Fatty liver in familial hypobetalipoproteinemia

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. 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. 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 (1Angulo P. Nonalcoholic fatty liver disease.N. Engl. J. Med. 2002; 346: 1221-1231Google Scholar, 2Teli M.R. James O.F. Burt A.D. Bennett M.K. Day C.P. The natural history of nonalcoholic fatty liver: a follow-up study.Hepatology. 1995; 22: 1714-1719Google Scholar, 3Matteoni C.A. Younossi Z.M. Gramlich T. Boparai N. Liu Y.C. McCullough A.J. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity.Gastroenterology. 1999; 116: 1413-1419Google Scholar, 4Marchesini G. Brizi M. Bianchi G. Tomassetti S. Bugianesi E. Lenzi M. McCullough A.J. Natale S. Forlani G. Melchionda N. 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Role of insulin resistance in human disease.Diabetes. 1988; 37: 1595-1607Google Scholar). 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 (12Shimano H. Horton J.D. Hammer R.E. Shimomura I. Brown M.S. Goldstein J.L. Overproduction of cholesterol and fatty acids causes massive liver enlargement in transgenic mice expressing truncated SREBP-1a.J. Clin. Invest. 1996; 98: 1575-1584Google Scholar), knockouts of genes of the hepatic fatty acid oxidation pathways (13Leone T.C. Weinheimer C.J. Kelly D.P. 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. 1999; 96: 7473-7478Google Scholar), and genes that regulate the development of adipose tissue (14Yu S. Matsusue K. Kashireddy P. Cao W.Q. Yeldandi V. 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Lonardo A. Ballarini G. Grisendi A. Pulvirenti M. Bagni A. Calandra S. Fatty liver in heterozygous hypobetalipoproteinemia caused by a novel truncated form of apolipoprotein B.Gastroenterology. 1996; 111: 1125-1133Google Scholar). 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 (23Linton M.F. Farese Jr., R.V. Young S.G. Familial hypobetalipoproteinemia.J. Lipid Res. 1993; 34: 521-541Google Scholar, 24Schonfeld G. The hypobetalipoproteinemias.Annu. Rev. Nutr. 1995; 15: 23-34Google Scholar). 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 (25Schonfeld G. Familial hypobetalipoproteinemia: a review.J. Lipid Res. 2003; 44: 878-883Google Scholar, 26Aguilar-Salinas C.A. Barrett P.H. Parhofer K.G. Young S.G. 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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. 2002; 277: 14135-14145Google Scholar); 2) FHBL linked to a susceptibility locus on chromosome 3p21 (31Yuan B. Neuman R. Duan S.H. Weber J.L. Kwok P.Y. Saccone N.L. Wu J.S. Liu K.Y. Schonfeld G. Linkage of a gene for familial hypobetalipoproteinemia to chromosome 3p21.1-22.Am. J. Hum. Genet. 2000; 66: 1699-1704Google Scholar, 32Neuman R.J. Yuan B. Gerhard D.S. Liu K.Y. Yue P. Duan S. Averna M. Schonfeld G. Replication of linkage of familial hypobetalipoproteinemia to chromosome 3p in six kindreds.J. Lipid Res. 2002; 43: 407-415Google Scholar); 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 (33Schonfeld G. Patterson B.W. Yablonskiy D.A. Tanoli T.S. Averna M. Elias N. Yue P. Ackerman J. Fatty liver in familial hypobetalipoproteinemia: triglyceride assembly into VLDL particles is affected by the extent of hepatic steatosis.J. Lipid Res. 2003; 44: 470-478Google Scholar), 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 (33Schonfeld G. Patterson B.W. Yablonskiy D.A. Tanoli T.S. Averna M. Elias N. Yue P. Ackerman J. Fatty liver in familial hypobetalipoproteinemia: triglyceride assembly into VLDL particles is affected by the extent of hepatic steatosis.J. Lipid Res. 2003; 44: 470-478Google Scholar). 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 (34Abate N. Garg A. Coleman R. Grundy S.M. Peshock R.M. 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. 1997; 65: 403-408Google Scholar) 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 (35Virtanen K.A. Lonnroth P. Parkkola R. Peltoniemi P. Asola M. Viljanen T. Tolvanen T. Knuuti J. Ronnemaa T. Huupponen R. Nuutila P. Glucose uptake and perfusion in subcutaneous and visceral adipose tissue during insulin stimulation in nonobese and obese humans.J. Clin. Endocrinol. Metab. 2002; 87: 3902-3910Google Scholar). 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. 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 (33Schonfeld G. Patterson B.W. Yablonskiy D.A. Tanoli T.S. Averna M. Elias N. Yue P. Ackerman J. Fatty liver in familial hypobetalipoproteinemia: triglyceride assembly into VLDL particles is affected by the extent of hepatic steatosis.J. Lipid Res. 2003; 44: 470-478Google Scholar). 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%) (36Hoyumpa Jr., A.M. Greene H.L. Dunn G.D. Schenker S. Fatty liver: biochemical and clinical considerations.Am. J. Dig. Dis. 1975; 20: 1142-1170Google Scholar).TABLE 1Clinical characteristics of study subjectsSubjects (Male/Female)Liver FatAgeTotal TGVLDL-TGTotal CholesterolVLDL-CLDL-CHDL-CapoA-IapoB%yearsmg/dlControl (12/20)5.2 ± 5.941 ± 16107 ± 7180 ± 72175 ± 3117 ± 13111 ± 2747 ± 11124 ± 2386 ± 28FHBLaThirty-three familial hypobetalipoproteinemia (FHBL) subjects were from the following families: F39 (apoB-4, n = 11), F37 (apoB-9, n = 3), F50 (apoB-29, n = 2), F40 (apoB-31, n = 1), F41 (apoB-38.9, n = 1), F45 (apoB-52, n = 8), F46 (apoB-54.8, n = 2), F48 (apoB-70.5, n = 1), F49 (apoB-75, n = 1), and F51 (apoB-89, n = 3). Of 32 control subjects, 10 were from F39 and 4 were from F52; the others were from the general population. (19/14)14.8 ± 12.044 ± 1862 ± 5049 ± 49104 ± 2611 ± 1241 ± 1852 ± 19124 ± 3228 ± 16P<0.0010.450.0040.06<0.0010.08<0.0010.190.24<0.001apoA-I, apolipoprotein A-I; C, cholesterol; TG, triglyceride.a Thirty-three familial hypobetalipoproteinemia (FHBL) subjects were from the following families: F39 (apoB-4, n = 11), F37 (apoB-9, n = 3), F50 (apoB-29, n = 2), F40 (apoB-31, n = 1), F41 (apoB-38.9, n = 1), F45 (apoB-52, n = 8), F46 (apoB-54.8, n = 2), F48 (apoB-70.5, n = 1), F49 (apoB-75, n = 1), and F51 (apoB-89, n = 3). Of 32 control subjects, 10 were from F39 and 4 were from F52; the others were from the general population. Open table in a new tab apoA-I, apolipoprotein A-I; C, cholesterol; TG, triglyceride. Plasma lipids and lipoproteins were quantified on plasma obtained after 12 h of fasting by enzymatic methods (Wako Chemicals, Richmond, VA) after separation of lipoproteins by combined ultracentrifugal and precipitation methods according to Lipid Research Clinic protocols (37National Institutes of Health Manual of Laboratory Operations Lipid Research Clinic Program in Lipid and Lipoprotein Analysis. National Institutes of Health, Bethesda, MD1982Google Scholar). Liver chemistry profiles were within normal limits. ApoB and apoA-I levels were determined by immunonephelometry (38Contois J.H. McNamara J.R. Lammi-Keefe C.J. Wilson P.W. Massov T. Schaefer E.J. Reference intervals for plasma apolipoprotein B determined with a standardized commercial immunoturbidimetric assay: results from the Framingham Offspring Study.Clin. Chem. 1996; 42: 515-523Google Scholar). Oral glucose tolerance tests were performed 12 h after fasting using 75 g of glucose. Plasma glucose and insulin measurements were performed in the Washington University General Clinical Research Center's Core Laboratory using routine methods. For the MR spectroscopy (MRS) study, subjects were instructed not to change their diets and to abstain from ethanol for at least 1 week before the studies. MRS and MRI studies were usually performed after fasting for 10 to 12 h. We have previously shown that diurnal variation of liver fat is small (33Schonfeld G. Patterson B.W. Yablonskiy D.A. Tanoli T.S. Averna M. Elias N. Yue P. Ackerman J. Fatty liver in familial hypobetalipoproteinemia: triglyceride assembly into VLDL particles is affected by the extent of hepatic steatosis.J. Lipid Res. 2003; 44: 470-478Google Scholar). 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 × 256 matrix within a 33.8 × 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 (34Abate N. Garg A. Coleman R. Grundy S.M. Peshock R.M. 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. 1997; 65: 403-408Google Scholar, 39Abate N. Burns D. Peshock R.M. Garg A. Grundy S.M. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers.J. Lipid Res. 1994; 35: 1490-1496Google Scholar)]. Thus, the mass of fat per slice = the average volume of adipose tissue in liters × 0.846 × 0.9196. 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 (40Bottomley P.A. Spatial localization in NMR spectroscopy in vivo.Ann. N. Y. Acad. Sci. 1987; 508: 333-348Google Scholar) without water suppression was used. Accurate voxel localization was achieved using specially designed numerically optimized RF pulses (41Raddi A. Klose U. A generalized estimate of the SLR B polynomial ripples for RF pulse generation.J. Magn. Reson. 1998; 132: 260-265Google Scholar). 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 × 2 × 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 (33Schonfeld G. Patterson B.W. Yablonskiy D.A. Tanoli T.S. Averna M. Elias N. Yue P. Ackerman J. Fatty liver in familial hypobetalipoproteinemia: triglyceride assembly into VLDL particles is affected by the extent of hepatic steatosis.J. Lipid Res. 2003; 44: 470-478Google Scholar). The regression of weight percentage liver triglyceride (chemical) on MRS liver fat percentage was y = 0.807x (R2 = 0.986). 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 α 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.). 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). Mean values of SAT, RPAT, and IPAT were comparable to those reported by others (34Abate N. Garg A. Coleman R. Grundy S.M. Peshock R.M. 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. 1997; 65: 403-408Google Scholar, 39Abate N. Burns D. Peshock R.M. Garg A. Grundy S.M. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers.J. Lipid Res. 1994; 35: 1490-1496Google Scholar), and the mean values were similar in the FHBL and control groups (Table 2).TABLE 2Indices of adiposity and abdominal fat massesSubjectsBMIW/HSATRPATIPATkg/m2cm/cmkg/sliceControl26.8 ± 4.90.84 ± 0.090.133 ± 0.0560.028 ± 0.0260.049 ± 0.044FHBL25.6 ± 3.90.86 ± 0.080.109 ± 0.0440.028 ± 0.0210.049 ± 0.040P0.290.330.110.940.98BMI, body mass index; IPAT, intraperitoneal; RPAT, retroperitoneal abdominal adipose tissue; SAT, subcutaneous; W/H, waist-hip ratio. n = 33 and 32 for FHBL and control groups, respectively, for BMI and W/H; n = 24 and 27 for FHBL and control groups, respectively, for SAT, IPAT, and RPAT. Open table in a new tab BMI, body mass index; IPAT, intraperitoneal; RPAT, retroperitoneal abdominal adipose tissue; SAT, subcutaneous; W/H, waist-hip ratio. n = 33 and 32 for FHBL and control groups, respectively, for BMI and W/H; n = 24 and 27 for FHBL and control groups, respectively, for SAT, IPAT, and RPAT. RPAT and IPAT were strongly correlated with each other (r > 0.87, P < 0.001 in both subject groups), but neither was significantly correlated with SAT (r = 0.2 and 0.4, P > 0.2 and 0.07, respectively, in FHBL subjects and controls). SAT, RPAT, and IPAT were varyingly correlated with BMI, waist-hip ratio, and age (Table 3).TABLE 3Correlations between indices of adiposity, abdominal fat masses, and ageVariableSATRPATIPATAgeBMI Control0.633 (0.0004)0.749 (0.0001)0.708 (0.0001)0.231 (0.2039) FHBL0.796 (0.0001)0.685 (0.0002)0.664 (0.0004)0.255 (0.1504)W/H Control0.407 (0.3049)0.609 (0.0008)0.727 (0.0001)0.051 (0.7829) FHBL0.511 (0.0107)0.699 (0.0001)0.722 (0.0001)0.262 (0.1421)Age Control0.043 (0.8301)0.429 (0.0254)0.271 (0.1705)– FHBL0.454 (0.0259)0.662 (0.0004)0.582 (0.0028)–Values in parentheses are P values. n = 24 and 27 for FHBL and control groups, respectively. Open table in a new tab Values in parentheses are P values. n = 24 and 27 for FHBL and control groups, respectively. Liver fat percentage was significantly correlated with several of the indices of adiposity in both subject groups, but the correlation coefficient was largest with IPAT (Table 4). Linear regression lines and equations of liver fat on IPAT are shown in Fig. 1. The slope of the line for FHBL subjects is statistically significantly steeper than the line for controls (P < 0.0001). The conclusion is not altered if log (liver triglyceride) is used on the ordinate (not shown).TABLE 4Correlations between liver fat contents, indices of adiposity, and abdominal fat massesSubjectsLiver Fat vs. BMILiver Fat vs. W/HLiver Fat vs. SATLiver Fat vs. RPATLiver Fat vs. IPATControlr 0.426 0.321 0.275 0.389 0.548P 0.015 0.078 0.164 0.045 0.003Sample size 32 31 27 27 27FHBLr 0.571 0.478 0.402 0.579 0.638P<0.001<0.006 0.052 0.003<0.001Sample size 32 32 24 24 24 Open table in a new tab Mean fasting glucose and insulin levels, HOMA index, and AUC insulin were similar in the two subject groups (Table 5), in agreement with previous results (33Schonfeld G. Patterson B.W. Yablonskiy D.A. Tanoli T.S. Averna M. Elias N. Yue P. Ackerman J. Fatty liver in familial hypobetalipoproteinemia: triglyceride assembly into VLDL particles is affected by the extent of hepatic steatosis.J. Lipid Res. 2003; 44: 470-478Google Scholar). Liver fat percentage was correlated with fasting insulin and the HOMA index in both FHBL and control groups. However, the correlation was stro