The apoB-to-PCSK9 ratio: A new index for metabolic risk in humans

Accepted Manuscript The apoB/ PCSK9 ratio: a new index for metabolic risk in humans Hanny Wassef, Simon Bissonnette, Nathalie Saint-Pierre, Valérie Lamantia, Yannick Cyr, Michel Chrétien, May Faraj PII:

S1933-2874(15)00294-9

DOI:

10.1016/j.jacl.2015.06.012

Reference:

JACL 779

To appear in:

Journal of Clinical Lipidology

Received Date: 10 February 2015 Revised Date:

8 May 2015

Accepted Date: 23 June 2015

Please cite this article as: Wassef H, Bissonnette S, Saint-Pierre N, Lamantia V, Cyr Y, Chrétien M, Faraj M, The apoB/ PCSK9 ratio: a new index for metabolic risk in humans, Journal of Clinical Lipidology (2015), doi: 10.1016/j.jacl.2015.06.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT The apoB/ PCSK9 ratio: a new index for metabolic risk in humans Hanny Wassef1,2,3 *, Simon Bissonnette1,2,3 *, Nathalie Saint-Pierre1,2,3, Valérie Lamantia1,2,3, Yannick Cyr1,2,3, Michel Chrétien2,3,4, May Faraj1,2,3

Institut de recherches cliniques de Montréal (IRCM), Montréal Québec Canada 3

Montreal Diabetes Research Center (MDRC), Montréal Québec Canada 4

Ottawa Health Research Institute (OHRI), Ottawa Ontario Canada

May Faraj, P.Dt., Ph.D.

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Send correspondence and reprints requests to;

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2

Faculty of Medicine, Université de Montréal, Québec Canada

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1

Institut de recherches cliniques de Montréal (IRCM) Office 1770.2 110, Pins Avenue West

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Montréal, Québec H2W 1R7 Tel: (514) 987-5655

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Email: [email protected]

Abbreviated title: High apoB/PCSK9 and metabolic risks in humans

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Abstract = 250 words, body = 4,161 words, total number of figures and tables = 6 Key words: Apolipoprotein B100, low-density lipoproteins, PCSK9, adipose tissue, type 2 diabetes mellitus, obesity

Disclosure statement: The authors have nothing to disclose * These authors contributed equally to the work

ACCEPTED MANUSCRIPT Abstract: Background: PCSK9 shuttles LDL-receptors for degradation, thus upregulates LDL plasma clearance. While PCSK9 loss-of-function is cardioprotective, its role in metabolic risks remains unknown. Increased apoB-lipoproteins uptake into non-hepatic tissues such as white adipose tissue

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(WAT) induces their dysfunction, which may be favoured by lower plasma PCSK9. We hypothesised that lower plasma PCSK9 relative to apoB, or higher apoB/PCSK9 ratio, is a better predictor of metabolic disturbances than PCSK9 alone in humans.

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Methods: 33 men and 48 postmenopausal women (>27 kg/m2, 45-74 years, normoglycemic) underwent in-depth assessment of glucose and fat metabolism using high-fat meals, WAT biopsies,

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intravenous-glucose-tolerance tests and hyperinsulinemia clamps.

Results: Plasma apoB correlated positively with fasting and postprandial TG and chylomicron clearance (R=0.44-0.66) and glucose-stimulated-insulin secretion (R=0.24), and negatively with insulin sensitivity (R=-0.28) and gynoid WAT in situ lipoprotein lipase activity (i.e. ex vivo WAT function, R2=0.34). Neither PCSK9 nor LDLC associated with these risks. In regression analysis that

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adjusted for BMI, lower plasma PCSK9 strengthened the association of apoB to WAT dysfunction and insulin resistance. Moreover, plasma apoB/PCSK9 ratio correlated positively with all these metabolic risks and further associated positively with android/gynoid fat ratio (R=0.41) and negatively with

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gynoid fat mass (R=-0.23, all p≤0.05). No significant sex-differences existed in these associations. Conclusions/interpretation: Lower plasma PCSK9 relative to apoB associates with metabolic risks

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and WAT dysfunction in normoglycemic obese subjects. We hypothesize that the plasma apoB/PCSK9 ratio provides a better clinical index than PCSK9 alone for monitoring early metabolic disturbances that may be promoted by reduction in plasma PCSK9.

ACCEPTED MANUSCRIPT Introduction: Normal fasting plasma glucose is maintained by the balance between insulin sensitivity and secretion (1). “Prediabetic” metabolic abnormalities that promote insulin resistance (IR), such as impaired glucose tolerance, increase the risk for type 2 diabetes (T2D) before the rise of fasting

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glucose. In time, these abnormalities are believed to promote hyperinsulinemia, β-cells exhaustion, apoptosis, and hypoinsulinemia, followed by fasting hyperglycemia and progression to T2D (1). While the etiology of IR is multifaceted, dysfunctional gynoid white adipose tissue (WAT) is believed to favor

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delayed clearance of triglyceride-rich lipoproteins (TRL) and increased lipid influx into peripheral tissues inducing lipotoxicity and IR (1;2).

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We previously reported that elevated numbers, but not cholesterol content, of apoB-lipoprotein particles, measured as plasma apoB concentrations, are strongly associated with risks for T2D, namely IR, chronic inflammation and dysfunctional WAT in obese women (3-5). Although the mechanisms behind these associations are not fully elucidated, we demonstrated that native human LDL have an acute inhibitory effect of lipoprotein lipase (LPL) activity in vitro and on in situ LPL activity

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in murine adipocytes and human WAT (5). Native human LDL also have a negative chronic effect on the differentiation and function of murine preadipocytes (5). Similarly, oxidized LDL (oxLDL) were reported to decrease adipocytes differentiation in a scavenger receptor (CD36)-dependent mechanism

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(6;7). In line, epidemiological evidence confirmed that the risk of T2D is related to elevated plasma apoB in several populations such as Canadian (8) and Korean (9), independent of traditional risk

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factors such as central adiposity (8), fasting glycemia (8;9), and HbA1c (9). Of importance to note however is that the abnormalities in the functions of WAT (5-7), skeletal muscle (10;11) and pancreas (12) induced by apoB-lipoproteins are dependent on receptor-mediated tissue-uptake of these particles. On the other hand, the lack of LDL receptors (LDLR) was suggested to protect against new onset T2DM in patients with familial hypercholesterolemia (FH) compared to unaffected relatives after correcting for confounders such as age, gender, BMI, statin use, HDL-C and TG (13). Taken together, these findings suggest that increasing or decreasing receptor-mediated uptake of apoB-lipoproteins into peripheral tissue increase or decrease T2D risks, respectively.

ACCEPTED MANUSCRIPT Receptor mediated-uptake of apoB-lipoproteins is controlled, in part, by PCSK9 (proprotein convertase subtilisin/kexin type 9). PCSK9 is the last member of a novel family of enzymes, proprotein convertases, whose existence was proposed following the prohormone theory published in 1967 (14). It is an endoproteolytic enzyme that forms a heterodimer with its cleaved prosegment and becomes an

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escort protein for hepatic LDLR (15). PCSK9 is a key regulator of LDL uptake as it shuttles LDLR to lysosomal degradation (16). Plasma PCSK9 associates positively with plasma LDLC, and variants that cause gain or loss of function of PCSK9 are associated with hyper- or hypo-cholesterolemia,

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increasing or decreasing CVD risk, respectively (17;18)

The association of PCSK9 with metabolic risks is, to date, suggestive but not conclusive in

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both mice (19;20) and humans (21;22). However, as LDLR are expressed by WAT (6;23), muscle (10) and pancreas (24), reducing plasma PCSK9 may reduce the “barrier” against apoB-lipoproteins uptake into peripheral tissues promoting their dysfunction. Given that the concentrations of PCSK9 does not always reflect its function (25), the interplay between plasma PCKS9 and apoB-lipoproteins may be a better indication of PCSK9 function and the overall clearance of apoB-lipoproteins.

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Accordingly, we hypothesized that lower plasma PCSK9 relative to apoB, or increased apoB/PCSK9 ratio, was associated with reduced WAT function ex vivo and delayed TG and chylomicron clearance, hyperinsulinemia and IR in vivo in non-diabetic obese subjects. Knowledge of plasma PCSK9 alone is

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unlikely to reveal these early “pre-diabetic” risks.

ACCEPTED MANUSCRIPT Material and methods: Study population: Metabolic studies measuring insulin sensitivity and secretion in vivo were conducted between 2010 and 2014 at the Clinical Research Institute of Montreal (IRCM). Subjects were recruited by

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newspaper advertisement with the following inclusion criteria; BMI > 27 kg/m2, age = 45 - 74 years, confirmed menopausal status (FSH ≥ 30 U/l, or more than 1 year without menses), non-smoker, sedentary (less than 2 hours of structured exercise/ week), low alcohol consumption (< 2 alcoholic

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drinks/day). The exclusion criteria were: present or prior history of CVD and hypertension requiring medication, diabetes (or fasting glucose > 7 mmol/L), cancer (within the last 3 years), untreated

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thyroid disease, kidney disease (or creatinine > 100 µmol/L), hepatic disease (or AST/ALT > 3 times normal limit), claustrophobia, anemia (Hb < 120 g/L), blood coagulation problems, current or past 3months use of drugs affecting metabolism (hormone replacement therapy except thyroid hormone at a stable dose, systemic corticosteroids, anti-psychotic/psycho-active drugs, anticoagulant, weight-loss, and adrenergic agonist), known substance abuse, exceeding the annual allowed radiation dose

physician.

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exposure, all other medical or psychological conditions deemed inappropriate according to the

One hundred and ten subjects were recruited, of whom 82 were eligible for inclusion in the

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principal study (49 women and 33 men). One woman felt malaise during the measurement of insulin secretion by an intravenous glucose tolerance test (IVGTT) and was excluded from continuing the test

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and study. Included subjects (N=81) were invited to be part of a sub-study measuring postprandial fat metabolism and WAT function. Thirty one subjects accepted and were included (17 women and 14 men). The study was carried out in accordance with the Declaration of Helsinki (2008) of the World Medical Association. All subjects signed an informed consent prior to the initiation of the 2 studies, and the studies were approved by the Ethics committee at IRCM. Anthropometry and metabolic measurements (N=81): After 4-weeks of weight-stabilization, body composition was measured by dual-energy x-ray absorptiometry (intelligent or iDXA, GE Healthcare), which measures total fat as well as android or

ACCEPTED MANUSCRIPT central fat (starting above the pelvis), and gynoid fat (comprising the hips and thighs). Plasma lipids and apolipoproteins were measured by an automated analyser (COBAS 400, Roche Diagnostic), glucose by automated analyser (YSI Incorporated, InterScience), and insulin by human RIA kit (Millipore Corporation). Plasma apoB48 was measured by ELISA (BioVendor) (5). Plasma PCSK9

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was measured using the commercial CircuLex Human PCSK9 ELISA Kit (MBL International). Lipoprotein size and cholesterol concentrations in lipoproteins subclass were measured by an automated FDA-approved electrophoresis system (Lipoprint®, Quantimetrix) (5;26).

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Insulin sensitivity and secretion in vivo (N=81):

Assessment of insulin sensitivity and secretion was conducted using a modified Botnia clamp.

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In brief, subjects underwent a 1-hour IVGTT using a bolus infusion of 20% dextrose (0.3 g glucose/kg body weight). This was followed by a 3-hours hyperinsulinemic euglycemic clamp, during which plasma insulin was elevated to a plateau concentration using insulin infusion (75 mU/m2/min), while plasma glucose was maintained at fasting levels by dextrose infusion. First phase, second phase, and total insulin secretion during the IVGTT were assessed as the area under the curve (AUC) of plasma

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insulin during the first 10, last 50 and total 60 minutes of the IVGTT, respectively. Insulin sensitivity was assessed during steady state (last 30 minutes of the clamp), and expressed as glucose infusion rate alone (GIR) or divided by steady state plasma insulin (M/I) (3;4). HOMA-IR was calculated as

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(fasting insulin (µU/ml) x fasting glucose (mM)/22.5). Subjects consumed a high carbohydrate diet (225 g/day) for the 3 days preceding the clamp to maximize glycogen stores.

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Postprandial fat clearance in vivo and gynoid WAT function ex vivo (N=31): The clearance of a high-fat meal was assessed as previously published (5;26). Briefly, fasting subcutaneous hip WAT samples were obtained by needle biopsy under local anaesthesia (Xylocaine 20 mg/ml, AstraZeneca) by a physician. Fasting blood samples were collected at T = 0 hr, followed by the consumption of a high-fat meal standardized to body surface area (600 kcal/m2, 68% fat, 18% carbohydrate). Serial collection of blood samples were conducted at 0, 1, 2, 4, and 6 hours. The area under the 6-hours’ time curve of plasma TG and apoB48 were calculated without (AUC6hrs) or with subtraction of fasting values (increment increase in AUC6hrs or iAUC6hrs).

ACCEPTED MANUSCRIPT We previously demonstrated that the measurement of heparin-releasable LPL activity alone does not reflect the overall capacity of adipocytes to clear circulating TRL, given the well-known inhibition of LPL activity by accumulating non-esterified fatty acids (NEFA) (27). Accordingly, WAT function is assessed here ex vivo as in situ LPL activity, which represent the overall process of

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hydrolysis of synthetic 3H-triolein-labelled-TRL (3H-TRL) and uptake and storage of released 3H-NEFA by WAT biopsies as published (5;26;27). Briefly, fresh subjects’ WAT samples (5-10 mg) taken from the gynoid area were incubated with 500 µL 3H-TRL (95% TG, 1.27 mM TG, 0.54 M Tris-HCl, pH 7.2,

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in DMEM/F12, 5.1% BSA, and 7.5% fasting serum) on a shaking plate for 4 hours. WAT samples were washed and 3H-lipids were extracted, counted, and expressed per mg of WAT. Total gynoid fat

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function was estimated by multiplying WAT function per mg by total gynoid fat mass (by DEXA). WAT biopsies from 3 subjects were not sufficient to conduct ex vivo analyses. The analysis presented here were from 28 subjects. Adipocyte Area (N=28):

Adipocyte area in the WAT biopsies were measured by digital-imaging as published (5;26).

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The average surface areas of adipocytes in 6 view fields, in 3 WAT slides are reported per subject (849 ± 280 adipocytes/ subject). The 3 slides were at least 48 µm apart to avoid multilayered images of one cell.

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Statistical analysis:

Data is presented as mean ± SEM. Pearson correlation analysis were used to examine the

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linear association between the independent and dependent variables. Data was log-transformed (base 10) then entered in the correlation analyses when the test for residues’ equal variance failed. Slope analysis indicated no significant sex-differences in the direction of association of the independent variables to dependent variables presented in table 2 and figures 1-4; accordingly, data for both sexes were pooled. Benjamini correction was applied to the correlation p-values in each dependent variable in Table 2 to control for false discovery rate resulting from multiple testing. Corrected and noncorrected p-values are presented so as not to lose new meaningful relationships. Table 3 was analysed using a stepwise forward regression analysis with co-linearity analysis and adjustment for

ACCEPTED MANUSCRIPT BMI and sex (entered as a factor as men=0 vs women=1) for dependent variables with N=81 and for BMI or sex for dependent variables measured in the sub-study (N=31). Statistical analysis was performed using SPSS V19, and slope and curve analyses using Prism V6.04. Significance was set at

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p ≤ 0.05.

ACCEPTED MANUSCRIPT Results Baseline fasting anthropometric and metabolic parameters of the subjects included are presented in Table 1. Both men and women were normoglycemic with similar adiposity (BMI or total fat mass), but men had higher abdominal obesity as indicated by all indices measured (waist

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circumference, android fat, and android/gynoid fat). Women had higher plasma cholesterol and PCSK9 with similar apoB. Accordingly, they had lower plasma apoB/PCSK9 ratio than men. On the other hand, women had lower plasma TG, higher HDLC, larger LDL diameter and higher fasting

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insulin sensitivity (lower HOMA-IR). There were no statistical differences between baseline characteristics of men and women in the principal study (N=81) and the sub-study (N=31).

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Plasma PCSK9 does not associate with metabolic risks in normoglycemic obese subjects: We first examined whether higher plasma PCSK9 was associated with plasma retention of apoB-lipoproteins. Plasma PCSK9 was positively associated with total cholesterol (Fig 1A) and nonHDL cholesterol (r=0.34, p=0.002), that was however limited to LDLC (Fig 1B) but not VLDLC (Fig 1D). PCSK9 was also not correlated with cholesterol contained in non-apoB-containing lipoproteins

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(HDL, Fig 1D). There was no significant association of plasma PCSK9 with the number of atherogenic particles measured as apoB (Fig 1E) nor with plasma TG in the fasting (Fig 1F) or postprandial states (Log10 [AUC6hrs and iAUC6hrs of total TG or apoB48], Table 2). No sex differences existed in these

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associations.

Plasma PCSK9 did not associate with glucose-induced insulin secretion (1st phase, 2nd phase

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or total, Table 2). It also did not associate with IR in the fasting state (insulin or HOMA-IR) as previously demonstrated by our group in a similar population of postmenopausal obese women (22), nor following the hyperinsulinemia euglycemia clamp assessed as GIR or M/I in this study (Table 2). Finally, there was no association between plasma PCSK9 with ex vivo WAT function assessed as in situ LPL activity, whether expressed per mg WAT or corrected for total gynoid fat mass. Of interest, despite lack of association of plasma PCSK9 with adiposity indices in general, its association was in the direction of higher risks when existed (i.e. higher android/gynoid fat mass ratio, Table 2).

ACCEPTED MANUSCRIPT Plasma apoB associates with metabolic risks in normoglycemic obese subjects, a relation which was strengthened by lower plasma PCSK9. As presented in Table 2, plasma apoB associated with fasting and postprandial hypertriglyceridemia and delayed chylomicrons clearance and with reduced WAT function (per mg and

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total). Moreover, plasma apoB associated positively with hyperinsulinemia (2nd phase and total insulin secretion) and negatively with insulin sensitivity (GIR and M/I, Table 2).

To examine whether concomitant evaluation of plasma PCSK9 with apoB improves the

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association of plasma apoB to these metabolic risks, we entered plasma apoB and PCSK9 as independent variables in a stepwise forward regression analysis to predict the risks. We adjusted for

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BMI (model 1) given the phenotype of the study population and the known association of adiposity with metabolic complications in humans. As presented in Table 3 (model 1), the associations of apoB to the metabolic risks were independent of BMI as plasma apoB predicted additional inter-subject variability in fasting and postprandial plasma TG (by 34% and 43%, respectively), postprandial chylomicron clearance (by 20%), gynoid WAT function assessed as in situ LPL activity per mg (by

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26%) and for total gynoid fat (by 21%), 2nd phase and total insulin secretion (by ~ 7%), and insulin sensitivity (by ~ 10%). All associations of apoB were in the direction of a higher risk. Lower plasma PCSK9 increased the power of the model including BMI and apoB to predict lower gynoid WAT

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function (per mg tissue by 14% and total by 18%) and lower insulin sensitivity (by 4% for GIR). In another model (model 2), adjustment for sex together with BMI increased the power of the

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final model including apoB to predict fasting plasma TG, 1st phase, 2nd phase or total insulin secretion, and insulin sensitivity expressed as M/I, while it eliminated plasma PCSK9 from the model predicting GIR. Adjustment for sex instead of BMI for the variables measured during the pilot study (postprandial plasma TG, apoB48 and WAT in situ LPL activity) also increased the prediction power of the model including apoB and/or PCSK9. Notably, higher plasma apoB and lower PCSK9 remained strongly linked to lower WAT function (per mg or total). Plasma apoB/PCSK9 ratio associates with hypertriglyceridemia, delayed postprandial clearance of chylomicrons, reduced in situ LPL activity in gynoid WAT, hyperinsulinemia and

ACCEPTED MANUSCRIPT IR in normoglycemic obese subjects: Since in regression analysis lower plasma PCSK9 relative to apoB increased the association of PCSK9, and in some cases apoB, to the metabolic risks, we examined whether considering their concentrations concomitantly in one index may be linked to the outcomes measured. Accordingly, we

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examined the association of apoB/PCSK9 to these risks. Plasma apoB/PCSK9 ratio correlated positively with all measures of plasma TG metabolism whether fasting (Fig 2A) or postprandial TG, evaluated as total AUC6hrs (Fig 2B) or increment increase in AUC6hrs (Fig 2C). Additionally, plasma

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apoB/PCSK9 associated with delayed postprandial clearance of chylomicrons in a hyperbolic nonlinear relationship (y= [2.19*apoB/PCSK9]/ [0.52+apoB/PCSK9], R2=0.19, p<0.05). Despite the lack of

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an association between plasma apoB/PCSK9 ratio with BMI, total and android fat, waist circumference, and adipocytes area (Table 2 and Figure 2), plasma apoB/PCSK9 correlated positively with android/gynoid fat mass ratio (Fig 1F) and negatively with gynoid fat mass (Fig 2F) and function, whether assessed per mg WAT (Fig 2G) or when corrected for total gynoid fat mass (Fig 2H). Of note, the negative relation of apoB/PCSK9 ratio to WAT function per mg of fat mass (Fig 2E) was best

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described by a non-linear (R2=0.34) rather than a linear regression (R2=0.20, p<0.05 for both, p=0.03 for comparison of curves fit), with a plateau of lowest WAT function at 1.18 nmol 3H-TG hydrolyzed per mg WAT (one phase decay curve; y=[24.0-1.18]*exp[-0.62*apoB/PCSK9] + 1.18). Similar findings

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were obtained when log10 WAT function (in Fig 2G) was used as a dependent variable (R2=0.37 nonlinear vs R2=0.21 linear, p<0.05 for both, p=0.015 for comparison of curves fit).

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In regards to glucose metabolism, plasma apoB/PCSK9 ratio correlated positively with 2nd phase (Fig 3B) and total insulin secretion (Fig 3C) but not with 1st phase insulin secretion (Fig 3A) during the IVGTT. Plasma apoB/PCSK9 also correlated negatively with peripheral insulin sensitivity measured during the hyperinsulinemia clamp (Fig 3E and 3F, respectively) but not with fasting measures of IR (HOMA-IR, Fig 3D). As evident in Figure 3, no sex differences existed in the direction of associations of plasma apoB/PCSK9 with these metabolic risks. Notably, the negative associations of these metabolic risks with plasma apoB/PCSK9 were stronger than those with LDLC/PCSK9 ratio, but were non-existent when LDL-C and PCSK9 were evaluated in isolation of each other (Table 2).

ACCEPTED MANUSCRIPT Discussion This study examines the hypothesis that reduced plasma PCSK9, when considered together with the concentrations of the particles it regulates, is associated with early “pre-diabetic” abnormalities more than when it is evaluated in isolation of them. Indeed, an elevated plasma

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apoB/PCSK9 ratio, but not plasma PCSK9 per se, was associated with hypertriglyceridemia, delayed chylomicron clearance, hyperinsulinemia, peripheral IR and reduced gynoid fat function assessed as in situ LPL activity in normoglycemic obese subjects. In a separate examination, regression analysis

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also showed that reduced plasma PCSK9 strengthened the association of plasma apoB to gynoid WAT dysfunction and insulin resistance independent of adiposity. Taken together, these findings

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suggest that a model that concomitantly evaluates the risk (apoB) relative to the barrier for their tissue uptake (PCSK9) improves the association of plasma PCSK9 with metabolic risks in humans. It should be underscored here that the correlative nature of this study does not allow causal relationships to be established between low plasma PCSK9 and the development of metabolic risks. However, it reveals the unfavorable direction of changes in glucose and fat metabolism when lower

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plasma PCSK9 relative to apoB-lipoproteins exists in humans. Moreover, it allows for the generation of novel hypotheses regarding underlying mechanisms and clinical impact of the findings. This is strengthened by the use of gold-standard in vivo and ex vivo techniques that measures

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complementary metabolic risks associated with WAT dysfunction in a non-fasting state that is rarely examined in clinical practice. Finally, this study included subjects who have a high risk for T2D given

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their age and adiposity, but are nevertheless normoglycemic and free of chronic diseases. While this strengthens the conclusions drawn for a prediabetic population, it necessitates additional studies in different populations such as diabetic, non-obese and/or younger to test whether the associations of high plasma apoB/PCSK9 to the metabolic disturbances remain valid. These are underway. While the bulk of apoB-lipoproteins uptake, the major form being LDL, occurs in the liver, the uptake of intact LDL by other non-peripheral tissues such as the muscle and WAT has long been reported in vivo in rats (28), pigs (29) and humans (30). In humans, WAT and skeletal muscle were reported to equally remove postprandial apoB48 and apoB100-TRL-remnants, mostly being of the

ACCEPTED MANUSCRIPT largest size (Sf>400) (30). Although, to our knowledge, the specific contribution of receptor-mediated uptake of apoB-lipoproteins in peripheral tissue has not been studied in vivo in human, several lines of evidence suggest that this is plausible. LDL are believed to pass through the intact endothelium through active transcytosis involving

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processes such as caveolin-1 and LDLR (31). More recently it was suggested that LDL transport across the peripheral endothelium in healthy humans is via ultrafiltration through intercellular pores without additional active process such as transcytosis (32). At the parenchymal cells, binding and

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internalization of LDL by pancreatic β-cell was reported to induce β-cell dysfunction and apoptosis (12). In muscle cells, TRL remnants (10) and ceramide-rich LDL (11) decreased insulin sensitivity and

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action in rat L6 muscle cells, effects which were reversed by inhibition of the LDLR family (10). Specifically in adipocytes, differentiation of murine preadipocytes with oxLDL was reported to increase proliferation and decrease cell-apoptotic rate and differentiation (6), a defect that was dependent on scavenger receptor, CD36 (7). Moreover, primary rabbit adipocytes take up oxLDL in a concentrationdependent manner (33) and the specific binding of native and modified (methylated) LDL to human

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adipocyte membranes is believed to be dependent in part on the lysine residues of apoB (34). PCSK9 targets cell-surface LDLR for degradation via 2 pathways in hepatocytes; 1) extracellular, where secreted PCSK9 binds to cell-surface LDLR leading to the intracellular

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degradation of the LDLR–PCSK9 complex and 2) intracellular in post-ER compartment, where PCSK9 targets the LDLR for degradation in lysosome (35). PCSK9 is also reported to regulate other apoB-

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lipoproteins receptors; such as VLDL receptors (36) LDL-receptor related protein-1 (37) and CD36 (36) particularly in hepatocytes and adipocytes. While the expression of PCSK9 is negligible in WAT, albeit in mice (36), intravenous injection of human PCSK9 in wild-type mice, or liver-specific expression of PCSK9 in knockout mice (36), reduced LDLR (38) and VLDL receptors (36) expressions in WAT. This underscores the important role of plasma PCSK9 in the regulation of apoB-lipoproteins receptors and, possibly, uptake in WAT. Nevertheless, the association of PCSK9 with metabolic risks is, to date, suggestive but not conclusive in both mice (19;20) and humans (21;22). In line, plasma PCSK9 per se was not

ACCEPTED MANUSCRIPT associated with metabolic risks in this study. However, PCSK9 gene is highly polymorphic with more than 50 reported exonic variations (15). Moreover, many PCSK9 loss-of-function variants are common and have variable potencies in reducing plasma LDLC, with or without changes in plasma PCSK9 or TG (25). This may explain why evaluating PCSK9 concentrations alone may not best reflect PCSK9

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function. On the other hand, once plasma apoB, more than LDLC, were considered, lower plasma PCSK9 was related to lower WAT in situ LPL activity and systemic insulin sensitivity.

Given these finding and the association of plasma apoB/PCSK9 with several in vivo and ex

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vivo metabolic risks examined, it is reasonable to question whether reduction in plasma PCSK9 is not without metabolic “costs”. We propose that while lowering plasma PCSK9 may be cardioprotective

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due to increased LDL-clearance by the liver, increased LDL-clearance by non-hepatic tissues promotes their dysfunction and the risks for T2D. Indeed, in humans, conditions that decrease or increase the expression of LDLR are associated with decreased or increased incidence for T2D, respectively. They are also associated with a discordance between risks for cardiovascular and T2D. For example, the lack of a functional LDLR was suggested to protect against new onset T2D in FH

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patients, and the higher the dysfunction in LDLR, the lower was the risk among the FH population (13). In contrast, statins are associated with an increased incidence of new onset T2D, particularly among patients with higher risks for T2D (39) and adherence to statins (40). Statins however

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upregulate cell-surface LDLR in hepatic as well as extra-hepatic tissues (41). Moreover, 2-weeks atorvastatin treatment in rabbits enhanced the uptake of oxLDL in adipocytes, which was closely

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related to reduced plasma LDLC and increased adipocytes CD36 mRNA expressions (33). Notably, like statins, PCSK9 monoclonal antibodies are expected to increase LDLR expression in the liver and extra-hepatic tissues such as WAT. Phase 2 clinical trials up to 12 weeks using anti-PCSK9 alone or in combination with statins and/or ezetimibe, have been shown to be safe and effective at reducing LDL-C in diverse patient populations (FH, non-FH, statin-intolerant) (42). However, if our hypothesis is correct, the long-term risk for T2D in patients treated with both statins and PCSK9 agents may further increase in susceptible patients. Intriguingly, among the 3 reported cases with complete PCSK9 deficiency, a 21 years African woman (43), a 32 years African American woman (44), and a 54 years

ACCEPTED MANUSCRIPT overweight Caucasian French man (45), the older man has T2DM that is controlled by hypoglycemic agents (45). He was initially hospitalized for rapid-onset insulin-requiring diabetes with moderate liver steatosis, the etiology of which was reported as unknown (45). While PCSK9 deficiency cannot be linked to T2D in this case, it also cannot be excluded.

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In conclusion, reduced plasma PCSK9 relative to apoB, but not reduced plasma PCSK9 per se, is associated with WAT dysfunction, fasting and postprandial hypertriglyceridemia, delayed postprandial chylomicron clearance, hyperinsulinemia and peripheral IR in normoglycemic obese

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subjects. We hypothesize that interventions that increase the expression of LDLR and its family members and decrease that of PCSK9, such as statins, anti-PCSK9 or others that upregulate the

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sterol regulatory element binding transcription factor 2 (SREBP2) (41), may increase apoBlipoproteins uptake into non-hepatic peripheral tissues reducing their function. This when added to other risk factors for T2D such as aging, obesity and unhealthy life style promotes the development of T2D in humans. The plasma apoB/PCSK9 ratio, but not PCSK9 per se, may provide a novel clinical

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agents.

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tool to detect “pre-diabetic” disturbances in high risk subjects especially when on hypercholesterolemic

ACCEPTED MANUSCRIPT Figure legends: Figure 1: Association of fasting plasma PCSK9 with fasting plasma A) total cholesterol, B) LDLC, C) VDLC, D) HDLC, E) apoB, and F) log10 [plasma TG] in all subjects; 48 women (open circles with

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dashed regression line) and 33 men (closed squares with dotted regression lines).

Figure 2: Association of fasting plasma apoB/ PCSK9 ratio with plasma TG metabolism and body fat indices measured as A) log10 [fasting plasma TG], B) log10 [AUC6hrs TG] and C) log10 [iAUC6hrs TG] and

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D) log10 [AUC6hrs apoB48] after the ingestion of the high-fat meal in in all subjects, E) android/ gynoid body fat ratio, F) gynoid fat, G) gynoid WAT function measured as in situ LPL activity per mg WAT,

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and H) total gynoid fat function in all subjects; 48 women and 33 men for panels A, E, F, and 17 women and 13 men for the remaining panels. Women are presented as open circles with dashed regression lines and men as closed squares with dotted regression lines.

Figure 3: Association of fasting plasma apoB/ PCSK9 ratio with glucose metabolism measured as A)

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1st phase, B) Log10 [2nd phase] and C) Log10 [total] insulin secretion during the IVGTT, and with D) fasting HOMA-IR, and insulin sensitivity measured during the hyperinsulinemia euglycemia clamp as E) GIR and F) M/I in all subjects; 48 women (open circles with dashed regression line) and 33 men

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(closed squares with dotted regression lines).

ACCEPTED MANUSCRIPT Author contribution: HW, SB, VL, NSP, and YC were responsible for samples collection and analysis, and HW and SB additionally for statistical analysis, data interpretation and manuscript writing. MC contributed to

supervision, data analysis and manuscript writing and review.

Acknowledgements:

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supervision of HW and to manuscript review. MF was responsible for project development,

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We acknowledge the invaluable help of Drs Remi Rabasa-Lhoret, Alexis Baass and Robert Dufour in conducting white adipose tissues biopsies and subjects’ medical screening and follow-up. We thank

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Mr Miguel Chagnon (Statistical Department, University of Montréal) for his advice on the statistical analysis of the data.

Sources of funding:

This work is supported by a grant from Canadian Institutes of Health Research (CIHR, MOP# 93581)

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to May Faraj, and by a grant from The Richard and Edith Strauss Canada Foundation to Michel Chrétien. May Faraj is the recipient of salary supports from CIHR and Fonds de recherche du Québec (FRQ) and Leader’s Opportunity Fund from Canadian Foundation for Innovation (CFI). Simon

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Bissonnette, Nathalie Saint-Pierre and Yannick Cyr are the recipients of Frederick Banting and Charles Best Canada graduate students salary awards from CIHR. Valerie Lamantia is the recipient of

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graduate study salary award from Fonds de recherche du Québec (FRQ).

Disclosures: None.

ACCEPTED MANUSCRIPT Reference List

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20. Langhi C, Le May C, Gmyr V, andewalle B, err-Conte J, rempf M, attou F, ostet P, ariou B. PCSK9 is expressed in pancreatic delta-cells and does not alter insulin secretion. Biochem Biophys Res Commun 2009; 390(4):1288-1293.

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21. Baass A, Dubuc Gv, Tremblay M, Delvin EE, O'Loughlin J, Levy E, Davignon J, Lambert M. Plasma PCSK9 Is Associated with Age, Sex, and Multiple Metabolic Markers in a Population-Based Sample of Children and Adolescents. Clin Chem 2009; 55(9):1637-1645. 22. Awan Z, Dubuc Gv, Faraj M, Dufour R, Seidah NG, Davignon J, Rabasa-Lhoret R+, Baass A. The effect of insulin on circulating PCSK9 in postmenopausal obese women. Clin Biochem 2014; 47(12):1033-1039.

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23. Wilsie L, Chanchani S, Navaratna D, Orlando R. Cell surface heparan sulfate proteoglycans contribute to intracellular lipid accumulation in adipocytes. Lipids Health Dis 2005; 4(1):2. 24. Grupping AY, Cnop M, Van Schravendijk C, Hannaert JC, van Berkel TJC, Pipeleers DG. Low Density Lipoprotein Binding and Uptake by Human and Rat Islet Beta-Cells. Endocrinology 1997; 138(10):40644068. 25. Mayne J, Ooi TC, Raymond A, Cousins M, Bernier L, Dewpura T, Sirois F, Mbikay M, Davignon J, Chretien M. Differential effects of PCSK9 loss of function variants on serum lipid and PCSK9 levels in Caucasian and African Canadian populations. Lipids in Health and Disease 2013; 12(1):70. 26. Wassef H, Salem H, Bissonnette S, Baass A, Dufour R, Davignon J, Faraj M. White adipose tissue-apoC-I secretion; relation to delayed plasma clearance of dietary fat in humans. Arterioscler Throm Vasc Biol 2012; 32:2785-2793. 19

ACCEPTED MANUSCRIPT 27. Faraj M, Sniderman A, Cianflone K. ASP enhances in situ lipoprotein lipase activity by increasing fatty acid trapping in adipocytes. J Lipid Res 2004; 45(4):657-666. 28. Stange EF, Dietschy JM. Age-related decreases in tissue sterol acquisition are mediated by changes in cholesterol synthesis and not low density lipoprotein uptake in the rat. J Lipid Res 1984; 25(7):703-713.

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29. Johnson JA, Beitz DC, Jacobson NL. Effects of Dietary Beef and Soy Protein on Tissue Composition and Low Density Lipoprotein Uptake in Young Pigs. The Journal of Nutrition 1989; 119(5):696-705. 30. Karpe F, Humphreys SM, Samra JS, Summers LK, Frayn KN. Clearance of lipoprotein remnant particles in adipose tissue and muscle in humans. J Lipid Res 1997; 38(11):2335-2343.

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31. von Eckardstein A, Rohrer L. Transendothelial lipoprotein transport and regulation of endothelial permeability and integrity by lipoproteins. Curr Opin Lipidol 2009; 20(3):197-205.

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32. Michel CC, Nanjee MN, Olszewski WL, Miller NE. LDL and HDL transfer rates across peripheral microvascular endothelium agree with those predicted for passive ultrafiltration in humans. J Lipid Res 2015; 56(1):122-128. 33. Zhao SP, Zhang DQ. Atorvastatin enhances cellular uptake of oxidized LDL in adipocytes from hypercholesterolemic rabbits. Clin Chim Acta 2004; 339(1-2):189-194. 34. Fong BS, Rodrigues PO, Angel A. Characterization of low density lipoprotein binding to human adipocytes and adipocyte membranes. J Biol Chem 1984; 259(16):10168-10174.

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35. Horton JD, Cohen JC, Hobbs HH. Molecular biology of PCSK9: its role in LDL metabolism. Trends in biochemical sciences 2007; 32(2):71-77. 36. Roubtsova A, Munkonda MN, Awan Z, Marcinkiewicz J, Chamberland A, Lazure C, Cianflone K, Seidah NG, Prat A. Circulating Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) Regulates VLDLR Protein and Triglyceride Accumulation in Visceral Adipose Tissue. Arterioscler Thromb Vasc Biol 2011; 31(4):785-791.

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37. Canuel M, Sun X, Asselin MC, Paramithiotis E, Prat A, Seidah NG. Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Can Mediate Degradation of the Low Density Lipoprotein Receptor-Related Protein 1 (LRP-1). PLoS ONE 2013; 8(5):e64145.

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38. Schmidt RJ, Beyer TP, Bensch WR, Qian YW, Lin A, Kowala M, Alborn WE, Konrad RJ, Cao G. Secreted proprotein convertase subtilisin/kexin type 9 reduces both hepatic and extrahepatic low-density lipoprotein receptors in vivo. Biochem Biophys Res Commun 2008; 370(4):634-640. 39. Ridker PM, Pradhan A, MacFadyen J, Libby P, Glynn RJ. Cardiovascular benefits and diabetes risks of statin therapy in primary prevention: an analysis from the JUPITER trial. Lancet 2012; 380(9841):565-571. 40. Corrao G, Ibrahim B, Nicotra F, Soranna D, Merlino L, Catapano AL, Tragni E, Casula M, Grassi G, Mancia G. Statins and the Risk of Diabetes: Evidence From a Large Population-Based Cohort Study. Diabetes Care 2014. 41. Dubuc G, Chamberland A, Wassef H, Davignon J, Seidah NG, Bernier L, Prat A. Statins Upregulate PCSK9, the Gene Encoding the Proprotein Convertase Neural Apoptosis-Regulated Convertase-1 Implicated in Familial Hypercholesterolemia. Arterioscler Thromb Vasc Biol 2004; 24(8):1454-1459. 20

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44. Zhao Z, Tuakli-Wosornu Y, Lagace TA, Kinch L, Grishin NV, Horton JD, Cohen JC, Hobbs HH. Molecular Characterization of Loss-of-Function Mutations in PCSK9 and Identification of a Compound Heterozygote. The American Journal of Human Genetics 2006; 79(3):514-523.

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ACCEPTED MANUSCRIPT Table 1: Baseline characteristics of the participants (N = 81) Men

N = 48

N = 33

PCSK9 (ng/ml)

340 ± 131

249 ± 74

(min-max)

(80 - 748)

(138 - 428)

0.95 ± 0.27

1.00 ± 0.24

(min-max)

(0.34 – 1.80)

(0.51 – 1.38)

Percentile of rangea

(<5th - > 90th)

(<5th - >75th)

3.35 ± 0.86

2.88 ± 0.64

(1.23 - 5.22)

(1.63 – 4.44)

LDLC (mM) (min-max)

3.13 ± 1.50

ApoB/PCSK9 (mg/ng)

(1.31 – 10.12)

4.32 ± 1.53

(1.99 – 7.65)

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(min-max)

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ApoB (g/L)

LDLC/PCSK9 (nmol/ng)

p-value

<0.0001

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Women

NS

0.008

<0.001

10.93 ± 4.31

12.44 ± 4.41

(3.44 – 24.66)

(6.08 – 22.21)

58.3 ± 5.7

56.6 ± 6.7

NS

BMI (kg/m )

32.1 ± 4.1

34.0 ± 5.6

NS

Waist (cm)

102.1 ± 10.2

116.3 ± 14.2

0.000

37.3 ± 8.5

38.4 ± 14.2

NS

3.48 ± 1.03

4.59 ± 1.81

<0.001

6.35 ± 1.42

5.14 ± 2.01

0.002

0.55 ± 0.12

0.91 ± 0.18

0.000

3209 ± 542

3328 ± 921

NS

5.11 ± 0.52

5.31 ± 0.47

NS

17.0 ± 5.6

25.5 ± 12.9

0.000

3.87 ± 1.38

6.01 ± 3.07

0.000

Cholesterol (mmol/L)

5.61 ± 1.01

4.99 ± 0.95

0.007

VLDLC (mmol/L)

0.95 ± 0.33

1.07 ± 0.34

0.120

HDLC (mmol/L)

1.52 ± 0.42

1.04 ± 0.20

0.000

TG (mmol/L)

1.62 ± 0.97

2.36 ± 1.66

0.013

6.86 ± 4.40

10.07 ± 8.34

0.180

1.67± 0.27

1.40 ± 0.18

0.000

269 ± 5

265 ± 6

0.013

(min-max) Age (yrs) 2

Android Fat (kg) Gynoid Fat (kg) Android/ gynoid fat

Glucose (mmol/L) Insulin (µU/mL)

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HOMA (mM.mU.L-1)

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Adipocyte Area (µm2)

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Total Fat (kg)

ApoB48 (mg/L)

a

ApoA-I (g/L) Mean LDL size (Å) Data presented as Avg ± SD.

a

NS

Percentile of apoB estimated in reference to a Canadian population

with the same sex- and age-range (46). a for N=17 women and N=14 men.

ACCEPTED MANUSCRIPT Table 2: Pearson correlation of fasting plasma apoB, LDL-C, PCSK9, apoB/PCSK9 and LDL-C/ PCSK9 with metabolic risks in the whole population (N=81) LDLC

PCSK9

ApoB/

LDLC/

(g/L)

(mM)

(ng/mL)

PCSK9

PCSK9

-0.03

-0.13

-0.11

0.05

0.01

Waist Circumference (cm)

0.01

-0.23

*

Android fat (g)

0.07

-0.11

2

BMI (kg/m )

Gynoid fat (g)

-0.08

0.04

Total body fat (g)

-0.01

-0.05

0.15

*

-0.22

**

-0.21

0.16

0.04

-0.15

0.13

0.04

-0.23

-0.04

-0.02

-0.02

**

0.41

0.20

-0.36

**

*$

0.15

**

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Android/gynoid fat

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ApoB

**

-0.15

0.76

0.86

0.42

0.08

0.17

Plasma HDLC (mM)

-0.30**

0.08

0.18

-0.36**

-0.18

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Plasma Cholesterol (mM)

Plasma apoA1 (g/L)

-0.06

0.18

0.21

-0.18

-0.09

**

-0.08

-0.12

-0.18

0.00

0.58**

0.16

0.10

0.31**

0.08

**

-0.46

Mean LDL size (Å) Log10 Plasma TG (mM) Log10 [AUC6hrs plasma TG (mmol/L)]

a

Log10 [iAUC6hrs plasma TG (mmol/L)]a Log10 [fasting plasma apoB48 (mg/L)]

a

0.66

0.35

0.00

0.50

0.42*

0.44*

0.18

-0.06

0.47**

0.40*

**

0.23

0.10

0.22

0.10

**

0.15

-0.05

0.30

0.18

-0.01

-0.24

0.33

0.51

a

0.46

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Log10 [AUC6hrs plasma apoB48 (mg/L)]

**

Log10 [iAUC6hrs plasma apoB48 (mg/L)]a

0.23

Log10 [WAT function (nmol TG/mg)]

-0.52

**

-0.28

Log10 [Total gynoid fat function (mmol TG)]b

-0.45*

b

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Adipocyte area (µm2)b Glucose (mM) Insulin (mM)

0.28

0.14

-0.46

*

-0.45*

-0.24

0.14

-0.45*

-0.44*

0.26

0.30

0.27

-0.19

-0.16

0.09

0.02

0.04

0.00

-0.05

*$

0.22

0.16

-0.03

-0.12

0.16

-0.03

-0.12

0.21

0.12

Log10 [1 phase insulin secretion (µU/mL)]

0.13

0.02

-0.12

0.15

0.10

Log10 [2nd phase Insulin secretion (µU/mL)]

0.26*

0.05

-0.15

0.27*

0.17

-0.16

*

0.16

**

-0.20 -0.21

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HOMA (mM*mU/L) st

Log10 [total insulin secretion (µU/mL)]

*$

0.24

0.04

0.25

GIR (mg/kg.min)

-0.28

*

0.04

0.22

-0.35

M/I (mg/kg/min)/( µU/mL)

-0.28*

-0.01

0.15

-0.32**

0.13

$

* p ≤ 0.05, ** p < 0.01 and *** p < 0.001 (2-tailed). Not significant if Benjamini correction was applied to the p-values, regression.

a

for N=31,

b

for N=28. The correlation value shown for WAT is that of non-linear

ACCEPTED MANUSCRIPT Table 3: Stepwise forward regression analysis for the association of fasting plasma apoB and PCSK9 with metabolic risks after correction for BMI (N=81).

Models

Log10 [Total insulin secretion]

Insulin sensitivity (GIR) Insulin sensitivity (M/I)

1 2 3

BMI ApoB -

BMI Sex ApoB

1

BMI

BMI

0.02

0.02

2

-

Sex

1 2 3

BMI ApoB -

BMI Sex ApoB

1 2 3

BMI ApoB -

BMI Sex ApoB

1 2 3

BMI ApoB PCSK9

BMI Sex ApoB

1 2 3

BMI ApoB -

BMI Sex ApoB

BMI

Sex

2.25 0.03 0.29 2.25 0.03 0.26 27.15 -0.39 -5.28 0.01 0.13 -0.002 -0.03 0.68 -0.00

-0.12 2.52 0.02 -0.19 0.25 2.70 0.02 -0.17 0.22 26.40 -0.37 2.32 -4.39 0.124 -0.002 0.01 -0.03 0.70 -0.08

2

ApoB

ApoB

0.48

0.48

1 2

BMI ApoB

Sex ApoB

1 2 3

BMI ApoB PCSK9

Sex ApoB PCSK9

1 2

BMI ApoB

Sex ApoB

1.28 0.01 0.36 0.82 0.002 -0.89 0.002 0.89 0.02 -0.88

1.60 -0.15 0.37 0.80 -0.11 -0.90 0.002 1.59 -0.09 -0.80

1

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Log10 [AUC plasma TG]a

Log10 [AUC6hrs plasma apoB48]a Log10 [WAT function/mg]b Log10 [Total gynoid fat function]b

2 -0.16 -0.00 -0.12 0.54 2.41

1

p-values 2

0.00 0.34 -

0.09 0.40

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0.08

1 0.065 0.998 <0.001 <0.001

2 0.397 0.596 0.008 <0.001 <0.001

0.013

0.031

0.093 <0.001 <0.001 <0.001 0.009 <0.001 <0.001 0.001 0.022 <0.001 <0.001 0.003 0.004 <0.001 0.002 0.020 0.009 0.001 0.166

-

0.11

0.24 0.31 -

0.37 0.42

0.21 0.27 -

0.32 0.37

0.23 0.32 0.36

0.32 0.39

0.13 0.23 -

0.20 0.28

0.00

0.04

<0.001 <0.001 0.005 <0.001 <0.001 0.012 <0.001 <0.001 0.001 0.035 <0.001 0.000 0.005 0.002 0.900

0.43

0.47

<0.001

<0.001

0.05 0.25

0.33 0.57

0.02 0.28 0.42

0.00 0.28 0.44

0.02 0.23

0.00 0.21

<0.001 0.237 0.010 0.066 0.827 0.001 0.025 0.056 0.070 0.001

<0.001 0.040 0.006 0.001 0.397 <0.001 0.015 <0.001 0.054 0.003

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Log10 [2nd phase insulin secretion]

1 -0.34 0.00 0.48 2.27

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Log10 [1st phase insulin secretion]

R2

Coefficients

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Log10 [Plasma TG]

Independent variables 1 2

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Steps

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for N=31 and

b

PCSK9

PCSK9

0.002

0.002

0.41

0.33

0.013

0.046

for N=28. Model 1 adjusted for BMI for all presented dependent variables. Model 2

adjusted for BMI and sex for dependent variables with N=81, and for sex only for variables with

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smaller sample size (N=31 or 28).

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Highlights 81 obese normoglycemic men and postmenopausal women were studied

-

Plasma apoB correlated positively with dysfunction in glucose and fat metabolism

-

Reduced plasma PCSK9 increased the association of apoB to metabolic risks

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Plasma apoB/PCSK9 ratio, not PCSK9 alone, associated with metabolic risks

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-