Postprandial lipaemia, due to elevated plasma apolipoprotein (apo) B-48 concentrations, contributes to increased cardiovascular (CV) risk in obesity. Proprotein convertase subtilisin/kexin type 9 (PCSK9) and apoC-III may play a role in regulating triacylglycerol-rich lipoprotein (TRL)–apoB-48 metabolism. We investigated the associations between plasma PCSK9 and apoC-III concentrations and the kinetics of apoB-48 in obese subjects. Seventeen obese subjects were given an oral fat load. ApoB-48 tracer/tracee ratios were measured after an intravenous 2H3-leucine administration using GC–MS. Kinetic parameters, including secretion and fractional catabolic rates (FCRs), were derived using a multi-compartmental model. Plasma PCSK9 and apoC-III concentrations were significantly and positively (P<0.05 in all) associated with the total area-under-curve (AUC) and incremental AUC for apoB-48 and inversely with TRL–apoB-48 FCR. Plasma PCSK9 and apoC-III concentrations were not correlated (P>0.05 in all) with basal secretion or the number of TRL–apoB-48 secreted over the postprandial period. In the stepwise regression analysis, plasma PCSK9 was the best predictor of the total and incremental AUCs for plasma apoB-48 and the FCR of TRL–apoB-48. The association between plasma PCSK9 and apoC-III and TRL–apoB-48 FCR remained significant (P<0.05 in all) after adjusting for age, homoeostasis model assessment (HOMA) score, hepatic lipase or lipoprotein lipase (LPL). In a multiple regression model, 31% of variance in TRL–apoB-48 FCR was accounted for by plasma PCSK9 and apoC-III concentrations (adjusted R2=0.306, P<0.05). However, their associations with TRL–apoB-48 FCR were not independent of each other. Our results suggest that the catabolism of TRL–apoB-48 in the postprandial state may be co-ordinated by PCSK9 and apoC-III in obese individuals.

CLINICAL PERSPECTIVES

  • Hypertriacylglycerolaemia is a risk factor for coronary artery disease and the most consistent lipid disorder in obesity. PCSK9 and apoC-III may play a role in regulating apoB-48 metabolism.

  • In the present study, plasma PCSK9 and apoC-III concentrations were significantly and positively associated with postprandial apoB-48 metabolism, favouring an inverse association with the catabolism of apoB-48 in obese subjects.

  • Our results suggest that the catabolism of TRL–apoB-48 in the postprandial state may be co-ordinated by apoC-III and PCSK9 in these subjects.

INTRODUCTION

Hypertriacylglycerolaemia is the most consistent lipid disorder in obesity and may contribute to increased risk of cardiovascular disease (CVD) in these subjects [1]. Humans consume multiple meals during the day and, hence, are continually in a dynamic state of altered postprandial chylomicron metabolism, which may contribute to risk of CVD [2].

There is an accumulating body of evidence to suggest that triacylglycerol-rich apolipoprotein (apo) B-48-containing chylomicrons play a central role in the development of atherosclerosis, contributing to endothelial dysfunction, inflammation and oxidative stress and foam cell formation [3]. We, previously, demonstrated that obese individuals have increased fasting and postprandial apoB-48 concentrations, related chiefly to over-production and delayed clearance of apoB-48 particles [4].

After ingesting a meal, dietary triacylglycerols are packaged and transported into the circulation by intestinally-derived apoB-48 containing chylomicrons [2]. The chylomicron remnants are rapidly taken up into the liver via the low-density lipoprotein (LDL) receptor and the LDL receptor-related protein (LRP). ApoC-III is also involved in chylomicron metabolism, via its effect in inhibiting lipoprotein lipase (LPL) activity and chylomicron remnant uptake by hepatic lipoprotein receptors [5]. Elevated apoC-III may accordingly result in accumulation of chylomicrons in the circulation and increased risk of CVD [5,6].

Recently, there has been intense interest in the potential affect of proprotein convertase subtilisin kexin type 9 (PCSK9) on lipoprotein metabolism [7]. A recent study suggests that a positive relationship between plasma triacylglycerols and cardiovascular (CV) events may be partly explained by PCSK9 [8]. PCSK9 is a secretory protease that regulates cell surface receptors, principally LDL, but potentially also very-low density lipoprotein (VLDL) and LRP [9,10]. In animal studies, PCSK9 deficiency is associated with reduced postprandial hypertriacylglycerolaemia, partly because of increased hepatic clearance of chylomicrons [11]. Treatment with recombinant human PCSK9 has been shown to increase intestinal apoB-48 secretion in human enterocytes [12]. However, the precise role for plasma PCSK9 in regulating postprandial triacylglycerol-rich lipoprotein (TRL)–apoB-48 kinetics in humans remains unclear.

We hypothesized that plasma PCSK9 concentrations are related to alteration in the postprandial kinetics of TRL–apoB-48, favouring an inverse association with the catabolism of apoB-48 in obese subjects. We also measured plasma apoC-III concentration and explored its relationship with indices of chylomicron metabolism.

MATERIALS AND METHODS

Subjects

Seventeen obese subjects (nine men and eight women) with waist circumference ≥94 cm for men and ≥80 cm for women while consuming ad libitum, weight-maintaining diets were recruited. None of the subjects were smokers, consumed >30 g of alcohol/day, nor had familial hypercholesterolaemia or Type 2 diabetes, APOE2/E2 genotype, proteinuria (>300 mg/l), creatininaemia (>120 μmol/l) with low glomerular filtration rate (<60 ml/min), hypothyroidism, elevated hepatic aminotransferase, a history of CVD or was taking statin medication. All subjects were requested to maintain their usual diets and level of physical activity 2 weeks prior to the study. The study was approved by the Ethics Committee of the Royal Perth Hospital.

Clinical protocols

All subjects were admitted to the metabolic ward in the morning after a 14-hour fast. They were studied in a semi-recumbent position and allowed to drink only water after the test-meal. Arterial blood pressure was recorded after 3 min in the supine position using a Dinamap1846 SX/P monitor (Critikon). Body composition was estimated using a Wedderburn Body Composition Analyzer (Wedderburn) from which total body fat and fat free mass (FFM) were derived. Dietary intake was assessed for energy and major nutrients using FoodWorks 7 Pro (Xyris).

Following a baseline fasting blood sample, a bolus of 2H3-leucine (5 mg/kg of body weight) was administered intravenously, within a 2-minute period, into an antecubital vein via a 21-gauge butterfly needle. A liquid-formulated high fat-test meal consisting of 100 ml of full-cream milk, 150 ml of pure cream, 70 ml of corn oil, 90 g of whole egg and 10 g of sugar (a total of 4800 kJ, 130 g of fat, 17 g of protein and 21 g of carbohydrate) was consumed over several minutes. Additional blood samples were obtained after 30 min and 1, 1.5, 2, 3, 4, 5, 6, 8, 10 and 24 h.

Isolation and determination of apoB-48 leucine enrichment

TRLs were isolated from 3.5 ml of plasma by ultracentrifugation at a density of 1.006 kg/l (40000 rev./min, overnight, 4°C). ApoB-48 was isolated using SDS/PAGE and hydrolysed with 200 μl of 6 M HCl at 110°C overnight. Derivatization of leucine to the oxazolinone derivative was described previously [13]. Tracer to tracee ratio was determined using GC–MS with selected ion-monitoring of each sample at a mass to charge ratio (m/z) of 212 and 209.

Mathematical modelling of apoB-48

A non-steady compartment model was developed using the SAAM II program (The Epsilon Group) to account for changes in plasma apoB-48 concentration following consumption of the fat meal. Two separate, but linked, models were developed, one to account for the leucine tracer data, including plasma leucine and apoB-48 leucine enrichment and the other model for apoB-48 concentration data (Figure 1). The leucine compartment model consists of a four-compartment subsystem (compartments 1–4) that describes plasma leucine kinetics. This subsystem is connected to an intra-hepatic delay compartment (compartment 5) that accounts for the time required for leucine tracer to be incorporated into apoB-48 and subsequently secreted into plasma, compartment 6. The apoB-48 concentration compartment model consists of compartment 7, a delay compartment that represents four compartments in series and compartment 8 represents plasma apoB-48 particles. Physiologically, the series of compartments in compartment 7 collectively represent intestinal motility and the absorption processes, hence the additional delay compartment.

Multi-compartmental model for apoB-48 non-steady-state metabolism

Figure 1
Multi-compartmental model for apoB-48 non-steady-state metabolism
Figure 1
Multi-compartmental model for apoB-48 non-steady-state metabolism

In this model, the initial conditions reflect the basal apoB-48 concentrations such as that in the fasting state. The concentration of apoB-48 in compartment 7 can be estimated assuming steady-state conditions before the consumption of the fat meal. The fat meal tested is represented as a bolus input into compartment 7, which acts as a delay prior to observing increased concentrations of the apoB-48 in plasma. This single bolus input into compartment 7 is an adjustable parameter, which reflects the number of new apoB-48 particles secreted into plasma as a result of the fat meal. This assumes that the fat meal increases the production of apoB-48 particles and, hence, the secretion of apoB-48 varies over time. By contrast, the catabolism of apoB-48 particles from the plasma was assumed to be constant over the non-steady state period, similar to the kinetic studies by Le et al. [14]. In addition, the model of the apoB-48 tracer and concentration data assumed that the rate constants from compartments 6 and 8 were equivalent. The model could be used to estimate apoB-48 secretion in the fasted state, in the postprandial state, apoB-48 fractional catabolic rate (FCR) and the number of apoB-48 secreted in response to the fat meal.

Biochemical measurements

The methods for measurements of biochemical analytes were previously described [4]. Plasma PCSK9 was measured by immunoassay (R&D Systems). Plasma apoB-48 levels were measured by enzyme immunoassay kit (Fujirebio). Plasma apoC-III was measured with ELISA (AssayPro). Preheparin hepatic lipase (Abnova) and LPL (Cell Biolabs) concentrations were assessed with ELISA. Postprandial metabolism was quantified by calculating the area under the curve (AUC) and incremental area under the curve (iAUC) respectively, for plasma triacylglycerols and apoB-48 (0–24 h) using the trapezium rule. The iAUC was estimated as the difference between the area defined below the baseline concentration and the area under the plasma curve between 0 and 24 h.

Statistical analyses

All analyses were performed using SPSS 21. Associations were examined by simple and partial linear regression methods. We restricted our selection of independent variables for multivariate regression analysis to those that were associated with the AUCs for plasma triacylglycerols and apoB-48 and the FCR of TRL-apoB-48 in univariate analyses (plasma PCSK9, apoC-III and hepatic lipase concentration). Statistical significance was defined at the 5% level using a two-tailed test.

RESULTS

The characteristics of the 17 subjects are summarized in Table 1. They were middle-aged normotensive and near-normolipidaemic, with body mass index (BMI) ranging from 26 to 51 kg/m2. Compared with 13 lean subjects (age 61±8 years and BMI 23±2 kg/m2) from our previous report [4], the obese subjects had significantly (P<0.05 in all) higher postprandial triacylglycerol and apoB-48 AUCs (total AUC and iAUC), basal secretion rate and lower apoB-48 FCR.

Table 1
Clinical, biochemical and lipoprotein kinetic characteristics of the 17 obese subjects

HDL, high-density lipoprotein; SR, secretion rate.

CharacteristicMean±S.D.Range
Age (years) 59±6.0 45–66 
Weight (kg) 93±14 70–121 
BMI (kg/m233±6.0 26–51 
Waist circumference (cm) 108±11 93–128 
Glucose (mmol/l) 5.3±0.6 4.1–6.2 
Insulin (m-units/l) 9.5±3.9 5.0–18 
HOMA score 2.3±1.0 1.0–4.3 
Systolic blood pressure (mmHg) 131±10 107–145 
Diastolic blood pressure (mmHg) 76±7.0 62–86 
Triacylglycerols (mmol/l) 1.9±0.7 1.0–3.4 
Total cholesterol (mmol/l) 5.1±0.9 3.9–6.6 
HDL-cholesterol (mmol/l) 1.1±0.3 0.6–1.7 
LDL-cholesterol (mmol/l) 3.4±0.9 1.3–4.9 
ApoB-100 (g/l) 0.9±0.2 0.7–1.2 
ApoB-48 (mg/l) 12±8.0 3.0–34 
ApoC-III (mg/l) 148±35 99–216 
PCSK9 (μmol/l) 287±57 195–461 
Pre-heaprin hepatic lipase (μg/l) 47±5.6 41–60 
Pre-heparin LPL (μg/l) 119±75 33–282 
Triacylglycerol 0–24 h AUC (mmol/l·h) 63±27 37–127 
Triacylglycerol 0–24 h iAUC (mmol/l·h) 17±14 5.8–58 
ApoB-48 0–24 h AUC (mg/l·h) 439±240 102–993 
ApoB-48 0-24 hr iAUC (mg/l·h) 141±69 24–314 
Basal TRL–apoB-48 SR (mg/kg of FFM per day) 8.2±6.1 1.8–29 
TRL–apoB-48 FCR (pool/day) 10±4.0 4.5–20 
TRL–apoB-48 secreted (mg/kg of FFM)* 0.59±0.31 0.31–1.45 
CharacteristicMean±S.D.Range
Age (years) 59±6.0 45–66 
Weight (kg) 93±14 70–121 
BMI (kg/m233±6.0 26–51 
Waist circumference (cm) 108±11 93–128 
Glucose (mmol/l) 5.3±0.6 4.1–6.2 
Insulin (m-units/l) 9.5±3.9 5.0–18 
HOMA score 2.3±1.0 1.0–4.3 
Systolic blood pressure (mmHg) 131±10 107–145 
Diastolic blood pressure (mmHg) 76±7.0 62–86 
Triacylglycerols (mmol/l) 1.9±0.7 1.0–3.4 
Total cholesterol (mmol/l) 5.1±0.9 3.9–6.6 
HDL-cholesterol (mmol/l) 1.1±0.3 0.6–1.7 
LDL-cholesterol (mmol/l) 3.4±0.9 1.3–4.9 
ApoB-100 (g/l) 0.9±0.2 0.7–1.2 
ApoB-48 (mg/l) 12±8.0 3.0–34 
ApoC-III (mg/l) 148±35 99–216 
PCSK9 (μmol/l) 287±57 195–461 
Pre-heaprin hepatic lipase (μg/l) 47±5.6 41–60 
Pre-heparin LPL (μg/l) 119±75 33–282 
Triacylglycerol 0–24 h AUC (mmol/l·h) 63±27 37–127 
Triacylglycerol 0–24 h iAUC (mmol/l·h) 17±14 5.8–58 
ApoB-48 0–24 h AUC (mg/l·h) 439±240 102–993 
ApoB-48 0-24 hr iAUC (mg/l·h) 141±69 24–314 
Basal TRL–apoB-48 SR (mg/kg of FFM per day) 8.2±6.1 1.8–29 
TRL–apoB-48 FCR (pool/day) 10±4.0 4.5–20 
TRL–apoB-48 secreted (mg/kg of FFM)* 0.59±0.31 0.31–1.45 
*

TRL–apoB-48 secreted in response to the fat meal on top of basal apoB-48 (or -apoB-100) secretion.

Table 2 shows the relationship between plasma PCSK9 and apoC-III and TRL–apoB-48 kinetic parameters. In univariate analysis, fasting plasma PCSK9 concentration was significantly and positively (P<0.05 in all) associated with plasma fasting triacylglycerol (r=0.718), apoB-48 (r=0.530), apoC-III concentrations (r=0.609), AUCs for plasma triacylglycerols (r=0.577) and apoB-48 (r=0.587) and incremental AUC for plasma apoB-48 (r=0.668) and inversely with TRL–apoB-48 FCR (r=−0.589, P<0.05). Plasma apoC-III concentration was positively associated with plasma triacylglycerol (r=0.825, P<0.01), apoB-48 (r=0.421, P=0.08), AUCs for plasma triacylglycerols (r=0.704, P<0.01) and apoB-48 (r=0.486, P<0.05) and incremental AUC for plasma apoB-48 (r=0.503, P<0.05) and inversely with TRL–apoB-48 FCR (r=−0.528, P<0.05). Figure 2 shows the associations of plasma PCSK9 and apoC-III with the total AUC and iAUC for plasma apoB-48 and the FCR of TRL–apoB-48. Plasma PCSK9 and apoC-III concentrations were not correlated (P>0.05 in all) with hepatic lipase, LPL concentrations, homoeostasis model assessment (HOMA) score, basal secretion or the number of TRL–apoB-48 secreted over the postprandial period. No significant association was found between plasma hepatic lipase, LPL concentrations or HOMA score with TRL–apoB-48 FCR (r=0.119, −0.100 and 0.289 respectively). Plasma hepatic lipase concentration was significantly associated with fasting and AUC for plasma triacylglycerols (r=−0.585 and −0.504 respectively). In partial correlational analysis, the associations between plasma PCSK9 and apoC-III with AUCs for plasma triacylglycerols, apoB-48 and TRL–apoB-48 FCR remained significant (P<0.05 in all) after adjusting for age, gender, HOMA score, hepatic lipase or LPL (Table 3).

Associations between (A and B) the total AUC, (C and D) the iAUC, and (E and F) the FCR of apoB-48 and plasma PCSK9 and apoC-III in 17 obese subjects

Figure 2
Associations between (A and B) the total AUC, (C and D) the iAUC, and (E and F) the FCR of apoB-48 and plasma PCSK9 and apoC-III in 17 obese subjects
Figure 2
Associations between (A and B) the total AUC, (C and D) the iAUC, and (E and F) the FCR of apoB-48 and plasma PCSK9 and apoC-III in 17 obese subjects
Table 2
Associations (Pearson correlation coefficients) of plasma PCSK9 and apoC-III with triacylglycerols, pre-heparin hepatic lipase and LPL concentrations, HOMA score and the kinetic indices for TRL–apoB-48 in the subjects studied

*P<0.01, and **P<0.05.

CharacteristicPCSK9ApoC-III
Triacylglycerols (mmol/l) 0.718* 0.825* 
PCSK9 (μmol/l) 0.609*  
ApoB-48 (mg/l) 0.530** 0.421 
ApoC-III (mg/l)  0.609* 
Pre-heparin hepatic lipase (μg/l) −0.362 −0.357 
Pre-heparin LPL (μg/l) −0.172 0.147 
HOMA score 0.160 0.054 
Triacylglycerol 0–24 h AUC (mmol/l·h) 0.577** 0.704* 
Triacylglycerol 0-24 h iAUC (mmol/l·h) 0.186 0.310 
ApoB-48 0–24 h AUC (mg/l·h) 0.587** 0.486** 
ApoB-48 0–24 h iAUC (mg/l·h) 0.668* 0.503** 
TRL–apoB-48 FCR (pool/day) −0.589** −0.528** 
Basal TRL–apoB-48 SR (mg/kg of FFM per day) 0.056 −0.153 
TRL–apoB-48 secreted (mg/kg of FFM) 0.324 0.031 
CharacteristicPCSK9ApoC-III
Triacylglycerols (mmol/l) 0.718* 0.825* 
PCSK9 (μmol/l) 0.609*  
ApoB-48 (mg/l) 0.530** 0.421 
ApoC-III (mg/l)  0.609* 
Pre-heparin hepatic lipase (μg/l) −0.362 −0.357 
Pre-heparin LPL (μg/l) −0.172 0.147 
HOMA score 0.160 0.054 
Triacylglycerol 0–24 h AUC (mmol/l·h) 0.577** 0.704* 
Triacylglycerol 0-24 h iAUC (mmol/l·h) 0.186 0.310 
ApoB-48 0–24 h AUC (mg/l·h) 0.587** 0.486** 
ApoB-48 0–24 h iAUC (mg/l·h) 0.668* 0.503** 
TRL–apoB-48 FCR (pool/day) −0.589** −0.528** 
Basal TRL–apoB-48 SR (mg/kg of FFM per day) 0.056 −0.153 
TRL–apoB-48 secreted (mg/kg of FFM) 0.324 0.031 

TRL–apoB-48 secreted in response to the fat meal on top of basal apoB-48 secretion.

Table 3
Partial correlational analysis of plasma PCSK9 and apoC-III with total AUC for plasma triacylglycerol, apoB-48 and the FCR of TRL–apoB-48 after adjustment for age, sex, HOMA score, hepatic lipase and LPL concentrations

*P<0.05 and **P<0.01. TG: triacylglycerol.

Partial coefficient (r)
Adjusted variablesCharacteristicsPCSK9ApoC-III
Age (years) AUC–TG (mmol/l·h) 0.555* 0.579* 
 AUC–apoB-48 (mg/l·h) 0.594* 0.562 
 TRL–apoB-48 FCR (pool/day) −0.566* −0.502* 
Gender (male/female) AUC–TG (mmol/l·h) 0.625** 0.678** 
 AUC–apoB-48 (mg/l·h) 0.586* 0.527* 
 TRL–apoB-48 FCR (pool/day) −0.602* −0.537* 
HOMA score AUC–TG (mmol/l·h) 0.563* 0.732** 
 AUC–apoB-48 (mg/l·h) 0.584* 0.502* 
 TRL–apoB-48 FCR (pool/day) −0.575* −0.671** 
Hepatic lipase (μg/l) AUC–TG (mmol/l·h) 0.534* 0.650** 
 AUC–apoB-48 (mg/l·h) 0.567* 0.512* 
 TRL–apoB-48 FCR (pool/day) −0.570* −0.504* 
LPL (μg/l) AUC–TG (mmol/l·h) 0.601* 0.703** 
 AUC–apoB-48 (mg/l·h) 0.617* 0.505* 
 TRL–apoB-48 FCR (pool/day) −0.556* −0.539* 
Partial coefficient (r)
Adjusted variablesCharacteristicsPCSK9ApoC-III
Age (years) AUC–TG (mmol/l·h) 0.555* 0.579* 
 AUC–apoB-48 (mg/l·h) 0.594* 0.562 
 TRL–apoB-48 FCR (pool/day) −0.566* −0.502* 
Gender (male/female) AUC–TG (mmol/l·h) 0.625** 0.678** 
 AUC–apoB-48 (mg/l·h) 0.586* 0.527* 
 TRL–apoB-48 FCR (pool/day) −0.602* −0.537* 
HOMA score AUC–TG (mmol/l·h) 0.563* 0.732** 
 AUC–apoB-48 (mg/l·h) 0.584* 0.502* 
 TRL–apoB-48 FCR (pool/day) −0.575* −0.671** 
Hepatic lipase (μg/l) AUC–TG (mmol/l·h) 0.534* 0.650** 
 AUC–apoB-48 (mg/l·h) 0.567* 0.512* 
 TRL–apoB-48 FCR (pool/day) −0.570* −0.504* 
LPL (μg/l) AUC–TG (mmol/l·h) 0.601* 0.703** 
 AUC–apoB-48 (mg/l·h) 0.617* 0.505* 
 TRL–apoB-48 FCR (pool/day) −0.556* −0.539* 

In stepwise regression analysis including plasma PCSK9 and apoC-III concentration, plasma PCSK9 was the best predictor of total AUC and iAUC for plasma apoB-48 and the FCR of TRL–apoB-48 (standardized β coefficient=0.587, 0.658 and −0.589 respectively). Conversely, plasma apoC-III was the best predictor of the fasting and total AUC for plasma triacylglycerols (standardized β coefficient=0.825 and 0.704 respectively) in a stepwise regression model including plasma PCSK9, apoC-III and hepatic lipase concentration. Moreover, 31% of variance in TRL–apoB-48 FCR was accounted for by plasma PCSK9 and apoC-III concentrations (adjusted R2=0.306, P<0.05). In partial correlational analysis, the significant association between plasma PCSK9 and TRL–apoB-48 FCR was not independent of plasma apoC-III (partial coefficient r=−0.397). Likewise, the significant associations between plasma apoC-III and TRL–apoB-48 FCR was not independent of plasma PCSK9 (partial coefficient r=−0.302).

DISCUSSION

Our major finding was that the plasma PCSK9 and the apoC-III concentration were significantly and inversely associated with the FCR of TRL–apoB-48. This association of PCSK9 and apoC-III with apoB-48 FCR is independent of age, gender, hepatic lipase, LPL or insulin resistance.

PCSK9 is an important regulator of LDL receptor and, hence, LDL metabolism [7]. Using a stable isotopic technique, we previously reported that plasma PCSK9 concentration was inversely associated with the FCR of LDL–apoB-100 in the post-absorptive state [15]. Given the competition between chylomicron and VLDL remnants for hepatic receptors, it is conceivable that PCSK9 will also modulate the removal of chylomicrons via these receptor pathways, thereby affecting the catabolism of apoB-48. This notion is consistent with our present data showing significant inverse associations between fasting plasma PCSK9 concentration and total AUC and iAUC for plasma apoB-48, as well as the FCR of TRL–apoB-48.

Consistent with our previous report [4], we confirm that plasma apoC-III concentration was significantly associated with TRL–apoB-48 FCR in obese subjects. Given that apoC-III has a dual role in triacylglycerol metabolism of inhibiting both LPL-mediated triacylglycerol lipolysis and hepatic uptake of TRL remnants [5], it is not unexpected that plasma apoC-III was a better predictor of fasting and postprandial AUC for plasma triacylglycerol than plasma PCSK9 concentrations in our stepwise regression model. Consistent with this, Sullivan et al. [16] found no relationship between plasma PCSK9 concentration and the catabolism of VLDL-triacylglycerols in obese subjects, implying that PCSK9 has a minimal role in LPL-mediated triacylglycerol hydrolysis. By contrast, we found that plasma PCSK9 was a better predictor of TRL–apoB-48 FCR than plasma apoC-III. Since no correlation was found between plasma concentrations of LPL or hepatic lipase and TRL–apoB-48 FCR, we postulate that the major pathway to determine TRL–apoB-48 FCR is at the level of hepatic remnant receptors and that plasma PCSK9 may have a more potent effect than apoC-III on this pathway. However, this speculation requires further investigation.

We also found that 31% of variance in TRL–apoB-48 FCR could be accounted for by plasma PCSK9 and apoC-III. This result further supports their roles in the regulation of TRL–apoB-48 catabolism. Given that both PCSK9 and apoC-III impair the hepatic uptake of TRL remnants, it is not unexpected that their associations with TRL–apoB-48 FCR were not independent of each other. The lack of significant associations between plasma heparin lipase LPL concentrations and HOMA score with TRL–apoB-48 FCR may reflect lack of statistical power. Hence, the findings need to be confirmed in a larger sample size. Further in vivo studies to measure post-heparin masses and activities of LPL and hepatic lipase and LDL receptor activity, may help to clarify the interaction between PCSK9 and apoC-III in regulating postprandial TRL–apoB-48 catabolism.

The role of PCSK9 in regulating apoB-48 secretion remains unclear. Rashid et al. [12] suggested that PCSK9 increased intestinal apoB-48 secretion in isolated human enterocytes. By contrast, Le May et al. [17] found that PCSK9 increased transintestinal cholesterol excretion which could conceivably contribute to reduced apoB-48 secretion. We found no correlation between plasma PCSK9 concentration and basal apoB-48 secretion rate or the number of apoB-48-containing particles secreted over the postprandial period. This result may preclude a significant intracellular role of PCSK9 in regulating apoB-48 secretion in the intestine. It is possible that the effect of insulin resistance overrides the affect of PCSK9 on apoB-48 secretion [18]; this would have diminished the strength of the association between PCSK9 concentration and apoB-48 secretion rate seen in our obese subjects. However, this requires further investigation.

Data from clinical trials demonstrate the association between PCSK9 and apoC-III concentrations and CVD events [6,8]. Beyond LDL metabolism, we now provide a kinetic basis for the association between plasma PCSK9 and apoC-III and chylomicron metabolism in obesity. Since our result was based on correlational analyses, it needs to be formally tested in intervention studies with a larger sample size (e.g. PCSK9 inhibitor and apoC-III antisense oligonucleotides)

Abbreviations

     
  • apo

    apolipoprotein

  •  
  • AUC

    area under the curve

  •  
  • BMI

    body mass index

  •  
  • CVD

    cardiovascular disease

  •  
  • FCR

    fractional catabolic rate

  •  
  • FFM

    fat free mass

  •  
  • HOMA

    homoeostasis model assessment

  •  
  • iAUC

    incremental area under the curve

  •  
  • LDL

    low-density lipoprotein

  •  
  • LPL

    lipoprotein lipase

  •  
  • LRP

    LDL receptor-related protein

  •  
  • PCSK9

    proprotein convertase subtilisin/kexin type 9

  •  
  • TRL

    triacylglycerol-rich lipoprotein

  •  
  • VLDL

    very-low density lipoprotein

AUTHOR CONTRIBUTION

Dick Chan, Hugh Barrett and Gerald Watts conceived and designed the study; Annette Wong, Dick Chan, Jing Pang, Gerald Watts and Hugh Barrett supervised or conducted research; Annette Wong, Dick Chan, Jing Pang and Hugh Barrett analysed the data; Annette Wong, Dick Chan, Gerald Watts and Hugh Barrett wrote the paper. All authors read and approved the final manuscript.

We would like to thank the nursing staff of the Clinical Research Studies Unit of the School of Medicine and Pharmacology (Royal Perth Hospital, University of Western Australia) for providing expert clinical assistance.

FUNDING

This work was supported by the National Health and Medical Research Council (NHMRC) [grant number 572585]. D.C.C. is a Career Development Fellow of the NHMRC. P.H.R.B. is an NHMRC Senior Research Fellow.

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