Use of soluble TREM-1 as a biomarker for assessing the risk of cardiovascular disease in asymptomatic subjects

Abstract

Early diagnosis of cardiovascular diseases can potentially cure subjects and save innumerable lives. Diagnosis and treatment of subjects at early stages by cardiologists remain a challenge. Here, the inventors have measured baseline sTREM-1 (triggering receptor expressed on myeloid cells-1) in 10,000 initially healthy participants without prevalent cardiovascular disease (CVD) from the Paris Prospective Study 3 (PPS3). They have quantified the association and predictive value of baseline sTREM-1 for incident CVD events over 10 years of follow-up. The analysis reveals strong, significant and independent association with incident CVD events combined and its subtypes (coronary heart disease, stroke, peripheral artery diseases, and heart failure). Furthermore, adding sTREM-1 to existing risk prediction algorithms such as SCORE2 or the US pooled risk equation improved the discrimination capacity of these models in a significant and more importantly in a clinically meaningful manner, including among those at moderate CVD risk. Furthermore, the inventors have examined CVD events such as aneurysm, arrythmias and thromboembolic events. Given the ongoing development of pharmacological molecules blocking blood sTREM-1, the inventors believe these findings support future intervention trials testing to which extent sTREM-1 could be a relevant target for the primary prevention of CVD in the general population.

Description

[0001] USE OF SOLUBLE TREM-1 AS A BIOMARKER FOR ASSESSING THE RISK OF CARDIOVASCULAR DISEASE IN ASYMPTOMATIC SUBJECTS

[0002] FIELD OF THE INVENTION:

[0003] The present invention is in the field of medicine, in particular cardiology.

[0004] BACKGROUND OF THE INVENTION:

[0005] The World health Organization estimates that nearly 17 million people die every year from CVDs, which accounts for approximately 31% of global deaths. Early diagnosis of CVD can potentially cure subjects and save innumerable lives. Diagnosis and treatment of subjects at early stages by cardiologists remain a challenge. The identification of traditional risk factors such as age, hypercholesterolemia, hypertension, diabetes mellitus, and smoking has improved primary prevention of CVD. Further scientific advances have led to the discovery of a broad range of novel biomarkers associated with cardiovascular risks, including B-type natriuretic peptide (BNP), N-terminal prohormone BNP (NT -proBNP), troponin, C-reactive protein (CRP), myeloperoxidase (MPO), lipoprotein-associated phospholipase A2, fibrinogen, TMAO, and cystatin C. Although these biomarkers have a prognostic value independent of the previous traditional risk factors, only a few have become important predictive tools in clinical practice. Additionally, several risk scores have been developed. For instance, the Framingham Risk Score (FRS) is one of a number of scoring systems used to determine an individual's risk of developing cardiovascular disease (Wilson, Peter WF, et al. "Prediction of coronary heart disease using risk factor categories." Circulation 97.18 (1998): 1837-1847). The European Society of Cardiology Systematic Coronary Risk Evaluation 2 (SCORE2; applicable to individuals aged 40-69 years) and SCORE2-Older Persons (SCORE2-OP; applicable to individuals aged 70-89 years) models can also be used for assessing the risk of CVD ("SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. "European heart journal 42, no. 25 (2021): 2439-2454). Every traditional CVD riskassessment model implicitly assumes each risk factor related to CVD outcome in a linear fashion. Such models have a tendency to oversimplify complex relationships, including several risk factors with non-linear interactions. Multiple risk factors should be properly incorporated, and more correlated nuances between the risk factors and outcomes should be determined. There is thus a need for identifying additional biomarkers that could be useful for improving the current scoring systems. Recently, WO2015018936 teaches that level of soluble Triggering Receptors Expressed on Myeloid cells- 1 (sTREM-1) would be suitable for identifying a subject at risk of developing a recurrent cardiovascular event or at risk of death occurring after a cardiovascular event but remain speculative for asymptomatic subjects.

[0006] SUMMARY OF THE INVENTION:

[0007] The present invention is defined by the claims. In particular, the present invention relates to the use of soluble Triggering Receptors Expressed on Myeloid cells-1 (sTREM-1) as a biomarker for assessing the risk of cardiovascular disease in asymptomatic subjects.

[0008] DETAILED DESCRIPTION OF THE INVENTION:

[0009] Methods of prediction:

[0010] The first object of the present invention relates to a method of assessing the risk of having a cardiovascular disease in an asymptomatic subject comprising determining the level of soluble Triggering Receptors Expressed on Myeloid cells-1 (sTREM-1) in a sample obtained from the subject wherein the level of sTREM-1 correlates with the risk of having a cardiovascular disease.

[0011] As used herein, the term “subject” refers to a human or another mammal (e.g., mouse, rat, rabbit, dog, cat, cattle, swine, sheep, horse or primate) that can be afflicted with or is susceptible to a cardiovascular disease but may or may not display symptoms of a cardiovascular disease. Thus, according to the present invention, the subject is asymptomatic. As used herein, the term "asymptomatic" refers to a subject who experiences no detectable symptoms for a cardiovascular disease, including, but not limited to chest pain, weakness or numb legs and / or arms, breathlessness, very fast or slow heartbeat, palpitations, feeling dizzy, lightheaded, fainting, and swollen limbs. In some embodiments, the subject is a human being. In one embodiment, the asymptomatic subject is a diabetic subject. Unless otherwise stated, the term “subject” does not denote a particular age, and thus encompasses adults and elderly.

[0012] In some embodiments, the subject has been classified as having a moderate risk according to at least one already known prediction algorithm selected from the group consisting of SCORE2 ("SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. " European heart journal 42, no. 25 (2021): 2439-2454), SCORE2-OP ("SCORE2-OP risk prediction algorithms: estimating incident cardiovascular event risk in older persons in four geographical risk regions. " European Heart Journal 42, no. 25 (2021): 2455-2467), Framingham Risk Score (FRS) (Wilson, Peter WF, et al. "Prediction of coronary heart disease using risk factor categories. " Circulation 97.18 (1998): 1837-1847), PREVENT risk equation (Development and Validation of the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENTTM) Equations. Circulation 2023), and the pooled cohort equations (Goff Jr, David C, et al. "2013 ACC / AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology / American Heart Association Task Force on Practice Guidelines." Circulation 129.25 suppl 2 (2014): S49- S73).

[0013] As used herein, the term "cardiovascular disease" or “CVD” has its general meaning in the art and is used to classify numerous conditions affecting the heart, heart valves, blood, and vasculature of the body and encompasses any disease affecting the heart or blood vessels. In particular, cardiovascular diseases include but is not limited to coronary heart disease, stroke, peripheral artery diseases, and heart failure. As used herein, the term “coronary heart disease” or “CHD” has its general meaning in the art and is a type of heart disease where the arteries of the heart cannot deliver enough oxygen-rich blood to the heart. As used herein, the term “stroke” has its general meaning in the art and refers to an episode of neurological dysfunction caused by focal cerebral, spinal, or retinal infarction (Easton et al., Stroke 2009, 40, 2276-2293). As used herein, the term “peripheral artery disease” or “PAD” has its general meaning in the art and refers to the narrowing of the peripheral arteries that carry blood away from the heart to other parts of the body. The most common type is lower-extremity PAD, in which blood flow is reduced to the legs and feet. As used herein, the term "heart failure" or “HF” has its general meaning in the art and embraces congestive heart failure and / or chronic heart failure. Functional classification of heart failure is generally done by the New York Heart Association Functional Classification (Criteria Committee, New York Heart Association. Diseases of the heart and blood vessels. Nomenclature and criteria for diagnosis, 6th ed. Boston: Little, Brown and co, 1964;114).

[0014] 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 disease, 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. "Risk evaluation," or "evaluation of risk" in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event or disease state may occur, the rate of occurrence of the event or conversion from one disease state to another, i.e., from a normal condition to a cardiovascular disease or to one at risk of developing a cardiovascular disease. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of a cardiovascular disease, either in absolute or relative terms in reference to a previously measured population. The methods of the present invention may be used to make continuous or categorical measurements of the risk of conversion to a cardiovascular disease, thus diagnosing and defining the risk spectrum of a category of subjects defined as being at risk for a cardiovascular disease. In the categorical scenario, the invention can be used to discriminate between low risk and other subject cohorts at higher risk for a cardiovascular disease.

[0015] In some embodiments, the methods of the present invention are particularly suitable for assessing the risk of having a cardiovascular event.

[0016] As used herein, the term “cardiovascular event" has its general meaning in the art and refers to sudden cardiac death, acute coronary syndromes such as, but not limited to, plaque rupture, myocardial infarction, unstable angina, as well as non-cardiac acute arteriovascular events such as blood clots of the leg, aneurysms or aneurysm progression, stroke and other arteriovascular ischemic events where arteriovascular blood flow and oxygenation is transiently or permanently interrupted.

[0017] As used herein, the term “sample" as used herein refer to a biological sample obtained for the purpose of in vitro evaluation. Typical biological samples to be used in the method according to the invention are blood samples (e.g. whole blood sample or serum sample). In some embodiments, said biological liquids comprise blood, plasma, serum, saliva and exsudates. Thus, in some embodiments, the sample is chosen from blood samples, plasma samples, saliva samples, exsudate samples and serum samples. Preferably, the sample is a blood sample, a serum sample or a plasma sample.

[0018] As used herein the term “TREM-1” has its general meaning in the art and refers to the triggering receptor expressed on myeloid cells-1 (TREM-1). TREM-1 is a member of the Ig-superfamily, the expression of which is up-regulated on phagocytic cells in the presence of bacteria or fungi (Bouchon A et al. Nature 2001; 230: 1103-7). An exemplary amino acid sequence is represented by SEQ ID NO: 1. It was previously described that TREM-1 can be shed or secreted from the membrane of activated phagocytes and can be found in a soluble form in body fluids. Accordingly, the term “sTREM-1” refers to the soluble form of the human TREM-1 receptor.

[0019] SEQ ID NO : l>sp | Q9NP99 | TREM1 HUMAN Triggering receptor expres sed on myeloid cells 1 0S=Homo sapiens OX=9606 GN=TREM1 PE=1 SV=1 MRKTRLWGLLWMLFVSELRAATKLTEEKYELKEGQTLDVKCDYTLEKFASSQKAWQI IRD GEMPKT LACTERPSKNSHPVQVGRI ILEDYHDHGLLRVRMVNLQVEDSGLYQCVIYQPPK EPHMLFDRIRLWTKGFSGTPGSNENSTQNVYKI PPTTTKALCPLYTSPRTVTQAPPKST ADVSTPDSEINLTNVTDI IRVPVFNIVILLAGGFLSKSLVFSVLFAVTLRSFVP

[0020] The measurement of the level of sTREM-1 in the sample is typically carried out using standard protocols known in the art. For example, the method may comprise contacting the sample with a binding partner capable of selectively interacting with sTREM-1 in the sample. In some embodiments, the binding partners are antibodies, such as, for example, monoclonal antibodies or even aptamers. For example the binding may be detected through use of a competitive immunoassay, a non-competitive assay system using techniques such as western blots, a radioimmunoassay, an ELISA (enzyme linked immunosorbent assay), a “sandwich” immunoassay, an immunoprecipitation assay, a precipitin reaction, a gel diffusion precipitin reaction, an immunodiffusion assay, an agglutination assay, a complement fixation assay, an immunoradiometric assay, a fluorescent immunoassay, a protein A immunoassay, an immunoprecipitation assay, an immunohistochemical assay, a competition or sandwich ELISA, a radioimmunoassay, a Western blot assay, an immunohistological assay, an immunocytochemical assay, a dot blot assay, a fluorescence polarization assay, a scintillation proximity assay, a homogeneous time resolved fluorescence assay, a lAsys analysis, and a BIAcore analysis. The aforementioned assays generally involve the binding of the partner (ie. antibody or aptamer) to a solid support. Solid supports which can be used in the practice of the invention include substrates such as nitrocellulose (e.g., in membrane or microtiter well form); polyvinylchloride (e.g., sheets or microtiter wells); polystyrene latex (e.g., beads or microtiter plates); polyvinylidine fluoride; diazotized paper; nylon membranes; activated beads, magnetically responsive beads, and the like. An exemplary biochemical test for identifying specific proteins employs a standardized test format, such as ELISA test, although the information provided herein may apply to the development of other biochemical or diagnostic tests and is not limited to the development of an ELISA test (see, e.g., Molecular Immunology: A Textbook, edited by Atassi et al. Marcel Dekker Inc., New York and Basel 1984, for a description of ELISA tests). Therefore ELISA method can be used, wherein the wells of a microtiter plate are coated with a set of antibodies which recognize sTREM-1. A sample containing or suspected of containing sTREM-1 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 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. Measuring the level of sTREM-1 (with or without immunoassay-based methods) may also include separation of the compounds: centrifugation based on the compound’s molecular weight; electrophoresis based on mass and charge; HPLC based on hydrophobicity; size exclusion chromatography based on size; and solid-phase affinity based on the compound's affinity for the particular solid-phase that is used. Once separated, said one or two biomarkers proteins may be identified based on the known "separation profile" e.g., retention time, for that compound and measured using standard techniques. Alternatively, the separated compounds may be detected and measured by, for example, a mass spectrometer. Typically, levels of immunoreactive sTREM-1 in a sample may be measured by an immunometric assay on the basis of a double-antibody "sandwich" technique, with a monoclonal antibody specific for sTREM-1 (Cayman Chemical Company, Ann Arbor, Michigan). According to said embodiment, said means for measuring sTREM-1 level are for example i) a sTREM-1 buffer, ii) a monoclonal antibody that interacts specifically with sTREM-1, iii) an enzyme-conjugated antibody specific for sTREM-1 and a predetermined reference value of sTREM-1.

[0021] In some embodiments, the level of sTREM-1 is compared to a predetermined reference value, wherein differential between the determined level of sTREM-1 and the predetermined reference value indicates the risk of having a cardiovascular disease. In some embodiments, the method further comprises the steps of i) determining the level of sTREM-1 in the sample obtained from the subject, ii) comparing the level of sTREM-1 with a predetermined reference value and iii) determining the risk of having a cardiovascular disease from said comparison. Typically, when the level of sTREM-1 is higher than the predetermined value, it is concluded that the subject is at risk of having a cardiovascular disease and conversely when the level of sTREM-1 is lower than the predetermined reference value, it is concluded that the subject is not at risk of having a cardiovascular disease.

[0022] Typically, the predetermined reference value is a threshold value or a cut-off value. 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 level of sTREM-1 in properly banked historical subject 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 determining the levels of sTREM-1 in a group of reference, one can use algorithmic analysis for the statistic treatment of the measured levels of sTREM-1 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 1 -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 9.2.0.1 medical statistical software, SPSS 9.0, ROCPOWER.SAS, DESIGNROC.FOR, MULTIREADER POWER. SAS, CREATE-ROC.SAS, GB STAT VIO.O (Dynamic Microsystems, Inc. Silver Spring, Md., USA), etc. This method has been extended for censored data. Here, the objective is to discriminate between those who will have the event in the future from those who will not.

[0023] The predetermined reference value can also be relative to a number or value derived from population studies, including without limitation, subjects adjusted for age, sex, BMI, physical activity, smoking status, LDL and HDL-chol esterol, SBP, Lipid lowering and BP lowering drugs, T2D, renal function (CKD-EPI) and / or having the same CVD risk as assessed by current methods (e.g. the same SCORE2 risk category). Such predetermined reference values can be derived from statistical analyses and / or risk prediction data of populations obtained from mathematical algorithms and computed indices. In some embodiments, the predetermined reference values are derived from the level of sTREM-1 in a control sample derived from one or more subject who do not develop a cardiovascular disease. Furthermore, retrospective measurement of the level of sTREM-1 in properly banked historical subject samples may be used in establishing these predetermined reference values.

[0024] 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. 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 subject may be assessed by comparing values obtained by measuring the level of sTREM-1, where values greater than 5 reveal that the subject is at risk of having a cardiovascular disease and values less than 5 reveal that the subject is not at risk of having a cardiovascular disease. In some embodiments, a subject may be assessed by comparing values obtained by measuring the level of sTREM-1 and comparing the values on a scale, where values above the range of 4- 6 indicate that the subject is at risk of having a cardiovascular disease and values below the range of 4-6 indicate that the subject is not at risk of having a cardiovascular disease, with values falling within the range of 4-6 indicate that further explorations are needed to conclude whether the subject is at risk of having a cardiovascular disease. In some embodiments, a given population of asymptomatic subjects is divided in five parts (or quintiles), each containing the same number of subjects wherein one quintile corresponding to the part of the given population with the highest level of sTREM-1, a second quintile corresponding to the part of the given population with the lowest level of sTREMl, and the three remaining quintiles corresponding to the rest of the population having intermediate level of sTREM-1. In said embodiment, a level of sTREM-1 above the fourth or the fifth quintile indicates that the subject is at high risk of having a cardiovascular disease.

[0025] In some embodiments, level of sTREM-1 higher than about 290pg / mL, preferably higher than about 335.4pg / mL indicates that the subject is at high risk of having a cardiovascular disease.

[0026] In some embodiments, the method of the present invention comprises the steps of a) assessing at least one parameter that is level of sTREM-1, b) implementing an algorithm on data comprising or consisting of the parameter assessed at step a) as to obtain an algorithm output, the implementing step being computer-implemented; and c) determining the risk of having a cardiovascular disease from the algorithm output obtained at step b).

[0027] As used herein, the term “algorithm” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous parameters and calculates an output value, sometimes referred to as an “index” or “index value”.

[0028] As used herein, the term “parameter” refers to any characteristic assessed when carrying out the method according to the invention. As used herein, the term “parameter value” refers to a value (a number for instance) associated to a parameter.

[0029] In some embodiments, the algorithm implements one or more additional parameters. Typically, the additional parameters are selected from the group consisting of age, sex, BMI, physical activity, smoking status, LDL and HDL-chole sterol levels, Systolic Blood Pressure level, status regarding lipid lowering, status regarding blood pressure lowering drugs, T2D status, and renal function such as eGFR.

[0030] In some embodiments, the level of sTREMl is implemented in one already known prediction algorithm selected from the group consisting of SCORE2 ("SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. " European heart journal 42, no. 25 (2021): 2439-2454), SCORE2-OP ("SCORE2-OP risk prediction algorithms: estimating incident cardiovascular event risk in older persons in four geographical risk regions." European Heart Journal 42, no. 25 (2021): 2455-2467), Framingham Risk Score (FRS) (Wilson, Peter WF, et al. "Prediction of coronary heart disease using risk factor categories. " Circulation 97.18 (1998): 1837-1847), PREVENT risk equation (Development and Validation of the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENTTM) Equations. Circulation 2023), and the pooled cohort equations (Goff Jr, David C, et al. "2013 ACC / AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology / American Heart Association Task Force on Practice Guidelines." Circulation 129.25 suppl 2 (2014): S49- S73).

[0031] Non other limiting examples of algorithms include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Non- limiting examples of algorithms thus include logistic regression, linear regression, random forests, classification and regression trees (C&RT), boosted trees, neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feedforward networks, support vector machines (e.g., Kernel methods), multivariate adaptive regression splines (MARS), Levenberg-Marquardt algorithms, Gauss-Newton algorithms, mixtures of Gaussians, gradient descent algorithms, learning vector quantization (LVQ), and combinations thereof. Of particular use in combining parameters are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of said parameters and the objective response to the preoperative adjuvant therapy. Of particular interest are structural and syntactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art.

[0032] In some embodiments, the method of the present invention comprises the use of a machine learning algorithm. The machine learning algorithm may comprise a supervised learning algorithm. Examples of supervised learning algorithms may include Average One-Dependence Estimators (AODE), Artificial neural network (e.g., Backpropagation), Bayesian statistics (e.g., Naive Bayes classifier, Bayesian network, Bayesian knowledge base), Case-based reasoning, Decision trees, Inductive logic programming, Gaussian process regression, Group method of data handling (GMDH), Learning Automata, Learning Vector Quantization, Minimum message length (decision trees, decision graphs, etc.), Lazy learning, Instance-based learning Nearest Neighbor Algorithm, Analogical modeling, Probably approximately correct learning (PAC) learning, Ripple down rules, a knowledge acquisition methodology, Symbolic machine learning algorithms, Subsymbolic machine learning algorithms, Support vector machines, Random Forests, Ensembles of classifiers, Bootstrap aggregating (bagging), and Boosting. Supervised learning may comprise ordinal classification such as regression analysis and Information fuzzy networks (IFN). Alternatively, supervised learning methods may comprise statistical classification, such as AODE, Linear classifiers (e.g., Fisher's linear discriminant, Logistic regression, Naive Bayes classifier, Perceptron, and Support vector machine), quadratic classifiers, k-nearest neighbor, Boosting, Decision trees (e.g., C4.5, Random forests), Bayesian networks, and Hidden Markov models. The machine learning algorithms may also comprise an unsupervised learning algorithm. Examples of unsupervised learning algorithms may include artificial neural network, Data clustering, Expectation-maximization algorithm, Self-organizing map, Radial basis function network, Vector Quantization, Generative topographic map, Information bottleneck method, and IBSEAD. Unsupervised learning may also comprise association rule learning algorithms such as Apriori algorithm, Eclat algorithm and FP-growth algorithm. Hierarchical clustering, such as Single-linkage clustering and Conceptual clustering, may also be used. Alternatively, unsupervised learning may comprise partitional clustering such as K-means algorithm and Fuzzy clustering. In some embodiments, the machine learning algorithms comprise a reinforcement learning algorithm Examples of reinforcement learning algorithms include, but are not limited to, temporal difference learning, Q-learning and Learning Automata. Alternatively, the machine learning algorithm may comprise Data Preprocessing. Methods of prevention:

[0033] A further object of the present invention relates to a method for the prophylactic treatment of a cardiovascular disease in a subject in need thereof comprising the steps of i) determining the risk of having a cardiovascular disease by performing the predictive method of the present invention and ii) comprising administering to the subject a therapeutically effective amount of a TREM-1 inhibitor.

[0034] As used herein, the term “TREM-1 inhibitor” refers to any compound, chemical, antibody, or peptide, naturally occurring or synthetic, that directly or indirectly decreases the activity and / or expression of TREM-1. Functionally conservative variations of known TREM-1 inhibitors are also intended to be covered by this description. This includes, for example only, deuterated variations of known inhibitors, inhibitors comprising non-naturally occurring amino-acids, functional variations of peptide inhibitors involving a different sequence of amino acids, inhibitors created by codon variations which code for the same amino-acid sequence of a known inhibitor or functional variation thereof, versions of peptides described herein in which one or more of the amino acids can be, individually, D or L isomers. The invention also includes combinations of L-isoforms with D-isoforms.

[0035] Common TREM-1 inhibitors include peptides which may be derived from TREM-1, or TREM- like-transcript-1 (“TLT-1”). Any peptide which competitively binds TREM-1 ligands, thereby reducing TREM-1 activity and / or expression is a TREM-1 inhibitor. These peptides may be referred to as “decoy receptors.”

[0036] In some embodiments, the TREM-1 inhibitor is a peptide that is disclosed in WO2014037565. Examples of such peptides are listed below in Table A. LR17 is a known, naturally occurring direct inhibitor of TREM-1 which functions by binding and trapping TREM-1 ligand. LR12 is a 12 amino-acid peptide derived from LR17. LR12 is composed of the N-terminal 12 amino- acids from LR17. Research suggests that LR12 is an equivalent TREM-1 inhibitor when compared to LR17. LR6-1, LR6-2 and LR6-3 are all 6 amino-acids peptides derived from LR17. These peptides may function in the same manner as LR12.

[0037] Table A: Different peptides that can be used as TREM-1 inhibitors

[0038] In some embodiments, the TREM-1 inhibitor is a peptide derived from TLT-1 or TREM-1, in particular peptides as described herein.

[0039] In some embodiments, the TREM-1 inhibitor is a short TLT-1 peptide consisting of less than 50 amino acids, preferably consisting of between 6 and 20 amino acids, more preferably consisting of between 6 and 17 amino acids, wherein said TLT-1 peptide comprises between 6 and 20 consecutive amino acids from the human TLT-1 having an amino acid sequence as set forth in SEQ ID NO: 12

[0040] (MGLTLLLLLLLGLEGQGIVGSLPEVLQAPVGSSILVQCHYRLQDVKAQKVWCRFLPE GCQPLVSSAVDRRAPAGRRTFLTDLGGGLLQVEMVTLQEEDAGEYGCMVDGARGP QILHRVSLNILPPEEEEETHKIGSLAENAFSDPAGSANPLEPSQDEKSIPLIWGAVLLVG LLVAAVVLFAVMAKRKQGNRLGVCGRFLSSRVSGMNPSSVVHHVSDSGPAAELPLD VPHIRLDSPPSFDNTTYTSLPLDSPSGKPSLPAPSSLPPLPPKVLVCSKPVTYATVIFPGG NKGGGTSCGPAQNPPNNQTPSS); or a sequence having at least 60, 65, 70, 75, 80, 85 or 90% identity with the amino acid sequence as set forth in SEQ ID NO: 12; or a functionconservative variant or derivative thereof.

[0041] In some embodiments, the TREM-1 inhibitor is a TLT-1 peptide consisting of 6 to 12, 13, 14, 15, 16, 17, 18, 19 or 20 amino acids and comprising an amino acid sequence as set forth in SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5 or SEQ ID NO: 6. or a sequence having at least 60, 65, 70, 75, 80, 85 or 90% identity with the amino acid sequence as set forth in SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, or SEQ ID NO: 6, respectively; or a function-conservative variant or derivative thereof.

[0042] In some embodiments, the TREM-1 inhibitor is a TLT-1 peptide comprising or consisting of an amino acid sequence as set forth in SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, or SEQ ID NO: 6; or a sequence having at least 60, 65, 70, 75, 80, 85 or 90% identity with the amino acid sequence as set forth in SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, or SEQ ID NO: 6, respectively; or a function-conservative variant or derivative thereof.

[0043] In some embodiments, the TREM-1 inhibitor is a TLT-1 peptide having an amino acid sequence as set forth in SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, or SEQ ID NO: 6; or a sequence having at least 60, 65, 70, 75, 80, 85 or 90% identity with the amino acid sequence as set forth in SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, or SEQ ID NO: 6, respectively; or a function-conservative variant or derivative thereof.

[0044] In some embodiments, the TREM-1 inhibitor is a TLT-1 peptide having an amino acid sequence as set forth in SEQ ID NO: 3, also known as LR12; or a sequence having at least 60, 65, 70, 75, 80, 85 or 90% identity with the amino acid sequence as set forth in SEQ ID NO: 3; or ; or a function-conservative variant or derivative of SEQ ID NO: 3.

[0045] In some embodiments, the TREM-1 inhibitor is a short TREM-1 peptide consisting of less than 50 amino acids, preferably consisting of between 6 and 20 amino acids, more preferably consisting of between 6 and 17 amino acids, wherein said TREM-1 peptide comprises between 6 and 20 consecutive amino acids from the human TREM-1 having an amino acid sequence as set forth in SEQ ID NO: 1 or a function-conservative variant or derivative thereof.

[0046] In some embodiments, the TREM-1 inhibitor is a TREM-1 peptide consisting of 6 to 12, 13, 14, 15, 16, 17, 18, 19 or 20 amino acids and comprising an amino acid sequence as set forth in SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10 or SEQ ID NO: 11; or a sequence having at least 60, 65, 70, 75, 80, 85 or 90% identity with the amino acid sequence as set forth in SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10 or SEQ ID NO: 11, respectively; or a function-conservative variant or derivative thereof. In some embodiments, the TREM-1 inhibitor is a TREM-1 peptide comprising or consisting of an amino acid sequence as set forth in SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10 or SEQ ID NO: 11; or a sequence having at least 60, 65, 70, 75, 80, 85 or 90% identity with the amino acid sequence as set forth in SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10 or SEQ ID NO: 11, respectively; or a function-conservative variant or derivative thereof.

[0047] In some embodiments, the TREM-1 inhibitor is a TREM-1 peptide having an amino acid sequence as set forth in SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10 or SEQ ID NO: 11 or a sequence having at least 60, 65, 70, 75, 80, 85 or 90% identity with the amino acid sequence as set forth in SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10 or SEQ ID NO: 11, respectively; or a function-conservative variant or derivative thereof.

[0048] As used herein, the term “identity” or “identical”, when used in a relationship between the sequences of two or more peptides, refers to the degree of sequence relatedness between peptides, as determined by the number of matches between strings of two or more amino acid residues. “Identity” measures the percent of identical matches between the smaller of two or more sequences with gap alignments (if any) addressed by a particular mathematical model or computer program (i.e., “algorithms”). Identity of related polypeptides can be readily calculated by known methods. Such methods include, but are not limited to, those described in Computational Molecular Biology, Lesk, A. M., ed., Oxford University Press, New York, 1988; Biocomputing: Informatics and Genome Projects, Smith, D. W., ed., Academic Press, New York, 1993; Computer Analysis of Sequence Data, Part 1, Griffin, A. M., and Griffin, H. G., eds., Humana Press, New Jersey, 1994; Sequence Analysis in Molecular Biology, von Heinje, G., Academic Press, 1987; Sequence Analysis Primer, Gribskov, M. and Devereux, J., eds., M. Stockton Press, New York, 1991; and Carillo et al., SIAM J. Applied Math. 48, 1073 (1988). Preferred methods for determining identity are designed to give the largest match between the sequences tested. Methods of determining identity are described in publicly available computer programs. Preferred computer program methods for determining identity between two sequences include the GCG program package, including GAP (Devereux et al., Nucl. Acid. Res. \2, 387 (1984); Genetics Computer Group, University of Wisconsin, Madison, Wis.), BLASTP, BLASTN, and FASTA (Altschul et al., J. Mol. Biol. 215, 403-410 (1990)). The BLASTX program is publicly available from the National Center for Biotechnology Information (NCBI) and other sources (BLAST Manual, Altschul et al. NCB / NLM / NIH Bethesda, Md. 20894; Altschul et al., supra). The well-known Smith Waterman algorithm may also be used to determine identity.

[0049] As used herein, the term “function-conservative variants” denotes peptides derived from the peptides as described herein, in which a given amino acid residue in a peptide has been changed without altering the overall conformation and function of said peptides, including, but not limited to, replacement of an amino acid with one having similar properties (such as, for example, similar polarity, similar hydrogen bonding potential, acidic or basic amino acid replaced by another acidic or basic amino acid, hydrophobic amino acid replaced by another hydrophobic amino acid, aromatic amino acid replaced by another aromatic amino acid). It is commonly known that amino acids other than those indicated as conserved may differ in a peptide so that the percent of amino acid sequence similarity between any two peptides of similar function may vary and may be, for example, from 70% to 99% as determined according to an alignment method such as by the Cluster Method, wherein similarity is based on the MEGALIGN algorithm. A “function-conservative variant” also includes peptides which have at least 20%, 30%, 40%, 50%, or 60% amino acid identity with the peptides as described herein, for example as determined by BLAST or FASTA algorithms, and which have the same or substantially similar properties or functions as the peptides as described herein. Preferably “function-conservative variants” include peptides which have at least 60%, 65%, 70%, 75%, 80%, 85% or 90% amino acid identity with the peptides as described herein and which have the same or substantially similar properties or functions as the peptides as described hereinabove.

[0050] As used herein, the term “derivative” refers to a variation of a peptide or of a functionconservative variant thereof that is otherwise modified in order to alter the in vitro or in vivo conformation, activity, specificity, efficacy or stability of the peptide. For example, said variation may encompass modification by covalent attachment of any type of molecule to the peptide or by addition of chemical compound(s) to any of the amino-acids of the peptide. In some embodiments, the peptide or function-conservative variants or derivatives thereof as described hereinabove may have D- or L-configuration. In some embodiments, the amino acid from the amino end of the peptide or function-conservative variant or derivative thereof as described hereinabove has an acetylated terminal amino group, and the amino acid from the carboxyl end has an amidated terminal carboxy group. In addition, the peptide or functionconservative variant or derivative thereof as described hereinabove may undergo reversible chemical modifications in order to increase its bioavailability (including stability and fat solubility) and its ability to pass the blood-brain barrier and epithelial tissue. Examples of such reversible chemical modifications include esterification of the carboxy groups of glutamic and aspartic amino acids with an alcohol, thereby removing the negative charge of the amino acid and increasing its hydrophobicity. This esterification is reversible, as the ester link formed is recognized by intracellular esterases which hydrolyze it, restoring the charge to the aspartic and glutamic residues. The net effect is an accumulation of intracellular peptide, as the internalized, de-esterified peptide cannot cross the cell membrane. Another example of such reversible chemical modifications includes the addition of a further peptide sequence, which allows the increase of the membrane permeability, such as a TAT peptide or Penetratin peptide (see - Charge-Dependent Translocation of the Trojan. A Molecular View on the Interaction of the Trojan Peptide Penetratin with the 15 Polar Interface of Lipid Bilayers. Biophysical Journal, Volume 87, Issue 1, 1 July 2004, Pages 332-343).

[0051] The peptides or function-conservative variants or derivatives thereof as described hereinabove may be obtained through conventional methods of solid-phase chemical peptide synthesis, following Fmoc and / or Boc-based methodology (see Pennington, M.W. and Dunn, B.N. (1994). Peptide synthesis protocols. Humana Press, Totowa.). Alternatively, the peptides or functionconservative variants or derivatives as described hereinabove may be obtained through conventional methods based on recombinant DNA technology, e.g., through a method that, in brief, includes inserting the nucleic acid sequence coding for the peptide into an appropriate plasmid or vector, transforming competent cells for said plasmid or vector, and growing said cells under conditions that allow the expression of the peptide and, if desired, isolating and (optionally) purifying the peptide through conventional means known to experts in these matters or eukaryotic cells that express the peptide. A review of the principles of recombinant DNA technology may be found, for example, in the text book entitled “Principles of Gene Manipulation: An Introduction to Genetic Engineering,” R.W. Old & S.B. Primrose, published by Blackwell Scientific Publications, 4th Edition (1989).

[0052] Additional examples of TREM-1 inhibitors include those disclosed by patent application WO 2015018936. These include, but are not limited to, antibodies directed to TREM-1 and / or sTREM-1 or TREM-1 and / or sTREM-1 ligand, small molecules inhibiting the function, activity or expression of TREM-1, peptides inhibiting the function, activity or expression of TREM-1, siRNAs directed to TREM-1, shRNAs directed to TREM-1, antisense oligonucleotide directed to TREM-1, ribozymes directed to TREM-1 and aptamers which bind to and inhibit TREM-1. Antibodies have been shown to inhibit TREM-1 as well. Representative antibodies are described, for example, in U.S. Publication No. 20130309239 and U.S. Pat. No. 9,000,127. Additional examples of TREM-1 inhibitors also include those disclosed in WO2011 047097. As described in U.S. patent publications 20090081199 and 20030165875, fusion proteins between human IgGl constant region and the extracellular domain of mouse TREM-1 or that of human TREM-1 can be used, as a decoy receptor, to inhibit TREM-1. Another TREM-1 inhibitor is TLT-1, as disclosed in Washington, et al., “A TREM family member, TLT-1, is found exclusively in the alpha-granules of megakaryocytes and platelets,” Blood. 2004 Aug. 15; 104(4): 1042-7. Additional TREM-1 inhibitors include MicroRNA 294, which has been shown to target TREM-1 by dual-luciferase assay activity. Naturally-occurring TREM-1 inhibitors include curcumin and diferuloylmethane, a yellow pigment present in turmeric. Inhibition of TREM-1 by curcumin is oxidant independent. Accordingly, curcumin and synthetic curcumin analogs, such as those described in U.S. Publication Nos. 20150087937, 20150072984, 20150011494, 20130190256; 20130156705, 20130296527, 20130224229, 20110229555; and 20030153512; U.S. Pat. Nos. 7,947,687, 8,609,723, and PCT WO 2003105751.

[0053] In some embodiments, the TREM-1 inhibitor is Nangibotide (CAS number 2014384-91-7) (Cuvier V, Lorch U, Witte S, Olivier A, Gibot S, Delor I, Garaud JJ, Derive M, Salcedo- Magguilli M: A first-in-man safety and pharmacokinetics study of nangibotide, a new modulator of innate immune response through TREM-1 receptor inhibition. Br J Clin Pharmacol. 2018 Oct;84(10):2270-2279. doi: 10.1111 / bcp.13668. Epub 2018 Jul 20).

[0054] As used herein, the term "therapeutically effective amount" refers to a sufficient amount of the TREM-1 inhibitor to prevent the cardiovascular disease in the subject. It will be understood, however, that the total daily usage of the agent is decided by the attending physician within the scope of sound medical judgment. The specific therapeutically effective dose level for any particular subject will depend upon a variety of factors including the disorder being treated and the severity of the disorder; activity of the specific compound employed; the specific composition employed, the age, body weight, general health, sex and diet of the subject; the time of administration, route of administration, and rate of excretion of the specific compound employed; the duration of the treatment; drugs used in combination or coincidential with the specific agent; and like factors well known in the medical arts. For example, it is well within the skill of the art to start doses of the compound at levels lower than those required to achieve the desired therapeutic effect and to gradually increase the dosage until the desired effect is achieved. However, the daily dosage of the agent may be varied over a wide range from 0.01 to 1,000 mg per adult per day. Preferably, the compositions contain 0.01, 0.05, 0.1, 0.5, 1.0, 2.5, 5.0, 10.0, 15.0, 25.0, 50.0, 100, 250 and 500 mg of the agent for the symptomatic adjustment of the dosage to the subject to be treated. A medicament typically contains from about 0.01 mg to about 500 mg of the active ingredient, preferably from 1 mg to about 100 mg of the active ingredient. An effective amount of the drug is ordinarily supplied at a dosage level from 0.0002 mg / kg to about 20 mg / kg of body weight per day, especially from about 0.001 mg / kg to 7 mg / kg of body weight per day.

[0055] Typically, the inhibitor of the present invention is combined with pharmaceutically acceptable excipients, and optionally sustained-release matrices, such as biodegradable polymers, to form pharmaceutical compositions. The term "Pharmaceutically" or "pharmaceutically acceptable" refer to molecular entities and compositions that do not produce an adverse, allergic or other untoward reaction when administered to a mammal, especially a human, as appropriate. A pharmaceutically acceptable carrier or excipient refers to a non-toxic solid, semisolid or liquid filler, diluent, encapsulating material or formulation auxiliary of any type. Typically, the pharmaceutical compositions contain vehicles, which are pharmaceutically acceptable for a formulation capable of being injected. These may be in particular isotonic, sterile, saline solutions (monosodium or disodium phosphate, sodium, potassium, calcium or magnesium chloride and the like or mixtures of such salts), or dry, especially freeze-dried compositions which upon addition, depending on the case, of sterilized water or physiological saline, permit the constitution of injectable solutions. The pharmaceutical forms suitable for injectable use include sterile aqueous solutions or dispersions; formulations including sesame oil, peanut oil or aqueous propylene glycol; and sterile powders for the extemporaneous preparation of sterile injectable solutions or dispersions. In all cases, the form must be sterile and must be fluid to the extent that easy syringability exists. It must be stable under the conditions of manufacture and storage and must be preserved against the contaminating action of microorganisms, such as bacteria and fungi. Sterile injectable solutions are prepared by incorporating the active ingredient at the required amount in the appropriate solvent with several of the other ingredients enumerated above, as required, followed by filtered sterilization. Generally, dispersions are prepared by incorporating the various sterilized active ingredients into a sterile vehicle which contains the basic dispersion medium and the required other ingredients from those enumerated above. In the case of sterile powders for the preparation of sterile injectable solutions, the preferred methods of preparation are vacuum-drying and freeze- drying techniques which yield a powder of the active ingredient plus any additional desired ingredient from a previously sterile-filtered solution thereof.

[0056] 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.

[0057] FIGURES:

[0058] Figure 1. Cumulative incidence of CVD events by quintiles of blood TREM-1.

[0059] Figure 2. Added value of TREM-1 for CVD risk prediction on top of established CVD risk equations.

[0060] Figure 3. Cumulative incidence of CVD events by tertiles of blood TREM-1 among diabetic patients.

[0061] EXAMPLE:

[0062] I. sTREM-1 and incident CVD events

[0063] We have measured baseline sTREM-1 (triggering receptor expressed on myeloid cells-1) in 10,000 initially healthy participants without prevalent cardiovascular disease (CVD) from the Paris Prospective Study 3 (PPS3). PPS3 is French INSERM supported prospective observational cohort study on novel risk factors for CVD in volunteers. We have quantified the association and predictive value of baseline sTREM-1 for incident CVD events over 10 years of follow-up. The analysis reveals strong, significant and independent association with incident CVD events combined and its subtypes (coronary heart disease, stroke, peripheral artery diseases, and heart failure). Furthermore, adding sTREM-1 to existing risk prediction algorithms such as SCORE2 or the US pooled risk equation improved the discrimination capacity of these models in a significant and more importantly in a clinically meaningful manner, including among those at moderate CVD risk. In a separate section (section II), we have examined CVD events such as aneurysm, arrythmias and thromboembolic events for which associations with sTREM-1 has been much less studied so far. Given the ongoing development of pharmacological molecules blocking blood sTREM-1, we believe these findings support future intervention trials testing to which extent sTREM-1 could be a relevant target for the primary prevention of CVD in the general population. TREM-1 is related to higher burden of CVD risk factors, comorbidities and treatment

[0064] Table 1 is reporting the baseline characteristics of the population by quintile of TREM-1. It shows for the first time the distribution of sTREM-1 in the population. It also indicates that higher sTREM-1 concentrations are related to higher level of the risk factors and more comorbidities and treatments. sTREM-1 is associated with higher risk of incident CVD

[0065] During a median follow-up period of 10.08 years (IQR= 9.17 to 12.02), 444 participants suffered from 479 CVD event (i.e. 35 participants had at least 2 events) including CHD (n=272), stroke (n=135), PAD (n=32) and HF (n=40). In addition, 450 died during follow-up. The Kaplan-Meier curves clearly indicates a gradient of risk for CVD events combined (Figure 1) and for its components (not shown but available on request) across the quintiles of sTREM-1 concentrations. As shown in Table 2, the crude incidence of CVD events combined increased 11.7- fold between quintile 1 (1.66 per 1000 PY, 1.31 to 2.00) and quintile 5 of sTREM-1 (19.54 per 1000 PY, 15.48 to 23.60). In multivariate analysis accounting for sociodemographic factors and standard CVD risk factors, and compared to quintile 1, the risk of CVD events combined increased three-fold from quintile 2 (HR:3.08, 95% CI: 2.29 to 4.15) up to 8.79-fold for quintile 5 of TREM-1 (HR:8.79, 95% CI: 6.42 to 12.03) respectively. Associations remained virtually unchanged after further adjustment for hs-CRP, IL-6, or IL-10 on separate models. sTREM-1 was associated with each CVD component, the strongest associations being observed for CHD and PAD, then stroke and then HF (Table 3). The association between sTREM-1 and CVD events combined did not differ by age group, sex, smoking status, hypertension status, lipid- lowering medication use, diabetes status or by SCORE2 risk category (all p values for interaction between 0.21 to 0.81). sTREM-1 improved substantially the performances of existing CVD risk prediction models As shown in Table 3, adding sTREM-1 to the SCORE 2 algorithm increased on average by 8.4% the discriminatory capacity of the model (C -index 0.79 vs. 0.71; boostraped C index difference: 0.084; 95% CI: 0.083 to 0.085). Improvement in risk discrimination was even stronger for those at moderate risk (Figure 2). Similarly, adding sTREM-1 to SCORE2 led to a significant and clinically meaningful improvement in reclassification, with a categorical net reclassification index (NRI) of 0.32 (95% CL 0.26 to 0.37) corresponding to a significant improvement in the reclassification of the cases (NRI: 0.39; 95% CI: 0.33 to 0.44). Very consistent findings were noted when using the US recalibrated pooled cohort risk equation, the Framingham general CVD risk profile or the recently published PREVENT risk equation.

[0066] To conclude, given the ongoing development of pharmacological molecules blocking blood TREM-1, we believe these findings may have in the mid-term strong implications for the primary prevention of CVD (i.e among the general population).

[0067] II. TREM-1 and less studied CVD events (arterial aneurysm, arrhythmias, thromboembolic events)

[0068] In the same PPS3 study and during the same study period, a total of 38 participants suffered from hospitalized arterial aneurysms requiring surgical procedures (including 33 aortic aneurysms), 202 had hospitalized arrythmias (AF, flutter, ventricular tachycardia, junctional arrhythmias) and 89 had hospitalized thromboembolic events. As shown in Table 2 and after multivariate analysis, the risk of aneurysm increased in a graded manner with higher sTREM- 1 concentrations, statistically significant associations being observed for the upper quintile of sTREM-1 only. Table 4 further shows association between the upper quintile of sTREM-1 with incident arrythmias and incident thromboembolic events.

[0069] III. TREM-1 and CVD events in diabetic patients

[0070] In a sub analysis of PPS3 focusing on diabetic patients at baseline (n=365), higher sTREM-1 concentrations were strongly associated with incident CVD events (n=31) over 12 years of follow-up. Compared to the first tertile of sTREM-1 (<257 pg / mL, calculated among the CVD cases to ensure a sufficient number of event per group), sTREM-1 in the second tertile, <307,56 pg / mL) and sTREM-1 in the third tertile (>=307,56 pg / mL) had respectively a 6.46 (95%CI 2.62; 15.94) and a 7.87 (95% CI: 3.12; 19.84) increased risk of CVD in multivariable analysis. The figure shows the KM curves of CVD per tertile of sTREM-1.

[0071]

[0072] Table 2. Crude CVD incidence rates and association of TREM-1 quintiles for CVD events combined (Cox analysis)

[0073] To ensure sufficient number of CVD events by quintiles, the thresholds for the quintiles of TREM-1 were calculated among the 444 participants who had an incident CVD event; this applies for Table 2 to Table 4. In Table 1 instead, the thresholds for the quintiles of TREM-1 were calculated in the whole population. Abbreviations: PY for person-year; HR for hazard ratios; CI for confidence intervals

[0074] Table 3. Association of TREM-1 quintiles with incident CVD subtypes (Cox analysis)

[0075] Cox models were adjusted for age, sex, BMI, physical activity, smoking status, LDL and HDL- cholesterol, SBP, Lipid lowering and BP lowering drugs, T2D and renal function (CKD-EPI) When appropriate, specific quintiles of TREM-1 were combined to ensure sufficient number of CVD events. Abbreviations: CHD: coronary heart diseases; PAD: peripheral artery diseases; HF: heart failure; HR for hazard ratios; CI for confidence intervals

[0076] Table 4. Multivariable analysis of blood TREM-1 quintiles with incident aneurysm, arrythmias and thromboembolic events Cox models were adjusted for age, sex, BMI, physical activity, smoking status, LDL and HDL- cholesterol, SBP, Lipid lowering and BP lowering drugs, T2D and renal function (CKD-EPI). Abbreviations: PY for person-year; HR for hazard ratios; CI for confidence intervals. Table 5. Multivariable analysis of blood TREM-1 tertiles with incident cardiovascular events

[0077] Cox models were adjusted for age, sex, BMI, physical activity, smoking status, LDL and HDL- cholesterol, SBP, Lipid lowering and BP lowering drugs, and renal function (CKD-EPI)

[0078] To ensure sufficient number of CVD events per group, the thresholds for the tertiles of TREM- 1 were calculated among the 31 participants who had an incident CVD event.

[0079] REFERENCES:

[0080] Throughout this application, various references describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure.

[0081] Joffre J, Potteaux S, Zeboudj L, et al. Genetic and Pharmacological Inhibition of TREM-1 Limits the

[0082] Development of Experimental Atherosclerosis. J Am Coll Cardiol 2016 ; 68: 2776-93.

[0083] Zysset D, Weber B, Rihs S, et al. TREM-1 links dyslipidemia to inflammation and lipid deposition in atherosclerosis. Nat Commun 2016 ; 7 : 13151.

[0084] Boufenzer A, Lemarie J, Simon T, et al. TREM-1 Mediates Inflammatory Injury and Cardiac Remodeling Following Myocardial Infarction. Circ Res 2015 ;116: 1772-82.

[0085] Wang YK, Tang JN, Shen YL, et al. Prognostic Utility of Soluble TREM-1 in Predicting Mortality and CardiovascularEvents in Patients With Acute Myocardial Infarction. J Am Heart Assoc. 2018;7:e008985.

[0086] Ait-Oufella H, Yu M, Kotti S, et al. Plasma and genetic determinants of soluble TREM-1 and major adverse cardiovascular events in a prospective cohort of acute myocardial infarction patients. Results from the FAST-MI 2010 study. Int J Cardiol 2021 ; 344:213-219 Lemarie J, Boufenzer A, Popovic B, et al. Pharmacological inhibition of the triggering receptor expressed on myeloid cells-1 limits reperfusion injury in a porcine model of myocardial infarction. ESC Heart Fail 2015; 2 : 90-9.

[0087] SCORE2 working group and ESC Cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J 2021;42:2439-2454.

[0088] Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC / AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology / American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014; 63: 2935- 2959. Ralph B D'Agostino Sr , Ramachandran S Vasan, Michael J Pencina, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008;117:743-53.

[0089] Khan SS, Matsushita K, Sang Y, et al. Development and Validation of the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) Equations. Circulation 2023. doi: 10.1161 / CIRCULATIONAHA.123.067626. Online ahead of print.

Claims

CLAIMS:

1. A method of assessing the risk of having a cardiovascular disease in an asymptomatic subject comprising determining the level of soluble Triggering Receptors Expressed on Myeloid cells-1 (sTREM-1) in a sample obtained from the subject wherein the level of sTREM-1 correlates with the risk of having a cardiovascular disease.

2. The method according to claim 1 wherein the subject has been classified as having a moderate risk according to at least one already known prediction algorithm selected from the group consisting of SCORE2, SCORE2-OP, Framingham Risk Score (FRS), PREVENT risk equation, or the pooled cohort equations.

3. The method according to claim 1 or 2 for assessing the risk of having a cardiovascular event.

4. The method according to any one of claims 1 to 3 wherein the level of sTREM-1 is compared to a predetermined reference value, wherein differential between the determined level of sTREM-1 and the predetermined reference value indicates the risk of having a cardiovascular disease.

5. The method according to any one of claims 1 to 4 that comprises the steps of i) determining the level of sTREM-1 in the sample obtained from the subject, ii) comparing the level of sTREM-1 with a predetermined reference value and iii) determining the risk of having a cardiovascular disease from said comparison.

6. The method of claim 5 wherein, when the level of sTREM-1 is higher than the predetermined value, it is concluded that the subject is at risk of having a cardiovascular disease and conversely when the level of sTREM-1 is lower than the predetermined reference value, it is concluded that the subject is not at risk of having a cardiovascular disease.

7. The method according to any one of claims 4 to 6 wherein a level of sTREM-1 above the fourth or the fifth quintile determined in a given population of asymptomatic subjects indicates that the subject is at high risk of having a cardiovascular disease.

8. The method according to any one of claims 1 to 7 that comprises the steps of a) assessing at least one parameter that is level of sTREM-1, b) implementing an algorithm on datacomprising or consisting of the parameter assessed at step a) as to obtain an algorithm output, the implementing step being computer-implemented; and c) determining the risk of having a cardiovascular disease from the algorithm output obtained at step b).

9. The method according to claim 8 wherein the algorithm implements one or more additional parameters selected from the group consisting of age, sex, BMI, physical activity, smoking status, LDL and HDL-cholesterol levels, Systolic Blood Pressure level, status regarding lipid lowering, status regarding blood pressure lowering drugs, T2D status, and renal function such as eGFR.

10. The method according to claim 8 or 9 wherein the level of sTREMl is implemented in one already known prediction algorithm selected from the group consisting of SCORE2,SCORE2-OP, Framingham Risk Score (FRS), PREVENT risk equation, or the pooled cohort equations.

11. A method for the prophylactic treatment of a cardiovascular disease in a subject in need thereof comprising the steps of i) determining the risk of having a cardiovascular disease by performing the predictive method according to any one of claims 1 to 10 and ii) comprising administering to the subject a therapeutically effective amount of a TREM- 1 inhibitor.

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