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The present invention is in the field of medicine in particular in the field of cardiology.


Atherosclerotic cardiovascular disease is the leading cause of death worldwide1. Since nearly 2 decades, several guidelines or consensus statements have highlighted that the detection of carotid plaque is an important clinical predictor of future adverse cardiovascular events2, 3. Among 13145 participants in the ARIC (Atherosclerosis Risk in Communities) study, free of cardiovascular disease at the beginning of the follow-up and followed for a mean of 15.1 years (accumulating a total of 1812 major cardiovascular events), the presence of carotid plaques was independently associated with incident major cardiovascular events and improved the predictive capacity of the clinical model using “classic” risk factors, such as age, smoking and cholesterol4. Whether a strategy aimed at modifying patients' risk factors based on the presence of carotid plaques would be effective in reducing cardiovascular risk is yet to be proven5.

Low-density lipoprotein cholesterol (LDLc) is a strong, independent and modifiable risk factor for developing cardiovascular disease6. Lowering LDLc, mainly with statins, has decreased the risk of cardiovascular events over the last decades7, 8. However, many patients do not achieve the desired LDLc levels and others experience side effects (eg. myalgia), that despite being mild in the majority of the cases, may lead patients to abandon statin therapy or to take low and insufficient doses9, 10. Medical misinformation with rapid spread over the internet has also contributed to the abandon of life saving therapies, such as statins11. Alternative therapies may be needed for those who cannot achieve the desired LDLc levels or experience side effects leading to therapy low adherence and/or withdrawal.

The proprotein convertase subtilisin/kexin type 9 (PCSK9) is produced by the liver and secreted into the plasma, acting as a low-density lipoprotein receptor (LDLr) binder at the surface of the hepatocytes that prevents the recycling of the LDLr12. In consequence, the LDLr becomes more susceptible to degradation and less efficient in performing the clearance of the LDLc, thereby increasing the circulating levels of LDLc and the atherosclerosis risk13. Recent cardiovascular outcome trials have shown that PCSK9-inhibitors (PCSK9i) effectively reduce LDLc, decrease the atheroma plaque burden, and reduce the rate of major cardiovascular events in high-risk patients with atherosclerotic cardiovascular disease and LDLc levels of 70 mg/dL (1.8 mmol/L) or higher who were receiving statin therapy14, 15. The rapid development of PCSK9i vaccines that are administered monthly or yearly, have the potential to increase the treatment adherence and to substantially decrease the negative impact of atherosclerotic cardiovascular disease16. PCSK9 is a highly polymorphic gene, and some variants of thePCSK9 gene are associated with variability of serum lipids levels especially the level of LDLc17. Gain-of-function mutations interfere with the recycling of the LDLr, reducing the LDLc uptake (and increasing LDLc levels)18. In particular, many studies have identified an association between the minor allele (A) of the rs562556 SNP (located on PCSK9 gene, and responsible of missense mutation I474V) and LDLc levels19.


As defined by the claims, the present invention relates to Methods of determining whether a subject is at risk of developing arterial plaques.


Proprotein convertase subtilisin/kexin type 9 (PCSK9) binds low-density lipoprotein receptor (LDLR), preventing its recycling. PCSK9 is a risk predictor and a biotarget in atherosclerosis progression. The PCSK9 rs562556 variant has been reported as a gain-of-function mutation. The aim of the inventors was to determine whether the PCSK9-LDLR axis can predict carotid artery plaques between two visits separated by almost 20 years in a longitudinal population cohort, and whether underlying genetic polymorphisms could be identified. The STANISLAS cohort is a longitudinal familial cohort from the Lorraine region of France. Participants attending two visits (visit 1 and visit 4) separated by 18.5 years (mean) were included (n=997). Carotid artery plaques were determined with standardized vascular echography. The mean age of the adult population at visit- 1 was 42±5 years. At visit-4, 203 (20.4%) participants had arterial plaques. At visit- 1 participants who developed arterial plaques were older (42.7±5.4 vs. 41.7±4.7years), more often male (60% vs. 49%), smokers (29% vs.18%), with diabetes (6% vs. 3%), and with higher cholesterol levels (LDLc 1.6±0.4 vs.

1.5±0.3g/L) (p<0.05 for all). The independent clinical predictors of arterial plaques were age, smoking and LDLc. Higher PCSK9 levels predicted arterial plaques on top of the clinical model, OR (95%CI) =2.14 (1.28-3.58); the missense mutation coding the single-nucleotide polymorphism (SNP) rs562556 is associated with both higher PCSK9 concentration and arterial plaques. In conclusion higher PCSK9 concentration predicted the development of arterial

plaques almost 20 years in advance in a healthy middle-aged population. Mutations of the SNP rs562556 associated with both PCSK9 levels and arterial plaques reinforce the potential causality of the findings. Finally PCSK9 inhibitors would be useful for cardiovascular prevention in patients considering having risk of developing arterial plaques.

Thus the first object of the present invention relates to a method of determining whether a subject is at risk of developing arterial plaques comprising determining the level of PCSK9 in a blood sample obtained from the subject wherein said level correlates with the risk of developing arterial plaque.

As used herein, the term "arterial plaque" has its general meaning in the art and is defined as a fatty build-up in the inner lining of walls or arteries, composed of deposits of smooth muscle cells, fatty substances, cholesterol, calcium, and cellular waste products, usually associated with atherosclerosis. The term is used herein interchangeably with the term "atherosclerotic plaque". In some embodiments, the arterial plaques are carotid artery plaques.

As used herein, the term “risk" relates to the probability that an event will occur over a specific time period, as in the conversion to a cardiovascular event, and can mean a subject's "absolute" risk or "relative" risk. Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p/(l-p) where p is the probability of event and (1- p) is the probability of no event) to no- conversion.

Thus the expression "determining whether a patient is at risk of developing arterial plaques" as used herein means that the patient to be analyzed by the method of the present invention is allocated either into the group of patients of a population having an elevated risk, or into a group having a reduced risk of developing arterial plaques. An elevated risk as referred to in accordance with the present invention, preferably, means that the risk of developing arterial plaques within a predetermined predictive window is elevated significantly (i.e. increased significantly) for a patient with respect to the average risk for a cardiovascular event or cardiac mortality in a population of patients. A reduced risk as referred to in accordance with the present invention, preferably, means that the risk of developing arterial plaques within a predetermined predictive window is reduced significantly for a patient with respect to the average risk for a cardiovascular event or cardiac mortality in a population of patients.

Particularly, a significant increase or reduction of a risk is an increase or reduction or a risk of a size which is considered to be significant for prognosis, particularly said increase or reduction is considered statistically significant. The terms "significant" and "statistically significant" are known by the person skilled in the art. Thus, whether an increase or reduction of a risk is significant or statistically significant can be determined without further ado by the person skilled in the art using various well known statistic evaluation tools.

Typically, the predictive window is about 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or 25 years.

As used herein the term “blood sample” means a whole blood, serum, or plasma sample obtained from the patient. Preferably the blood sample, according to the invention, is a plasma sample. A plasma sample may be obtained using methods well known in the art. For example, blood may be drawn from the patient following standard venipuncture procedure on tri-sodium citrate buffer. Plasma may then be obtained from the blood sample following standard procedures including but not limited to, centrifuging the blood sample at about 2500*g for about 15 minutes (room temperature), followed by pipeting of the plasma layer. Platelet-free plasma (PFP) will be obtained following a second centrifugation at about 2500*g for 15 min. Analyses can be performed directly on this PFP. Alternatively, microvesicles (MPs) may be more specifically isolated by further centrifuging the PFP at about 15,000 to about 25,000*g at 4°C. Different buffers may be considered appropriate for resuspending the pelleted cellular debris which contains the MPs. Such buffers include reagent grade (distilled or deionized) water and phosphate buffered saline (PBS) pH 7.4 or NaCl 0.9%. Preferably, PBS buffer (Sheath fluid) is used.

As used herein, the term “PCSK9” has its general meaning in the art and refers to the proprotein convertase subtilisin/kexin type 9. An exemplary amino acid sequence is as set forth in SEQ ID NO:l.

SEQ ID NO:l>sp|Q8NBP7|PCSK9_HUMAN Proprotein convertase subtilisin/kexin type 9 OS=Homo sapiens OX=9606 GN=PCSK9 PE=1 SV=3













Methods for determining the level of PCSK9 in a blood sample are well known in the art and typically involves an immunoassay. In some embodiments, an ELISA (enzyme-linked immunosorbent assay) method is used, wherein the wells of a microtiter plate are coated with a set of antibodies which recognize PCSK9. The blood sample is then added to the coated wells. After a period of incubation sufficient to allow the formation of antibody-antigen complexes, the plate(s) can be washed to remove unbound moieties and a detectably labelled secondary binding molecule is added. The secondary binding molecule is allowed to react with any captured sample marker protein, the plate washed and the presence of the secondary binding molecule detected using methods well known in the art.

In some embodiments, the level of PCSK9 is compared to a predetermined reference value. Typically, the predetermined reference value is a threshold value or a cut-off value. Typically, a "threshold value" or "cut-off value" can be determined experimentally, empirically, or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and/or clinical conditions, as would be recognized by a person of ordinary skilled in the art. For example, retrospective measurement of expression levels in properly banked historical patient samples may be used in establishing the predetermined reference value. The threshold value has to be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (clinical consequences of false positive and false negative). Typically, the optimal sensitivity and specificity (and so the threshold value) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data. For example, after quantifying the expression level in a group of reference, one can use algorithmic analysis for the statistic treatment of the determined levels in samples to be tested, and thus obtain a classification standard having significance for sample classification. The full name of ROC curve is Receiver Operator Characteristic Curve, which is also known as receiver operation characteristic curve. It is mainly used for clinical biochemical diagnostic tests. ROC curve is a comprehensive indicator that reflects the continuous variables of true positive rate (sensitivity) and false positive rate (1-specificity). It reveals the relationship between sensitivity and specificity with the image composition method. A series of different cut-off values (thresholds or critical values, boundary values between normal and abnormal results of diagnostic test) are set as continuous variables to calculate a series of sensitivity and specificity values. Then sensitivity is used as the vertical coordinate and specificity is used as the horizontal coordinate to draw a curve. The higher the area under the curve (AUC), the higher the accuracy of diagnosis. On the ROC curve, the point closest to the far upper left of the coordinate diagram is a critical point having both high sensitivity and high specificity values. The AUC value of the ROC curve is between 1.0 and 0.5. When AUC>0.5, the diagnostic result gets better and better as AUC approaches 1. When AUC is between 0.5 and 0.7, the accuracy is low. When AUC is between 0.7 and 0.9, the accuracy is moderate. When AUC is higher than 0.9, the accuracy is quite high. This algorithmic method is preferably done with a computer. Existing software or systems in the art may be used for the drawing of the ROC curve, such as: MedCalc medical statistical software, SPSS 9.0, ROCPOWER. S AS, DESIGNROC.FOR, MULTIREADER POWER S AS, CREATE-ROC.SAS, GB STAT VIO.O (Dynamic Microsystems, Inc. Silver Spring, Md., USA), etc.

In some embodiments, the predetermined reference value was established in a population of patients who did not develop arterial plaques. Accordingly when the level of PCSK9 is higher than the predetermined reference value, it is concluded that the patient is at risk of developing arterial plaques. On contrary, when the level of PCSK9 is lower than the predetermined reference value, then is it concluded that the patient is not at risk of developing arterial plaques.

In some embodiments, high statistical significance values (e.g. low P values) are obtained for a range of successive arbitrary quantification values, and not only for a single arbitrary quantification value. Thus, in some embodiments, instead of using a definite predetermined reference value, a range of values is provided. Therefore, a minimal statistical significance value (minimal threshold of significance, e.g. maximal threshold P value) is arbitrarily set and a range of a plurality of arbitrary quantification values for which the statistical significance value calculated at step g) is higher (more significant, e.g. lower P value) are retained, so that a range of quantification values is provided. This range of quantification values includes a "cut-off value as described above. For example, according to this specific embodiment of a "cut-off value, the outcome can be determined by comparing the expression level with the range of values which are identified. In some embodiments, a cut-off value thus consists of a range of quantification values, e.g. centered on the quantification value for which the highest statistical significance value is found (e.g. generally the minimum p value which is found). For example, on a hypothetical scale of 1 to 10, if the ideal cut-off value (the value with the highest statistical significance) is 5, a suitable (exemplary) range may be from 4-6. For example, a patient may be assessed by comparing values obtained by determining the level of PCSK9, where values greater than 5 reveal that the patient is at risk of developing arterial plaques and values less than 5 reveal that the patient is not at risk of developing arterial plaques. In some embodiments, a patient may be assessed by comparing values obtained by measuring the level of PCSK9 and comparing the values on a scale, where values above the range of 4-6

indicate that the patient is at risk of developing arterial plaques and values below the range of 4-6 indicate that the patient is not at risk of developing arterial plaques, with values falling within the range of 4-6 indicating an intermediate risk.

A further object of the present invention relates to a method of determining whether a subject is at risk of developing arterial plaques comprising determining in a nucleic acid sample obtained from the subject the presence or absence of at least one genetic variant in the PCSK9 gene.

As used herein, the term "nucleic acid sample” refers to any biological sample isolated from the subject liable to contain nucleic acid for the purpose of the present invention. Samples can include by way of example and not limitation, body fluids (e;g. saliva) and/or tissue extracts such as homogenates or solubilized tissue obtained from the subject. In some embodiments, the sample is a blood sample. The term “blood sample” means any blood sample derived from the patient that contains nucleic acids. Peripheral blood is preferred, and mononuclear cells (PBMCs) are the preferred cells. The term “PBMC” or “peripheral blood mononuclear cells” or “unfractionated PBMC”, as used herein, refers to whole PBMC, i.e. to a population of white blood cells having a round nucleus, which has not been enriched for a given sub-population. Typically, these cells can be extracted from whole blood using Ficoll, a hydrophilic polysaccharide that separates layers of blood, with the PBMC forming a cell ring under a layer of plasma. Additionally, PBMC can be extracted from whole blood using a hypotonic lysis which will preferentially lyse red blood cells. Such procedures are known to the expert in the art. The template nucleic acid need not be purified. Nucleic acids may be extracted from a sample by routine techniques such as those described in Diagnostic Molecular Microbiology: Principles and Applications (Persing et al. (eds), 1993, American Society for Microbiology, Washington D.C.).

As used herein, the term "genetic variant" has its general meaning in the art and denotes any of two or more alternative forms of a gene occupying the same chromosomal locus. The alteration typically consists in a substitution, an insertion, and/or a deletion, at one or more (e.g., several) positions in the gene. Genetic variation arises naturally through mutation, and may result in phenotypic polymorphism within populations. Gene mutations can be silent (no change in the encoded polypeptide) or may encode polypeptides having altered amino acid sequence. The term is also known as “polymorphism”.

In some embodiments, the method of the present invention comprises detecting one or more single nucleotide polymorphism (SNP).

In some embodiments, the genetic variant is a single nucleotide polymorphism. As used herein, the term "single nucleotide polymorphism" or "SNP" has its general meaning in the art and refers to a single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.

In some embodiments, the method of the present invention comprises detecting rs562556.

As used herein, the term “rs562556” has its general meaning in the art and refers to the genetic variant responsible for the responsible of I474V mutation in PCSK9 protein.

Typically the presence of the minor allele “A” indicates that the subject is at risk of developing arterial plaques.

Detecting the genetic variant may be determined according to any genotyping method known in the art. Typically, common genotyping methods include, but are not limited to, TaqMan assays, molecular beacon assays, nucleic acid arrays, allele-specific primer extension, allele-specific PCR, arrayed primer extension, homogeneous primer extension assays, primer extension with detection by mass spectrometry, sequencing, multiplex primer extension sorted on genetic arrays, ligation with rolling circle amplification, homogeneous ligation, OLA, multiplex ligation reaction sorted on genetic arrays, restriction-fragment length polymorphism, single base extension-tag assays, and the Invader assay. Such methods may be used in combination with detection mechanisms such as, for example, luminescence or chemiluminescence detection, fluorescence detection, time-resolved fluorescence detection, fluorescence resonance energy transfer, fluorescence polarization, mass spectrometry, and electrical detection. Various methods for detecting polymorphisms include, but are not limited to, methods in which protection from cleavage agents is used to detect mismatched bases in RNA/RNA or RNA/DNA, comparison of the electrophoretic mobility of variant and wild type nucleic acid molecules, and assaying the movement of polymorphic or wild-type fragments in polyacrylamide gels containing a gradient of denaturant using denaturing gradient gel electrophoresis. Sequence variations at specific locations can also be assessed by nuclease protection assays such as RNase and SI protection or chemical cleavage methods.

In some embodiments, genotyping is performed using the TaqMan assay, which is also known as the 5' nuclease assay. The TaqMan assay detects the accumulation of a specific amplified product during PCR. The TaqMan assay utilizes an oligonucleotide probe labeled with a fluorescent reporter dye and a quencher dye. The reporter dye is excited by irradiation at an appropriate wavelength, it transfers energy to the quencher dye in the same probe via a

process called fluorescence resonance energy transfer (FRET). When attached to the probe, the excited reporter dye does not emit a signal. The proximity of the quencher dye to the reporter dye in the intact probe maintains a reduced fluorescence for the reporter. The reporter dye and quencher dye may be at the 5' most and the 3' most ends, respectively, or vice versa.

Alternatively, the reporter dye may be at the 5' or 3' most end while the quencher dye is attached to an internal nucleotide, or vice versa. In yet another embodiment, both the reporter and the quencher may be attached to internal nucleotides at a distance from each other such that fluorescence of the reporter is reduced. During PCR, the 5' nuclease activity of DNA polymerase cleaves the probe, thereby separating the reporter dye and the quencher dye and resulting in increased fluorescence of the reporter. Accumulation of PCR product is detected directly by monitoring the increase in fluorescence of the reporter dye. The DNA polymerase cleaves the probe between the reporter dye and the quencher dye only if the probe hybridizes to the target SNP-containing template which is amplified during PCR, and the probe is designed to hybridize to the target SNP site only if a particular SNP allele is present. Preferred TaqMan primer and probe sequences can readily be determined using the SNP and associated nucleic acid sequence information provided herein. A number of computer programs, such as Primer Express (Applied Biosystems, Foster City, Calif.), can be used to rapidly obtain optimal primer/probe sets. It will be apparent to one of skill in the art that such primers and probes for detecting the nucleic acids of the present invention are useful in diagnostic assays for stenosis and related pathologies, and can be readily incorporated into a kit format.

Detecting the genetic variant may also be performed by sequencing. A variety of automated sequencing procedures can be used, including sequencing by mass spectrometry. The nucleic acid sequences of the present invention enable one of ordinary skill in the art to readily design sequencing primers for such automated sequencing procedures. Commercial instrumentation, such as the Applied Biosystems 377, 3100, 3700, 3730, and 3730x1 DNA Analyzers (Foster City, Calif.), is commonly used in the art for automated sequencing. Nucleic acid sequences can also be determined by employing a high throughput mutation screening system, such as the SpectruMedix system.

In some embodiments, the methods of the present invention further comprises measuring at least one other risk factor.

In some embodiments, the method of the present invention further comprises a step consisting of calculating a score, representing an estimation value of the risk that the subject develops arterial plaques. Typically, the score is based on the level of PCSK9 and/or the presence or absence of at least one genetic variant in the PCSK9 gene and may typically include another risk factor. Typically, other risk factors may include clinical features such as age, gender, diabetes mellitus, smoking, family history of arterial event, LDLc levels, and body mass index. Based the above input features obtained from the subject, an operator can calculate a numerical function of the above list of inputs by applying an algorithm. For instance this numerical function may return a number, i.e. risk score (R), for instance between zero and one, where zero is the lowest possible risk indication and one is the highest. This numerical output may also be compared to a threshold (T) value between zero and one. If the risk score exceeds the threshold T, it is meant than the subject has a high risk of having a coagulation related disorder and if the risk score is under the threshold T, it is meant than the subject has a low risk of having a coagulation related disorder.

In some embodiments, the methods of the invention thus comprises the use of an algorithm.

In some embodiments, the algorithm is a classification algorithm typically selected from Multivariate Regression Analysis, Linear Discriminant Analysis (LDA), Topological Data Analysis (TDA), Neural Networks, Support Vector Machine (SVM) algorithm and Random Forests algorithm (RF). As used herein, the term "classification algorithm" has its general meaning in the art and refers to classification and regression tree methods and multivariate classification well known in the art such as described in US 8,126,690; WO2008/156617. As used herein, the term “support vector machine (SVM)” is a universal learning machine useful for pattern recognition, whose decision surface is parameterized by a set of support vectors and a set of corresponding weights, refers to a method of not separately processing, but simultaneously processing a plurality of variables. Thus, the support vector machine is useful as a statistical tool for classification. The support vector machine non-linearly maps its n-dimensional input space into a high dimensional feature space, and presents an optimal interface (optimal parting plane) between features. The support vector machine comprises two phases: a training phase and a testing phase. In the training phase, support vectors are produced, while estimation is performed according to a specific rule in the testing phase. In general, SVMs provide a model for use in classifying each of n subjects to two or more disease categories based on one k-dimensional vector (called a k-tuple) of biomarker measurements per subject. An SVM first transforms the k-tuples using a kernel function into a space of equal or higher dimension. The kernel function projects the data into a space where the categories can be better separated using hyperplanes than would be possible in the original data space. To determine the hyperplanes with which to discriminate between categories, a set of support vectors, which lie closest to the boundary between the disease categories, may be chosen. A hyperplane is then selected by known SVM techniques such that the distance between the support vectors and the hyperplane is maximal within the bounds of a cost function that penalizes incorrect predictions. This hyperplane is the one which optimally separates the data in terms of prediction (Vapnik, 1998 Statistical Learning Theory. New York: Wiley). Any new observation is then classified as belonging to any one of the categories of interest, based where the observation lies in relation to the hyperplane. When more than two categories are considered, the process is carried out pairwise for all of the categories and those results combined to create a rule to discriminate between all the categories. As used herein, the term "Random Forests algorithm" or "RF" has its general meaning in the art and refers to classification algorithm such as described in US 8,126,690; WO2008/156617. Random Forest is a decision-tree-based classifier that is constructed using an algorithm originally developed by Leo Breiman (Breiman L, "Random forests," Machine Learning 2001, 45:5-32). The classifier uses a large number of individual decision trees and decides the class by choosing the mode of the classes as determined by the individual trees. The individual trees are constructed using the following algorithm: (1) Assume that the number of cases in the training set is N, and that the number of variables in the classifier is M; (2) Select the number of input variables that will be used to determine the decision at a node of the tree; this number, m should be much less than M; (3) Choose a training set by choosing N samples from the training set with replacement; (4) For each node of the tree randomly select m of the M variables on which to base the decision at that node; (5) Calculate the best split based on these m variables in the training set. In some embodiments, the score is generated by a computer program.

In some embodiments, the method of the present invention thus comprises a) determining the level of PSCK9 in a blood sample obtained from the subject and/or detecting at least one genetic variant in the PCSK9 gene; b) implementing an algorithm on data comprising the level of PCSK9 and/or presence or absence of the genetic variant so as to obtain an algorithm output; c) determining the probability that the subj ect will develop arterial plaques.

The result given by the methods of the invention may be used as a guide in determining how frequently the development of arterial plaques should be screened and/or in selecting a therapy or treatment regimen for the patient. For example, when the patient has been determined as having a high risk of a cardiovascular event development, he can be eligible for a therapy with a PSCK9 antagonist.

In some embodiments, the PCSK9 antagonist is an anti-PCSK9 antagonist antibody. As used herein, an "anti-PCSK9 antagonist antibody" or "PCSK9 antagonist antibody" refers to an anti-PCSK9 antibody that is able to inhibit PCSK9 biological activity and/or downstream pathway(s) mediated by PCSK9 signaling, including PCSK9-mediated down-regulation of the LDLR, and PCSK9-mediated decrease in LDL blood clearance. A PCSK9 antagonist antibody encompasses antibodies that block, antagonize, suppress or reduce (to any degree including significantly) PCSK9 biological activity, including downstream pathways mediated by PCSK9 signaling, such as LDLR interaction, or elicitation of a cellular response to PCSK9. For purpose of the present invention, it will be explicitly understood that the term "PCSK9 antagonist antibody" encompasses all the previously identified terms, titles, and functional states and characteristics whereby the PCSK9 itself, a PCSK9 biological activity (including but not limited to its ability to mediate any aspect of interaction with the LDLR, down regulation of LDLR, and decreased blood LDL clearance), or the consequences of the biological activity, are substantially nullified, decreased, or neutralized in any meaningful degree. In some embodiments, a PCSK9 antagonist antibody binds PCSK9 and prevents interaction with the LDLR. Examples of PCSK9 antagonist antibodies are provided in, e.g., U.S. Patent Application Publication No. 20100068199, which is herein incorporated by reference in its entirety. In some embodiments, the PCSK9 antagonist antibody is alirocumab (PRALUENT™); evolocumab (REPATHA™); REGN728; LGT209; RG7652; LY3015014; J16, LI L3 (bococizumab); 31 H4, 11 FI , 12H1 1 , 8A3, 8A1 , or 3C4 (see, e.g., US8,030,457); 300N (see, e.g., US8,062,640); or 1 D05 (see, e.g., US8, 188,234). In some embodiments, the anti-PCSK9 antibody is bococizumab, alirocumab (PRALUENT™), or evolocumab (REPATHA™).

In some embodiments, the PCSK9 antagonist is an inhibitor of expression. An “inhibitor of expression” refers to a natural or synthetic compound that has a biological effect to inhibit the expression of a gene. In a preferred embodiment of the invention, said inhibitor of gene expression is a siRNA, an antisense oligonucleotide or a ribozyme. For example, anti-sense oligonucleotides, including anti-sense RNA molecules and anti-sense DNA molecules, would act to directly block the translation of PCSK9 mRNA by binding thereto and thus preventing protein translation or increasing mRNA degradation, thus decreasing the level of PCSK9, and thus activity, in a cell. For example, antisense oligonucleotides of at least about 15 bases and complementary to unique regions of the mRNA transcript sequence encoding PCSK9 can be synthesized, e.g., by conventional phosphodiester techniques. Methods for using antisense techniques for specifically inhibiting gene expression of genes whose sequence is known are well known in the art (e.g. see U.S. Pat. Nos. 6,566,135; 6,566,131; 6,365,354; 6,410,323; 6,107,091; 6,046,321; and 5,981,732). Small inhibitory RNAs (siRNAs) can also function as inhibitors of expression for use in the present invention. PCSK9 gene expression can be reduced by contacting a patient or cell with a small double stranded RNA (dsRNA), or a vector or construct causing the production of a small double stranded RNA, such that PCSK9 gene expression is specifically inhibited (i.e. RNA interference or RNAi). Antisense oligonucleotides, siRNAs, shRNAs and ribozymes of the invention may be delivered in vivo alone or in association with a vector. In some embodiments, the inhibition of expression is Inclisiran that is a chemically synthesized small interfering RNA (siRNA) molecule, which targets the hepatic production of PCSK9.

The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.


Figure 1. Multivariable predictors (visit 1) of carotid artery plaque (at visit 4)

N =997; N. Plaque =203.

Median time between visit 1 (VI) and visit 4 (V4) =18.5 years.

Legend: LDLc, low-density lipoprotein cholesterol; PCSK9, proprotein convertase subtilisin/kexin type 9; LDLr, LDL receptor; NPX, Olink® log2 standardized units.



Study population

A detailed description of the STANISLAS cohort has been previously published20. The STANISLAS cohort was established with the primary objective of investigating gene-gene and gene-environment interactions in the field of cardiovascular diseases. In order to assess the effect of genetics on the variability of intermediate phenotypes on the transition toward pathology, the families were deemed healthy and free of declared acute and/or chronic illness at VI. The implementation of the fourth visit (V4) enabled a follow-up of 18 to 23 years. The collected information was enriched with new biomarkers and detailed clinical phenotyping (including vascular echography). The STANISLAS-V4 also allows the long-term evaluation of clinical, biological and morphological data, such as the advent of atherosclerotic plaques in an initially healthy population.

In the present analysis we included the 997 adults that attended both the VI and V4, and had also performed vascular echography, biomarker, and 949 of them have been successfully genotyped.

Study design

All participants were observed at the Centre d'Investigation Clinique Plurithematique Pierre Drouin at Nancy Hospital Center (CIC-P de Nancy) in the morning after a 12- to 14-hour fast. Blood samples were taken. Medical history, medications, anthropometric parameters, blood pressure, carotid plaques, carotid-femoral pulse-wave velocity (PWV), and carotid intima-media thickness (cIMT) were recorded.

Arterial plaques, carotid intima-media thickness, and pulse wave velocity

High-resolution echotracking in STANISLAS-V4 was performed to assess both carotid plaques, diameter, distension and IMT on the right common carotid artery21. Intima-media thickness was also measured in a subset of STANISLAS-V1 participants, but information regarding carotid plaques was not recorded22. The non-invasive investigations were performed in a controlled environment at 22±1°C after 10 minutes of rest in the supine position23. Four measurements were obtained per each participant. Examinations were performed with the wall track system (WTS, ESOATE, Maastricht, The Netherlands) and the ART. LAB (ESAOTE, Maastricht, The Netherlands) in immediate succession. The reproducibility and agreement (intra/interoperator/devices) of the measurements were excellent.

Pulse wave velocity was determined with the Complior® (Complior® SP, Alam Medical, France) and Sphygmocor® CVMS (AtCor, Australia) devices. Peripheral blood pressure was measured after at least 10 min of rest in supine position, in a quiet room. Carotid to femoral PWV was assessed with Complior® using the recommendations of the European Network for Noninvasive Investigation of Large Arteries24.

Biomarkers and gene-candidate analysis

All samples were collected at the CIC-P de Nancy with minimally traumatic venepuncture. Standardized sample handling procedures enabled the collection of serum and plasma (EDTA, heparin) as well as huffy coat fraction. Blood DNA of all the participants to the Stanislas V4 was extracted using Gentra Puregene Blood Kit (Qiagen, Hilden Germany) and stored at -20°C. Genotyping was conducted at CNRGH (Evry, France) using two chips: the Illumina Global Screening Array (GSA) which is composed of 687 572 intronic and exonic

markers and the Illumina Exome Array, which is constituted of 244 330 SNPs, mostly exonic. All blood-derived bio-samples are stored in a central biobank facility with temperatures between -80°C and -196°C (as required).

Baseline plasma samples were analyzed for protein biomarkers by the TATAA-biocenter using the Olink Proseek® Multiplex cardiovascular (CVD) II panel that includes both PCSK9 and LDLr, using a proximity extension assay (PEA) technology25, where 92 oligonucleotide-labelled antibody probe pairs per panel are allowed to bind to their respective targets in the sample in 96-well plate format. When binding to their correct targets, they give rise to new DNA amplicons with each ID-barcoding their respective antigens. The amplicons are subsequently quantified using a Fluidigm BioMark™ HD real-time PCR platform. The platform provides log2-normalized protein expression (NPX) data.

For the genetic analyses of the present study, we focused on the PCSK9 gene which encodes the PCSK9 protein. First, we defined an interval that encompassed the PCSK9 gene boundaries (+/- 20kb) on chromosome 1, based on the reference genome built 37 from the Ensembl database (Chrl; position 55485221-55550525; http :// grch37 ensembl . 26. Then, we
selected all the SNPs comprised between these boundaries in the two chips. 24 SNPs were selected from the GSA chip and 12 from the Exome chip. However, 3 markers were duplicated between the two chips and one of each was excluded. Hence 33 SNPs were selected. After the quality control steps, 1 SNP was excluded for monomorphism (i.e. minor allele frequency (maf)=0); no SNP had more than 1% of missing data (i.e. all SNPs had callrate >0.99), and no SNP deviated from the Hardy-Weinberg Equilibrium at a threshold of p <1.108. We also excluded 7 SNPs that are very rare (maf <0.01). Tests for linkage disequilibrium in the subset of the 26 SNPs were conducted, and 4 SNPs were highly linked with r2>0.90 (not shown).

Statistical considerations

For the baseline clinical characteristics, continuous variables are expressed as means and respective standard deviation (SD). Categorical variables are presented as frequencies and percentages. Participant baseline characteristics were compared between those without plaques vs. those with plaques at V4 using Chi2 tests for categorical variables and t-tests for continuous variables.

The main aim of this study was to test the association of PCSK9 and LDLr with incident carotid plaques. Logistic regression models were performed. Firstly, a stepwise backward model including all the clinical variables with a p<0.1 from Table 1 was performed, to select the clinical features with stronger association with carotid plaques. Secondly, the potential association of PCSK9 and LDLr with carotid plaques was tested on top of the clinical model built in the previous step.

Since PCSK9 and LDLr proteins were measured using NPX (Normalized Protein expression) values on a log2 scale, the odds ratio (OR) for each protein estimates the increase in the odds of carotid plaques associated with a doubling in the protein concentration. A p-value <0.05 was considered statistically significant. The analyses were performed using STATA version 15 software (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LP).

The genetic analyses were performed using R (version 3.4.1). Association tests were performed using the R package Gaston

( The association of the genetic variants with the study outcomes was tested using a linear model, with age and sex used as covariates. The statistical significance level was fixed at 0.05, after applying a Benjamini-Hochberg correction for multiple testing.


Characteristics of the population

In the present analyses 997 adult participants were included. The mean age of the adult population at VI was 42±5 years. Of these, 203 (20.4%) had carotid plaque(s) at V4. Participants who developed arterial plaques (from VI to V4) were older than the other participants (42.7±5.4 vs. 41.7±4.7 years), more often male (60% vs. 49%), smokers (29% vs.

18%), with diabetes (6% vs. 3%), and with higher cholesterol levels (LDLc 1.6±0.4 vs. 1.5±0.3 g/L) (p<0.05 for all) at VI (see Table 1). Participants who developed arterial plaques also had higher circulating PCSK9 and LDLr levels (Table 1).

The median (pct25-7s) follow-up time from VI to V4 was 18.5 (17.7-19.7) years. The mean age at V4 was 59.2±5.7 years. No patient had carotid artery stenosis >50% of the lumen diameter.

Predictors of carotid plaques

The independent clinical predictors of arterial plaques were age (OR [95%CI] per 5 years increase=1.26 [1.06-1.49], p =0.007), smoking (OR [95%CI] =1.79 [1.23-2.60], p =0.002), and LDLc (OR [95%CI] per 10 mg/dL increased.72 [1.10-2.69], p =0.018). Table 2.

PCSK9 predicted arterial plaques on top of the clinical model (OR [95%CI] =2.14 [1.28-3.58], p =0.004) and LDLr did not (OR [95%CI] =0.91 [0.66-1.25], p =0.57). The PCSK9/LDLr

ratio also predicted arterial plaques driven by the PCSK9 levels (OR [95%CI] =1.32 [1.04-1.67], p =0.023). Table 2 and Figure 1. PCSK9 levels at V4 was also “transversally” associated with carotid plaques at V4 (OR [95%CI] =1.66 [1.01-2.72], p =0.045).

Sensitivity analysis in children at baseline

The proportion of children at VI (n =647, mean age =15±4 years) who developed plaques at V4 (mean age =33±5 years) was 0.9% (n=6). Despite an almost ten-year difference, the age of these participants at V4 was closer to their parents at VI, possibly indicating that our population might have low prevalence of carotid plaques at VI.

Association of PCSK9 and LDLr with cIMT and PWV

At VI LDLr was independently associated with V4 PWV as continuous variable: b-coef. (95%CI) =0.4 (0.1-0.6), p =0.006. At V4, LDLr was independently associated with V4 cIMT as continuous and categorical variable: b-coef. (95%CI) =20.9 (4.5-37.2), p =0.012 and OR (95%CI) =1.32 (1.00-1.73), p =0.042 for cIMT above the 90th percentile, respectively.

Genetic considerations

We tested the association between polymorphisms of PCSK9 gene and presence of atherosclerotic plaques at V4. We found 3 SNPs (rs540796, rs562556, and rs631220) associated with the presence of plaques (corrected p-value =0.02). Minor alleles of these 3 SNPs are also associated with LDLc and PCSK9 levels both at VI and V4. These 3 SNPs are highly correlated among them (r2 >0.9) and one of them (rs562556) carried a missense gain-of-function mutation (I474V). Subjects who carried the minor allele (genotypes AA or GA) had higher cholesterol level and more often carotid plaques. However, no significant association was found between PCSK9 polymorphisms and PWV or cIMT.

The partial correlation analyses suggest that rs562556 contributes with 16 to 17% of the variance of PCSK9 levels both at VI and V4 (p <0.001 for both).


As main finding, the present study shows that increased circulating levels of PCSK9 in middle-aged people, could independently predict the presence of carotid plaques 20 years later, adjusting for important contributors of carotid plaque formation (e.g. age, smoking, LDLc). Moreover, PCSK9 levels were similarly associated with carotid plaques “transversally” at visit 4. The identification of the SNP rs562556 (responsible for the missense mutation I474V), provides a potential causal link between the PCSK9 levels and the atherosclerotic plaques.

Higher LDLc concentrations have causal association with increased cardiovascular risk. Evidence derived from multiple randomized controlled trials and meta-analyses, shows a consistent and graded reduction in cardiovascular risk in response to reductions in the LDLc levels (grade of evidence IA)8, 27-3 f There is no apparent threshold at which LDLc lowering is not associated with reduced cardiovascular risk8. Moreover, the higher the initial LDLc level, the greater the absolute reduction in risk, while the relative risk reduction remains constant at any given baseline LDLc level32, 33. Statins, effective lower LDLc and are generally well tolerated. The only (rare) adverse events that have been reliably shown to be caused by statins are myalgias34. However, many patients are reluctant in taking statins, many due to medical misinformation, while many others cannot achieve the desired levels of LDLc despite moderate-to-high intensity statin therapy11, 35.

The PCSK9, LDLr, and apolipoprotein B genes have been associated with autosomal dominant forms of familial hypercholesterolemia (FH), caused by a “gain-of-function” mutation in the PCSK9 gene; resulting in excessive LDLc and, consequently, atherosclerotic plaques and cardiovascular events36. Mutations in PCSK9 gene are reported to be responsible for 10-25% of autosomal-dominant form of FH cases without mutations in LDLr or apolipoprotein B37. On the other hand, individuals that have a “loss-of-function” mutation in the PCSK9 gene, express lower levels of LDLc and have low cardiovascular risk38, 39. The physiological explanation is that the circulating PCSK9 binds to the LDLr, increasing its degradation in the lysosomal compartments. Inhibiting the PCSK9 activity (e.g. with a monoclonal antibody), results in lower concentrations of free PCSK9, and in consequence, fewer LDLr are degraded, thus being available for the uptake of LDLc, decreasing its blood concentrations40. The advent of PCSK9i has enabled their testing in Phase 3 trials. Overall, PCSK9i significantly reduced LDLc, regardless patient population and/or background statin treatment13, 41, 42. The rapid development and human testing of PCSK9i provides promising data for a more widespread use of these molecules when they become more affordable and its use more practical (e.g. one sole yearly injection)13; this could be of potential interest for cardiovascular prevention in a near future (e.g. target therapy to patients with elevated PCSK9 levels and in cases in whom statin therapy is not tolerated or sufficient to achieve the desired LDLc levels).

Previous to the present study, evidence regarding an independent predictive effect of PCSK9 in atherosclerosis progression was scarce. Prior cross-sectional studies reported

conflicting results regarding the association between PCSK9 levels and cIMT4344. In concordance with our results, in a Chinese cohort of 643 participants free of cardiovascular disease at baseline, plasma PCSK9 levels were associated with 10-year progression of atherosclerosis (measured by the total plaque area), independently from LDL45. However, in this study the association of polymorphisms of PCSK9 gene and the progression of atherosclerotic plaques were not evaluated.

In our study, we found that the rs562556 missense mutation was independently associated with incident carotid atherosclerotic plaques, LDLc, and PCSK9 levels. The SNP rs562556 has already been found to be associated with LDLc and total cholesterol, whereby patients with the mutation have higher levels of cholesterol19. Another recent study showed that genetically low LDL cholesterol due to PCSK9 variation (including the rs562556 SNP) was causally associated with low risk of cardiovascular mortality, but not with low all-cause mortality in a general population46. These findings are in agreement with our results.

Our predictive models, retained age, smoking and LDLc as the factors with stronger association with the occurrence of plaques, in concordance with previous studies47. Hence, the independent associated of PCSK9 on top of these “classic” risk markers reinforces our hypothesis of targeting PCSK9 for preventing the occurrence of atherosclerotic disease. As described in the introduction section, the presence of atherosclerotic plaques has been associated with the occurrence of major cardiovascular events beyond cIMT or PWV2· 448.


PCSK9 could predict arterial plaques almost 20 years in advance in a healthy middle-aged population. The association of plaque and the SNP rs562556, responsible of I474V mutation, may reinforce the potential causality of our findings. It thus credible to consider that PCSK9 inhibitors could be useful for cardiovascular prevention in the population identified at risk of developing arterial plaques.

Table 1. Baseline (visit 1) characteristics of the study population by the presence of carotid plaques at visit 4.

Median time between visit 1 (VI) and visit 4 (V4) =18.5 years.

Legend: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate calculated by the CKD-EPI formula; LDL, low-density lipoprotein; PCSK9, proprotein convertase subtilisin/kexin type 9; NPX, Olink® log2 standardized units.

Table 2. Predictors (visit 1) of carotid artery plaque (at visit 4)

N =997; N. Plaque =203.

Median time between visit 1 (VI) and visit 4 (V4) =18.5 years.

Legend: LDLc, low-density lipoprotein cholesterol; PCSK9, proprotein convertase subtilisin/kexin type 9; LDLr, LDL receptor; NPX, Olink® log2 NPX.


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