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Lipids, Lipoproteins, and Cardiovascular Risk Factors |
1 School of Medicine and Pharmacology, Western Australian Institute for Medical Research, University of Western Australia, Perth, Western Australia, Australia.
2 Fujirebio Inc., Research and Development Division, Tokyo, Japan.
3 Department of Internal Medicine and Molecular Science, Osaka University Graduate School of Medicine, Osaka, Japan.
aAddress correspondence to this author at: School of Medicine and Pharmacology, University of Western Australia, Royal Perth Hospital, GPO Box X2213, Perth Western Australia 6847, Australia. Fax 61-8-9224-0246; e-mail gfwatts{at}cyllene.uwa.edu.au.
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Abstract |
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Methods: Fasting adiponectin, leptin, resistin, interleukin-6 (IL-6), tumor necrosis factor-
(TNF-), apolipoprotein (apo) B-48, apo C-III, and remnant-like particle (RLP)-cholesterol concentrations were measured by immunoassays and insulin resistance by homeostasis assessment (HOMA) score in 41 nondiabetic men with a body mass index of 22–35 kg/m2. Visceral and subcutaneous adipose tissue masses (ATMs) were determined by magnetic resonance imaging and total ATM by bioelectrical impedance.
Results: In univariate regression, plasma adiponectin and leptin concentrations were inversely and directly associated with plasma apoB-48, apoC-III, RLP-cholesterol, triglycerides, VLDL-apoB, and VLDL-triglycerides (P <0.05). Resistin, IL-6, and TNF- were not significantly associated with any of these variables, except for a direct correction between apoC-III and IL-6 (P <0.05). In multivariate regression including HOMA, age, nonesterified fatty acids, and adipose tissue compartment, adiponectin was an independent predictor of plasma apoB-48 (ß coefficient = –0.354; P = 0.048), apoC-III (ß coefficient = –0.406; P = 0.012), RLP-cholesterol (ß coefficient = –0.377; P = 0.016), and triglycerides (ß coefficient = –0.374; P = 0.013). By contrast, leptin was not an independent predictor of these TRL markers. Plasma apoB-48, apoC-III, RLP-cholesterol, and triglycerides were all significantly and positively associated with plasma insulin, HOMA, and visceral, subcutaneous, and total ATMs (P <0.05).
Conclusions: These data suggest that the plasma adiponectin concentration may not only link abdominal fat, insulin resistance, and dyslipidemia, but may also exert an independent role in regulating TRL metabolism.
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Introduction |
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The precise relationships between dyslipoproteinemia, adiposity, and insulin resistance are complex and undefined (2)(4)(6). Adipose tissue has recently been shown to secrete a variety of bioactive peptides, called adipocytokines, that can potentially impact on glucose and lipid metabolism (7)(8)(9). These adipocytokines include adiponectin, leptin, resistin, interleukin-6 (IL-6), and tumor necrosis factor- (TNF-). Adiponectin, also known as adipocyte complement-related protein of 30 kDa (ACRP30), adipoQ, and gelatin-binding protein of 28 kDa (GBP28), is a protein present at relatively high concentrations in the circulation (9). Unlike other adipocytokines, plasma adiponectin concentrations are decreased in obese and insulin-resistant individuals, including those with type 2 diabetes (10)(11). Experimental and clinical evidence suggests that other adipocytokines may exert their effects on insulin sensitivity by influencing adipocyte expression and secretion of adiponectin (12). Hypertriglyceridemia, low HDL-cholesterol concentrations, and decreased LDL particle size have recently been shown in humans to be correlated with low plasma adiponectin concentrations independent of the amount of intraabdominal fat and degree of insulin resistance (13)(14). Moreover, a recent intervention trial reported that changes in adiponectin concentrations after weight loss are correlated with improvements in plasma lipid concentrations independent of changes in adiposity and insulin sensitivity (13). However, the association between plasma adiponectin and specific markers of TRLs in relation to insulin resistance and body fat distribution has not been investigated previously.
In the present study, we hypothesized that, in men, adiponectin would be the adipocytokine most closely associated with changes in TRLs, namely apoB-48 and RLP-cholesterol, and that this association would be independent of body fat compartments and insulin resistance. Our principal aims were (a) to examine the association of markers of TRL metabolism, as reflected by plasma concentrations of apoB-48, apoC-III, and RLP-cholesterol, with plasma adiponectin, leptin, resistin, TNF-, and IL-6 concentrations; and (b) to explain these associations in relation to variations in body fat compartments and insulin resistance, measured by magnetic resonance imaging (MRI) and homeostasis assessment (HOMA) score, respectively.
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Materials and Methods |
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clinical protocols
Clinical tests.
Body weight, height, and waist and hip circumference were recorded as described previously (15); BMI and waist-to-hip ratio were calculated. Blood pressure was recorded semiautomatically by use of a Dinamap recorder (Critilzon). Body composition was estimated, with participants at rest in the supine position after emptying their bladders, by use of a Holtain Body Composition Analyser (Holtain Ltd.) from which total adipose tissue mass (ATM), fat mass, and fat-free mass (FFM) were derived; FFM was calculated by use of the formula of Lukaski et al. (16): FFM = (0.85 x H2/Z) + 3.04, where H is the height (cm) of the individual and Z is impedance. For this measure, participants were also asked to refrain from alcoholic beverages for 24 h; they were then studied in the morning, 15 min after emptying their bladder and in a temperature-controlled room. The technical error for FFM was <3%.
Dietary and energy expenditure records.
Participants completed a 7-day food intake diary that recorded all dietary, alcohol, and energy intake; data were analyzed by use of DIET4 Nutrient Calculation Software (Xyris Software) based on the Australian Food Composition Database (NUTTAB 95; Australian Government Nutrient Database).
MRI.
MRI of eight transaxial segments (field of view, 40–48 cm; 10-mm thickness) at intervertebral disc positions from T11 to S1 was carried out with a 1.0-Tesla Picker MR scanner (Picker International) and a T1-weighted fast-spin-echo sequence with a high fat-to-water signal ratio. Subcutaneous abdominal ATM (SAATM), intraperitoneal ATM (IPATM), and retroperitoneal ATM (RPATM) areas were calculated by summing the relevant adipose tissue pixel units with purpose-designed software, as used previously (15). Fat anterior to the posterior peritoneum and anterior abdominal wall was defined as IPATM, and fat posterior to the posterior peritoneum was defined as RPATM. Anterior and posterior subcutaneous abdominal compartments was separated by drawing a perpendicular line at the midpoint of the anterior–posterior line passing through midpoints of the vertebral bodies in the MRI images. Anterior SAATM was obtained by subtracting posterior SAATM from total SAATM. The imaging protocol has a technical error of <10% and is highly correlated (R2 = 99%) with measurements obtained from imaging of the abdominal region by contiguous transaxial slices. On the basis of phantom studies using oil/water emulsions, the accuracy of our method for delineating regional adipose tissue was 100.1 (0.01)%. The reproducibility of duplicate in vivo measures of IPATM and SAATM had a CV <3.5%. Further details are described elsewhere (15).
biochemical analyses
Fasting plasma cholesterol, triglycerides, and HDL-cholesterol were determined by standard enzymatic methods (interassay CVs <3%). LDL-cholesterol was calculated by use of the Friedewald equation. Non-HDL cholesterol was derived as total cholesterol minus HDL-cholesterol. The VLDL fraction was isolated from 3 mL of plasma by ultracentrifugation (Kontron Instruments) at 147 000g for 16 h at 4 °C, and the triglyceride concentration was measured as described above. VLDL-apoB concentrations were determined by a modified Lowry method (interassay CV <5%) (5). Plasma nonesterified fatty acids (NEFAs) and glucose were measured by enzymatic methods and insulin by an immunosorbent assay. Insulin resistance was estimated by the HOMA score (17). Plasma apoB-48 concentrations were measured by a sandwich ELISA using anti-human apoB-48 monoclonal antibodies (designated B-48-151) as reported previously (interassay CV <5%) (18). This direct ELISA measurement of apoB-48 in plasma was highly correlated with the traditional method using sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) coupled with immunoblotting and enhanced chemiluminescence in frozen plasma samples (n = 30; r = 0.805; P <0.001) with a wide range of triglyceride concentrations (0.4–5.0 mmol/L) (19). Values were 0.3–17.7 mg/L [mean (SD), 5.5 (4.8) mg/L] for the ELISA method and 9.51–54.40 mg/L [52.3 (11.6) mg/L] for the SDS-PAGE method. Plasma apoC-III was measured by immunoturbidimetric assay (Daiichi). Plasma RLP-cholesterol was determined with a JIMRO-II (Japan Immunoresearch Laboratories) assay using an immunoseparation technique (interassay CV <5%) (20). Plasma adiponectin, leptin, IL-6, TNF-, and resistin concentrations were measured by enzyme immunoassays (R & D Systems and Phoenix Pharmaceuticals); the interassay CV for these methods were <7%.
statistical analyses
All analyses were performed with SPSS 11.5 (SPSS). The data are reported as the mean (SD). Skewed data were log-transformed where appropriate. Associations were examined by simple and stepwise linear regression methods. Because we carried out multiple comparisons, we considered that only P <0.01 was of major importance in univariate analysis, but we also considered the conventional P <0.05 as being statistically significant. Multiple regression models were used to determine the variables that best predicted plasma apoB-48, apoC-III, RLP-cholesterol, triglyceride, and VLDL-apoB concentrations. The adipose tissue compartment the most significantly correlated with corresponding dependent variable in stepwise regression analysis was included in the regression models.
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Results |
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. They were generally middle aged and normotensive. Thirteen were nonobese, and 28 were obese, defined as BMI 
30 kg/m2. The mean proportions of total adipose tissue as IPATM, RPATM, and SAATM were 11%, 1.5%, and 12.1%, respectively. Of total SAATM, 35% was in the anterior and 65% in the posterior compartment.
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The biochemical characteristics in the individuals studied are shown in Table 2
. On average, the group was dyslipidemic, with increased triglycerides and low HDL-cholesterol, and insulin resistant. Four had impaired fasting glucose (6.1–6.9 mmol/L). Mean (SD) dietary intake per day was as follows: energy, 9276 (2030) kJ; total fat, 36 (7)%; carbohydrates, 38 (8)%; protein, 21 (4)%; alcohol, 6 (6)%; and cholesterol, 385 (176) g. Compared with the 13 nonobese men, the 28 obese men had significantly increased plasma glucose, insulin, triglyceride, apoB-48, apoC-III, RLP-cholesterol, non-HDL cholesterol, VLDL-apoB, VLDL-triglycerides, and leptin concentrations and HOMA scores (P <0.01), and significantly lower plasma HDL-cholesterol and adiponectin concentrations (P <0.01), with no significant group differences in plasma resistin, IL-6, or TNF- concentrations.
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The univariate associations of plasma lipid and lipoprotein concentrations with plasma adipocytokine concentrations, measures of insulin resistance, and adipose tissue compartments are shown in Table 3
. Plasma adiponectin concentration was significantly and negatively correlated with plasma apoB-48, apoC-III, RLP-cholesterol, triglyceride, total cholesterol, non-HDL cholesterol, VLDL-apoB, and VLDL-triglyceride concentrations and positively with HDL-cholesterol (P <0.05 for both). By contrast, plasma leptin concentration was positively associated with plasma apoB-48, apoC-III, RLP-cholesterol, triglyceride, non-HDL cholesterol, VLDL-apoB, and VLDL-triglyceride concentrations and inversely with HDL-cholesterol (both P <0.05). Plasma VLDL-apoB concentration was significantly and positively correlated with triglyceride (r = 0.799; P <0.001), cholesterol (r = 0.447; P <0.01), and non-HDL cholesterol (r = 0.534; P <0.001) concentrations and negatively with HDL-cholesterol (r = 0.402; P <0.01) concentration. The associations of apoB-48, apoC-III, RLP-cholesterol, and triglycerides with plasma adiponectin and leptin are shown in Figs. 1
and 2
, respectively. Plasma resistin, IL-6, and TNF- concentrations were not significantly associated with any of these lipid and lipoprotein variables except for a direct correlation between apoC-III and IL-6 (r = 0.321; P <0.05). Plasma apoB-48, apoC-III, RLP-cholesterol, triglyceride, VLDL-apoB, and VLDL-triglyceride concentrations were positively associated with insulin, HOMA score, and the masses of all adipose tissue compartments except for total ATM in the case of VLDL-triglycerides. Plasma apoB-48 concentrations were also highly significant associated (both P <0.01) with plasma concentrations of triglycerides (r = 0.826), RLP-cholesterol (r = 0.732), non-HDL cholesterol (r = 0.517), and VLDL-apoB (r = 0.829). Moreover, plasma adiponectin and leptin concentrations were significantly associated with insulin concentrations, HOMA score, and the masses of all adipose tissue compartments except for RPATM. Plasma resistin, IL-6, and TNF- were not significantly associated with insulin concentration, HOMA score, and the masses of all adipose tissue compartments except for IPATM in the case of IL-6 and TNF- (data not shown).
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As shown in Table 4
, plasma adiponectin concentration was a significant independent predictor of plasma apoB-48, apoC-III, RLP-cholesterol, and triglyceride concentrations (P <0.05) in regression models including HOMA score, adipose tissue compartment, age, and NEFAs. Plasma adiponectin concentration was also a significant independent predictor of plasma VLDL-apoB (ß coefficient = –0.377; P = 0.016) and VLDL-triglyceride (ß coefficient = –0.364; P = 0.042) concentrations. In these models, IPATM was also an independent predictor of plasma apoC-III and VLDL-apoB concentrations, whereas total SAATM was an independent predictor of plasma triglyceride concentrations (Table 4
). In contrast to adiponectin, plasma leptin was not an independent predictor of plasma apoB-48, apoC-III, RLP-cholesterol, and triglyceride concentrations in the same regression models (Table 4
). In these models, total SAATM was an independent predictor of plasma apoB-48 and triglyceride concentrations, whereas HOMA score was an independent predictor of plasma RLP-cholesterol and triglyceride concentrations. Plasma IL-6 concentration was not an independent predictor of plasma apoC-III in the regression models including HOMA, age, NEFAs, and IPATM (data not shown).
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Discussion |
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) were not significantly associated with these markers except for a direct association between plasma apoC-III and IL-6 concentrations. In the case of leptin, significant associations were not independent of body fat compartments and insulin resistance. We also showed good agreement across a wide range of plasma triglyceride concentrations between a new direct ELISA for apoB-48 and a previously published method based on SDS-PAGE coupled with immunoblotting and enhanced chemiluminescence (19). Our data are consistent with previous findings that low adiponectin concentrations are associated with an atherogenic lipid profile, including increased triglycerides and low HDL-cholesterol (13)(14)(21). We have extended these studies by investigating the association of plasma adiponectin concentrations with markers of TRL metabolism as measured by plasma apoB-48, apoC-III, and RLP-cholesterol concentrations and demonstrating that low adiponectin concentrations are most closely correlated with accumulation of TRLs independent of insulin resistance and body fat distribution. We also provide new data, based on comprehensive investigation of body fat compartments by MRI, that plasma concentrations of apoB-48, apoC-III, and RLP-cholesterol are strongly associated with adipose tissue compartments, including IPATM, RPATM, anterior SAATM, posterior SAATM, and total ATM.
Dyslipidemia in obesity and insulin resistance is fundamentally related to expansion in the plasma pool of TRLs (6)(22). Accumulation of adipose fat, particularly in the abdominal region, leads to a markedly increased flux of NEFAs to the liver (6)(23), which stimulates triglyceride synthesis (24). Insulin resistance increases hepatic synthesis of lipid substrates and the secretion of VLDL apoB-100; it also down-regulates LDL receptors (22)(25). These effects potentially increase the plasma concentrations of remnant lipoproteins containing apoB-100 and increase competition for hepatic uptake between chylomicron and VLDL remnants (26). However, the lack of a significant correlation of plasma NEFAs with VLDL-triglyceride and/or total triglyceride concentration in our study suggests that measurement of circulating NEFAs in plasma may not simply reflect portal flow of NEFAs to the liver. We have previously reported that, in obese men, accumulation of TRL remnants is attributable to defective lipolysis and impaired clearance of chylomicron remnants, as reflected by increased apoC-III concentrations and a reduced catabolic rate of a remnant-like emulsion (3). The metabolic differences between obese and nonobese men in this study were consistent with our previous data (3). We also demonstrated that insulin resistance and body fat distribution were strongly and independently predictive of plasma apoB-48, apoC-III, RLP-cholesterol, triglyceride, and VLDL-apoB concentrations.
The effect of adiponectin on TRL metabolism may principally involve intrinsic changes in skeletal muscle lipid metabolism and effects on lipoprotein lipase activity in both skeletal muscle and adipocytes (8)(27)(28). Adiponectin may decrease accumulation of triglycerides in skeletal muscle by enhancing fatty acid oxidation through activation of acetyl-CoA oxidase, carnitine palmitoyltransferase-1, and AMP kinase (27). Adiponectin may also stimulate both lipoprotein lipase (29), the lipolytic enzyme that catabolizes VLDL, and apoC-III by increasing the expression of peroxisome proliferator-activated receptor- in the liver and adipocytes (30). At the hepatic level, adiponectin may decrease the supply of NEFAs to the liver for gluconeogenesis, hence decreasing triglyceride synthesis. Taken together, low circulating adiponectin concentrations could lead to delayed removal of TRLs by the liver and peripheral tissue by increasing competition between chylomicrons and VLDL for LPL lipolysis, and between chylomicron remnants and VLDL remnants for LDL-receptor-mediated clearance (26). Because resistin, IL-6, and TNF- were not associated with insulin resistance and total body fat in the present study, it was not surprising that we found no significant association of these peptides with markers of TRLs. Our findings also suggest that plasma leptin may not per se have a direct impact on the metabolism of TRLs and may simply reflect changes in body fat stores (31).
Several methods have been used for the measurement of apoB-48 in plasma (18)(19)(32). The Western blotting method is time-consuming and is less quantitative than the standard ELISA technique; the specificity of polyclonal antibodies in the competitive ELISA is also questionable. In the present study, we used a novel ELISA system that incorporates monoclonal antibodies against apoB-48 to measure apoB-48 in plasma (18). This method enhances the specificity and sensitivity of apoB-48 measurements in plasma without the need for time-consuming isolation of TRLs. Differences in fasting apoB-48 values reported by different methods reflect differences in standardization (32). Despite the analytical shortcomings listed above, we found that the apoB-48 values obtained by our ELISA and the SDS-PAGE methods were well correlated.
We used a surrogate estimate of insulin resistance, the HOMA score, which is well correlated with the hyperinsulinemic, euglycemic clamp technique (17). Measurements of apoB-48 may not differentiate between the nascent chylomicron and its remnant. However, because participants were fasted for at least 12 h to ensure minimal intestinal secretion of nascent chylomicrons, the apoB-48 concentration was probably indicative of small, dense chylomicrons and their remnants. In addition, fasting RLP-cholesterol is not a specific marker of chylomicron and VLDL remnants because it quantifies apoE-rich lipoproteins of intestinal origin as well as some hepatic lipoproteins (20)(33). The association of plasma adipocytokines with apoC-III kinetics also requires further investigation. Future studies should examine the effect of adiponectin genotypes on TRL metabolism (34)(35). In addition, the individual effects of the full-length peptide as well as the low- and high-molecular-weight forms of adiponectin on TRL metabolism also merit further investigation (36).
Several studies have clearly demonstrated the close relationship between the impaired metabolism of TRLs and the development of CVD and type 2 diabetes (33)(37)(38)(39). Clinical and experimental data have also recently demonstrated that adiponectin is a strongly protective predictor of CVD, having several antiatherogenic properties (28)(40)(41). Our study therefore suggests that the relationship between low adiponectin concentrations and CVD may in part be mediated by the accumulation of TRLs in plasma. However, definitive evidence of the role of adiponectin in regulating TRL metabolism will require further investigation using adiponectin-knockout animals and recombinant adiponectin replacement therapy (29).
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Acknowledgments |
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Footnotes |
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, tumor necrosis factor-; MRI, magnetic resonance imaging; HOMA, homeostasis assessment; BMI, body mass index; ATM, adipose tissue mass; FFM, fat-free mass; SAATM, subcutaneous abdominal adipose tissue mass; IPATM, intraperitoneal adipose tissue mass; RPATM, retroperitoneal adipose tissue mass; NEFA, nonesterified fatty acid; and SDS-PAGE, sodium dodecyl sulfate–polyacrylamide gel electrophoresis. 
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