Biomarkers for predicting and diagnosing severe atherosclerosis

Abstract

The present invention relates to the use of the expression level of B2MG, PIBA, FIBB, FIBG, GPV, PLF4 and TSP1 proteins as protein biomarkers for severe atherosclerosis and the use of same in methods for the prognosis and / or diagnosis of severe atherosclerosis in groups of patients with dyslipidaemia. Therefore, the method of the invention can also be used to predict the risk of atherosclerotic cardiovascular diseases associated with severe forms of atherosclerosis.

Description

[0001] BIOMARKERS FOR THE PREDICTION AND DIAGNOSIS OF SEVERE ATHEROSCLEROSIS

[0002] DESCRIPTION

[0003] TECHNICAL SECTOR

[0004] The invention falls within the field of Medicine, specifically in the area of ​​cardiovascular disease diagnosis. In particular, it relates to biomarkers for the prediction and diagnosis of severe atherosclerosis and the development of associated atherosclerotic cardiovascular diseases (ACED).

[0005] BACKGROUND OF THE INVENTION

[0006] Atherosclerotic cardiovascular diseases (ASCVDs) significantly reduce the quality of life of affected patients and are a leading cause of mortality worldwide. ASCVDs include coronary artery disease, stroke, and peripheral artery disease. In all these cases, atherosclerosis is the underlying disease. Atherosclerosis is a multifactorial disease that begins to develop in childhood and involves the accumulation of lipids and fibrous material in the arterial walls, followed by oxidation and aggregation of pro-atherogenic lipid particles. This process promotes an inflammatory response, endothelial damage, fibrosis, and ultimately, the formation of atheromatous plaques. Atheromatous plaques can significantly narrow the arterial lumen, reducing blood flow and causing ischemia.This situation can progress to complex conditions where unstable atherosclerotic plaques can rupture and block blood flow, leading to thrombotic events that result in myocardial infarction, ischemic heart disease, or stroke. "Vulnerable" atherosclerotic plaques, meaning those susceptible to rupture, are characterized by a thin fibrous cap, a large lipid core, and a high content of inflammatory cells. The fibrous cap can rupture or erode in response to shear forces or inflammation, allowing the release of highly thrombogenic material into the bloodstream.

[0007] Identifying specific markers associated with the progression of atherosclerosis to severe forms of the disease and the presence of vulnerable atheromatous plaques is a fundamental step, both to understand the molecular mechanisms that take place during the formation and rupture of atheromatous plaques, and to predict the risk of rupture and the appearance of the most serious clinical symptoms, and therefore, to reduce their incidence.

[0008] Currently, there are imaging techniques that allow the definition and identification of certain structures indicative of vulnerable plates susceptible to rupture, although these techniques require expensive equipment and expert technicians for the interpretation of results.

[0009] On the other hand, some biological markers have also been described, mainly in serum, but also in urine or even the secretome of atheromatous plaques [1]. Many of these markers are not exclusive to the atherosclerotic process, and therefore cannot help to predict progression to more complex atherosclerotic states.

[0010] Given the relationship between lipid molecule levels and the risk of acute coronary syndrome (ACS), the diagnosis of atherosclerosis focuses primarily on evaluating blood lipid profiles, as well as other risk factors such as diabetes, age, sex, obesity, etc. However, many factors are involved (inflammation, hyperglycemia, etc.), including individual predisposition, making it necessary to identify individuals at higher risk of developing acute vascular events in order to provide targeted therapeutic interventions.

[0011] DESCRIPTION OF THE INVENTION

[0012] The present invention describes a panel of protein biomarkers, the analysis of which serves to predict and / or diagnose advanced stages of the atherosclerotic process, and therefore, predict the appearance of ECAT, especially in patients at risk of developing this type of disease, such as patients with dyslipidemia.

[0013] In vitro analysis of the differential expression level of various proteins in serum samples isolated from patients with severe atherosclerosis, patients with dyslipidemia, and healthy control patients allowed the identification of several proteins with significantly different expression levels among the different experimental groups.

[0014] The differentially expressed proteins identified are ACTB, APOB, B2MG, C4BPA, CO1A1, CORO1A, FIBA, FIBB, FIBG, GPV, MMP9, PCOC1, PLF4, and TSP1. All of these are therefore potential biomarkers for the prognosis and / or diagnosis of severe atherosclerosis, and consequently, of the risk of developing cardiovascular disease.

[0015] ACTB is the human cytoplasmic actin 1 protein (uniprot code: P60709).

[0016] APOB is human apolipoprotein B (uniprot code: P04114).

[0017] B2MG or B2M is the human Beta-2-microglobulin protein (uniprot code: P61769) with canonical sequence SEQ. ID. No. 1.

[0018] C4BPA is the human C4b-binding protein alpha chain (uniprot code: P04003) with canonical sequence SEQ. ID. No. 2.

[0019] C01A1 is the human protein alpha-1 collagen (I) (uniprot code: P02452).

[0020] C0R01A is the human protein coronain-1A (uniprot code: P31146).

[0021] FIBA is the human protein fibrinogen, alpha chain (uniprot code: P02671) with canonical sequence SEQ. ID. No. 3.

[0022] FIBB is the human protein fibrinogen, beta chain (uniprot code: P02675) with canonical sequence SEQ. ID. No. 4.

[0023] FIBG is the human protein fibrinogen, gamma chain (uniprot code: P02679) with canonical sequence SEQ. ID. No. 5.

[0024] GPV is the human platelet glycoprotein-V protein (uniprot code: P40197) with canonical sequence SEQ. ID. No. 6.

[0025] MMP9 is the human extracellular matrix metalloproteinase 9 protein (uniprot code: P14780) with canonical sequence SEQ. ID. No. 7.

[0026] PCOC1 is the human protein procollagen C-endopeptidase enhancer 1 (uniprot code: Q15113).

[0027] PLF4 is platelet factor-4 (uniprot code: P02776) of canonical sequence SEQ. ID.

[0028] No. 8.

[0029] TSP1 is the human thrombospondin-1 protein (uniprot code: P07996) with canonical sequence SEQ. ID. No. 9. After a detailed analysis of the differences in the expression levels of these proteins between the different groups of individuals (with severe atherosclerosis, with dyslipidemia and healthy individuals), a series of proteins of special interest were identified due to their sensitivity and specificity in establishing a prognosis and / or diagnosis of severe atherosclerosis.

[0030] Therefore, a first aspect of the invention relates to the in vitro use of the expression level of the proteins B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4 and / or TSP1, in a biological sample isolated from an individual without dyslipidemia, as a prognostic and / or diagnostic biomarker of severe atherosclerosis, and therefore, of the risk of suffering from ECAT.

[0031] B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4 and / or TSP1 constitute the “proteins of the invention” and we will refer to them as such from now on. Preferably, the expression level of the proteins of the invention is measured simultaneously.

[0032] In a preferred embodiment of this aspect of the invention, the expression level of the proteins B2MG, FIBA, FIBB, FIBG, GPV, PLF4, and / or TSP1, measured in vitro in a biological sample isolated from an individual, serves as a prognostic and / or diagnostic biomarker of severe atherosclerosis and, therefore, of the risk of developing cardiovascular disease in individuals with dyslipidemia. Preferably, the expression level of the aforementioned proteins is measured simultaneously.

[0033] A “biological sample” is a small part of a subject, representative of the whole. In the present invention, the biological sample is preferably a sample of blood, plasma, or serum, preferably serum.

[0034] The term “biomarker” refers to a biological molecule, or a fragment of a biological molecule, whose change and / or detection can be correlated with a particular condition or physical state. The terms “marker” and “biomarker” are used interchangeably throughout this description. The biomarkers of the present invention are correlated with the probability of developing cardiovascular disease associated with the progression of the atherosclerotic process.

[0035] In the present invention, "diagnosis" means the process of identifying a disease, condition, or injury by its signs and symptoms. In the present invention, "prognosis" means the assessment of the probability that an individual may develop a disease and progress to more severe forms of the same, worsen, exhibit more severe or acute symptoms, or show values ​​that are more significantly altered compared to the average values ​​of the healthy population. The prognosis of the disease includes the assessment prior to the onset of symptoms in individuals belonging to risk groups or in the general population. It also includes the identification of the stage of development or progression of the disease, or its regression to the onset of the disease or after diagnosis. Therefore, it includes both the probability of the disease appearing and the probability of it presenting in severe or critical forms in the future.

[0036] Both the diagnosis and prognosis of the disease may not be accurate for 100% of the subjects undergoing diagnostic and / or prognostic testing. However, it is required that a statistically significant proportion of subjects be identifiable as having the disease, being predisposed to it, or having the potential to progress to more aggressive, acute, or severe forms of it. Whether a proportion is statistically significant can be determined by a subject matter expert using various well-known statistical evaluation tools, such as determining confidence intervals, calculating p-values, Student's t-test or Fisher's discriminant functions, non-parametric Mann-Whitney U tests, Spearman's rank correlation coefficient, logistic regression, linear regression, and the area under the receiver operating characteristic (ROC) curve.Preferably, the confidence intervals are at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%. Preferably, the p-value is less than 0.1, 0.05, 0.01, 0.005, or 0.0001.

[0037] Atherosclerosis is a specific type of arteriosclerosis, that is, the thickening and hardening of blood vessels that restricts blood flow (stenosis), caused primarily by the accumulation of fats and cholesterol that form atheromatous plaques. Atherosclerosis is a chronic, widespread, and progressive disease that mainly affects medium-sized arteries. In its severe forms, atherosclerosis can completely block blood flow. Atheromatous plaques can also break off, producing clots that can block blood flow in areas distant from the plaque's origin. Clinically, it manifests as various "atherosclerotic cardiovascular diseases" or ACDs.“Atherosclerotic cardiovascular diseases” or ECATs associated with the progression of the atherosclerotic process, that is, with severe forms of atherosclerosis, include coronary artery disease, stroke, peripheral artery disease, carotid artery disease, renal artery stenosis, vertebral artery disease, ischemic heart disease, acute myocardial infarction, acute stroke, transient ischemic attack, intermittent claudication, and acute ischemia, among others.

[0038] In the present invention, “dyslipidemia” is understood to mean a quantitative or qualitative disorder of lipids and lipoproteins in the blood that increases the risk of developing cardiovascular disease: Specifically, we refer to the increase in plasma concentrations of cholesterol, triglycerides or both, or the decrease in the level of cholesterol associated with HDL-C, which contributes to the development of atherosclerosis and therefore represents a risk factor for suffering from atherosclerosis.

[0039] The determination of the expression level of the proteins of the invention can be carried out by any quantitative or semi-quantitative method known in the prior art, preferably by a quantitative method. The present invention also contemplates the determination of the expression level of the genes encoding the proteins of the invention, specifically the mRNA levels of each of these genes, individually or jointly, or in any combination thereof, for this same purpose.

[0040] Method of the invention

[0041] A second aspect of the invention relates to a method for obtaining data useful for predicting and / or diagnosing severe atherosclerosis and / or ECAT resulting from the atherosclerotic process in an individual without dyslipidemia, comprising measuring the expression levels of the proteins B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4, and / or TSP1 in a biological sample isolated from the individual. Preferably, the expression levels of the proteins of the invention are measured simultaneously.

[0042] Preferably, the results obtained are compared to those of a healthy reference sample.

[0043] A third aspect of the invention relates to an in vitro method for predicting and / or diagnosing severe atherosclerosis and / or ACE2-associated cardiovascular disease (ACED) in an individual without dyslipidemia. This method comprises the aforementioned method for obtaining useful data, wherein an individual is considered to have or be at risk of having severe atherosclerosis and / or ACED when the expression levels of the proteins B2MG, C4BPA, FIBA, FIBB, and / or FIBG are significantly higher than those of the reference sample, and / or the expression levels of the proteins GPV, MMP9, PLF4, and / or TSP1 are significantly lower than those of the reference sample. Preferably, the expression levels of the proteins of the invention are measured simultaneously.

[0044] A fourth aspect of the invention relates to an in vitro method for obtaining data useful for predicting and / or diagnosing severe atherosclerosis and / or ECAT resulting from the atherosclerotic process in individuals with dyslipidemia, comprising measuring the expression levels of the proteins B2MG, FIBA, FIBB, FIBG, GPV, PLF4, and TSP1 in a biological sample isolated from the individual. Preferably, the expression levels of these proteins are measured simultaneously.

[0045] Preferably, the results obtained are compared to those of a reference sample of patients with dyslipidemia.

[0046] A fifth aspect of the invention relates to an in vitro method for predicting and / or diagnosing severe atherosclerosis and / or ACE2-associated cardiovascular disease (ACED) in individuals with dyslipidemia. This method comprises the aforementioned method for obtaining useful data, wherein an individual with dyslipidemia is considered to have, or be at risk of having, severe atherosclerosis and / or ACED when the expression levels of the proteins B2MG, FIBA, FIBB, and / or FIBG are significantly higher than those of the reference sample and / or the expression levels of the proteins GPV, PLF4, and / or TSP1 are significantly lower than those of the reference sample. Preferably, the expression levels of these proteins are measured simultaneously.

[0047] The quantification of the expression levels of the proteins of the invention present in an isolated biological sample can be performed by any method known in the prior art. The measurement of the amount or concentration of protein present in the biological sample can be performed semi-quantitatively or quantitatively. The term “amount” refers to, but is not limited to, the absolute or relative amount of the proteins of the panel of the present invention, as well as any other value or parameter related to or derived from them. Such values ​​or parameters may include signal intensity values ​​obtained from any measurement of the physical or chemical properties of said proteins, such as the measurement of the intensity levels of said proteins detected by mass spectrometry.

[0048] The measurement of the quantity or concentration of the proteins of the present invention can be carried out using different techniques such as immunoassay, clinical turbidimetry, mass spectrometry, or the "proximity extension assay" method, among others.

[0049] The term “immunoassay,” as used herein, refers to any analytical technique based on the conjugation reaction of an antibody with an antigen. Examples of prior art immunoassays include, but are not limited to: enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), and immunofluorescence. The ELISA, or enzyme-linked immunosorbent assay, is based on the premise that an antigen or antibody can be immobilized on a solid support, and this system is then brought into contact with a fluid phase containing a complementary reagent that can bind to a marker—that is, a compound capable of producing a chromogenic, fluorogenic, radioactive, and / or chemiluminescent signal that allows the detection and quantification of antibody levels that recognize the proteins of interest described herein.

[0050] Clinical turbidimetry is a technique that allows the measurement of the amount of suspended particles in biological fluids, such as blood, urine, and cerebrospinal fluid. This technique is based on measuring the turbidity of the sample, that is, the amount of light scattered through the solution due to the presence of suspended particles. In the present invention, turbidimetry will allow the measurement of the presence and quantity of proteins of interest.

[0051] The proximity extension assay (PEA) is a method for detecting and quantifying the amount of many specific proteins present in a biological sample such as serum or plasma. PEA is performed in three fundamental steps: Immune reaction: Paired antibody pairs bound to complementary oligo.DNA sequences recognize and bind to the protein(s) of interest in the biological sample, preferably serum or plasma. The unique DNA sequences attached to these antibody pairs, called proximity probes, then act as barcodes. These barcodes are amplified by a DNA polymerase, which forms a specific, bound DNA sequence for each detected target protein via a PCR reaction. Amplification is only possible when the antibodies that recognize the protein of interest are in close proximity (through detection and binding to that protein).The amplified amount or the PCR-derived products will be representative of the protein of interest to which the antibodies coupled to specific DNA sequences had bound.

[0052] The methods of the invention involve comparing the expression levels of the proteins of the invention in isolated biological samples with the expression levels of the proteins of the invention in a reference sample or with a median value.

[0053] In the context of the present invention, a "reference sample" is understood to be the sample used to determine the variation in the expression level of the proteins of the invention. Reference samples are generally taken from healthy individuals without cardiovascular risk factors or associated complications, so that the reference value reflects the average value of each of these molecules in the population of individuals who do not have severe atherosclerosis and / or cardiovascular disease (CVD) resulting from the atherosclerotic process. To carry out any of the methods of the invention in a group of individuals with dyslipidemia, the reference sample is taken from individuals with dyslipidemia who do not have severe atherosclerosis and / or CVD resulting from the atherosclerotic process.

[0054] The reference sample is taken from isolated samples of cells, tissues, and / or biological fluids from an organism, obtained by any method known to an expert in the field. Preferably, the isolated biological sample is blood, serum, or plasma.

[0055] “Reference value” refers to the average protein levels in a reference sample. Reference levels are determined by measuring the levels of the proteins of the present invention, preferably simultaneously. The expression profile in the reference sample can preferably be generated from a population of two or more individuals. The population, for example, may contain 10, 15, 20, 30, 40, 50, or more individuals. Reference values ​​can be tailored to specific populations; for example, a reference level may be age-related, allowing comparisons to be made between subjects with a particular disease, phenotype, or within a specific age group.

[0056] The term "comparison," as used in the description, refers to, but is not limited to, the comparison of the expression level of the proteins of the invention in the isolated biological sample to be analyzed, also called the biological sample under investigation, with the expression level of the proteins of the invention in one or more reference samples. The reference sample may be analyzed, for example, simultaneously or consecutively with the biological sample under investigation.

[0057] Once the expression level of the proteins of the invention has been determined in relation to the reference values, it is necessary to identify whether there are alterations in the expression level (increase or decrease in the expression level). The level of expression is considered increased in a sample of the subject matter under study when the increase with respect to the reference sample is at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more.Similarly, the level of expression is considered decreased when it decreases with respect to the reference sample by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100% (i.e., absent).

[0058] The invention allows the classification of individuals into at least two groups: individuals with a probability of suffering from or who suffer from severe atherosclerosis and / or of suffering from or who suffer from a CTD derived from the atherosclerotic process, and individuals without severe atherosclerosis or without a probability of suffering from severe atherosclerosis or a CTD derived from the atherosclerotic process.

[0059] Identifying individuals likely to develop or who have severe atherosclerosis and / or who have or who have a cardiovascular disease resulting from the atherosclerotic process allows for the application of personalized therapies to prevent them.

[0060] The present invention further provides an in vitro method for evaluating the efficacy of a treatment with pharmacological agents that reduce the risk of severe atherosclerosis and / or cardiovascular disease (CVD) resulting from the atherosclerotic process, by determining the effect of said drugs on the levels of the proteins of the invention B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4, and / or TSP1, preferably simultaneously, in an isolated biological sample. In the case of drugs specifically targeted at patients with dyslipidemia, the protein levels measured to determine the effect of said drugs are B2MG, FIBA, FIBB, FIBG, GPV, PLF4, and / or TSP1, preferably simultaneously.

[0061] In particular, the pharmacological agent is an anti-hypertensive, anti-inflammatory, antithrombotic, antiplatelet, fibrinolytic agent, lipid-reducing agent, direct thrombin inhibitor, glycoprotein IIb / IIIa inhibitor, calcium channel blocker, alpha or beta adrenergic receptor blocker, cyclooxygenase-2 inhibitor, or angiotensin inhibitor system.

[0062] In particular, the determination of the levels of these proteins is carried out in a biological sample, where the alteration of the levels of these proteins indicates the effectiveness or not of a treatment to reduce the risk of suffering from severe atherosclerosis and / or an ECAT derived from the atherosclerotic process.

[0063] More specifically, the biological sample is a physiological fluid, more specifically blood, serum, or plasma.

[0064] Kit or device of the invention and uses

[0065] Another aspect of the invention relates to a kit or device, hereinafter referred to as the kit or device of the invention, comprising the elements necessary to quantify the expression levels of the proteins of the invention B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4 and / or TSP1, preferably all of them simultaneously, in the isolated biological samples.

[0066] In another preferred embodiment of the kit or device of the invention, it comprises the elements necessary to quantify the expression levels of B2MG, FIBA, FIBB, FIBG, GPV, PLF4 and / or TSP1, preferably all of them simultaneously, in isolated biological samples.

[0067] Another aspect of the invention relates to the use of said kit to carry out the in vitro prognostic and / or diagnostic methods of the invention.

[0068] More specifically, the kit contains the reagents necessary to determine the levels of the proteins of the invention by mass spectrometry, specific antibodies, protein microarrays, PEA assay or proximity extension assay or assay

[0069] ELISA.

[0070] More specifically, the kit contains the reagents necessary to determine the levels of these proteins by means of a PCR assay to measure the levels of genetic precursors of the proteins of the panel of the invention.

[0071] Another particular object of the invention is a kit for evaluating in vitro the efficacy of a treatment to reduce the risk of suffering from severe atherosclerosis and / or an ECAT derived from the atherosclerotic process, which contains the reagents necessary to carry out the determination of the expression levels of the proteins of the invention.

[0072] Another aspect of the invention relates to a kit or device, hereinafter referred to as the kit or device of the invention, comprising the elements necessary to quantify the expression levels of the proteins of the invention B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4, and / or TSP1, preferably all of them simultaneously, in isolated biological samples where alterations in the levels of these proteins indicate the efficacy or ineffectiveness of a treatment. In the case of drugs specifically targeting patients with dyslipidemia, the protein levels measured to determine the effect of these drugs are B2MG, FIBA, FIBB, FIBG, GPV, PLF4, and / or TSP1, preferably simultaneously.

[0073] Another aspect of the invention relates to a computer program comprising instructions for performing the procedure according to any of the methods of the invention.

[0074] In particular, the invention covers computer programs arranged on or within a carrier. The carrier can be any entity or device capable of supporting the program. As an example, the carrier could be an integrated circuit containing the program and adapted to execute, or be used in the execution of, the corresponding processes.

[0075] For example, programs could be embedded in a storage medium, such as ROM, CD-ROM, semiconductor ROM, USB flash drive, or magnetic recording media, such as a floppy disk or hard drive. Alternatively, programs could be carried on a transmissible carrier signal; for example, an electrical or optical signal that could be transmitted via electrical or optical cable, radio, or any other means.

[0076] The invention also extends to computer programs adapted so that any processing device can implement the methods of the invention. The computer programs also encompass cloud applications based on this procedure.

[0077] Other aspects of the invention relate to the readable storage medium and the transmissible signal comprising program instructions necessary for the execution of the method of invention by a computer.

[0078] The term “individual,” as used in the description, refers to animals, preferably mammals, and more preferably, humans. The terms “individual,” “human subject,” and “subject” are used interchangeably in this specification and are synonymous with “patient,” and are not intended to be limiting in any respect, as the individual may be of any age, sex, and physical condition. The methods of the invention can be applied to patient samples of any sex, i.e., male or female, and of any age.

[0079] The terms “one or more” or “at least one” or “vapes”, as used herein, include one and the individualized specification of any number that is more than one, such as two, three, four, five, six, etc.

[0080] The term "comprises" may also be interpreted, in a particular embodiment, as "consists of." The term "comprises" and its variants are not intended to exclude other technical features, additives, components, or steps.

[0081] For experts in the field, other objects, advantages, and features of the invention will become apparent partly from the description and partly from the practice of the invention.

[0082] BRIEF DESCRIPTION OF THE FIGURES

[0083] Figure 1: Characteristics, biochemical parameters, expression levels of certain proteins by turbidimetry, fibrinogen levels by coagulometry, and complete blood count of each experimental group: healthy control individuals (CTRL), dyslipidemic individuals (DLP), and atherosclerotic patients with carotid stenosis (AT). Figure 2: A) Number of proteins with altered expression levels (increased or decreased) of the 304 proteins identified in the serum of the different study groups: healthy controls (CTRL), dyslipidemic individuals (DLP), and atherosclerotic patients (AT); and B) Principal component analysis.

[0084] Figure 3: Graphical representation of the protein expression levels identified by mass spectrometry in control individuals (CTRL, n:42), dyslipidemic individuals (DLP: n:44), and atherosclerotic patients with carotid stenosis (AT, n:40) for the 14 proteins initially identified: ACTB, APOB, B2M, C4BPA, CO1A1, CORO1A, FIBA, FIBB, FIBG, GPV, MMP9, PCOC1, PLF4, TSP. The ROC curves recorded for each of the three comparison conditions are included: ATvsDLP; ATvsCTRL; and DLPvsCTRL, with the Area under the curve (AUC) and the recorded p-values.

[0085] Figure 4: ROC curves calculated jointly for the markers: B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4, and TSP1 in atherosclerotic patients with carotid stenosis vs. healthy controls. (AT vs. CTRL). Analysis models used: Extremely Randomized Trees Classifier; Linear Support Vector Machine; Random Forest; AdaBoost; and Naive Bayes.

[0086] Figure 5: ROC curves calculated jointly for the markers: B2MG, FIBA, FIBB, FIBG, GPV, PLF4 and TSP1 in atherosclerotic patients with carotid stenosis vs dyslipidemic patients (AT vs DLP). Analysis models used: Extremely Randomized Trees Classifier; Linear Support Vector Machine; Random Forest, AdaBoost and Naive Bayes.

[0087] Figure 6: Overall contribution or weight (on average) of each characteristic in the classification of patients into the CTRL (healthy controls), ATER (atherosclerotic) and DLP (dyslipidemic) groups for Linear Support Vector Machine.

[0088] Figure 7: Overall contribution or weight (on average) of each characteristic in the classification of patients into the CTRL (healthy controls), ATER (atherosclerotic) and DLP (dyslipidemic) groups for Random Forest.

[0089] Figure 8: Overall contribution or weight (average) of each characteristic in the classification of patients into the CTRL (healthy controls), ATER (atherosclerotic), and DLP (dyslipidemic) groups for the Extremely Randomized Trees Classifier. DETAILED DESCRIPTION OF THE INVENTION

[0090] Characteristics of the studied population

[0091] For the study of the invention's biomarkers, a total of 181 individuals, aged 18 to 75 years, both men and women, were recruited between 2018 and 2022. These individuals were divided into healthy subjects (n=66), subjects with dyslipidemia (n=55), and subjects with carotid stenosis due to severe atherosclerosis (n=60). The study took place at the Joaquin Pecce Health Center, Puerta del Mar University Hospital, and Jerez de la Frontera University Hospital, Cádiz, Spain. Dyslipidemia and severe atherosclerosis represent two clinical stages of the same pathology: the former represents a subclinical atherosclerotic state or early atherosclerosis, while the latter, with carotid stenosis, represents an advanced or severe atherosclerotic clinical state. The protocol was approved by the Biomedical Ethics Committee of Cádiz. All patients provided informed consent before sample collection, and the principles of the Declaration of Helsinki were followed.

[0092] The group of individuals with severe atherosclerosis (n:60) consisted of individuals diagnosed with carotid artery stenosis > 70% by Doppler system.

[0093] The group of individuals with dyslipidemia (n:55) consisted of individuals so diagnosed based on biochemical parameters, according to Garg R and collaborators [2]: high density lipoprotein (HDLc) values ​​< 40 mg / dL (men); <50 mg / dL (women) and low density lipoprotein (LDLc) > 160mg / dL.

[0094] Healthy controls (CTRL, n:66) were individuals with HDL values ​​>40 mg / dL (men); >50 mg / dL (women); and LDL <160 mg / dL. Exclusion criteria for healthy controls were: treatment with non-steroidal anti-inflammatory drugs or antiplatelet therapy during the previous year, except for acetylsalicylic acid <325 mg / day; heparin treatment; acute myocardial infarction, stroke, or major surgery in the previous 6 months; malignancy; infectious / inflammatory disease; pregnancy, lactation, or fertility treatments.

[0095] Blood and serum samples were obtained from these patients. Blood samples were collected in the early morning after fasting, using SST II Advance, K2E (EDTA), and citrate / CTAD tubes (BD Vacutainer, Plymouth, UK). Biochemical parameters, complete blood count (CBC), and fibrinogen data were obtained from each sample. Biochemical parameters were obtained using photometric assays. Samples from dyslipidemic and atherosclerotic patients were analyzed using a Hitachi Cobas c702 (Roche Diagnostics, Rotkreuz, Switzerland), while serum from healthy controls was analyzed using an Alinity c (Abbott, IL, USA). CBC parameters were obtained using a Sysmex analyzer (Sysmex, Kobe, Japan).The levels of the proteins APOA1, AG1A1, APOB, B2MG, and APOE were measured by turbidimetry using a BS-200E analyzer (Mindray, Shenzhen, China) with a quantitative turbidimetric assay (a1-Ac GLY, APO1A1, APO-B, APO2E, and p2-m TURBI, Spinreact, Girona, Spain), following the manufacturer's instructions. Finally, fibrinogen levels were measured using a coagulometric assay on an ACL Top system (Werfen, Barcelona, ​​Spain). The results of these analyses, along with the other characteristics of each experimental group, are shown in Figure 1.

[0096] Table 1: Patients / samples included for each analysis throughout the trial.

[0097] All measurements of the recorded clinical variables (hypertension, type 2 diabetes mellitus, smoking, alcohol intake), corresponding to cardiovascular disease risk factors, were statistically significantly higher in patients with atherosclerosis. The recorded biochemical values ​​(glucose, HbA1c, CRP) were also statistically higher in patients with atherosclerosis, except for the lipid profile and remnant lipoproteins (total cholesterol, HDL, IDL), which were higher in patients with dyslipidemia.

[0098] Proteomic analysis

[0099] For proteomic analysis, serum samples were also obtained, including one additional tube of SST II Advance collected on the same day (baseline). Briefly after clot formation, the samples were centrifuged at 2000 g for 10 minutes at 4 °C. All serum samples were aliquoted and stored at -80 °C for later analysis. A total of 10 µL of serum was used for proteomic analysis. The samples were denatured, reduced, and alkylated with 100 µL of 1% SDS, 5 mM TCEP, 10 mM CAA, 100 mM Ths (pH 8.5), and protease inhibitors for 10 minutes at 95 °C and 1400 rpm. The samples were digested using the PAC method [3, 4]. Briefly, the samples (600 pg) were incubated in 70% acetonitrile with amine magnetic beads (ReSyn Biosciences) at a protein / bead ratio of 1:3. Protein aggregation was carried out in two 1-min steps, under shaking, followed by a 10-min pause each.The samples were then washed sequentially for 5 min, in triplicate with 95% acetonitrile and twice with 70% ethanol, without detaching the microspheres from the magnet. The digestion step was performed at 37°C for 12 h with agitation using a 1:50 Trypsin / LysC Mix (Promega) in 50 mM ammonium bicarbonate solution. Digestion was stopped by acidification with 1% trifluoroacetic acid. Finally, 20 µl of plasma samples plus 17 µl of 0.1% formic acid were loaded directly into Evotips (Evosep) for complete proteome analysis.

[0100] Liquid chromatography-tandem mass spectrometry (LC-MS / MS)

[0101] The proteomic analysis used serum samples isolated from healthy individuals (n:42, taken as a reference), dyslipidemic individuals (n:44) and individuals with carotid stenosis due to severe atherosclerosis (n:40).

[0102] Proteomic analysis was performed using a tide-free strategy with a TIMS-TOF pro mass spectrometer (Bruker) coupled to an Evosep One chromatograph. A 15 cm, 75 µm internal diameter capillary column was used, coated with 1.9 µm Reprosil-Pur C18 microspheres (AccIaim™ PepMap™ 100 C18, Thermo Scientific). The column temperature was set to 60 °C using an integrated column oven (PRSO-V1, Sonation, Biberach, Germany), and the column was connected online to an Orbitrap Exploris 480 MS (Thermo Fisher Scientific, Bremen, Germany) using Xcalibur (tuning version 1.1). Peptides were separated on the analytical column using a pre-programmed gradient for 30 samples per day. For all samples, the Orbitrap Exploris operated in independent data acquisition (DIA) mode.

[0103] Raw data processing

[0104] The raw files were processed using Spectronaut (v15.4) with a directDIA approach and the human database (Uniprot Reference Proteome 2020 release, 20,600 entries). Carbamylation of cisternae was set as a fixed modification, while methionine oxidation and N-terminal protein acetylation were set as possible variable modifications. The enzyme was set to trypsin, data filtering was disabled, and cross-normalization was turned off. All other variables were set using default settings.

[0105] Analysis of mass spectrometry data

[0106] Data processing was performed using Perseus (v1.6.15.0). Briefly, the data were Iog2 transformed, valid values ​​were filtered to 70%, missing values ​​were imputed from a normal distribution, and the data were normalized by quantiles using R. A two-sample Student's t-test was performed with a permutation-based FDR. Proteins were considered differentially expressed between groups when the p-value was <0.05.

[0107] As a result of mass spectrometry analysis, a total of 304 proteins were identified in the serum of the different study groups: healthy controls (CTRL), dyslipidemic patients (DLP), and atherosclerotic patients (AT). The levels of some proteins were found to be altered (increased or decreased) among these groups: 123 altered proteins in the serum of AT patients compared to CTRL, 68 altered proteins in the serum of AT vs. DLP, and 17 altered proteins in the serum of DLP vs. CTRL. Of these, 54 proteins were altered in both AT vs. CTRL and AT vs. DLP, while only 3 proteins were altered in all 3 comparisons.

[0108] The serum proteomic profile of AT patients was entirely different from that of DLP or CTRL individuals, while the latter two groups, although distinguishable, presented similar profiles to each other, as indicated in the principal component analysis (PCA) (Figure 2). Thus, by observing the protein profiles, it would be possible to determine whether an individual could be included in the AT group.

[0109] Functional analysis

[0110] Functional analysis was performed using Reactome (Home - Reactome Pathway Database), and Ingenuity® Pathway Analysis (IPA®, QIAGEN Redwood City).

[0111] Statistical analysis

[0112] Statistical analysis of baseline characteristics was performed using SPSS Statistics v.20.0.0 (Chicago, IL, USA). For categorical variables, data are expressed as absolute numbers and percentages. For continuous variables, data are presented as median and interquartile range (IQR). Normality of variables was assessed using the Kolmogorov-Smirnov or Shapiro-Wilk tests, as appropriate. Where applicable, chi-square tests for independent samples, Kruskal-Wallis tests, and Mann-Whitney U tests were performed. The null hypothesis was rejected if the p-value was <0.05.

[0113] Graphs of individual protein changes (LFQ intensities or turbidimetric results) were obtained using GrapHPad Prism 7 software (Boston, MA, USA). The ROUT method was used to identify outliers, and one-test ANOVA was performed, along with Tukey's post hoc test.

[0114] Additionally, the C-statistic or the area under the receiver operating characteristic (ROC) curves was obtained, and the AUG (95% CI) was calculated by plotting the sensitivity of the selected proteins against 1-specificity, using the Youden index as the cutoff value. Finally, a multivariate analysis (Spearman) was performed using RStudio software to find correlations between the variables of interest. Correlation analyses were performed using the cor and rcorr functions, located in the stats and Hmisc packages, respectively. Data imputation was performed using the k-nearest neighbor method, with three neighbors for each value. This step was carried out using the kNN algorithm, available in the VI M package. Correlations with an R² coefficient greater than 0.6 or less than -0.6 were selected.

[0115] Application of massive analysis tools

[0116] The data obtained by mass spectrometry analysis were combined with clinical data (biochemical parameters, blood count) from AT, DLP patients and healthy controls, and were analyzed using 6 different massive analysis tools (MLCA models or machine learning algorithms): Naive Bayes (NB) [5], Linear Support Vector Machine (SVM) [6], Random Forest (RF) [7], Extremely Randomized Trees Classifier [8], Gaussian Power (GP) and AdaBoost [9],

[0117] Table 2. Variables included in the analyses.

[0118]

[0119] These analysis systems based on recursive feature elimination (RFE) allow feature selection by recursively considering increasingly smaller sets of features, given an external estimator (classifier) ​​that assigns weights to the features. First, the estimator is trained on the initial set of features, and weights are assigned to each feature. Then, the features with the smallest absolute weights are removed from the current set of features. This procedure is repeated recursively on the trimmed set until the desired number of features to be selected is reached.

[0120] The imputation of null values ​​was performed using a local similarity method, i.e., k- nearest neighbors (kNN)

[0010] . Therefore, k:5 was chosen as the number of nearest neighbors and weighted Euclidean distance as the distance metric.

[0121] Before training the MLCA models, a feature selection stage was applied to optimize the classifiers' performance in exploiting the information contained in the strongly discriminatory attributes. Recursive feature removal

[0011] was chosen as the selection strategy, employing a cross-validation strategy.

[0122] The partitioning of the dataset for model training and validation followed a repeated stratified K-fold cross-validation procedure, using 3 folds and 20 replicates. The combination of clinical and proteomic data allowed the identification of 14 serum proteins, shown in Table 3, that were highly discriminating between the study groups: healthy individuals, dyslipidemic individuals, and individuals with carotid stenosis due to severe atherosclerosis. Table 3. Features that were selected at least four times (out of five) by the estimators considered.

[0123] Figure 3 graphically represents the intensity changes and ROC-AUC curves of these proteins.

[0124] Significant differences in expression levels were observed between groups (healthy controls, dyslipidemics and those with severe atherosclerosis) for the 14 markers detected.

[0125] For a more in-depth analysis of the diagnostic / prognostic value of the 14 detected markers, an individual analysis of each of the proposed biomarkers was performed, obtaining ROC-AUC curves of sensitivity-specificity by comparing conditions in pairs, specifically: AT vs CTRL (severe atherosclerosis vs control); DLP vs CTRL (dyslipidemic vs control); and AT vs DLP (severe atherosclerosis vs dyslipidemic). AUC values ​​above 0.75 and a p-value < 0.001 were considered optimal.

[0126] Thanks to this analysis, it was determined that nine of the markers—B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4, and TSP1—were particularly sensitive and specific for identifying patients with severe atherosclerosis compared to healthy controls. Furthermore, seven of these markers—specifically B2MG, FIBA, FIBB, FIBG, GPV, PLF4, and TSP1—also allowed for the identification of patients with severe atherosclerosis within the dyslipidemic risk group. Subsequently, ROC curves were calculated for the combined use of the nine markers that identify patients with severe atherosclerosis compared to healthy controls: B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4, and TSP1 (Figure 4). The results of these analyses reveal AUC values ​​greater than 0.97 for all analysis models used (Extremely Randomized Trees Classifier; Linear Support Vector Machine; Random Forest, AdaBoost and Naive Bayes).

[0127] Furthermore, ROC curves were calculated for the combined use of the 7 markers that allow the identification of patients with severe atherosclerosis compared to dyslipidemic patients: B2MG, FIBA, FIBB, FIBG, GPV, PLF4, and TSP1 (Figure 5). The results of these analyses reveal AUC values ​​greater than 0.96 for all the analysis models used (Extremely Randomized Trees Classifier; Linear Support Vector Machine; Random Forest, AdaBoost, and Naive Bayes).

[0128] Figures 6, 7 and 8 show the overall contribution or weight (on average) of each feature in the classification of patients into the CTRL, AT and DLP groups for three of the classifiers considered: Linear Support Vector Machine (Figure 6), Random Forest (Figure 7) Extremely Randomized Trees Classifier (Figure 8).

[0129] References

[0130] 1. Eslava-Alcon S, Extremera-Garcia MJ, González-Rovira A, Rosal-Vela A, Rojas-Torres M, Beltran-Camacho L, Sanchez-Gomar I, Jiménez-Palomares M, Alonso-Piñero JA, Conejero R, et al. Molecular signatures of atherosclerotic plaques: An up-dated panel of protein related markers. J Proteomics. 2020;221: 103757. doi: 10.1016 / j.jprot.2020.103757.

[0131] 2. Garg R, Aggarwal S, Kumar R, Sharma G. Association of atherosclerosis with dyslipidemia and co-morbid conditions: A descriptive study. J Nat Sci Biol Med. 2015;6:163-168. doi: 10.4103 / 0976-9668.149117.

[0132] 3. Batth TS, Tollenaere MX, Ruther P, Gonzalez-Franquesa A, Prabhakar BS, Bekker- Jensen S, Deshmukh AS, Olsen JV. Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation. Mol Cell Proteomics. 2019;18:1027-1035. doi: 10.1074 / mcp.TIR118.001270

[0133] 4. Martinez-Vai A, Bekker-Jensen DB, Steigerwald S, Koenig C, Ostergaard O, Mehta A, Tran T, Sikorski K, Torres-Vega E, Kwasniewicz E, et al. Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution. Nat Commun. 2021;12:7113. doi: 10.1038 / S41467-021 -27398-y.

[0134] 5. Zhang H. The optimality of naive Bayes. Proceedings of 17th International Florida Artificial Intelligence Research Society Conference, Menlo Park. 2004:562-567. 6. Fan RE CK, Hsieh CJ, Wang XR, Lin CJ. . LIBLINEAR: A Library for Large Linear Classification. The Journal of Machine Learning Research. 2008;9:1871-1874.

[0135] 7. L. B. Random Forest. Machine learning. 2001 ;45:5-32.

[0136] 8. Geurts P ED, Wehenkel L. Extremely randomized trees. Machine learning. 2006;63:3- 42. 9. Hastie T RS, Zhu J, Zou H. Multi-class adaboost. Statistics and its Interface. 2009;2:349-360.

[0137] 10. Rashid W GM. A perspective of missing value imputation approaches. Advances in Computational Intelligence and Communication Technology: Proceedings of CICT 2019 Springer Singapore. 2021 :307-315. 11. Guyon I WJ, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine learning. 2002;46:389-422.

Claims

CLAIMS 1. In vitro use of the expression level of the proteins B2MG, FIBA, FIBB, FIBG, GPV, PLF4 and TSP1 in a biological sample isolated from an individual as a prognostic and / or diagnostic biomarker of severe atherosclerosis in individuals with dyslipidemia.

2. In vitro use of the expression level of the proteins B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4 and TSP1 in an isolated biological sample from an individual as a prognostic and / or diagnostic biomarker of severe atherosclerosis in individuals without dyslipidemia.

3. The use according to any of claims 1 or 2, characterized in that the isolated biological sample is selected from blood, serum, and plasma.

4. In vitro method for obtaining useful data to predict and / or diagnose severe atherosclerosis in individuals with dyslipidemia comprising: a) measuring the expression level of the proteins B2MG, FIBA, FIBB, FIBG, GPV, PLF4 and TSP1 in a biological sample isolated from the individual, and b) comparing the results with those of a reference sample from an individual with dyslipidemia.

5. An in vitro method for predicting and / or diagnosing severe atherosclerosis in individuals with dyslipidemia comprising the method of obtaining useful data according to the preceding claim and further comprising: c) determining that the individual with dyslipidemia suffers from or is at risk of suffering from severe atherosclerosis if the values ​​of B2MG, FIBA, FIBB and FIBG are significantly higher than those of the reference sample and those of GPV, PLF4 and TSP1 are significantly lower than those of the reference sample.

6. In vitro method for obtaining useful data to predict and / or diagnose severe atherosclerosis in individuals without dyslipidemia comprising: a) measuring the expression level of the proteins B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4 and TSP1 in a biological sample isolated from the individual, and b) comparing the results with those of a reference sample from a healthy individual.

7. An in vitro method for predicting and / or diagnosing severe atherosclerosis comprising the method of obtaining useful data according to the preceding claim and further comprising: c) determining that the individual suffers from or is at risk of suffering from severe atherosclerosis if the values ​​of B2MG, C4BPA, FIBA, FIBB and FIBG are significantly higher than those of the reference sample and those of GPV, MMP9, PLF4 and TSP1 are significantly lower than those of the reference sample.

8. An in vitro method for evaluating the efficacy of a treatment to prevent and / or treat severe atherosclerosis in individuals without dyslipidemia comprising: a) measuring the expression level of the proteins B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4 and TSPI in a biological sample isolated from the individual prior to starting the treatment, b) measuring the expression level of the proteins B2MG, C4BPA, FIBA, FIBB, FIBG, GPV, MMP9, PLF4 and TSP1 in a biological sample isolated from the same individual after administering the treatment, c) comparing the results and d) determining that the treatment is effective if the values ​​of B2MG, C4BPA, FIBA, FIBB and FIBG decrease and those of GPV, MMP9, PLF4 and TSP1 increase.

9. An in vitro method for evaluating the efficacy of a treatment to prevent and / or treat severe atherosclerosis in individuals with dyslipidemia comprising: a) measuring the expression level of the proteins B2MG, FIBA, FIBB, FIBG, GPV, PLF4 and TSPI in a biological sample isolated from the individual prior to starting treatment, b) measuring the expression level of the proteins B2MG, FIBA, FIBB, FIBG, GPV, PLF4 and TSP1 in a biological sample isolated from the same individual after administering the treatment, c) comparing the results and d) determining that the treatment is effective if the values ​​of B2MG, FIBA, FIBB and FIBG decrease and those of GPV, PLF4 and TSP1 increase.

10. In vitro method according to any of claims 3 to 9 characterized in that the isolated biological sample is selected from blood, serum and plasma.

11. A kit or device comprising the elements necessary to measure in vitro the expression levels of the proteins of claim 1 or 2.

12. The kit or device according to the preceding claim, further comprising the elements necessary to compare the expression levels of the proteins with respect to the values ​​of a reference sample.

13. Use of the kit according to the preceding claim to predict and / or diagnose severe atherosclerosis.

Images