Goldstein. 1996. Overproduction of cholesterol and fatty acids causes massive liver enlargement in transgenic mice expressing truncated SREBP-1a. J. Clin. Invest. 98: 1575–1584.[Medline]

13. Leone, T. C., C. J. Weinheimer, and D. P. Kelly. 1999. A critical role for the peroxisome proliferator-activated receptor alpha (PPARalpha) in the cellular fasting response: the PPARalpha-null mouse as a model of fatty acid oxidation disorders. Proc. Natl. Acad. Sci. USA. 96: 7473–7478.[Abstract/Free Full Text]

14. Yu, S., K. Matsusue, P. Kashireddy, W. Q. Cao, V. Yeldandi, A. V. Yeldandi, M. S. Rao, F. J. Gonzalez, and J. K. Reddy. 2003. Adipocyte-specific gene expression and adipogenic steatosis in the mouse liver due to peroxisome proliferator-activated receptor gamma1 (PPARgamma1) overexpression. J. Biol. Chem. 278: 498–505.[Abstract/Free Full Text]

15. Wishingrad, M., B. Paaso, and G. Garcia. 1994. Fatty liver due to heterozygous hypobetalipoproteinemia. Am. J. Gastroenterol. 89: 1106–1107.[Medline]

16. Hagve, T. A., L. E. Myrseth, E. Schrumpf, J. P. Blomhoff, B. Christophersen, K. Elgjo, E. Gjone, and H. Prydz. 1991. Liver steatosis in hypobetalipoproteinemia. A case report. J. Hepatol. 13: 104–111.[CrossRef][Medline]

17. Castellano, G., C. Garfia, D. Gomez-Coronado, J. Arenas, J. Manzanares, F. Colina, and J. A. Solis-Herruzo. 1997. Diffuse fatty liver in familial heterozygous hypobetalipoproteinemia. J. Clin. Gastroenterol. 25: 379–382.[CrossRef][Medline]

18. Ogata, H., K. Akagi, M. Baba, A. Nagamatsu, N. Suzuki, K. Nomiyama, and M. Fujishima. 1997. Fatty liver in a case with heterozygous familial hypobetalipoproteinemia. Am. J. Gastroenterol. 92: 339–342.[Medline]

19. Tarugi, P., A. Lonardo, C. Gabelli, F. Sala, G. Ballarini, I. Cortella, L. Previato, S. Bertolini, R. Cordera, and S. Calandra. 2001. Phenotypic expression of familial hypobetalipoproteinemia in three kindreds with mutations of apolipoprotein B gene. J. Lipid Res. 42: 1552–1561.[Abstract/Free Full Text]

20. Tarugi, P., and A. Lonardo. 1997. Heterozygous familial hypobetalipoproteinemia associated with fatty liver. Am. J. Gastroenterol. 92: 1400–1402.

21. Tarugi, P., A. Lonardo, G. Ballarini, L. Erspamer, E. Tondelli, S. Bertolini, and S. Calandra. 2000. A study of fatty liver disease and plasma lipoproteins in a kindred with familial hypobetalipoproteinemia due to a novel truncated form of apolipoprotein B (APO B-54.5). J. Hepatol. 33: 361–370.[CrossRef][Medline]

22. Tarugi, P., A. Lonardo, G. Ballarini, A. Grisendi, M. Pulvirenti, A. Bagni, and S. Calandra. 1996. Fatty liver in heterozygous hypobetalipoproteinemia caused by a novel truncated form of apolipoprotein B. Gastroenterology. 111: 1125–1133.[CrossRef][Medline]

23. Linton, M. F., R. V. Farese, Jr., and S. G. Young. 1993. Familial hypobetalipoproteinemia. J. Lipid Res. 34: 521–541.[Medline]

24. Schonfeld, G. 1995. The hypobetalipoproteinemias. Annu. Rev. Nutr. 15: 23–34.[CrossRef][Medline]

25. Schonfeld, G. 2003. Familial hypobetalipoproteinemia: a review. J. Lipid Res. 44: 878–883.[Abstract/Free Full Text]

26. Aguilar-Salinas, C. A., P. H. Barrett, K. G. Parhofer, S. G. Young, D. Tessereau, J. Bateman, C. Quinn, and G. Schonfeld. 1995. Apoprotein B-100 production is decreased in subjects heterozygous for truncations of apoprotein B. Arterioscler. Thromb. Vasc. Biol. 15: 71–80.[Abstract/Free Full Text]

27. Elias, N., B. W. Patterson, and G. Schonfeld. 2000. In vivo metabolism of ApoB, ApoA-I, and VLDL triglycerides in a form of hypobetalipoproteinemia not linked to the ApoB gene. Arterioscler. Thromb. Vasc. Biol. 20: 1309–1315.[Abstract/Free Full Text]

28. Elias, N., B. W. Patterson, and G. Schonfeld. 1999. Decreased production rates of VLDL triglycerides and ApoB-100 in subjects heterozygous for familial hypobetalipoproteinemia. Arterioscler. Thromb. Vasc. Biol. 19: 2714–2721.[Abstract/Free Full Text]

29. Chen, Z., R. L. Fitzgerald, M. R. Averna, and G. Schonfeld. 2000. A targeted apolipoprotein B-38.9-producing mutation causes fatty livers in mice due to the reduced ability of apolipoprotein B-38.9 to transport triglycerides. J. Biol. Chem. 275: 32807–32815.[Abstract/Free Full Text]

30. Chen, Z., R. L. Fitzgerald, and G. Schonfeld. 2002. Hypobetalipoproteinemic mice with a targeted apolipoprotein (Apo) B-27.6-specifying mutation: in vivo evidence for an important role of amino acids 1254–1744 of ApoB in lipid transport and metabolism of the apoB-containing lipoprotein. J. Biol. Chem. 277: 14135–14145.[Abstract/Free Full Text]

31. Yuan, B., R. Neuman, S. H. Duan, J. L. Weber, P. Y. Kwok, N. L. Saccone, J. S. Wu, K. Y. Liu, and G. Schonfeld. 2000. Linkage of a gene for familial hypobetalipoproteinemia to chromosome 3p21.1-22. Am. J. Hum. Genet. 66: 1699–1704.[CrossRef][Medline]

32. Neuman, R. J., B. Yuan, D. S. Gerhard, K. Y. Liu, P. Yue, S. Duan, M. Averna, and G. Schonfeld. 2002. Replication of linkage of familial hypobetalipoproteinemia to chromosome 3p in six kindreds. J. Lipid Res. 43: 407–415.[Abstract/Free Full Text]

33. Schonfeld, G., B. W. Patterson, D. A. Yablonskiy, T. S. Tanoli, M. Averna, N. Elias, P. Yue, and J. Ackerman. 2003. Fatty liver in familial hypobetalipoproteinemia: triglyceride assembly into VLDL particles is affected by the extent of hepatic steatosis. J. Lipid Res. 44: 470–478.[Abstract/Free Full Text]

34. Abate, N., A. Garg, R. Coleman, S. M. Grundy, and R. M. Peshock. 1997. Prediction of total subcutaneous abdominal, intraperitoneal, and retroperitoneal adipose tissue masses in men by a single axial magnetic resonance imaging slice. Am. J. Clin. Nutr. 65: 403–408.[Abstract/Free Full Text]

35. Virtanen, K. A., P. Lonnroth, R. Parkkola, P. Peltoniemi, M. Asola, T. Viljanen, T. Tolvanen, J. Knuuti, T. Ronnemaa, R. Huupponen, and P. Nuutila. 2002. Glucose uptake and perfusion in subcutaneous and visceral adipose tissue during insulin stimulation in nonobese and obese humans. J. Clin. Endocrinol. Metab. 87: 3902–3910.[Abstract/Free Full Text]

36. Hoyumpa, A. M., Jr., H. L. Greene, G. D. Dunn, and S. Schenker. 1975. Fatty liver: biochemical and clinical considerations. Am. J. Dig. Dis. 20: 1142–1170.[CrossRef][Medline]

37. National Institutes of Health. 1982. Manual of Laboratory Operations Lipid Research Clinic Program in Lipid and Lipoprotein Analysis. National Institutes of Health. Bethesda, MD.

38. Contois, J. H., J. R. McNamara, C. J. Lammi-Keefe, P. W. Wilson, T. Massov, and E. J. Schaefer. 1996. Reference intervals for plasma apolipoprotein B determined with a standardized commercial immunoturbidimetric assay: results from the Framingham Offspring Study. Clin. Chem. 42: 515–523.[Abstract/Free Full Text]

39. Abate, N., D. Burns, R. M. Peshock, A. Garg, and S. M. Grundy. 1994. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. J. Lipid Res. 35: 1490–1496.[Abstract]

40. Bottomley, P. A. 1987. Spatial localization in NMR spectroscopy in vivo. Ann. N. Y. Acad. Sci. 508: 333–348.[Abstract]

41. Raddi, A., and U. Klose. 1998. A generalized estimate of the SLR B polynomial ripples for RF pulse generation. J. Magn. Reson. 132: 260–265.[CrossRef][Medline]

42. Doron, Z., L. Linova, A. Tamara, V. Lebedev, I. Polychuck, I. Leibovitz, T. Reitblat, Z. Weiler, R. Peled, and O. Lebovici. 2003. Prediction of fatty liver: comparison of body mass index, visceral fat, and subcutaneous fat [letter]. J. Clin. Gastroenterol. 36: 281–282.[CrossRef][Medline]

43. Tiikkainen, M., R. Bergholm, S. Vehkavaara, A. Rissanen, A. M. Hakkinen, M. Tamminen, K. Teramo, and H. Yki-Jarvinen. 2003. Effects of identical weight loss on body composition and features of insulin resistance in obese women with high and low liver fat content. Diabetes. 52: 701–707.[Abstract/Free Full Text]

44. Nguyen-Duy, T. B., M. Z. Nichaman, T. S. Church, S. N. Blair, and R. Ross. 2003. Visceral fat and liver fat are independent predictors of metabolic risk factors in men. Am. J. Physiol. Endocrinol. Metab. 284: E1065–E1071.[Abstract/Free Full Text]

45. Kahn, B. B., and J. S. Flier. 2000. Obesity and insulin resistance. J. Clin. Invest. 106: 473–481.[Medline]

46. Steiner, G., and G. F. Lewis. 1996. Hyperinsulinemia and triglyceride-rich lipoproteins. Diabetes. 45 (Suppl. 3): 24–26.

47. Aarsland, A., D. Chinkes, and R. R. Wolfe. 1996. Contributions of de novo synthesis of fatty acids to total VLDL-triglyceride secretion during prolonged hyperglycemia/hyperinsulinemia in normal man. J. Clin. Invest. 98: 2008–2017.[Medline]

48. Lin, X., G. Schonfeld, P. Yue, and Z. Chen. 2002. Hepatic fatty acid synthesis is suppressed in mice with fatty livers due to targeted apolipoprotein B38.9 mutation. Arterioscler. Thromb. Vasc. Biol. 22: 476–482.[Abstract/Free Full Text]

49. Samaras, K., T. D. Spector, T. V. Nguyen, K. Baan, L. V. Campbell, and P. J. Kelly. 1997. Independent genetic factors determine the amount and distribution of fat in women after the menopause. J. Clin. Endocrinol. Metab. 82: 781–785.[Abstract/Free Full Text]

50. Kissebah, A. H., G. E. Sonnenberg, J. Myklebust, M. Goldstein, K. Broman, R. G. James, J. A. Marks, G. R. Krakower, H. J. Jacob, J. Weber, L. Martin, J. Blangero, and A. G. Comuzzie. 2000. Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proc. Natl. Acad. Sci. USA. 97: 14478–14483.[Abstract/Free Full Text]

51. Arya, R., J. Blangero, K. Williams, L. Almasy, T. D. Dyer, R. J. Leach, P. O'Connell, M. P. Stern, and R. Duggirala. 2002. Factors of insulin resistance syndrome-related phenotypes are linked to genetic locations on chromosomes 6 and 7 in nondiabetic Mexican-Americans. Diabetes. 51: 841–847.[Abstract/Free Full Text]

52. Ridderstrale, M., E. Carlsson, M. Klannemark, A. Cederberg, C. Kosters, H. Tornqvist, H. Storgaard, A. Vaag, S. Enerback, and L. Groop. 2002. FOXC2 mRNA expression and a 5' untranslated region polymorphism of the gene are associated with insulin resistance. Diabetes. 51: 3554–3560.[Abstract/Free Full Text]

53. Savage, D. B., G. D. Tan, C. L. Acerini, S. A. Jebb, M. Agostini, M. Gurnell, R. L. Williams, A. M. Umpleby, E. L. Thomas, J. D. Bell, A. K. Dixon, F. Dunne, R. Boiani, S. Cinti, A. Vidal-Puig, F. Karpe, V. K. Chatterjee, and S. O'Rahilly. 2003. Human metabolic syndrome resulting from dominant-negative mutations in the nuclear receptor peroxisome proliferator-activated receptor-gamma. Diabetes. 52: 910–917.[Abstract/Free Full Text]

54. Mehrabian, M., P. Z. Wen, J. Fisler, R. C. Davis, and A. J. Lusis. 1998. Genetic loci controlling body fat, lipoprotein metabolism, and insulin levels in a multifactorial mouse model. J. Clin. Invest. 101: 2485–2496.[Medline]

55. Colombo, C., M. Haluzik, J. J. Cutson, K. R. Dietz, B. Marcus-Samuels, C. Vinson, O. Gavrilova, and M. L. Reitman. 2003. Opposite effects of background genotype on muscle and liver insulin sensitivity of lipoatrophic mice. Role of triglyceride clearance. J. Biol. Chem. 278: 3992–3999.[Abstract/Free Full Text]

CiteULike

Complore

Connotea

Del.icio.us

Digg

Reddit

Technorati

This article has been cited by other articles:

				A. van der Graaf, S. W. Fouchier, M. N. Vissers, J. C. Defesche, A. Wiegman, R. R. Sankatsing, B. A. Hutten, M. D. Trip, and J. J.P. Kastelein Familial Defective Apolipoprotein B and Familial Hypobetalipoproteinemia in One Family: Two Neutralizing Mutations Ann Intern Med, May 6, 2008; 148(9): 712 - 714. [Full Text] [PDF]

				J. R. Burnett, S. Zhong, Z. G. Jiang, A. J. Hooper, E. A. Fisher, R. S. McLeod, Y. Zhao, P. H. R. Barrett, R. A. Hegele, F. M. van Bockxmeer, et al. Missense Mutations in APOB within the beta{alpha}1 Domain of Human APOB-100 Result in Impaired Secretion of ApoB and ApoB-containing Lipoproteins in Familial Hypobetalipoproteinemia J. Biol. Chem., August 17, 2007; 282(33): 24270 - 24283. [Abstract] [Full Text] [PDF]

				A. J. Hooper, K. Robertson, P. H. R. Barrett, K. G. Parhofer, F. M. van Bockxmeer, and J. R. Burnett Postprandial Lipoprotein Metabolism in Familial Hypobetalipoproteinemia J. Clin. Endocrinol. Metab., April 1, 2007; 92(4): 1474 - 1478. [Abstract] [Full Text] [PDF]

				Y.-L. Zhang, A. Hernandez-Ono, P. Siri, S. Weisberg, D. Conlon, M. J. Graham, R. M. Crooke, L.-S. Huang, and H. N. Ginsberg Aberrant Hepatic Expression of PPAR{gamma}2 Stimulates Hepatic Lipogenesis in a Mouse Model of Obesity, Insulin Resistance, Dyslipidemia, and Hepatic Steatosis J. Biol. Chem., December 8, 2006; 281(49): 37603 - 37615. [Abstract] [Full Text] [PDF]

				R. R. Sankatsing, S. W. Fouchier, S. de Haan, B. A. Hutten, E. de Groot, J. J.P. Kastelein, and E. S.G. Stroes Hepatic and Cardiovascular Consequences of Familial Hypobetalipoproteinemia Arterioscler. Thromb. Vasc. Biol., September 1, 2005; 25(9): 1979 - 1984. [Abstract] [Full Text] [PDF]

				X. Lin, P. Yue, Z. Chen, and G. Schonfeld Hepatic triglyceride contents are genetically determined in mice: results of a strain survey Am J Physiol Gastrointest Liver Physiol, June 1, 2005; 288(6): G1179 - G1189. [Abstract] [Full Text] [PDF]

				A. J. Whitfield, P. H. R. Barrett, K. Robertson, M. F. Havlat, F. M. van Bockxmeer, and J. R. Burnett Liver Dysfunction and Steatosis in Familial Hypobetalipoproteinemia Clin. Chem., January 1, 2005; 51(1): 266 - 269. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) All Versions of this Article: M300508-JLR200v1 45/5/941    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Tanoli, T. Articles by Schonfeld, G. Search for Related Content PubMed PubMed Citation Articles by Tanoli, T. Articles by Schonfeld, G. Social Bookmarking                      What's this?