Diabetologia

, Volume 49, Issue 4, pp 755–765

Overproduction of large VLDL particles is driven by increased liver fat content in man

Authors

  • M. Adiels
    • Wallenberg LaboratoryGothenburg University
  • M.-R. Taskinen
    • Division of CardiologyUniversity of Helsinki Biomedicum
  • C. Packard
    • Department of Pathological BiochemistryGlasgow Royal Infirmary
  • M. J. Caslake
    • Department of Pathological BiochemistryGlasgow Royal Infirmary
  • A. Soro-Paavonen
    • Division of CardiologyUniversity of Helsinki Biomedicum
  • J. Westerbacka
    • Division of DiabetesUniversity of Helsinki
  • S. Vehkavaara
    • Division of DiabetesUniversity of Helsinki
  • A. Häkkinen
    • Department of OncologyUniversity of Helsinki
  • S.-O. Olofsson
    • Wallenberg LaboratoryGothenburg University
  • H. Yki-Järvinen
    • Division of DiabetesUniversity of Helsinki
    • Wallenberg LaboratoryGothenburg University
    • Wallenberg LaboratorySahlgrenska University Hospital
Article

DOI: 10.1007/s00125-005-0125-z

Cite this article as:
Adiels, M., Taskinen, M., Packard, C. et al. Diabetologia (2006) 49: 755. doi:10.1007/s00125-005-0125-z

Abstract

Aims/hypothesis

We determined whether hepatic fat content and plasma adiponectin concentration regulate VLDL1 production.

Methods

A multicompartment model was used to simultaneously determine the kinetic parameters of triglycerides (TGs) and apolipoprotein B (ApoB) in VLDL1 and VLDL2 after a bolus of [2H3]leucine and [2H5]glycerol in ten men with type 2 diabetes and in 18 non-diabetic men. Liver fat content was determined by proton spectroscopy and intra-abdominal fat content by MRI.

Results

Univariate regression analysis showed that liver fat content, intra-abdominal fat volume, plasma glucose, insulin and HOMA-IR (homeostasis model assessment of insulin resistance) correlated with VLDL1 TG and ApoB production. However, only liver fat and plasma glucose were significant in multiple regression models, emphasising the critical role of substrate fluxes and lipid availability in the liver as the driving force for overproduction of VLDL1 in subjects with type 2 diabetes. Despite negative correlations with fasting TG levels, liver fat content, and VLDL1 TG and ApoB pool sizes, adiponectin was not linked to VLDL1 TG or ApoB production and thus was not a predictor of VLDL1 production. However, adiponectin correlated negatively with the removal rates of VLDL1 TG and ApoB.

Conclusions/interpretation

We propose that the metabolic effect of insulin resistance, partly mediated by depressed plasma adiponectin levels, increases fatty acid flux from adipose tissue to the liver and induces the accumulation of fat in the liver. Elevated plasma glucose can further increase hepatic fat content through multiple pathways, resulting in overproduction of VLDL1 particles and leading to the characteristic dyslipidaemia associated with type 2 diabetes.

Keywords

Apolipoprotein BCompartmental modellingDiabetesDyslipidaemiaKineticsStable isotopeTriglyceridesVLDL

Abbreviations

ApoB

apolipoprotein B

ChREBP

carbohydrate responsive-element binding protein

FCR

fractional catabolic rate

FDCR

fractional direct catabolic rate

FTR

fractional transfer rate

HOMA-IR

homeostasis model assessment of insulin resistance

PPAR

peroxisome proliferator activated receptor

SREBP1c

sterol regulatory element binding protein 1c

TG

triglyceride

Introduction

Insulin resistance is associated with atherogenic dyslipidaemia characterised by high levels of triglyceride (TG)-rich lipoproteins and their cholesteryl ester-rich remnant particles, low levels of HDL-cholesterol, and small dense LDL [1]. Previously, we reported that elevation of large VLDL particles is the major determinant of plasma TG levels in subjects with and without type 2 diabetes mellitus [2] and that the production rate of large VLDL1 particles is increased in type 2 diabetic patients [3]. Thus, in diabetic dyslipidaemia, overproduction of large VLDL particles seems to initiate a sequence of lipoprotein changes that increase the risk of atherosclerosis, including an increase in smaller LDL and a decrease in HDL particles. Importantly, these lipid abnormalities precede the clinical diagnosis of type 2 diabetes and may explain the ‘ticking clock’ hypothesis for macrovascular disease that develops long before diabetes. Thus, it is of great importance to elucidate the mechanisms behind the overproduction of large VLDL particles in diabetic dyslipidaemia.

Non-alcoholic fatty liver disease has emerged as a new component of the metabolic syndrome [4] and a predictor of type 2 diabetes [57]. In observational studies, liver fat content was associated with common features of insulin resistance (e.g. increased fasting insulin levels, high C-peptide levels, glucose intolerance and intra-abdominal fat) both in patients with type 2 diabetes and in non-obese and moderately obese subjects without diabetes [46, 8, 9]. Therefore, the strong relationship between the hepatic fat content and hepatic insulin sensitivity is not surprising [8, 10, 11]. Liver fat content correlates closely with serum TGs, small dense LDL, and HDL-cholesterol [4, 8, 9]. Intriguingly, the fatty liver seems to exacerbate the dyslipidaemia in type 2 diabetic patients [10]. The mechanisms underlying the relationship between dyslipidaemia and liver fat have not been fully defined.

Excessive visceral fat appears to be a key factor in the development of the disturbed metabolism in type 2 diabetes. Intra-abdominal fat is considered the major source of hepatic NEFA released directly into the portal vein [12]. Thus, hepatic delivery of NEFA increases as the visceral fat compartment expands, and visceral adiposity has been reported to predict liver fat content [9, 10]. Recent evidence suggests that dyslipidaemia is mainly attributable to increased intra-abdominal fat [13].

Adiponectin is an adipocyte-secreted hormone that acts as an anti-diabetic and anti-atherogenic adipocytokine [14, 15]. Decreased plasma concentrations of adiponectin, unlike all other adipocytokines, correlate negatively with liver fat content and liver insulin sensitivity [7, 9, 16]. Thus, the plasma adiponectin concentration is lower in human obesity and in other insulin-resistant states [14], suggesting that adiponectin may be an important marker of the metabolic syndrome. Emerging evidence suggests that adiponectin also influences lipid metabolism. Adiponectin levels correlate inversely with plasma TGs but positively with HDL-cholesterol levels and LDL size [4, 8, 9, 1720]. Importantly, these relations persist even after adjustment for measures of overall obesity and visceral fat, consistent with a direct action of adiponectin on lipoprotein metabolism [19, 20]. Thus, adiponectin may be a mediator between adipose tissue and the liver, influencing both glucose and lipid metabolism.

To determine whether hepatic fat content and plasma adiponectin concentration are key regulators of VLDL1 production, we used a novel multicompartment model that allows the kinetic parameters of TG and ApoB in VLDL1 and VLDL2 to be simultaneously determined after a bolus of [2H3]leucine and [2H5]glycerol [21]. Magnetic resonance proton spectroscopy was used to determine hepatic fat content and MRI to determine intra-abdominal and s.c. fat volumes. We studied both non-diabetic and type 2 diabetic subjects with different levels of insulin resistance, which permitted us to explore the effects of disturbances in lipid and glucose metabolism on VLDL kinetics over a wide range of liver fat and intra-abdominal fat content. Here we present the first human in vivo study on the relationship between liver and abdominal fat content and the production rate of VLDL1 TG and ApoB. From the results we hypothesise that liver fat and plasma glucose are the major determinants of VLDL1 TG and ApoB production rates.

Subjects and methods

Subjects

We studied ten subjects with type 2 diabetes and 18 non-diabetic subjects, all of whom were men of white European origin. The subjects were recruited by advertisement in local newspapers and from a Finnish database, Health 2000, which is a population-based cohort. All subjects underwent a physical examination and laboratory tests to exclude hepatic abnormalities (other than hepatic steatosis) and renal, thyroid and haematological abnormalities. Subjects with CHD, diabetic retinopathy or microalbuminuria, total cholesterol greater than 7.0 mmol/l, TG greater than 5.0 mmol/l, or BMI greater than 40 kg/m2 were excluded. All non-diabetic subjects had lipid values representative of a Western population; three had IGT according to WHO criteria [22]. The diagnosis of type 2 diabetes was based on glucose tolerance tests according to WHO criteria [22] or on use of oral anti-diabetic medication. The age at onset of type 2 diabetes was at least 35 years. None of the subjects was taking lipid-lowering therapy or insulin. Four patients with type 2 diabetes and one non-diabetic subject were taking acetylsalicylic acid. Two patients with type 2 diabetes were treated with diet alone, one with diet and metformin, two with diet and sulfonylurea, and five with a combination therapy of sulfonylurea, metformin and diet. One patient with type 2 diabetes was using antihypertensive therapy (felodipine). The medications were continued throughout the study. The mean duration of diabetes was 7.0 years (range 0–13 years). The study design was approved by the ethics committee of Helsinki University Central Hospital, and each subject gave written informed consent. All samples were collected in accordance with the Declaration of Helsinki. One subject did not have intra-abdominal or s.c. fat measured. Fifteen of the control subjects and six of the type 2 diabetic subjects were presented in a previous paper [3].

ApoB and TG turnover

Turnover of ApoB and TG was measured after an overnight fast. Particle composition and ApoB mass of the VLDL1 and VLDL2 fractions were determined 30 min before and 0, 4, and 8 h after a bolus injection of [2H3]leucine and [2H5]glycerol [21]. The subjects fasted until 17.00 h, when the last blood sample was taken.

Kinetics analysis

Measured ApoB and TG pool sizes in VLDL1 and VLDL2 were converted into leucine and glycerol equivalents. The injected amount of [2H3]leucine and [2H5]glycerol, the leucine and glycerol pool sizes in VLDL1 and VLDL2, and the enrichment curves of leucine in plasma and leucine and glycerol in VLDL1 and VLDL2 were used as the data set for a multicompartment model that allowed simultaneous modelling of ApoB and TG kinetics as described [21]. The outputs from the model are the production rates and fractional catabolic rate (FCR) of VLDL1 and VLDL2 (the fractional loss of mass due to transfer and direct catabolism), the fractional direct catabolic rate (FDCR; the fractional loss of mass due to direct catabolism from VLDL1) and the fractional transfer rate (FTR; the fractional transfer from VLDL1 to VLDL2). In this model, we cannot distinguish between transfer from VLDL2 to intermediate-density lipoprotein and direct catabolism of VLDL2 particles; hence they are represented by the FCR. The ApoB and TG production rates were calculated as mg day−1 kg body weight−1. FCR, FDCR and FTR were calculated for both ApoB and TG as pools per day.

Isolation of lipoproteins and biochemical analyses

VLDL1 and VLDL2 were isolated from 8.4 ml of plasma [23]. ApoB and TG concentrations in these lipoprotein fractions were measured in samples obtained at 0, 4 and 8 h. ApoB and TG pool sizes were calculated as the product of plasma volume (4.5% of body weight) and the plasma concentration of ApoB and TG in VLDL1 and VLDL2. The leucine and glycerol contents of each pool were determined [21], and the TG, cholesterol, ApoB, glucose, insulin, NEFA, HbA1c and protein concentrations in lipoprotein fractions were analysed as described [21]. LDL peak particle diameter (LDL size) was measured with 2–10% gradient polyacrylamide gel electrophoresis [24]. Serum adiponectin concentrations were measured with an ELISA kit (B-Bridge International, San Jose, CA, USA) that detected full-length peptides. The HOMA-IR (homeostasis model assessment for insulin resistance) was calculated from the fasting glucose and serum insulin concentrations as fasting insulin (mU/l)×fasting glucose (mmol/l)/22.5 [25].

Liver fat analysis

Image-guided proton magnetic resonance spectroscopy was performed with a 1.5 T whole-body device (Magnetom Vision; Siemens, Erlangen, Germany) using a combination of whole-body and loop surface coils for the radio frequency transmitting and receiving of signals [9, 11]. Volumes of interest (8 cm3) in the liver were placed, avoiding vascular structures and s.c. fat tissue. These areas were typically within the right lobe. Chemical shifts were measured relative to water signal intensity at 4.8 ppm (Swater). Methylene signal intensity, which represents intracellular TGs in the liver [11], was measured at 1.4 ppm (Sfat). The measurement of the percentage of hepatic fat by proton spectroscopy has been validated against the lipid content of liver biopsies in humans [26] and against liver density measurements performed by computed tomography [11]. The percentage of liver fat was calculated as 100×Sfat/(Sfat+Swater). Regarding the error introduced by the use of a single rather than multiple voxels, we have shown in 24 normal subjects that the Spearman’s correlation coefficient for liver fat between the left and right lobes is 0.95 (95% CI 0.89–0.98, p<0.001) and the CV is 6%.

Determination of intra-abdominal and s.c. fat volumes

Intra-abdominal and s.c. fat volumes were determined by MRI. A series of 16 T1-weighted transaxial scans were acquired from a region extending from 8 cm above to 8 cm below the fourth and fifth lumbar interspaces [11]. Intra-abdominal and s.c. fat areas were measured with an image analysis program (Alice 3.0; Parexel, Waltham, MA, USA) [11].

Statistical analysis

Differences between non-diabetic and type 2 diabetic subjects were evaluated with unpaired, two-sided t tests. Correlations were evaluated by linear regression and multiple interactions by forward stepwise multivariate linear regression and by exhaustive check of all possible multivariate models. Variables with univariate correlation with p<0.05 were included in the multivariate regression but HOMA-IR was left out of the multivariate when both plasma glucose and fasting insulin were included. Also, we included all fat compartment variables in the multivariate analysis, even though we did not find a univariate correlation. The correlations were calculated for all subjects as well as for the type 2 diabetic subjects and the non-diabetic subjects. For non-normally distributed variables, values were log-transformed before correlation analysis. Values of p<0.05 were considered significant.

Statistical analysis was performed with SPSS, Microsoft Excel and Matlab (MathWorks, Natick, MA, USA).

Results

Subject characteristics

Characteristics of the subjects are summarised in Table 1. As expected, type 2 diabetic subjects were more obese than non-diabetic subjects and had a typical dyslipidaemia, with higher plasma TG, lower HDL levels and smaller LDL particles than non-diabetic subjects, and mildly increased plasma glucose, insulin and serum alanine aminotransferase. Type 2 diabetic subjects had a mean glycosylated haemoglobin level of 6.9% (range 6.1–8.1%, reference range 4–6%) and significantly higher hepatic and intra-abdominal fat content than non-diabetics subjects. S.c. fat levels were similar in the two groups. Type 2 diabetic subjects had approximately 2.0-fold larger TG and ApoB VLDL1 pools (TG, 20.4±15.3 vs 39.8±21.9 mg/kg; ApoB, 0.8±0.7 vs 1.6±0.9 mg/kg) and approximately 1.5-fold larger VLDL2 pools (TG, 7.4±3.8 vs 12.0±6.5 mg/kg; ApoB, 1.3±0.7 vs 1.9±0.9 mg/kg). Importantly, there was a strong linear correlation between the VLDL1 ApoB and VLDL1 TG pools in all subjects (r=0.98, p<0.001) (Fig. 1). Thus, the two groups represent an overlapping continuum of VLDL TG and ApoB pools. The TG:ApoB ratios of VLDL1 and VLDL2 were similar in the two groups.
Table 1

Basic characteristics of the subjects

 

Non-diabetic subjects (n=18)

Type 2 diabetic subjects (n=10)

Mean±SD

Range

Mean±SD

Range

Age (years)

50±10

25–64

56±9

37–67

Weight (kg)

82±9

70–102

94±9b

82–106

BMI (kg/m2)

26±2

22–30

30±4a

23–35

Liver fat (%)

4±2

1–10

11±7c

3–24

Total abdominal fat (cm3)

3,845±1,270

986–5,760

5,103±942a

3,441–6,539

S.c. fat (cm3)

2,209±592

946–3,313

2,689±814

1,614–4,334

Intra-abdominal fat (cm3)

1,636±886

40–3,341

2,414±595a

1,583–3,660

Fasting serum glucose (mmol/l)

5.9±0.6

5.0–7.2

8.5±1.5c

6.8–11.7

HbA1c(%)

  

6.8±0.82

6.1–8.1

Fasting serum insulin (mU/l)

7±3

2.0–11.0

10.1±3.6b

5.5–14.7

HOMA-IR

1.8±0.8

0.5–3.0

3.9±1.6c

1.9–6.5

Serum adiponectin (mg/l)

9.1±4.1

4.6–18.9

6.9±2.4

3.4–11.0

Serum ALT (U/l)

26±7

14–39

36±14a

24–72

Serum NEFA (μmol/l)

558±89

352–714

656±152a

377–876

Plasma TG (mmol/l)

1.5±0.61

0.67–3.14

2.00±0.77

1.17–3.81

Cholesterol (mmol/l)

5.17±0.89

3.76–7.90

4.92±0.37

4.53–5.62

HDL-cholesterol (mmol/l)

1.33±0.25

0.78–1.68

1.07±0.28a

0.82–1.68

ApoB (mg/dl)

106±19

66–140

121±15a

104–156

LDL size (nm)

26.3±1.0

23.4–27.8

25.2±1.1a

23.2–26.4

ALT Alanine aminotransferase

ap<0.05, bp<0.01, cp<0.001 vs non-diabetic subjects

https://static-content.springer.com/image/art%3A10.1007%2Fs00125-005-0125-z/MediaObjects/125_2005_125_Fig1_HTML.gif
Fig. 1

Measured VLDL1 ApoB pool size plotted against measured VLDL1 TG pool sizes in type 2 diabetic subjects (filled boxes, n=10) and control subjects (open boxes, n=18). There was a strong linear correlation between the ApoB and TG pools in all subjects (r=0.98, p<0.001). This finding is consistent with our earlier results [3].

Production and clearance rates

The VLDL1 and VLDL2 TG and ApoB pool sizes and enrichment data were entered into a multicompartment model that simultaneously determined VLDL1 and VLDL2 TG and ApoB kinetics [21]. Type 2 diabetic subjects produced significantly (p<0.01) more VLDL1 TG and ApoB than non-diabetic subjects (Table 2), confirming our previous findings [3]. There was also a significant increase (p=0.04) in direct production of VLDL2, TG but no significant difference in direct production of VLDL2 ApoB. The FCR, the FDCR and the FTR did not differ between non-diabetic and type 2 diabetic subjects. Consistent with the conversion of large VLDL1 particles to small dense LDL, the VLDL1 TG (p<0.01) and ApoB (p<0.05) production rates correlated inversely with LDL size.
Table 2

Calculated parameters

 

Non-diabetic subjects (n=18)

Type 2 diabetic subjects (n=10)

Mean±SD

Range

Mean±SD

Range

VLDL TG total production (mg kg−1 day−1)

249±84

138–387

382±114b

194–525

VLDL1 TG production rate (mg kg−1 day−1)

222±79

107–352

345±105b

178–495

VLDL1 TG FCR (pools/day)

14.9±7.7

4.4–35.3

11.0±5.2

3.4–21.5

VLDL1 TG FDCR (pools/day)

10.8±6.7

3.9–31.2

8.0±4.3

1.6–17.0

VLDL1 TG FTR (pools/day)

4.1±2.0

0.5–8.6

2.9±1.2

1.3–5.1

VLDL1 TG to VLDL2 transfer (mg kg−1 day−1)

63.0±34.6

20.9–146

93.1±23.3a

61.5–128

VLDL2 TG direct production rate (mg kg−1 day−1)

27.5±9.2

14.4–51.4

37.4±14.7a

15.6–61.7

VLDL2 TG FCR (pools/day)

14.9±8.1

4.4–39.4

12.8±5.2

6.2–23.7

VLDL ApoB total production (mg kg−1 day−1)

9.2±3.6

4.1–15.1

13.6±3.6b

7.4–18.6

VLDL1 ApoB production rate (mg kg−1 day−1)

7.1±3.1

2.9–12.5

11.1±3.5b

6.4–16.5

VLDL1 ApoB FCR (pools/day)

12.4±6.9

4.0–31.0

9.1±5.1

3.1–21.5

VLDL1 ApoB FDCR (pools/day)

4.6±6.0

0.0–23.3

3.8±3.5

0.0–11.3

VLDL1 ApoB FTR (pools/day)

7.8±3.7

1.2–13.4

5.3±2.7

1.6–10.2

VLDL1 ApoB to VLDL2 transfer (mg kg−1 day−1)

4.7±2.6

1.8–10.0

6.2±1.1

4.2–7.7

VLDL2 ApoB direct production rate (mg kg−1 day−1)

2.1±0.8

1.0–3.3

2.5±0.8

1.0–3.5

VLDL2 ApoB FCR (pools/day)

6.1±2.9

2.1–12.3

5.7±2.5

2.3–8.9

ap<0.05, bp<0.01 vs non-diabetic subjects

Hepatic lipid and intra-abdominal fat predictors

In the univariate regression analysis, liver fat correlated negatively with adiponectin (Fig. 2a) and positively with plasma glucose, plasma TG, insulin, alanine aminotransferase and HOMA-IR (p<0.01), but less strongly with plasma ApoB and BMI (Table 3). Liver fat also correlated positively with intra-abdominal fat (Fig. 2b) and total abdominal fat (Table 3). All fat compartments were highly correlated with BMI (Table 3). Notably, intra-abdominal fat correlated positively with s.c. fat (r=0.59, p<0.001). In a multivariate regression analysis, only adiponectin and plasma glucose were independently associated with liver fat (Table 4). Intra-abdominal fat also correlated strongly with insulin and HOMA-IR, but less strongly with plasma glucose, TG and ApoB.
https://static-content.springer.com/image/art%3A10.1007%2Fs00125-005-0125-z/MediaObjects/125_2005_125_Fig2_HTML.gif
Fig. 2

Correlation between liver fat and a adiponectin (all subjects, r=−0.65, p<0.001; non-diabetic subjects, r=−0.62, p<0.01; subjects with type 2 diabetes mellitus, r=−0.59, NS) and b intra-abdominal fat (all subjects, r=0.53, p<0.01; non-diabetic subjects, r=0.47, p<0.05; subjects with type 2 diabetes mellitus, r=0.41, NS). Correlation between VLDL1 TG production and c plasma insulin (all subjects, r=0.50, p<0.01; non-diabetic subjects, r=0.46, NS; subjects with type 2 diabetes mellitus, r=−0.04, NS) and d plasma glucose (all subjects, r=0.59, p<0.001; non-diabetic subjects, r=0.12, NS; subjects with type 2 diabetes mellitus, r=0.39, NS). Filled boxes, subjects with type 2 diabetes mellitus; open boxes, non-diabetic subjects; open circles, IGT subjects. Data were log-transformed before the regression analysis.

Table 3

Univariate regressions of plasma concentrations and adiposity measures

 

Liver fat

Adiponectin

Glucose

Insulin

HOMA-IR

Plasma TG

ApoB

BMI

Liver fat

−0.647c

0.589c

0.513b

0.589c

0.490b

0.402a

0.467a

NEFA

0.163

−0.061

0.494b

0.156

0.320

−0.103

0.262

0.355

Adiponectin

−0.647c

−0.168

−0.347

−0.326

−0.445a

−0.348

−0.275

Glucose

0.589c

−0.168

0.511b

0.793c

0.258

0.295

0.427a

Insulin

0.513b

−0.347

0.511b

0.955c

0.501b

0.393a

0.666c

HOMA-IR

0.589c

−0.326

0.793c

0.955c

0.479b

0.407a

0.656c

Total abdominal fat

0.481a

−0.213

0.498b

0.698c

0.710c

0.301

0.423a

0.800c

Subcutaneous fat

0.227

0.098

0.391a

0.553b

0.561b

−0.088

0.252

0.745c

Intra-abdominal fat

0.528b

−0.380

0.466a

0.635c

0.631c

0.450a

0.526b

0.602c

Non-normally distributed numbers were log transformed before the analysis.

r values for univariate regressions

ap<0.05, bp<0.01, cp<0.001

Table 4

Multivariate regression models searching for independent correlates of liver fat content, VLDL1 TG and ApoB production, and VLDL1 TG and ApoB FTR: results of stepwise multivariate regression

 

Coefficient

Standard error

Significance

Liver fat

Plasma glucose

1.73a

0.54a

0.004a

Adiponectin

−1.16a

0.27a

0.000a

Insulin

  

0.16b

S.c. fat

  

0.53b

Intra-abdominal fat

  

0.24b

Plasma TG

  

0.55b

BMI

  

0.32b

VLDL1 TG production rate

Liver fat

0.22a

0.085a

0.018a

Plasma glucose

0.84a

0.34a

0.021a

Insulin

  

0.62b

Adiponectin

  

0.79b

S.c. fat

  

0.49b

Intra-abdominal fat

  

0.58b

VLDL1 apoB production rate

Liver fat

0.22a

0.10a

0.039a

Plasma glucose

1.03a

0.40a

0.016a

Insulin

  

0.36b

Adiponectin

  

0.91b

S.c. fat

  

0.69b

Intra-abdominal fat

  

0.43b

VLDL1 TG FTR

Insulin

−0.50a

0.22a

0.031a

Adiponectin

  

0.32b

Plasma glucose

  

0.92b

Liver fat

  

0.34b

S.c. fat

  

0.62b

Intra-abdominal fat

  

0.41b

VLDL1 ApoB FTR

Insulin

−0.56a

0.23a

0.024a

Adiponectin

  

0.39b

Plasma glucose

  

0.79b

Liver fat

  

0.29b

S.c. fat

  

0.17b

Intra-abdominal fat

  

0.56b

Non-normally distributed numbers were log-transformed before the analysis.

Significance level for excluded variables is the significance it would have if only that variable were added to the model.

The best models found for liver fat, VLDL1 TG production rate and VLDL1 ApoB production rate were also best among all multivariate models with two independent parameters.

aSignificant predictor variables; bexcluded variables

Production and clearance rates

Univariate regression analysis showed that liver fat correlated positively with VLDL1 TG and apoB production rates (p<0.01) (Fig. 3a,b Table 5). Liver fat also showed a weak negative correlation with VLDL1 apoB and TG FTRs (p<0.05). VLDL1 TG production rate also correlated with plasma glucose, insulin (p<0.01) (Fig. 2c,d) and HOMA-IR (p<0.001); the same correlation was observed for the VLDL1 apoB production rate, consistent with earlier results [3]. Intra-abdominal fat correlated positively with VLDL1 TG and apoB production rates (p<0.05) (Fig. 3c,d) and negatively with VLDL2 TG and apoB FTR (p<0.05). Adiponectin correlated negatively with VLDL1 TG and apoB pool sizes (p<0.01) and positively with the corresponding FCR (p<0.05) but was not linked to VLDL1 TG or ApoB production rates.
https://static-content.springer.com/image/art%3A10.1007%2Fs00125-005-0125-z/MediaObjects/125_2005_125_Fig3_HTML.gif
Fig. 3

Correlation between liver fat and a VLDL1 TG production (all subjects, r=−0.58, p<0.01; non-diabetic subjects, r=0.48, p<0.05; subjects with type 2 diabetes mellitus, r=0.17, NS) and b VLDL1 ApoB production (all subjects, r=0.52, p<0.01; non-diabetic subjects, r=0.41, p=0.09; subjects with type 2 diabetes mellitus, r=0.06, NS). Correlation between intra-abdominal fat and c VLDL1 TG production (all subjects, r=0.46, p<0.05; non-diabetic subjects, r=0.40, NS; subjects with type 2 diabetes mellitus, r=0.14, NS) and d VLDL1 ApoB production (all subjects, r=0.47, p<0.05; non-diabetic subjects, r=0.41, NS; subjects with type 2 diabetes mellitus, r=0.10, NS). Filled boxes, subjects with type 2 diabetes mellitus; open boxes, non-diabetic subjects; open circles, IGT subjects. Data were log-transformed prior to the regression analysis.

In a stepwise multivariate regression analysis of VLDL1 TG or apoB production rate as the dependent variable and plasma glucose, insulin, liver fat, intra-abdominal fat, subcutaneous fat and adiponectin as independent variables, only plasma glucose and liver fat remained significant (Table 4). When VLDL1 TG or ApoB FTR served as the independent variable, insulin was the best predictor of VLDL1 TG and ApoB (Table 4).

Discussion

This study shows that liver fat, insulin and plasma glucose are independent determinants of VLDL1 TG and ApoB production rates. The key metabolic determinants of liver fat content were intra-abdominal fat, adiponectin and plasma glucose. These findings emphasise the critical role of substrate fluxes and lipid availability in the liver as the driving force for the overproduction of VLDL1 in subjects with type 2 diabetes [21].

The liver fat content reflects the balance between NEFA flux, fatty acid oxidation, de novo lipogenesis and VLDL secretion. Increased NEFA flux is a major contributor to the availability of TG for VLDL assembly [27, 28]. Indeed, increased delivery of NEFA to the liver in humans is associated with fatty liver [29, 30] and has been linked to hepatic insulin resistance [3133]. These findings are consistent with the strong relationship we observed between intra-abdominal fat and liver fat content.

Both hepatic and intra-abdominal fat content correlated with VLDL1 TG and ApoB production rates in the univariate analysis, but only the hepatic fat content was significant in multiple regression models. Our data suggest that hepatic steatosis is the proximate cause of the characteristic atherogenic dyslipidaemia seen in type 2 diabetes [1, 10]. This conclusion is consistent with the fact that VLDL is produced by liver fat, not visceral adipose tissue. VLDL1 particles are converted in the circulation by cholesteryl ester transport protein and hepatic lipase – which are commonly increased in type 2 diabetes – to small atherogenic LDL particles [1]. This explains the strong inverse correlation between VLDL1 TG production rate and LDL particle size. In a parallel process, excess of VLDL1 particles alters the lipid composition of HDL particles, leading to enhanced catabolism of these particles [34]; this explains the strong inverse correlation between HDL-cholesterol and liver fat in the present study (p<0.001, r=−0.65). Thus, overexpression of VLDL1 leads to the generation of both small dense LDL and low HDL, the two hallmarks of diabetic dyslipidaemia. Our data highlight liver fat as a major driving force for diabetic dyslipidaemia. However, we recognise that regulatory factors not measurable in man (i.e. enzymatic activities in the liver, transcription factors and substrate fluxes) can influence hepatic lipid metabolism and influence VLDL production.

The correlation of the direct production rates of both VLDL2 TG and ApoB with liver fat content suggests that excessive accumulation of hepatic fat also influences the VLDL2 production rate (Table 5).
Table 5

Univariate regression analyses of plasma concentrations and adiposity measures vs VLDL1 and VLDL2 production and clearance rates

TG

ApoB

 

VLDL1 FCR

VLDL1 FTR

VLDL1 production rate

VLDL2 FCR

VLDL2 DPR

VLDL1 FCR

VLDL1 FTR

VLDL1 production rate

VLDL2 FCR

VLDL2 DPR

Liver fat

−0.349

−0.392a

0.583b

−0.307

0.392a

−0.405a

−0.416a

0.523b

−0.249

0.439a

NEFA

0.128

−0.125

0.317

−0.165

0.050

0.192

0.060

0.378a

0.121

0.146

Adiponectin

0.402a

0.377

−0.355

0.343

−0.418a

0.436a

0.300

−0.272

0.350

−0.467a

Glucose

−0.120

−0.326

0.593c

−0.160

0.437a

−0.081

−0.369

0.568b

−0.094

0.356

Insulin

−0.401a

−0.449a

0.505b

−0.263

0.282

−0.359

−0.487b

0.540b

−0.259

0.271

HOMA-IR

−0.348

−0.422a

0.594c

−0.219

0.368

−0.296

−0.487b

0.617c

−0.206

0.331

Total abdominal fat

−0.216

−0.398a

0.346

−0.364

−0.047

−0.135

−0.347

0.430a

−0.235

−0.048

S.c. fat

−0.155

−0.268

0.115

−0.253

−0.213

−0.012

−0.128

0.258

−0.010

−0.105

Intra-abdominal fat

−0.266

−0.382a

0.459a

−0.438a

0.127

−0.252

−0.415a

0.474a

−0.392a

0.129

Non-normally distributed numbers were log transformed before the analysis

r values for univariate regressions

ap<0.05, bp<0.01, cp<0.001

DPR Direct production rate

Insulin appears to be pivotal in regulating VLDL assembly in the liver [35, 36]. Insulin acts on two key factors that regulate the initiation of VLDL assembly and the maturation of VLDL particles: microsomal triglyceride transfer protein and phosphatidylinositol 3-kinase [3740]. Consequently, impaired insulin signalling results in increased VLDL production. Consistent with previous studies [3, 41], we observed significant relationships between fasting insulin and HOMA-IR and VLDL1 TG and apoB production rates. However, these relationships were not significant in regression models that included liver fat. HOMA-IR can be considered at best a surrogate marker for type 2 diabetes, and, notably, is influenced more strongly by fasting glucose than by insulin, since fasting glucose closely reflects hepatic glucose production [42]. Thus, liver fat content may influence hepatic insulin resistance by blunting the insulin signalling pathways in the liver. This would explain why fasting glucose predicted VLDL1 production, especially in subjects with type 2 diabetes (Fig. 2d).

Insulin appears to be pivotal in regulating VLDL assembly in the liver [35, 36]. Insulin acts on two key factors that regulate the initiation of VLDL assembly and the maturation of VLDL particles: microsomal triglyceride transfer protein and phosphatidylinositol 3-kinase [3740]. Consequently, impaired insulin signalling results in increased VLDL production. Consistent with previous studies [3, 41], we observed significant relationships between fasting insulin and HOMA-IR and VLDL1 TG and ApoB production rates. However, these relationships were not significant in regression models that included liver fat. HOMA-IR can be considered at best a surrogate marker for type 2 diabetes, and, notably, is influenced more strongly by fasting glucose than by insulin, since fasting glucose closely reflects hepatic glucose production [42]. Thus, liver fat content may influence hepatic insulin resistance by blunting the insulin signalling pathways in the liver. This would explain why fasting glucose predicted VLDL1 production, especially in subjects with type 2 diabetes (Fig. 2d).

The strong relation between fasting glucose and VLDL1 production probably reflects disturbed regulation of VLDL1 production. The relation between plasma glucose and VLDL1 TG production could be explained by increased expression of sterol regulatory element binding protein 1c (SREBP1c) because of the increased levels of insulin combined with increased production of glycerol 3-phosphate. Glycerol 3-phosphate could either be formed during the glycolysis, but more probably as a result of the increased gluconeogenesis present in insulin resistance. The observation that fasting glucose in insulin resistance is closely linked to the production of glucose in the liver would support such a mechanism.

Elevated plasma glucose concentrations may also contribute directly to increased VLDL1 production by providing the carbon skeleton for glycerol utilised in the re-esterification of NEFA. Therefore, in the setting of increased NEFA flux, hyperglycaemia may enhance TG synthesis. Along these lines, it has been indicated that glucose could influence the bulk lipid addition step of VLDL assembly [43]. Another potential source of liver TG is de novo lipogenesis [29, 30]. In liver insulin resistance, hyperinsulinaemia upregulates SREBP1c expression and leads to activation of key enzymes for lipogenesis [44, 45]. Carbohydrate responsive-element binding protein (ChREBP) is also stimulated by hyperglycaemia and activates key lipogenic enzymes [46]. Thus, concomitant up regulation of SREBP1c and ChREBP by insulin and glucose in type 2 diabetes can increase hepatic lipogenesis. Production of malonyl-CoA is increased by fatty acid synthesis, but is also increased directly by acetyl-CoA carboxylase-2, which is activated by SREBP1c. Malonyl-CoA inhibits carnitine palmitoyl transferase-1, which results in decreased fatty acid oxidation [47, 48]. Thus, elevated plasma glucose acts via multiple pathways to increase liver fat content. Not surprising, plasma glucose strongly correlated with both liver fat content and VLDL1 production rate.

Adiponectin also appears to contribute to the metabolic dysregulation in type 2 diabetes. Adiponectin strongly predicted liver fat content, as in previous studies [16]. By activating AMP-activated protein kinase in hepatocytes [49], which stimulates β-oxidation and simultaneously inhibits lipogenesis [50], adiponectin may regulate fatty acid oxidation. By activating peroxisome proliferator activated receptor (PPAR)α, adiponectin may be pivotal in upregulating the expression of key enzymes of fatty acid oxidation [14]. Thus, hypoadiponectinaemia in type 2 diabetes may impair fatty acid oxidation through at least two mechanisms. In the setting of increased NEFA flux, this would direct NEFA for re-esterification and enhance TG accumulation in the liver.

Several studies have reported a strong association between dyslipidaemia and adiponectin. Despite the close relationship between adiponectin and the liver fat content, adiponectin did not predict VLDL1 production and therefore must influence plasma TGs by mechanisms other than effects on VLDL1 production. Interestingly, adiponectin regulates plasma lipoprotein lipase activity independently of insulin sensitivity and BMI [51]. Thus, adiponectin can influence the intravascular catabolism of VLDL particles by several mechanisms. This hypothesis is consistent with our finding that adiponectin correlated with the FCR of VLDL1 TG and ApoB and the FTR of VLDL1 TG, and is consistent with recent data by Ng et al. [52]. Notably, adiponectin stimulates (via its action on PPARα) the expression of ApoCIII, which regulates the metabolism of TGs [53].

A potential weakness of our study is that the data are derived from two populations with different characteristics. Thus, the reported findings could be a function of the predictors or of the diabetic state in itself. However, the two groups are not entirely separate but represent an overlapping continuum of VLDL TG and ApoB pools.

In summary, we propose that the metabolic effect of insulin resistance, partly mediated by depressed plasma adiponectin levels, increases fatty acid flux from adipose tissue to the liver and induces the accumulation of fat in the liver. Elevated plasma glucose can further increase hepatic fat content though multiple pathways, resulting in overproduction of VLDL1 particles and leading to the characteristic dyslipidaemia associated with type 2 diabetes.

Acknowledgements

We thank H. Hilden, H. Perttunen-Nio, K. Skålén and D. Bedford for expert technical assistance and S. Ordway for editing the manuscript. This work was supported by The Swedish Research Council, The Swedish Heart-Lung Foundation, The Göran Gustafsson Foundation, The Sigrid Juselius Foundation and the Helsinki University Central Hospital Research Foundation.

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© Springer-Verlag 2006