WO2025109147A1 - Method to predict the risk of a cardiovascular event in a patient with type 2 diabetes - Google Patents

Abstract

The present invention relates to cardiovascular event in a patient with type 2 diabetes. In a cross-sectional study population of 672 participants with T2D, the inventors demonstrated that the risk of CV events, evaluated by coronary artery calcium (CAC) score, is positively associated with blood leukocyte and monocyte counts. Then, considering frequencies of the 3 monocyte subtypes, they propose 3 endotypes of participants with T2D differing in blood monocyte counts, classical monocyte frequency (CD45+ CD14++ CD16-) and the risk of CV events. The predictive association between monocyte count and major adverse cardiovascular events (MACE) was validated through an independent prospective T2D cohort. In fact, monocyte count increase was associated with a statistically significant 2.8-fold increase of MACE and a 5.1-fold increased risk of CV deaths. T2D participants with median monocyte count > 0.5 x 109/L, suffered significantly more MACE. Thus, the invention relates to a method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event.

Description

METHOD TO PREDICT THE RISK OF A CARDIOVASCULAR EVENT IN A

PATIENT WITH TYPE 2 DIABETES

FIELD OF THE INVENTION:

The present invention relates to a method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event.

BACKGROUND OF THE INVENTION:

Responsible for 16% of globally recorded deaths, ischemic heart disease is the leading cause of death worldwide¹. Ischemic heart diseases are linked to atherogenesis and its progression in coronary arteries. Atherosclerosis initiation and progression is mainly due to circulating monocytes being recruited to the sub-endothelial space of coronary arteries, where they differentiate into macrophages². These newly differentiated macrophages become foam cells by capturing oxidized-low density lipoproteins (oxLDL) via scavenger receptors and produce inflammatory cytokines. Inflammatory cytokines and matrix metallopeptidases (enzymes capable of degrading extracellular matrix) produced by monocytes and macrophages further amplify endothelial activation. This increases recruitment and adhesion of more immune cells, including monocytes, simultaneously enlarging and destabilizing the plaque³'⁴. Circulating monocytes are subdivided into three main subtypes: classical, intermediate and non- classical⁵, and an increase of specific subtypes has been positively associated with plaque vulnerability⁶. This indicates potentially different atherogenic roles between these 3 monocyte subsets. Diabetes is one of the major risk factors for atherosclerotic cardiovascular (CV) diseases with a 2-fold higher risk of CV events in patients with T2D, compared to those without⁷'⁹. Nevertheless, CV risk assessment in the T2D population, through cross-sectional studies based on coronary artery calcium scoring, showed risk heterogeneity inside this population^10,11. This heterogeneity could be explained by differences in other concomitant clinical risk factors and their management (sex, age, dyslipidemia, high blood pressure, smoking, etc.). Although algorithms have been developed to take into account multiple clinical variables, including those specific to T2D (diabetes duration, glycated hemoglobin (HbAlc)), the CV risk scores they derive perform poorly¹². This reflects the lack of effective diagnostic scores specifically tailored to individuals with T2D. Furthermore, many CV risk scores take into account the traditional risk factors, but only one score includes total white blood cell counts and none include circulating immune cell population counts, despite atherosclerosis being an inflammatory disease¹³'²⁰. As inflammatory effector cells and more specifically monocytes are both involved in T2D and atherosclerosis pathophysiology^21-24, they could be additional sub- clinical variables included in these scores to better measure and define CV risk heterogeneity among the population with T2D.

SUMMARY OF THE INVENTION:

In a cross-sectional study population of 672 participants with T2D, the inventors demonstrated that the risk of CV events, evaluated by coronary artery calcium (CAC) score, is positively associated with blood leukocyte and monocyte counts. Then, considering frequencies of the 3 monocyte subtypes, they propose 3 endotypes of participants with T2D differing in blood monocyte counts, classical monocyte frequency (CD45+ CD14++ CD16-) and the risk of CV events. The predictive association between monocyte count and major adverse cardiovascular events (MACE) was validated through an independent prospective T2D cohort. In fact, monocyte count increase was associated with a statistically significant 2.8-fold increase of MACE and a 5.1 -fold increased risk of CV deaths. T2D participants with median monocyte count > 0.5 x 10⁹/L, suffered significantly more MACE.

Complementary analysis of monocyte transcriptome and plasma metabolome also revealed deregulated genes involved in matrix remodeling and mitochondrial function. Among these genes, MMP25, encoding Matrix Metalloproteinase 25, and specific metabolite like Itaconate were the most significantly associated with the risk of CV events. MMP25 expression was also observed in macrophages in human atherosclerotic plaque. Altogether, the results of the inventors provide evidence that monocytes count predict CV events and a specific phenotypic switch of monocyte profile is closely linked to the CV risk increase in patients with T2D.

Thus, the present invention relates to a method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event comprising the steps of: i) determining in a sample obtained from the patient the quantity of monocytes ii) comparing said quantity determined at step i) with a predetermined reference value and iii) providing that a high quantity of monocytes compared to said predetermined reference value is indicative of whether a patient is at a risk of developing a cardiovascular event and that a low quantity of monocytes compared to said predetermined reference value is indicative of whether a patient is not at a risk of developing a cardiovascular event.

Particularly, the invention is defined by its claims. DETAILED DESCRIPTION OF THE INVENTION:

A first aspect of the invention relates to a method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event comprising the steps of: i) determining in a sample obtained from the patient the quantity of monocytes ii) comparing said quantity determined at step i) with a predetermined reference value and iii) providing that a high quantity of monocytes compared to said predetermined reference value is indicative of whether a patient is at a risk of developing a cardiovascular event and that a low quantity of monocytes compared to said predetermined reference value is indicative of whether a patient is not at a risk of developing a cardiovascular event.

In a particular embodiment, the cardiovascular event is a major adverse cardiovascular event (MACE).

According to the invention, the inventor determined a predetermined reference value (or threshold) of 0.5 x 10⁹ cells (monocytes)/L.

Thus and accordingly, the invention relates to a method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event comprising the steps of: i) determining in a sample obtained from the patient the quantity of monocytes ii) comparing said quantity determined at step i) with a predetermined reference value and iii) providing that a quantity of monocytes superior to 0.5 x 10⁹/L is indicative of whether a patient is at a risk of developing a cardiovascular event and a quantity of monocytes inferior of 0.5 x 10⁹/L is indicative of whether a patient is not at a risk of developing a cardiovascular event.

According to the invention, the sample can be blood and peripheral-blood.

As used herein, the term “patient” denotes a mammal, such as a rodent, a feline, a canine, and a primate. Particularly, the patient according to the invention is a human and more particularly, the patient is a human with a type 2 diabetes (T2D) according to the invention.

As used herein, the term “monocyte” denote a type of leukocyte. They are the largest type of leukocyte in blood and can differentiate into macrophages and monocyte-derived dendritic cells. As a part of the vertebrate innate immune system monocytes also influence adaptive immune responses and exert tissue repair functions. There are at least three subclasses of monocytes in human blood based on their phenotypic receptors. The three types of monocytes are:

The classical monocyte which is characterized by high level expression of the CD14 cell surface receptor (CD45⁺CD14⁺⁺ GD I 6 monocyte)

The non-classical monocyte which shows low level expression of CD 14 and additional coexpression of the CD16 receptor (CD45⁺CD14⁺CD16⁺ monocyte). The intermediate monocyte which expresses high levels of CD 14 and low levels of CD 16 (CD45⁺CD14⁺⁺CD16⁺ monocyte).

In a particular embodiment, the quantity of classical monocytes is determined to perform the method of the invention.

In a particular embodiment, the quantity of leukocytes is also determined to perform the method of the invention.

As used herein, the term “cardiovascular event” denotes all disease characterised with a cardiac problem. A cardiovascular event included but is not limited to ischemic heart disease, atherosclerotic cardiovascular (CV) disease like atherosclerosis, coronary artery disease, peripheral arterial disease, cardiomyopathy, hypertensive heart disease, heart failure, pulmonary heart disease or myocarditis.

According to the invention, a cardiovascular event is predicted by the Coronary artery calcium (CAC) score (see the references 26 and 27 for the calculation).

In another particular embodiment, the invention relates to a method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event comprising the steps of: i) determining in a sample obtained from the patient the expression level of MMP25 ii) comparing said expression level determined at step i) with its predetermined reference value and iii) providing that when the expression levels of MMP25 determined at step i) is higher than its predetermined reference value it is indicative of whether a patient is at a risk of developing a cardiovascular event and providing that when the expression levels of MMP25 determined at step i) is lower than its predetermined reference value it is indicative of whether a patient is not at risk of developing a cardiovascular event.

As used herein, the term “MMP25” for matrix metalloproteinase-25 is an enzyme of the family of the matrix metalloproteinase (MMP) involved in the breakdown of extracellular matrix in normal physiological processes, such as embryonic development, reproduction, and tissue remodeling, as well as in disease processes, such as arthritis and metastasis. The Entrez accession number of MMP25 is 64386.

In a particular embodiment, the method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event can comprises the steps of determining in a sample obtained from the patient the expression level of specific metabolites like ITACONATE or Glutamine)).

Thus in particular embodiment, the invention relates to a method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event comprising the steps of: i) determining in a sample obtained from the patient the expression level of metabolites ii) comparing said expression level determined (at step i) with its predetermined reference value and iii) providing that when the expression levels of metabolites determined at step i) is higher than its predetermined reference value it is indicative of whether a patient is at a risk of developing a cardiovascular event and providing that when the expression levels of metabolites determined at step i) is lower than its predetermined reference value it is indicative of whether a patient is not at risk of developing a cardiovascular event.

As used herein and according to the invention, the term “metabolites” denotes for example itaconate or glutamine.

As use d herein, the term “itaconate” or “itaconic acid” denotes an organic compound. This dicarboxylic acid is a white solid that is soluble in water, ethanol, and acetone. Historically, itaconic acid was obtained by the distillation of citric acid, but currently it is produced by fermentation. The name itaconic acid was devised as an anagram of aconitic acid, another derivative of citric acid.

Determination of the quantity of monocytes of the invention:

As used herein, the term “quantity” refers to the quantity (cell by liter) or frequency of monocytes cells and particularly classical monocytes. Particularly, the term “frequency” refers to the percentage of monocytes compared to the total leukocytes. Typically, the quantity or frequency of monocytes cells may be determined by any technology known by a person skilled in the art. In some embodiments, the expression of the phenotypic marker is assessed at the mRNA level. Methods for assessing the transcription level of a molecule are well known in the prior art. Examples of such methods include, but are not limited to, RT-PCR, RT-qPCR, Northern Blot, hybridization techniques such as, for example, use of microarrays, and combination thereof including but not limited to, hybridization of amplicons obtained by RT- PCR, sequencing such as, for example, next-generation DNA sequencing (NGS) or RNA-seq (also known as “Whole Transcriptome Shotgun Sequencing”) and the like. In some embodiments, the expression of the phenotypic marker is assessed at the protein level. Methods for determining a protein level in a sample are well-known in the art. Examples of such methods include, but are not limited to, immunohistochemistry, Multiplex methods (Luminex), western blot, enzyme-linked immunosorbent assay (ELISA), sandwich ELISA, fluorescent-linked immunosorbent assay (FLISA), enzyme immunoassay (EIA), radioimmunoassay (RIA), flow cytometry (FACS) and the like.

In some embodiment, the sample of the present invention (i.e., blood sample) is analyzed by flow cytometry to determine the quantity or frequency of monocytes cells. As used herein, the term "flow cytometry methods" refers to a technique for counting cells of interest, by suspending them in a stream of fluid and passing them through an electronic detection apparatus. Flow cytometry methods allow simultaneous multiparametric analysis of the physical and/or chemical parameters of up to thousands of particles per second, such as fluorescent parameters. Modern flow cytometry instruments usually have multiple lasers and fluorescence detectors. A common variation of flow cytometry techniques is to physically sort particles based on their properties, so as to purify or detect populations of interest, using "fluorescence-activated cell sorting".

As used herein, "fluorescence-activated cell sorting" (FACS) refers to a flow cytometric method for sorting a heterogeneous mixture of cells from a biological sample into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell and provides fast, objective and quantitative recording of fluorescent signals from individual cells as well as physical separation of cells of particular interest. Accordingly, FACS can be used with the methods described herein to isolate and detect the subpopulation of monocytes cells.

In some embodiments, the preferred agents are antibodies that specifically bind the cellsurface markers, and can include polyclonal and monoclonal antibodies, and antigen-binding derivatives or fragments thereof. Well-known antigen binding fragments include, for example, single domain antibodies (dAbs; which consist essentially of single VL or VH antibody domains), Fv fragment, including single chain Fv fragment (scFv), Fab fragment, and F(ab')2 fragment. Methods for the construction of such antibody molecules are well known in the art.

Accordingly, as used herein, the term "antibody" refers to an intact immunoglobulin or to a monoclonal or polyclonal antigen-binding fragment with the Fc (crystallizable fragment) region or FcRn binding fragment of the Fc region. Antigen-binding fragments may be produced by recombinant DNA techniques or by enzymatic or chemical cleavage of intact antibodies. "Antigen-binding fragments" include, inter alia, Fab, Fab', F(ab')2, Fv, dAb, and complementarity determining region (CDR) fragments, single -chain antibodies (scFv), single domain antibodies, chimeric antibodies, diabodies and polypeptides that contain at least a portion of an immunoglobulin that is sufficient to confer specific antigen binding to the polypeptide. The terms Fab, Fc, pFc', F(ab') 2 and Fv are employed with standard immunological meanings (Roitt, I. (1991) Essential Immunology, 7th Ed., (Blackwell Scientific Publications, Oxford)]. Such antibodies or antigen-binding fragments are available commercially from vendors such as R&D Systems, BD Biosciences, e-Biosciences, Proimmune and Miltenyi, or can be raised against these cell-surface markers by methods known to those skilled in the art.

In some embodiments, an agent that specifically bind to a cell-surface marker, such as an antibody or antigen-binding fragment, is labelled with a tag to facilitate the isolation and detection of the cell populations of the invention.

As used herein, the terms "label" or "tag" refer to a composition capable of producing a detectable signal indicative of the presence of a target, such as, the presence of a specific cellsurface marker in a biological sample. Suitable labels include fluorescent molecules, radioisotopes, nucleotide chromophores, enzymes, substrates, chemiluminescent moieties, magnetic particles, bioluminescent moieties, and the like. As such, a label is any composition detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical or chemical means needed for the methods to isolate and detect the cell populations of the invention. Non-limiting examples of fluorescent labels or tags for labeling the agents such as antibodies for use in the methods of invention include

Hydroxycoumarin, Succinimidyl ester, Aminocoumarin, Succinimidyl ester, Methoxycoumarin, Succinimidyl ester, Cascade Blue, Hydrazide, Pacific Blue, Maleimide, Pacific Orange, Lucifer yellow, NBD, NBD-X, R-Phycoerythrin (PE), a PE-Cy5 conjugate (Cy chrome, R670, Tri-Color, Quantum Red), a PE-Cy7 conjugate, Red 613, PE-Texas Red, PerCP, Peridinin chlorphyll protein, TruRed (PerCP-Cy5.5 conjugate), FluorX, Fluoresceinisothyocyanate (FITC), BODIPY-FL, TRITC, X-Rhodamine (XRITC), Lissamine Rhodamine B, Texas Red, Allophycocyanin (APC), an APC-Cy7 conjugate, Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500, Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, Alexa Fluor 750, Alexa Fluor 790, Cy2, Cy3, Cy3B, Cy3.5, Cy5, Cy5.5 or Cy7.

Determination of the expression level of MMP25:

Measuring the expression level of MMP25 can be done by measuring the gene expression level of MMP25 or by measuring the level of the protein of MMP25 and can be performed by a variety of techniques well known in the art.

Typically, the expression level of a gene may be determined by determining the quantity of mRNA. Methods for determining the quantity of mRNA are well known in the art. For example the nucleic acid contained in the samples (e g., cell or tissue prepared from the patient) is first extracted according to standard methods, for example using lytic enzymes or chemical solutions or extracted by nucleic-acid-binding resins following the manufacturer's instructions. The extracted mRNA is then detected by hybridization (e. g., Northern blot analysis, in situ hybridization) and/or amplification (e.g., RT-qPCR). Particularly, nanostring assay can be used.

Other methods of Amplification include ligase chain reaction (LCR), transcription- mediated amplification (TMA), strand displacement amplification (SDA) and nucleic acid sequence based amplification (NASBA).

Nucleic acids having at least 10 nucleotides and exhibiting sequence complementarity or homology to the mRNA of interest herein find utility as hybridization probes or amplification primers. It is understood that such nucleic acids need not be identical, but are typically at least about 80% identical to the homologous region of comparable size, more preferably 85% identical and even more preferably 90-95% identical. In certain embodiments, it will be advantageous to use nucleic acids in combination with appropriate means, such as a detectable label, for detecting hybridization.

Typically, the nucleic acid probes include one or more labels, for example to permit detection of a target nucleic acid molecule using the disclosed probes. In various applications, such as in situ hybridization procedures, a nucleic acid probe includes a label (e.g., a detectable label). A “detectable label” is a molecule or material that can be used to produce a detectable signal that indicates the presence or concentration of the probe (particularly the bound or hybridized probe) in a sample. Thus, a labeled nucleic acid molecule provides an indicator of the presence or concentration of a target nucleic acid sequence (e.g., genomic target nucleic acid sequence) (to which the labeled uniquely specific nucleic acid molecule is bound or hybridized) in a sample. A label associated with one or more nucleic acid molecules (such as a probe generated by the disclosed methods) can be detected either directly or indirectly. A label can be detected by any known or yet to be discovered mechanism including absorption, emission and/ or scattering of a photon (including radio frequency, microwave frequency, infrared frequency, visible frequency and ultra-violet frequency photons). Detectable labels include colored, fluorescent, phosphorescent and luminescent molecules and materials, catalysts (such as enzymes) that convert one substance into another substance to provide a detectable difference (such as by converting a colorless substance into a colored substance or vice versa, or by producing a precipitate or increasing sample turbidity), haptens that can be detected by antibody binding interactions, and paramagnetic and magnetic molecules or materials.

Particular examples of detectable labels include fluorescent molecules (or fluorochromes). Numerous fluorochromes are known to those of skill in the art, and can be selected, for example from Life Technologies (formerly Invitrogen), e.g., see, The Handbook — A Guide to Fluorescent Probes and Labeling Technologies). Examples of particular fluorophores that can be attached (for example, chemically conjugated) to a nucleic acid molecule (such as a uniquely specific binding region) are provided in U.S. Pat. No. 5,866, 366 to Nazarenko et al., such as 4-acetamido-4'-isothiocyanatostilbene-2,2' disulfonic acid, acridine and derivatives such as acridine and acridine isothiocyanate, 5-(2'-aminoethyl) aminonaphthal ene-1 -sulfonic acid (EDANS), 4-amino -N- [3 vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate (Lucifer Yellow VS), N-(4-anilino-l- naphthyl)maleimide, antllranilamide, Brilliant Yellow, coumarin and derivatives such as coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4- trifluoromethylcouluarin (Coumarin 151); cyanosine; 4',6-diaminidino-2-phenylindole (DAPI); 5',5"dibromopyrogallol-sulfonephthalein (Bromopyrogallol Red); 7 -diethylamino -3 (4'-isothiocyanatophenyl)-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4'- diisothiocyanatodihydro-stilbene-2,2'-disulfonic acid; 4,4'-diisothiocyanatostilbene-2,2'- disulforlic acid; 5 -[dimethylamino] naphthalene-l-sulfonyl chloride (DNS, dansyl chloride); 4-(4'-dimethylaminophenylazo)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl- 4'-isothiocyanate (DABITC); eosin and derivatives such as eosin and eosin isothiocyanate; erythrosin and derivatives such as erythrosin B and erythrosin isothiocyanate; ethidium; fluorescein and derivatives such as 5-carboxyfluorescein (FAM), 5-(4,6diclllorotriazin-2- yDarninofluorescein (DTAF), 2'7'dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein, fluorescein isothiocyanate (FITC), and QFITC Q(RITC); 2',7'-difluorofluorescein (OREGON GREEN®); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4- methylumbelliferone; ortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B- phycoerythrin; o-phthaldialdehyde; pyrene and derivatives such as pyrene, pyrene butyrate and succinimidyl 1 -pyrene butyrate; Reactive Red 4 (Cibacron Brilliant Red 3B-A); rhodamine and derivatives such as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, rhodamine green, sulforhodamine B, sulforhodamine 101 and sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives. Other suitable fluorophores include thiol -reactive europium chelates which emit at approximately 617 nm (Heyduk and Heyduk, Analyt. Biochem. 248:216-27, 1997; J. Biol. Chem. 274:3315-22, 1999), as well as GFP, LissamineTM, diethylaminocoumarin, fluorescein chlorotriazinyl, naphthofluorescein, 4,7-dichlororhodamine and xanthene (as described in U.S. Pat. No. 5,800,996 to Lee et al.) and derivatives thereof. Other fluorophores known to those skilled in the art can also be used, for example those available from Life Technologies (Invitrogen; Molecular Probes (Eugene, Oreg.)) and including the ALEXA FLUOR® series of dyes (for example, as described in U.S. Pat. Nos. 5,696,157, 6, 130, 101 and 6,716,979), the BODIPY series of dyes (dipyrrometheneboron difluoride dyes, for example as described in U.S. Pat. Nos. 4,774,339, 5,187,288, 5,248,782, 5,274,113, 5,338,854, 5,451,663 and 5,433,896), Cascade Blue (an amine reactive derivative of the sulfonated pyrene described in U.S. Pat. No. 5,132,432) and Marina Blue (U.S. Pat. No. 5,830,912).

In addition to the fluorochromes described above, a fluorescent label can be a fluorescent nanoparticle, such as a semiconductor nanocrystal, e.g., a QUANTUM DOTTM (obtained, for example, from Life Technologies (QuantumDot Corp, Invitrogen Nanocrystal Technologies, Eugene, Oreg.); see also, U.S. Pat. Nos. 6,815,064; 6,682,596; and 6,649, 138). Semiconductor nanocrystals are microscopic particles having size-dependent optical and/or electrical properties. When semiconductor nanocrystals are illuminated with a primary energy source, a secondary emission of energy occurs of a frequency that corresponds to the handgap of the semiconductor material used in the semiconductor nanocrystal. This emission can he detected as colored light of a specific wavelength or fluorescence. Semiconductor nanocrystals with different spectral characteristics are described in e.g., U.S. Pat. No. 6,602,671. Semiconductor nanocrystals that can he coupled to a variety of biological molecules (including dNTPs and/or nucleic acids) or substrates by techniques described in, for example, Bruchez et al., Science 281 :20132016, 1998; Chan et al., Science 281:2016-2018, 1998; and U.S. Pat. No. 6,274,323. Formation of semiconductor nanocrystals of various compositions are disclosed in, e.g., U.S. Pat. Nos. 6,927, 069; 6,914,256; 6,855,202; 6,709,929; 6,689,338; 6,500,622; 6,306,736; 6,225,198; 6,207,392; 6,114,038; 6,048,616; 5,990,479; 5,690,807; 5,571,018; 5,505,928; 5,262,357 and in U.S. Patent Publication No. 2003/0165951 as well as PCT Publication No. 99/26299 (published May 27, 1999). Separate populations of semiconductor nanocrystals can he produced that are identifiable based on their different spectral characteristics. For example, semiconductor nanocrystals can he produced that emit light of different colors based on their composition, size or size and composition. For example, quantum dots that emit light at different wavelengths based on size (565 nm, 655 nm, 705 nm, or 800 nm emission wavelengths), which are suitable as fluorescent labels in the probes disclosed herein are available from Life Technologies (Carlshad, Calif). Additional labels include, for example, radioisotopes (such as 3 H), metal chelates such as DOTA and DPTA chelates of radioactive or paramagnetic metal ions like Gd3+, and liposomes.

Detectable labels that can he used with nucleic acid molecules also include enzymes, for example horseradish peroxidase, alkaline phosphatase, acid phosphatase, glucose oxidase, beta-galactosidase, beta-glucuronidase, or beta-lactamase.

Alternatively, an enzyme can he used in a metallographic detection scheme. For example, silver in situ hybridization (SISH) procedures involve metallographic detection schemes for identification and localization of a hybridized genomic target nucleic acid sequence. Metallographic detection methods include using an enzyme, such as alkaline phosphatase, in combination with a water-soluble metal ion and a redox-inactive substrate of the enzyme. The substrate is converted to a redox-active agent by the enzyme, and the redoxactive agent reduces the metal ion, causing it to form a detectable precipitate. (See, for example, U.S. Patent Application Publication No. 2005/0100976, PCT Publication No. 2005/ 003777 and U.S. Patent Application Publication No. 2004/ 0265922). Metallographic detection methods also include using an oxido-reductase enzyme (such as horseradish peroxidase) along with a water soluble metal ion, an oxidizing agent and a reducing agent, again to form a detectable precipitate. (See, for example, U.S. Pat. No. 6,670,113).

Probes made using the disclosed methods can be used for nucleic acid detection, such as ISH procedures (for example, fluorescence in situ hybridization (FISH), chromogenic in situ hybridization (CISH) and silver in situ hybridization (SISH)) or comparative genomic hybridization (CGH).

In situ hybridization (ISH) involves contacting a sample containing target nucleic acid sequence (e.g., genomic target nucleic acid sequence) in the context of a metaphase or interphase chromosome preparation (such as a cell or tissue sample mounted on a slide) with a labeled probe specifically hybridizable or specific for the target nucleic acid sequence (e.g., genomic target nucleic acid sequence). The slides are optionally pretreated, e.g., to remove paraffin or other materials that can interfere with uniform hybridization. The sample and the probe are both treated, for example by heating to denature the double stranded nucleic acids. The probe (formulated in a suitable hybridization buffer) and the sample are combined, under conditions and for sufficient time to permit hybridization to occur (typically to reach equilibrium). The chromosome preparation is washed to remove excess probe, and detection of specific labeling of the chromosome target is performed using standard techniques. For example, a biotinylated probe can be detected using fluorescein-labeled avidin or avidin-alkaline phosphatase. For fluorochrome detection, the fluorochrome can be detected directly, or the samples can be incubated, for example, with fluorescein isothiocyanate (FITC)- conjugated avidin. Amplification of the FITC signal can be effected, if necessary, by incubation with biotin-conjugated goat antiavidin antibodies, washing and a second incubation with FITC- conjugated avidin. For detection by enzyme activity, samples can be incubated, for example, with streptavidin, washed, incubated with biotin-conjugated alkaline phosphatase, washed again and pre-equilibrated (e.g., in alkaline phosphatase (AP) buffer). For a general description of in situ hybridization procedures, see, e.g., U.S. Pat. No. 4,888,278.

Numerous procedures for FISH, CISH, and SISH are known in the art. For example, procedures for performing FISH are described in U.S. Pat. Nos. 5,447,841; 5,472,842; and 5,427,932; and for example, in Pirlkel et al., Proc. Natl. Acad. Sci. 83:2934-2938, 1986; Pinkel et al., Proc. Natl. Acad. Sci. 85:9138-9142, 1988; and Lichter et al., Proc. Natl. Acad. Sci. 85:9664-9668, 1988. CISH is described in, e.g., Tanner et al., Am.l. Pathol. 157:1467-1472, 2000 and U.S. Pat. No. 6,942,970. Additional detection methods are provided in U.S. Pat. No. 6,280,929.

Numerous reagents and detection schemes can be employed in conjunction with FISH, CISH, and SISH procedures to improve sensitivity, resolution, or other desirable properties. As discussed above probes labeled with fluorophores (including fluorescent dyes and QUANTUM DOTS®) can be directly optically detected when performing FISH. Alternatively, the probe can be labeled with a nonfluorescent molecule, such as a hapten (such as the following nonlimiting examples: biotin, digoxigenin, DNP, and various oxazoles, pyrrazoles, thiazoles, nitroaryls, benzofurazans, triterpenes, ureas, thioureas, rotenones, coumarin, courmarin-based compounds, Podophyllotoxin, Podophyllotoxin-based compounds, and combinations thereof), ligand or other indirectly detectable moiety. Probes labeled with such non-fluorescent molecules (and the target nucleic acid sequences to which they bind) can then be detected by contacting the sample (e.g., the cell or tissue sample to which the probe is bound) with a labeled detection reagent, such as an antibody (or receptor, or other specific binding partner) specific for the chosen hapten or ligand. The detection reagent can be labeled with a fluorophore (e.g., QUANTUM DOT®) or with another indirectly detectable moiety, or can be contacted with one or more additional specific binding agents (e.g., secondary or specific antibodies), which can be labeled with a fluorophore.

In other examples, the probe, or specific binding agent (such as an antibody, e.g., a primary antibody, receptor or other binding agent) is labeled with an enzyme that is capable of converting a Anorogenic or chromogenic composition into a detectable fiuorescent, colored or otherwise detectable signal (e.g., as in deposition of detectable metal particles in SISH). As indicated above, the enzyme can be attached directly or indirectly via a linker to the relevant probe or detection reagent. Examples of suitable reagents (e.g., binding reagents) and chemistries (e.g., linker and attachment chemistries) are described in U.S. Patent Application Publication Nos. 2006/0246524; 2006/0246523, and 2007/ 01 17153.

It will be appreciated by those of skill in the art that by appropriately selecting labelled probe-specific binding agent pairs, multiplex detection schemes can he produced to facilitate detection of multiple target nucleic acid sequences (e.g., genomic target nucleic acid sequences) in a single assay (e.g., on a single cell or tissue sample or on more than one cell or tissue sample). For example, a first probe that corresponds to a first target sequence can he labelled with a first hapten, such as biotin, while a second probe that corresponds to a second target sequence can be labelled with a second hapten, such as DNP. Following exposure of the sample to the probes, the bound probes can he detected by contacting the sample with a first specific binding agent (in this case avidin labelled with a first fluorophore, for example, a first spectrally distinct QUANTUM DOT®, e.g., that emits at 585 nm) and a second specific binding agent (in this case an anti-DNP antibody, or antibody fragment, labelled with a second fiuorophore (for example, a second spectrally distinct QUANTUM DOT®, e.g., that emits at 705 nm). Additional probes/binding agent pairs can he added to the multiplex detection scheme using other spectrally distinct fluorophores. Numerous variations of direct, and indirect (one step, two step or more) can he envisioned, all of which are suitable in the context of the disclosed probes and assays.

Probes typically comprise single-stranded nucleic acids of between 10 to 1000 nucleotides in length, for instance of between 10 and 800, more preferably of between 15 and 700, typically of between 20 and 500. Primers typically are shorter single-stranded nucleic acids, of between 10 to 25 nucleotides in length, designed to perfectly or almost perfectly match a nucleic acid of interest, to be amplified. The probes and primers are “specific” to the nucleic acids they hybridize to, i.e. they preferably hybridize under high stringency hybridization conditions (corresponding to the highest melting temperature Tm, e.g., 50 % formamide, 5x or 6x SCC. SCC is a 0.15 M NaCl, 0.015 M Na-citrate).

The nucleic acid primers or probes used in the above amplification and detection method may be assembled as a kit. Such a kit includes consensus primers and molecular probes. A preferred kit also includes the components necessary to determine if amplification has occurred. The kit may also include, for example, PCR buffers and enzymes; positive control sequences, reaction control primers; and instructions for amplifying and detecting the specific sequences.

In a particular embodiment, the methods of the invention comprise the steps of providing total RNAs extracted from cumulus cells and subjecting the RNAs to amplification and hybridization to specific probes, more particularly by means of a quantitative or semi- quantitative RT-PCR (or q RT-PCR).

In another preferred embodiment, the expression level is determined by DNA chip analysis. Such DNA chip or nucleic acid microarray consists of different nucleic acid probes that are chemically attached to a substrate, which can be a microchip, a glass slide or a microsphere-sized bead. A microchip may be constituted of polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, or nitrocellulose. Probes comprise nucleic acids such as cDNAs or oligonucleotides that may be about 10 to about 60 base pairs. To determine the expression level, a sample from a test subject, optionally first subjected to a reverse transcription, is labelled and contacted with the microarray in hybridization conditions, leading to the formation of complexes between target nucleic acids that are complementary to probe sequences attached to the microarray surface. The labelled hybridized complexes are then detected and can be quantified or semi-quantified. Labelling may be achieved by various methods, e.g. by using radioactive or fluorescent labelling. Many variants of the microarray hybridization technology are available to the man skilled in the art (see e.g. the review by Hoheisel, Nature Reviews, Genetics, 2006, 7:200-210).

Expression level of a gene may be expressed as absolute expression level or normalized expression level. Typically, expression levels are normalized by correcting the absolute expression level of a gene by comparing its expression to the expression of a gene that is not a relevant for determining a cardiovascular event, e.g., a housekeeping gene that is constitutively expressed. Suitable genes for normalization include housekeeping genes such as the actin gene ACTB, ribosomal 18S gene, GUSB, PGK1, TFRC, GAPDH, TBP and ABL1. This normalization allows the comparison of the expression level in one sample, e.g., a patient sample, to another sample, or between samples from different sources.

According to the invention, the level of the proteins MMP25 may also be measured and can be performed by a variety of techniques well known in the art. For measuring the expression level of MMP25, techniques like ELISA (see below) allowing to measure the level of the soluble MMP25 are particularly suitable.

In the present application, the “level of protein” or the “protein level expression” or the “protein concentration” means the quantity or concentration of said protein. In another embodiment, the “level of protein” means the level of the proteins fragments. In still another embodiment, the “level of protein” means the quantitative measurement of the proteins of the invention expression relative to a negative control.

According to the invention, the proteins of the invention may be measured at the surface of the tumor cells or in an extracellular context (for example in blood or plasma).

Typically protein concentration may be measured for example by capillary electrophoresis-mass spectroscopy technique (CE-MS) or ELISA performed on the sample.

Such methods comprise contacting a sample with a binding partner capable of selectively interacting with proteins present in the sample. The binding partner is generally an antibody that may be polyclonal or monoclonal, preferably monoclonal.

The presence of the protein can be detected using standard electrophoretic and immunodiagnostic techniques, including immunoassays such as competition, direct reaction, or sandwich type assays. Such assays include, but are not limited to, Western blots; agglutination tests; enzyme-labeled and mediated immunoassays, such as ELISAs; biotin/avidin type assays; radioimmunoassays; immunoelectrophoresis; immunoprecipitation, capillary electrophoresismass spectroscopy technique (CE-MS). etc. The reactions generally include revealing labels such as fluorescent, chemioluminescent, radioactive, enzymatic labels or dye molecules, or other methods for detecting the formation of a complex between the antigen and the antibody or antibodies reacted therewith.

The afore mentioned assays generally involve separation of unbound protein in a liquid phase from a solid phase support to which antigen-antibody complexes are bound. 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.

More particularly, an ELISA method can be used, wherein the wells of a microtiter plate are coated with a set of antibodies against the proteins to be tested. A sample containing or suspected of containing the marker protein 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 labeled secondary binding molecule is added. The secondary binding molecule is allowed to react with any captured sample marker protein, the plate is washed and the presence of the secondary binding molecule is detected using methods well known in the art. Methods of the invention may comprise a step consisting of comparing the proteins and fragments concentration in circulating cells with a control value. As used herein, "concentration of protein" refers to an amount or a concentration of a transcription product, for the proteins of the invention. Typically, a level of a protein can be expressed as nanograms per microgram of tissue or nanograms per milliliter of a culture medium, for example. Alternatively, relative units can be employed to describe a concentration. In a particular embodiment, "concentration of proteins" may refer to fragments of the proteins of the invention. Thus, in a particular embodiment, fragment of the proteins of the invention may also be measured.

In a particular embodiment, the detection of the level of the proteins of the invention can be performed by flow cytometry.

Predetermined reference value

Predetermined reference value used for comparison of the quantity of monocytes or for the expression level of MMP25 may comprise “cut-off’ or “threshold” values that may be determined as described herein. Each reference (“cut-off’) value for the monocytes or for MMP25 may be predetermined by carrying out a method comprising the steps of: a) providing a collection of samples from patients suffering of a DT2; b) determining the quantity of monocytes or the level of MMP25 of the invention for each sample contained in the collection provided at step a); c) ranking the samples according to said level d) classifying said samples in pairs of subsets of increasing, respectively decreasing, number of members ranked according to their expression level, e) providing, for each sample provided at step a), information relating to the presence of a cardiovascular event; f) for each pair of subsets of samples, obtaining a Kaplan Meier percentage of survival curve; g) for each pair of subsets of samples calculating the statistical significance (p value) between both subsets h) selecting as reference value for the level, the value of level for which the p value is the smallest.

For example the quantity of monocytes or the expression level of MMP25 of the invention has been assessed for 100 TD2 samples of 100 patients. The 100 samples are ranked according to the quantity of monocytes or to the expression level of MMP25. Sample 1 has the best expression level and sample 100 has the worst expression level. A first grouping provides two subsets: on one side sample Nr 1 and on the other side the 99 other samples. The next grouping provides on one side samples 1 and 2 and on the other side the 98 remaining samples etc., until the last grouping: on one side samples 1 to 99 and on the other side sample Nr 100. According to the information relating to the actual presence or not of a cardiovascular event, Kaplan Meier curves are prepared for each of the 99 groups of two subsets. Also for each of the 99 groups, the p value between both subsets was calculated.

The reference value is selected such as the discrimination based on the criterion of the minimum p value is the strongest. In other terms, the expression level corresponding to the boundary between both subsets for which the p value is minimum is considered as the reference value. It should be noted that the reference value is not necessarily the median value of expression levels.

In routine work, the reference value (cut-off value) may be used in the present method to discriminate samples and therefore the corresponding patients.

Kaplan-Meier curves of percentage of survival as a function of time are commonly used to measure the fraction of patients living for a certain amount of time after treatment and are well known by the man skilled in the art.

The man skilled in the art also understands that the same technique of assessment of the expression level of a protein should of course be used for obtaining the reference value and thereafter for assessment of the expression level of a protein of a patient subjected to the method of the invention. Particularly, other methods like median thresholding”, “max-stat” and “Youden’s J statistic can be used to determine a threshold.

Therapeutics applications

In a further aspect, the invention also relates to a method for treating a cardiovascular event in a patient with a risk of cardiovascular event above comprising the administration to said patient of an anti-cardiovascular disease treatment.

As used herein, the term “anti-cardiovascular disease treatment” relates to any treatment of a cardiovascular event as described above like statins, anticoagulants, GLP1 analogs and SGLT2 inhibitor.

The invention also relates to a pharmaceutical composition comprising an anti- cardiovascular disease treatment for use in the treatment of a cardiovascular event in a subject with a bad prognosis as described above.

Any therapeutic agent of the invention may be combined with pharmaceutically acceptable excipients, and optionally sustained-release matrices, such as biodegradable polymers, to form therapeutic compositions. "Pharmaceutically" or "pharmaceutically acceptable" refers 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, semi-solid or liquid filler, diluent, encapsulating material or formulation auxiliary of any type.

The form of the pharmaceutical compositions, the route of administration, the dosage and the regimen naturally depend upon the condition to be treated, the severity of the illness, the age, weight, and sex of the patient, etc.

The pharmaceutical compositions of the invention can be formulated for a topical, oral, intranasal, parenteral, intraocular, intravenous, intramuscular, intrathecal or subcutaneous administration and the like.

Particularly, 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 doses used for the administration can be adapted as a function of various parameters, and in particular as a function of the mode of administration used, of the relevant pathology, or alternatively of the desired duration of treatment.

In addition, other pharmaceutically acceptable forms include, e.g. tablets or other solids for oral administration; time release capsules; and any other form currently can be used.

Typically the anti-cardiovascular disease treatment according to the invention are administered to the subject in a therapeutically effective amount.

By a "therapeutically effective amount" the anti-cardiovascular disease treatment is meant a sufficient amount of the anti-cardiovascular disease treatment for treating a cardiovascular event at a reasonable benefit/risk ratio applicable to any medical treatment. It will be understood, however, that the total daily usage of the anti-cardiovascular disease treatment will be 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) the anti-cardiovascular disease treatment 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 the anti- cardiovascular disease treatment employed; the duration of the treatment; drugs used in combination or coincidental with the specific anti-cardiovascular disease treatment employed; 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 anti-cardiovascular disease treatment 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 products may be varied over a wide range from 0.01 to 1,000 mg per adult per day. Typically, 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 anti-cardiovascular disease treatment 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 anti-cardiovascular disease treatment, preferably from 1 mg to about 100 mg of the anti -cardiovascular disease treatment. 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.

In a particular embodiment, the anti-cardiovascular disease treatment may be used in a concentration between 0.01 pM and 20 pM, particularly, the anti -cardiovascular disease treatment may be used in a concentration of 0.01, 0.05, 0.1, 0.5, 1.0, 2.5, 5.0, 10.0, 15.0, 20.0 pM.

According to the invention, the anti -cardiovascular disease treatment is administered to the subject in the form of a pharmaceutical composition.

Typically, the anti -cardiovascular disease treatment may be combined with pharmaceutically acceptable excipients, and optionally sustained-release matrices, such as biodegradable polymers, to form therapeutic compositions. "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, semi-solid or liquid filler, diluent, encapsulating material or formulation auxiliary of any type.

In the pharmaceutical compositions of the present invention for oral, sublingual, subcutaneous, intramuscular, intravenous, transdermal, local or rectal administration, the active principle, alone or in combination with another active principle, can be administered in a unit administration form, as a mixture with conventional pharmaceutical supports, to animals and human beings. Suitable unit administration forms comprise oral-route forms such as tablets, gel capsules, powders, granules and oral suspensions or solutions, sublingual and buccal administration forms, aerosols, implants, subcutaneous, transdermal, topical, intraperitoneal, intramuscular, intravenous, subdermal, transdermal, intrathecal and intranasal administration forms and rectal administration forms.

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 inj ectable 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. Solutions comprising anti-cardiovascular disease treatment as free base or pharmacologically acceptable salts can be prepared in water suitably mixed with a surfactant, such as hydroxypropylcellulose. Dispersions can also be prepared in glycerol, liquid polyethylene glycols, and mixtures thereof and in oils. Under ordinary conditions of storage and use, these preparations contain a preservative to prevent the growth of microorganisms. The anti -cardiovascular disease treatment of the invention can be formulated into a composition in a neutral or salt form. Pharmaceutically acceptable salts include the acid addition salts (formed with the free amino groups of the protein) and which are formed with inorganic acids such as, for example, hydrochloric or phosphoric acids, or such organic acids as acetic, oxalic, tartaric, mandelic, and the like. Salts formed with the free carboxyl groups can also be derived from inorganic bases such as, for example, sodium, potassium, ammonium, calcium, or ferric hydroxides, and such organic bases as isopropylamine, trimethylamine, histidine, procaine and the like. The carrier can also be a solvent or dispersion medium containing, for example, water, ethanol, polyol (for example, glycerol, propylene glycol, and liquid polyethylene glycol, and the like), suitable mixtures thereof, and vegetables oils. The proper fluidity can be maintained, for example, by the use of a coating, such as lecithin, by the maintenance of the required particle size in the case of dispersion and by the use of surfactants. The prevention of the action of microorganisms can be brought about by various antibacterial and antifungal agents, for example, parabens, chlorobutanol, phenol, sorbic acid, thimerosal, and the like. In many cases, it will be preferable to include isotonic agents, for example, sugars or sodium chloride. Prolonged absorption of the injectable compositions can be brought about by the use in the compositions of agents delaying absorption, for example, aluminium monostearate and gelatin. Sterile injectable solutions are prepared by incorporating the active compounds in 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 agent of the present inventions 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 typical methods of preparation are vacuumdrying and freeze-drying techniques which yield a powder of the anti-cardiovascular disease treatment of the invention plus any additional desired ingredient from a previously sterile- filtered solution thereof. The preparation of more, or highly concentrated solutions for direct injection is also contemplated, where the use of DMSO as solvent is envisioned to result in extremely rapid penetration, delivering high concentrations of the active agents to a small tumor area. Upon formulation, solutions will be administered in a manner compatible with the dosage formulation and in such amount as is therapeutically effective. The formulations are easily administered in a variety of dosage forms, such as the type of injectable solutions described above, but drug release capsules and the like can also be employed. For parenteral administration in an aqueous solution, for example, the solution should be suitably buffered if necessary and the liquid diluent first rendered isotonic with sufficient saline or glucose. These particular aqueous solutions are especially suitable for intravenous, intramuscular, subcutaneous and intraperitoneal administration. In this connection, sterile aqueous media which can be employed will be known to those of skill in the art in light of the present disclosure. Some variation in dosage will necessarily occur depending on the condition of the subject being treated. The person responsible for administration will, in any event, determine the appropriate dose for the individual subject.

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.

FIGURES:

Figure 1: Monocyte subtypes frequencies determine groups with different cardiovascular risk. A) Forest plot of Spearman correlation between CAC-score and CV risk factors from 672 T2D participants (population 1). B) Box plot of circulating monocytes and leukocytes blood counts per CV risk group defined by participants’ CAC-score in 672 T2D participants (population 1), Kruskal-Wallis test with post-hoc comparisons. C) Box plot of circulating monocytes and leukocytes blood counts of surdiagene T2D population (n=1041) subdivided in primary prevention (n=757) versus secondary prevention MACE groups (n = 284). Kruskal-Wallis test with post-hoc comparisons, *: p <= 0.05, ***:p<=0.001. D) Box plot of monocyte subtypes flow cytometry percentages per CV risk group defined by participants’ CAC-score in 266 T2D participants (population 2), Kruskal-Wallis test with post-hoc comparisons. CAC-score = coronary artery calcium score, MACE = major adverse cardiovascular event.

Figure 2: Clustering analysis reveals groups of T2D individuals with different monocyte subtype distribution and distinct CV risk. A) Schematic of clustering workflow, and principal component analysis representation of the T2D participants (n=266, Population 2) clustering based on circulating monocyte subpopulation percentages among CD45+ cells. By k-means method 3 clusters of n = 78, n = 141, and n = 33 patients with T2D were identified. B) Heatmap of the monocyte subtypes distribution among the T2D individuals ((n=266, Population 2) of each cluster. C) Left: Spearman correlation plot between log2(CAC-score) and classical monocytes flow cytometry percentages in 266 T2D participants (Population 2), colored by cluster. Right: Barplot of the percentage of cluster 1 or cluster 2 individuals that have a CAC-score > 400 (CV3 group). D) Boxplot of the log2 values of monocyte blood levels (10e9/L among the T2D individuals ((n=266, Population 2) of each cluster, Kruskal-Wallis test. E) Kaplan Meier Curves of cumulative incidence of MACE or CV death for each quartile of monocyte blood levels (10e9/L), in the Surdiagene cohort of 757 T2D individuals in primary prevention. F) Distribution in percentage of MACE-defined high (Q4+Q3) or low (Q1+Q2) CV risk quartiles of monocytes per cluster in 266 T2D participants (Population 2). CAC-score = coronary artery calcium score, MACE = major adverse cardiovascular event. G) Box plot of monocyte subtypes flow cytometry percentages per MACE-defined monocyte quartile in 266 T2D participants (Population 2), Kruskal-Wallis test with post-hoc comparisons.

Figure 3: Matrix Metalloproteinase 25 expression is associated with high CV risk and monocyte differentiation. A) Barplot of MMP25 protein expression at the surface of circulating CD 16+ (intermediate and non-classical, left) or CD 16- classical (right) monocytes after 6 hours of incubation in the serum of 6 low or 6 high CV risk T2D patients. Kruskal- Wallis comparisons *: p < 0.05. B) Immunohistochemistry of CD68 monocyte and macrophage marker (left panel), and MMP-25 (middle panel) in the atheromatous plaque, in 3 independent patients. The right panel represents the merged image of both stainings. L: Localization. MFI: Mean Fluorescent Intensity. EXAMPLE:

Material & Methods

Patient recruitment

We included patients from the AngioSafe-2 cohort (ClinicalTrials.gov Identifier: NCT02671864). The overall study aims to include 7200 patients who will be followed-up at 3 years to evaluate Diabetic Retinopathy presence, incidence or progression according to the International classification of Diabetic Retinopathy. The study was approved by local institutions and ethical committees (CPP He de France V number 15070) and was conducted in accordance with the principles of the Declaration of Helsinki. All patients provided informed consent.

To answer our ancillary question, we focused on patients recruited in two out of three AngioSafe-2 cohort centers (Hopital Lariboisiere, and Groupe Hospitalier Bichat - Claude Bernard, both AP-HP (Paris, France)), and who underwent a coronary calcium score and hemogram on the day of their inclusion.

For carotid plaque analysis, the study protocol was reviewed and approved by the regional Ethics Committee (Comite de Protection des Personnes Est, Dijon, France CPP Estill, CHRU Nancy, N° 2017-A00022-51) and recorded on ClinicalTrials.gov (clinical registration number: NCT03202823). Patients enrolled in the present study were admitted to the Department of Cardiovascular Surgery at the University Hospital of Dijon (Bourgogne- Franche-Comte, France) for the surgical treatment of an atheromatous internal carotid artery (ICA) stenosis. According to the trial category (Interventional research that does not involve drugs or non-CE-marked medical devices and involves only minimal risks and constraints for the patient) and the ethics committee, all patients received a written information note on the study. Oral consent was obtained from the patient and a certificate of the patient's oral consent was signed by the investigator physician and countersigned by the patient, according to the French law in this type of trial.

We used the data from the prospective T2D monocenter SURDIAGENE cohort to validate Patients were recruited in a single center (Poitiers University Hospital, France) from 2002 to 2012 and prospectively followed until 2015²⁵. Of 1,468 participants of the cohort, 1042 with WBC available within 6-month from enrollment were included in the current study. The study design was approved by the local ethics committee (CPP Quest III) and written informed consent was obtained from all participants.

CAC-score Coronary artery calcium (CAC) score is a widely used predictor of CV event risk in asymptomatic patients. The description of this test and the precise calculations to obtain the CAC-score was first described in 1990- 1991^26,27. To this day, it is still used as a good predictor of CV events in the general and diabetic populations. We stratified the 672 T2D participants according the CV risk assessed by CAC-score: very low with CAC-score = 0 (CVO group), low with CAC-score between 1 and 100 (CV1 group), intermediate with CAC- score between 101 and 400 (CV2 group), and high with CAC-score greater than 400 (CV3 group).

Experimental Design

We included 672 participants with T2D that underwent a CAC-score test, and measured their circulating monocyte levels by hemogram. Blood was also drawn to perform immunophenotyping of the peripheral blood mononuclear cells (PBMCs) by flow cytometry (n = 266) and analyze (among others) monocyte subtypes. We also studied the circulating blood metabolome of 86 patients with T2D to identify blood metabolites linked to CV risk increase. Finally, we analyzed the transcriptome of 78 males with T2D to associate a specific gene expression profile with CV risk increase (Supplementary material online clinical Table 1).

Study outcomes for replication study

The primary endpoint was a composite of CV death, non-fatal myocardial infarction (MI) and non-fatal stroke (major adverse CV events [MACE])²⁸. Vital status and CV endpoints were determined from participants’ hospital records, interviews with their general practitioners and inquiry to the French National Death Registry. Each endpoint was reviewed by an independent adjudication committee according to the international definitions of clinical outcomes. We defined history of CV disease by a history of MI and/or stroke.

Immunophenotyping by flow cytometry

Blood cells were obtained from 1 ml of venous blood, after red blood cell lysis, and resuspended into the FACS buffer as previously described²⁹. After 10 min incubation with an Fc blocker (120-000-422; Miltenyi), cells were stained for surface markers with the appropriate antibodies and a Live/Dead viability dye (L34957; Thermo Fisher Scientific) according to manufacturer's protocol. The following antibodies were used: anti-CD14 (MOP9), from BD Biosciences; anti-CD16 (3G8), and anti-MMP25 (141811) from Bio-techne R&D. Antibodies were diluted in the following ratios: 1 :200 for anti-CD14, anti-CD16, and 1 :50 for anti-MMP25. For dedicated analysis, Viability dye was diluted at 1: 1,000 following manufacturer's protocol. Acquisition was performed on a LSR Fortessa flow cytometer (BD Biosciences) and analyzed with FlowJo software (BD Biosciences). Immune lineage markers and antibodies were chosen based on the Immunological Genome Consortium guidelines. Gating strategies have been previously published^30-32. For analyses dedicated to the evaluations of mitochondrial function in monocytes, we used, in addition to the previously described CD45, CD16, CD14, and viability stainings, two MitoTracker® dyes: one dedicated to mitochondrial mass (MitoMass, M46750; Thermo Fisher Scientific, Dilution 1:100) and one dedicated to mitochondrial activity (MitoActivity, M7510;Thermo Fisher Scientific, Dilution 1:100).

Extracellular flux measurements

Real-time extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) were measured using Seahorse XFe96 extracellular flux analyser (Agilent). PBMC (800,000 cells per well) were seeded in an XFe96 cell culture plate pre-treated with CellTak (Corning). Cells were incubated in Seahorse XF base medium supplemented with either 2 mM L- glutamine, 10 mM glucose and 1 mM sodium pyruvate (pH = 7.4) for mitochondrial stress test or only 2 mM L-glutamine (pH = 7.4) for glycolysis stress test, for 1 h at 37 °C in a non-CO2 incubator. ECAR and OCR were measured in response to injections of either glucose (10 mM), oligomycin (1 pM) and 2-deoxyglucose (2-DG) (50 mM) for glycolysis stress test or oligomycin (1 pM), carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP) (1 pM) and rotenone/antimycin A (0.5 pM) for mitochondrial stress test. Three measurements were made under basal conditions and after each drug injection. Each measurement cycle had the following time parameters: ‘mix’ 3 min, ‘wait’ 2 min, ‘measure’ 3 min.

Immunohistochemistry

Carotid endarterectomy (CEA) was performed within the carotid bulb, with removal of the entire plaque (n=9). The plaque samples were fixed in neutral buffered formalin, dehydrated in graded alcohols, cleared in xylene and embedded in paraffin. 5 pm thick slices were deposited on a slide for immunohistochemical analysis. For multiple labeling experiments, tissue sections were incubated in 3% hydrogen peroxide for 15 min and in 3% bovine serum albumin for 20 min before adding the primary antibody, followed by Polymer-HRP detection system (Leica, Novolink Polymer Detection Systems), and chromogenic revelation was performed using AEC (Vector, SK-4200). Tissue sections were then mounted with aqueous mounting medium (Aquatex®, Merck #108562), and scanned for digital imaging. After scanning, slide coverslips were directly removed in water, and tissue sections were unstained in ethanol. Then, the slides were subjected to the next round of staining with the next primary antibody. Individual images were converted to 8-bit grayscale files with ImageJ software and merged to RGB files. The following antibodies were used: mouse monoclonal anti-CD68 antibody (1:200, Invitrogen, Cat#14-0688-82), rabbit polyclonal anti-MMP25 antibody (1:200, Thermo Fisher, # PA5-90790.) Secondary antibodies were goat anti-mouse CF Dye 568 (1:500, Biotium, #20100) or Impress HRP -horse anti-rabbit (Vector Labs, #MP7401, ready to use).

Blood Metabolome

For 86 participants with T2D, 1.5mL of whole blood was centrifuged 5 minutes at 1800 rpm at room temperature, and supernatant plasma was collected and stored at -80°C. Metabolic extracts obtained after methanol-assisted protein precipitation were analyzed by liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) using a combination of 2 complementary chromatographic methods and Exactive/Q-Exactive instruments, following previously published procedures^33,34. Data processing and statistical analysis were performed using the Workflow4Metabolomics (W4M) platform³⁵. Among 5,653 quantified metabolites, annotation of 253 metabolite features was realized using an internal spectral database³⁴ (Supplementary Data Table 1). For analysis between high and low cardiovascular risk, non-parametric Mann-Whitney test p-values were calculated for all metabolites, and Benjamini & Hochberg correction was applied to adjust p-values. Metabolites with both adjusted p-value <= 0.05 and annotation were selected (n=72 fingerprintings, corresponding to 67 metabolites) and heatmap was plotted after annotation confirmation (n=63 metabolites).

CD14⁺ Monocyte Immunoselection and RNA extraction

20mL of whole blood of 78 males with T2D was drawn and PBMCs were isolated using Ficoll Paque+. CD14⁺ circulating monocytes were immunoselected using anti-human CD14 Milteny beads. A minimum of 2 million cells were lysed with RLT plus buffer and 3% 0- mercaptoethanol for subsequent RNA extraction using the RNeasy Plus extraction kit (Qiagen), following user manual instructions.

PBMC culture

Whole blood samples from random patients were obtained from the French Blood Establishment (EFS). The whole blood was then transferred into UniSep tubes and centrifuged for 20 minutes at 1000g and 20°C to obtain the peripheral blood mononuclear cell (PBMC) ring. Subsequently, they were divided into two groups: one for extracellular flux measurements and the other for flow cytometry. For flow cytometry, PBMCs were cultured in RPMI medium supplemented with 30% serum, 1% PS, and 1% AANE. PBMCs cultures were incubated for 6h with serum from diabetic patients with low (n=6, cluster 1) or high (n=6, cluster 2) cardiovascular risk. After the exposure period, PBMCs were collected, washed, and subjected to the flow cytometry protocol as described above. For Extracellular flux measurements, PBMCs were directly cultured in standard Seahorse medium in Seahorse plates. The same culture conditions used in flow cytometry were applied to these PBMCs, and the Seahorse protocol was subsequently followed.

RNA sequencing

78 RNA-seq samples were sequenced in 2 batches. For the first batch of 48 participants, libraries were prepared following Tru-seq mRNA protocol, and single-end sequencing was performed on a Nextseq500 sequencer (Illumina) at Ecole Normale Superieure Genomic Platform (Paris, France). For other samples, libraries were prepared following the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina, and paired-end (2x75) strand specific sequencing was performed on Nextseq500 sequencer (Illumina) in two batches at the UVSQ Genomic Platform (Montigny-le-Bretonneux, France). FASTQ files were pre-processed using fastp vO. 19. 11. Reads were mapped to the GRCh38 primary human genome assembly version 38 with STAR v2.7³⁶. Genes with less than 5 raw counts in more than half of participants were removed. Reads were assigned to Gencode version 32 features using featureCounts from subread-1.6.4 package³⁷. Normalization and differential gene expression (DGE) analyses were performed with R package DESeq2 vl.32.0, including batch in the design formula, and Benjamini -Hochberg correction was applied with threshold for significance set at adjusted p- values < 0.05.

WGCNA

Weighted gene co-expression network analysis (WGCNA) was performed using the R package WGCNA vl .70-3. Only the most expressed features were selected, 10,717 for RNA- seq analysis and 3,400 for metabolome. RNA-seq analysis was driven on batch-corrected and variance-stabilizing transformed expression data from all available samples. Best soft- thresholds were picked by visual inspection of the pickSoftTreshold function result (12 for RNA-seq and 3 for metabolome analysis). Module eigengenes and clinical traits were correlated with Spearman correlation, and p-values less than 0.05 were retained as significant.

Pathway enrichment analysis

For all the pathway overrepresentation analyses (ORA) on transcriptome data, we used the online tool metascape (https://metascape.org), and selected the KEGG³⁸, GO³⁹ and wikipathways⁴⁰ gene sets as references. We considered the pathways to be overrepresented when the nominal p-value was below 0.01. We performed multiple gene set enrichment analyses (GSEA) with the GSEA tool (https://www. sea-msi db.org/gsea/index.jsp), and selected the KEGG gene set as reference. We used all the 18,815 expressed genes of our cohort, ordered by increasing adjusted p-value signed with the fold change (FC) sign of the DGE analysis. Pathways with False Discovery Rate (FDR) value below 0.05 and Normalized Enrichment Score (NES) below -1.5 or above +1.5 were considered as significantly enriched. For metabolome enrichment pathways analyses, we used MetaboAnalyst online tools (www.metaboanalyst.ca), Functional Analysis module for group comparison from complete peak list including retention time information, and Enrichment Analysis module for WGCNA modules analyses.

Statistical analyses

All statistical analyses and figures were done using the R software (version R 4.1.0). Non-parametric tests were used to compare continuous clinical features between each CV risk group (Kruskal -Wallis test for multiple groups and Wilcoxon test for two groups comparison), while categorical clinical feature distributions were compared with Pearson’s Chi squared tests. The time to event was plotted by the Kaplan-Meier method curves according to quartiles of monocyte counts and compared with the log-rank test. Risk-prediction established by Cox proportional hazard models were used to analyze the effect on MACE after adjustment for age, sex, NT -proBNP levels, eGFR, uACR, and HbAlc. The HR and 95% CI are presented. Figures were plotted with R packages ggplot2 v3.3.5⁴¹ and ComplexHeatmap v2.8.0⁴². Cluster analysis was done on classical, non-classical and intermediate monocyte levels expressed in percentage of CD45⁺ cells, after scaling to obtain a mean of 0 and a SD of 1 for each variable. Optimal cluster number was determined by Silhouette method (fviz_nbclust function from factoextra package) and good quality of clustering was checked at the first step of TwoSteps Cluster with SPSS version 26, then kmeans function (nstart = 500, iter.max = 500) in R version 4.1.0 was used to do k-means clustering with a k value of 3. Medians are indicated with interquartiles.

Data Availability

RNA-Sequencing data are available from the NCBI Gene Expression Omnibus with GEO accession number GSE201087.

Results

Increased classical monocyte frequency identifies high CV risk individuals amongst people with T2D.

Our first aim was to evaluate the relationship between monocyte count and CV risk quantified by coronary artery calcium (CAC) score, a clinical test that reflects coronary calcification, in 672 participants with T2D from the angiosafe-2 cohort. In this cohort, 60% were males, the median age was 61 years and body mass index (BMI) 28.4 kg/m², with an average diabetes duration of 13 years and a mean HbAlc of 7.6% (data not shown). Correlative analyses revealed that CAC-score distribution was positively correlated with monocyte and leukocyte counts (Spearman correlation coefficient rho = 0.16, p-value < 0.001), in addition to age and diabetes duration (Figure 1A). Correlation between CAC-score and monocyte counts was near-significant when age was taken into account (Spearman’s coefficient partial correlation controlling p-value = 0.057). Stratification of participants with T2D into 4 groups ranging from CVO to CV3 based on their CAC score (data not shown) confirmed the gradual increase of both monocytes (p-value = 2.3e-03) and leukocytes (p-value = 1.0e-02) when CAC- score increases (Figure IB). To confirm this link between monocytes and CV risk among people with T2D, we compared the blood leukocyte and monocyte counts between 757 participants with T2D of the SURDIAGENE cohort (data not shown) in either primary or secondary CV prevention. Interestingly, we observed significantly higher counts of both circulating monocytes and leukocytes compared to the participants with T2D in the primary prevention group (Figure 1C). Taken together, our human observations suggest that monocyte counts could be a CV risk marker in populations with T2D. Blood monocytes are composed of 3 subtypes based on variable surface marker expression: classical (CD45⁺ CD14⁺⁺ CD16'), intermediate (CD45⁺ CD14⁺⁺ CD16⁺) and non-classical (CD45⁺ CD14⁺ CD16⁺). To evaluate the monocyte subtype distribution related to CV risk, we performed blood monocyte subtyping by flow cytometry in 266 participants with T2D of the AngioSafe-2 cohort (Population 2, data not shown). We found a significant increase (p = 2.8e-02) of classical monocytes in higher CV risk groups (Figure ID) compared to the lowest CV risk group (CVO). In contrast, we did not observe similar changes in the frequencies of intermediate (p = 8.4e-01) or non-classical (p = 3.7e-02) monocytes, between CV risk groups (Figure ID).

Blood monocytes count and classical monocyte frequency can predict CV event in T2D.

As we observed a difference in monocyte subtypes distribution between groups at different CV risk categories, we performed an unsupervised K-means clustering on the frequency of monocyte subpopulations obtained by flow cytometry of the 266 participants (Figure 2A). Our approach identified 3 clusters of participants (Figure 2A): 147 participants (55%) in the main cluster (clust.1), 87 participants (33%) in clust.2, characterized by a higher percentage of classical monocytes (Figure 2B), and 32 participants (12%) in clust.3, characterized by a higher percentage of intermediate and non-classical monocytes (Figure 2B). Our unsupervised analyses revealed that participants from the cluster 2 are characterized by i) a higher proportion of classical monocytes (Figures 2B, and 2C), ii) higher counts of blood monocytes (median = 0.6xl0e9/mL, Kruskal -Wallis p-value < 0.001, Figure 2D) and iii) a higher CAC-score (median = 97, Kruskal-Wallis p-value = 0.004, Figure 2C) iv) with a greater proportion of CV3 individuals (Figure 2C). Importantly, CAC-score differences persisted after age and sex adjustments (p-value = 0.048 after sex and age adjustments). We provide evidence, using complementary approaches, that blood monocyte count and frequency of classical monocytes are associated with the increased risk of CV events in a T2D population.

To assess the association between circulating monocyte count and MACE amongst individuals with T2D, 757 primary CV prevention participants with T2D of the SURDIAGENE cohort (data not shown) were divided into quartiles based on baseline monocyte counts (Quartile 1 [0-0.4[, to Quartile 2 [0.4-0.5[, to Quartile 3 [0.5-0.6[ and Quartile 4 [0.6-1.2] per 10⁹/L). We then compared the cumulative incidence of both MACE (n=269, 3.55 per 100 person year) and CV death (n=196, 2.55 per 100 person year) between quartiles. Interestingly, participants with monocyte count above the median 0.5 10⁹/L (i.e Q3 and Q4), suffered significantly more MACE (p log-rank < 0.001) and CV deaths (p log-rank < 0.001) than individuals below the median (i.e QI and Q2), as is shown in the Kaplan Meier curves (Figure 2E). Furthermore, CV risk factor-adjusted (age, sex, NT-proBNP levels, eGFR, uACR, and HbAlc) Cox analysis confirmed that a monocyte count increase was associated with a statistically significant 2.8-fold increase of MACE and a 5.1-fold increased risk of CV deaths (p-value = 0.03 and p-value = 0.003, respectively). Of note, we observed no such association between leukocyte blood levels and MACE or CV death (p log-rank = 0.0112 and 0.166, respectively; data not shown). Interestingly, in the cross-sectional study, 71% of participants belonging to cluster 2 have monocytes above the median 0.5 x 10⁹/L (qHigh=Q3 and Q4), compared to only 40% of the cluster 1 participants (Figure 2F). Applying quartiles stratification in the Angiosafe-2 cohort (n=266, Population 2) revealed that people belonging to Q4 [0.6-1.2 per 10⁹/L] were enriched in classical monocytes compared to other quartiles while no differences were observed for non-classical and intermediate monocytes (Figure 2G).

TCA cycle and mitochondrial oxidative pathways are deregulated in classical monocytes of participants with T2D and high CV risk.

To functionally characterize monocytes and identify the pathways that may influence monocyte subset proportions previously linked with CV risk heterogeneity, we analyzed CD14⁺ monocyte transcriptomes by RNA sequencing (RNA-seq) in a subset of 78 males with T2D (data not shown). First, we performed a weighted gene correlation network analysis (WGCNA, data not shown) to identify modules of coregulated genes associated with clusters, monocyte count, monocyte subtypes frequencies, CAC-score and CV risk groups. Our approach revealed 12 co-expressed gene modules (data not shown). Gene expression of each transcriptome WGCNA module (tWGCNA) was summarized by an eigengene, a surrogate for all genes belonging to the module for each participant (data not shown). Two main modules (tWGCNA- 1 with 751 co-expressed genes and tWGCNA-2 with 295 co-expressed genes) were significantly associated with cluster type (p-value = 0.001 and 0.01 respectively, data not shown). Interestingly, tWGCNA-1 was also significantly associated with classical monocyte frequencies (p-value = 0.05) and CV3 or CV0 risk group (p = 0.01, data not shown). Functional over-representation analysis (ORA) revealed that the gene network belonging to this tWGCNA- 1 module is involved in the TCA cycle (p-value < le-6) and mitochondrial OxPhos pathway (p-value < le-6, data not shown). To associate a specific monocyte transcriptome signature to high CV risk T2D participants from clust.2, we compared the CD14⁺ monocyte transcriptome of 19 participants from clust.l (cluster with the lowest SCC median) and 27 participants from clust.2, of the same sex (only males) and similar age (data not shown). This analysis revealed 2,529 differentially expressed genes (DEG) (adjusted p-value < 0.05), with 1,343 up-regulated and 1,186 down-regulated genes in clust.2 (data not shown). Pathway enrichment analysis of the DEG displayed significant enrichment of genes involved in epigenetic pathways such as Histone Modification (adjusted-p = 1.4e-12), and Chromatin Organization (adjusted-p < l.Oe- 12) (data not shown). Noticeably, both the TCA cycle and OxPhos pathways were also enriched (gene set enrichment analysis: adjusted-p = 2.06e-14 and 8.37e-l l respectively, (data not shown), and mainly composed of downregulated genes in cluster 2 (data not shown). To identify genes specifically linked to increased coronary artery calcification, we performed differential gene expression (DGE) analysis between groups with the lowest (CV0) and the highest (CV3) CAC-score. By comparing CV0 (n = 21) and CV3 (n = 16) groups (data not shown), 315 genes were significantly differentially expressed (adjusted-p < 0.05, data not shown), with 245 up- regulated and 70 down-regulated genes in the CV3 group. It should be noted that although participants belonging to cluster 2 have a higher CAC-score compared to the other clusters, only 21% are also part of CV3, thus the individuals from the clustering and the CAC-score analysis are not identical. Plasma membrane organization, Glycerolipid metabolism and Glycerophospholipid metabolism were the top over-represented pathways (data not shown). The enrichment of these lipid pathways was independent of the presence of dyslipidemia (glycerolipid p = 3.6e-04 and glycerophospholipid p = 3.1e-03 after adjustment). In addition, decreased expression of genes involved in the OxPhos pathway, glutathione metabolism, TCA cycle and fatty acid metabolism was observed in the highest (CV3) CV risk’s group according to CAC-score (data not shown). Of note, age adjustment confirms our observations (data not shown). Altogether, combination and integration of our different transcriptomic analyses revealed that deregulation of the cellular metabolism including unsaturated fatty acids, glutathione, TCA cycle and OxPhos pathways might strongly contribute to the phenotypic reprogramming of blood monocytes associated with CV risk (data not shown). Furthermore, it seems that a downregulation of these mitochondrial pathways in monocytes occurs when CAC- score increases and between clusters (data not shown) and affects 4 out of 5 of the mitochondrial complexes impacting a subset of TCA genes (data not shown).

To confirm these transcriptomic results, we compared the PBMC energy metabolism through Seahorse experiments of 34 participants with T2D from CVO or CV3 groups. We found that CV3 individuals (n=26) had significant lower oxygen consumption rate compared to CVO individuals (n=8) with T2D (data not shown). Levels of basal and maximal mitochondrial respiration as well as ATP production are reduced in PBMCs of participants with T2D from CV3 group suggesting a decreased mitochondrial function (data not shown). Quantification of extracellular acidification rate (ECAR) revealed that the glycolytic capacity (and reserve) is also significantly reduced in PBMCs of participants with T2D from CV3 group (data not shown). Integration of basal and maximal OCR and ECAR levels revealed an overall decreased energetic profile of PBMCs of participants with T2D from CV3 group compared to CVO group (data not shown). Additionally, we measured the mitochondrial mass in the blood monocytes using mitotracker assay in the same population. We found a decrease in the mitochondrial mass of CD14⁺ monocytes in participants with T2D belonging to the CV3 high CV risk group (data not shown). We also observed a significant decrease of mitochondrial mass and activity in both total blood monocytes and classical monocytes of high CV risk participants (high level of monocytes, high CAC-score and belonging to cluster 2) with T2D compared to low CV risk participants (low level of blood monocytes, low CAC-score and belonging to cluster 1) with T2D (data not shown). Interestingly, plasma metabolome of participants with T2D (data not shown) from low CV risk and high CV risk were characterized by an enrichment of metabolic pathways such as the TCA cycle and multiple amino acids including tryptophan, alanine, aspartate and glutamate (data not shown). These results may reflect a contribution of amino acid metabolism in the monocyte phenotype associated with high CV risk increase. Hence, the monocyte transcriptome and more specifically the mitochondrial metabolism genes downregulated in high CV risk group (CV3) could be a consequence of blood metabolome rewiring (data not shown). To test this hypothesis, we incubated the PBMCs of a healthy donor with serum of either high CV risk (high level of monocytes, high CAC-score (CV3 group) and belonging to cluster 2, n = 6) or low CV risk (low level of blood monocytes, low CAC-score (CVO group) and belonging to cluster 1, n = 6) participants with T2D for 6 hours and then measured the frequency of classical monocytes and, mitochondrial mass and activity of these circulating monocytes. In line with our previous results, we found that after incubation in the serum of high CV risk individuals the percentage of classical monocytes significantly increased compared to the monocytes incubated with the serum of low CV risk participants (data not shown). Interestingly, this increase of monocyte frequency is associated with a decrease of mitochondrial mass, as observed in monocytes of individuals with T2D at high CV risk (data not shown). However, the mitochondrial activity increased which might be an initial transient compensation for compromised mitochondrial function. Altogether, functional characterization of monocytes from participants with T2D at high CV risk revealed a decrease of mitochondrial mass and activity which might be provoked by blood metabolites.

MMP25 expression levels were positively correlated to CV risk and monocyte activation.

As a significant number of genes involved in cell surface remodeling, immune functions and signaling were overexpressed in monocytes of participants with T2D at high CV risk, we aimed to find a monocyte cell surface marker linked to CV risk. To do so, we identified the common differentially expressed genes between the 3 CV risk comparisons (CV3 vs CVO, CV3 vs CV1, and CV3 vs CVO, data not shown), that remained significant upon age adjustment (data not shown), and were also up-regulated in high CV risk cluster 2 (data not shown). Among them, the most differentially expressed gene after adjusting for age VJ& -1MP25 (encoding the matrix metallopeptidase (MMP) enzyme 25) (log2FC = 1.85, adjusted-p = 7.46e-03), that was also significantly positively correlated to CAC-score as a continuous trait (data not shown) MMP -25, at the cell surface or secreted, degrades extracellular matrix components and promotes adhesion of cells to endothelium. MMP25 expression was also significantly differentially expressed between CV3 and CVO prior to age adjustment (log2FC = 1.74, data not shown), between CV3 and CV1 groups (log2FC = 1.2, data not shown), between CV3 and CV2 groups (log2FC = 1.4, data not shown). To confirm this at the protein level, we performed flow cytometry analysis on blood cells of an independent cohort of participants with T2D (n=40 ; data not shown). MMP -25 had a 2.5-fold higher expression in monocytes compared to other CD45⁺ cells (mean = 838.6 and 296.6 respectively, data not shown), and in CD14⁺⁺CD16⁺ intermediate and CD14⁺CD16⁺ non-classical monocytes compared to CD14⁺⁺CD16- classical monocytes (data not shown). MMP-25 gene and protein expressions were positively correlated to CD 16 gene and protein expressions in blood monocytes (data not shown). Moreover, 6h incubation of PBMCs from a healthy donor with serum from individuals with T2D at high CV risk (n=6) revealed that CD 16+ monocytes had a significantly higher MMP25 protein expression compared to monocytes of PBMCs incubated with serum from low CV risk T2D individuals (n=6) (Figure 3A). To evaluate the relevance of MMP25 expression in the context of human atherosclerosis, we analyzed MMP25 expression in carotid plaques from 9 individuals by immunohistochemistry. MMP25 was detected in situ, in macrophage-enriched regions, and near necrotic areas. Furthermore, double-staining experiments indicated that MMP25 was mainly expressed in CD68+ cells (Figure 3B). Altogether, our results suggest that MMP25 is a marker of monocyte activation and phenotypic switch towards non-classical monocytes, in the context of CVD, as it was identified in CD 14+ monocytes of high CV participants, induced by the serum of high CV risk participants, and found in macrophage-rich regions of atherosclerotic plaques.

REFERENCES:

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.

1. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death [1] [visit date: August 2nd 2023]

2. Moore KJ, Sheedy FJ, Fisher EA. Macrophages in atherosclerosis: a dynamic balance. Nat Rev Immunol. 2013 Oct; 13(10):709-21. doi: 10.1038/nri3520. Epub 2013 Sep 2.

3. Tacke F, Alvarez D, Kaplan TJ, Jakubzick C, Spanbroek R, Llodra J, Garin A, Liu J, Mack M, van Rooijen N, Lira SA, Habenicht AJ, Randolph GJ. Monocyte subsets differentially employ CCR2, CCR5, and CX3CR1 to accumulate within atherosclerotic plaques. J Clin Invest. 2007 Jan;117(l):185-94. doi: 10.1172/JCI28549.

4. Weber C, Noels H. Atherosclerosis: current pathogenesis and therapeutic options. Nat Med. 2011 Nov 7;17(11): 1410-22. doi: 10.1038/nm.2538.

5. Ziegler-Heitbrock L, Ancuta P, Crowe S, Dalod M, Grau V, Hart DN, Leenen PJ, Liu YJ, MacPherson G, Randolph GJ, Scherberich J, Schmitz J, Shortman K, Sozzani S, Strobl H, Zembala M, Austyn JM, Lutz MB. Nomenclature of monocytes and dendritic cells in blood. Blood. 2010 Oct 21;116(16):e74-80. doi: 10.1182/blood-2010-02-258558. Epub 2010 Jul 13.

6. van Diepen JA, Thiem K, Stienstra R, Riksen NP, Tack CJ, Netea MG. Diabetes propels the risk for cardiovascular disease: sweet monocytes becoming aggressive? Cell Mol Life Sci. 2016 Dec;73(24):4675-4684. doi: 10.1007/s00018-016-2316-9. Epub 2016 Jul 28.

7. Gu K, Cowie CC, Harris ML Diabetes and decline in heart disease mortality in US adults. JAMA. 1999 Apr 14;281(14): 1291-7. doi: 10.1001/jama.28L 14.1291.

8. Martin-Timon I, Sevillano-Collantes C, Segura-Galindo A, Del Canizo-Gomez FJ. Type 2 diabetes and cardiovascular disease: Have all risk factors the same strength? World J Diabetes. 2014 Aug 15;5(4):444-70. doi: 10.4239/wjd.v5.i4.444. 9. Rafieian-Kopaei M, Setorki M, Doudi M, Baradaran A, Nasri H. Atherosclerosis: process, indicators, risk factors and new hopes. Int J Prev Med. 2014 Aug; 5 (8): 927-46.

10. Lei MH, Wu YL, Chung SL, Chen CC, Chen WC, Hsu YC. Coronary Artery Calcium Score Predicts Long-Term Cardiovascular Outcomes in Asymptomatic Patients with Type 2 Diabetes. J Atheroscler Thromb. 2021 Oct l;28(10): 1052-1062. doi: 10.555 l/jat.59386. Epub 2020 Nov 7.

U . S, Zhao Y, Budoff M, Nasir K, Blumenthal RS, Bertoni AG, Wong ND. Coronary Artery Calcium Score for Long-term Risk Classification in Individuals With Type 2 Diabetes and Metabolic Syndrome From the Multi-Ethnic Study of Atherosclerosis. JAMA Cardiol. 2017 Dec l;2(12):1332-1340. doi: 10.1001/jamacardio.2017.4191.

12. Dziopa K, Asselbergs FW, Gratton J, Chaturvedi N, Schmidt AF. Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings. Diabetologia. 2022 Apr;65(4):644-656. doi: 10.1007/s00125-021-05640-y. Epub 2022 Jan 15.

13. Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J. 1991 Jan;121(l Pt 2):293-8. doi: 10.1016/0002-8703(91)90861-b.

14. Clarke PM, Gray AM, Briggs A, Farmer AJ, Fenn P, Stevens RJ, Matthews DR, Stratton IM, Holman RR; UK Prospective Diabetes Study (UKDPS) Group. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia. 2004 Oct;47(10): 1747-59. doi: 10.1007/s00125-004-1527-z. Epub 2004 Oct 27

15. Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB Sr, Gibbons R, Greenland P, Lackland DT, Levy D, O'Donnell CJ, Robinson JG, Schwartz JS, Shero ST, Smith SC Jr, Sorlie P, Stone NJ, Wilson PWF. 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 Jul 1;63(25 Pt B):2935-2959. doi: 10.1016/j.jacc.2013.11.005. Epub 2013 Nov 12. Erratum in: J Am Coll Cardiol. 2014 Jul 1;63(25 Pt B):3026.

16. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, Brindle P. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008 Jun 28;336(7659): 1475-82. doi: 10.1136/bmj.39609.449676.25. Epub 2008 Jun 23.

17. Basu S, Sussman JB, Berkowitz SA, Hayward RA, Yudkin JS. Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials. Lancet Diabetes Endocrinol. 2017 Oct;5(10):788-798. doi: 10.1016/S2213-8587(17)30221-8. Epub 2017 Aug 10.

18. Morris AD, Boyle DI, MacAlpine R, Emslie-Smith A, Jung RT, Newton RW, MacDonald TM. The diabetes audit and research in Tayside Scotland (DARTS) study: electronic record linkage to create a diabetes register. DARTS/MEMO Collaboration. BMJ. 1997 Aug 30;315(7107):524-8. doi: 10.1136/bmj.315.7107.524.

19. Clarke PM, Gray AM, Briggs A, Farmer AJ, Fenn P, Stevens RJ, Matthews DR, Stratton IM, Holman RR; UK Prospective Diabetes Study (UKDPS) Group. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia. 2004 Oct;47(10): 1747-59. doi: 10.1007/s00125-004-1527-z. Epub 2004 Oct 27.

20. Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM, Kronmal RA, Kuller LH, Manolio TA, Mittelmark MB, Newman A, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol. 1991 Feb;l(3):263-76. doi: 10.1016/1047-2797(91)90005-w.

21. Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nat Rev Immunol. 2011 Feb;l l(2):98-I07. doi: 10.1038/nri2925. Epub 2011 Jan 14.

22. Alexandraki K, Piperi C, Kalofoutis C, Singh J, Alaveras A, Kalofoutis A. Inflammatory process in type 2 diabetes: The role of cytokines. Ann N Y Acad Sci. 2006 Nov;1084:89-117. doi: 10.1196/annals.l372.039.

23. Groh L, Keating ST, Joosten LAB, Netea MG, Riksen NP. Monocyte and macrophage immunometabolism in atherosclerosis. Semin Immunopathol. 2018 Feb;40(2):203-214. doi: 10.1007/s00281-017-0656-7. Epub 2017 Oct 2.

24. Tabas I, Lichtman AH. Monocyte-Macrophages and T Cells in Atherosclerosis. Immunity. 2017 Oct 17;47(4):621-634. doi: 10.1016/j.immuni.2017.09.008.

25. Hadjadj S, Fumeron F, Roussel R, Saulnier PJ, Gallois Y, Ankotche A, Travert F, Abi Khalil C, Miot A, Alhenc-Gelas F, Lievre M, Marre M; DIAB HYCAR Study Group; DIAB2NEPHROGENE Study Group; SURDIAGENE Study Group. Prognostic value of the insertion/deletion polymorphism of the ACE gene in type 2 diabetic subjects: results from the Non-insulin-dependent Diabetes, Hypertension, Microalbuminuria or Proteinuria, Cardiovascular Events, and Ramipril (DIABHYCAR), Diabete de type 2, Nephropathie et Genetique (DIAB2NEPHROGENE), and Survie, Diabete de type 2 et Genetique (SURDIAGENE) studies. Diabetes Care. 2008 Sep;31(9): 1847-52. doi: 10.2337/dc07-2079. Epub 2008 Jun 3. PMID: 18523145; PMCID: PMC2518357. 26. Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990; 15(4): 827-32. doi: 10.1016/0735-1097(90)90282-t.

27. Janowitz WR, Agatston AS, Viamonte M. Comparison of serial quantitative evaluation of calcified coronary artery plaque by ultrafast computed tomography in persons with and without obstructive coronary artery disease. Am J Cardiol. 1991 Jul 1;68(1): 1-6. doi: 10.1016/0002-9149(91)90700-u.

28. Hicks KA, Tcheng JE, Bozkurt B, Chaitman BR, Cutlip DE, Farb A, Fonarow GC, Jacobs JP, Jaff MR, Lichtman JH, Limacher MC, Mahaffey KW, Mehran R, Nissen SE, Smith EE, Targum SL. 2014 ACC/AHA Key Data Elements and Definitions for Cardiovascular Endpoint Events in Clinical Trials: A Report of the American College of Cardiol ogy/Ameri can Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Cardiovascular Endpoints Data Standards). J Am Coll Cardiol. 2015 Jul 28;66(4):403-69. doi: 10.1016/j.jacc.2014.12.018. Epub 2014 Dec 29. Erratum in: J Am Coll Cardiol. 2015 Aug 25;66(8):982. PMID: 25553722.

29. Julia J-B, Ballaire R, Ejlalmanesh T, Gautier J-F, Venteclef N, Alzaid F. Isolation and Analysis of Human Monocytes and Adipose Tissue Macrophages. Methods Mol Biol Clifton NJ. 2019;1951:33-48. doi: 10.1007/978-1-4939-9130-3 _3.

30. Heng TSP, Painter MW, Immunological Genome Project Consortium. The Immunological Genome Project: networks of gene expression in immune cells. Nat Immunol. 2008 Oct;9(10): 1091-4. doi: 10.1038/ni 1008- 1091.

31. Autissier P, Soulas C, Burdo TH, Williams KC. Evaluation of a 12-color flow cytometry panel to study lymphocyte, monocyte, and dendritic cell subsets in humans. Cytometry A. 2010;9999A:NA-NA. doi: 10.1002/cyto.a.20859.

32. Melzer S, Zachariae S, Bocsi J, Engel C, Loffler M, Tarnok A. Reference intervals for leukocyte subsets in adults: Results from a population-based study using 10-color flow cytometry. Cytometry B Clin Cytom. 2015 Aug;88(4):270-81. doi: 10.1002/cyto.b.21234. Epub 2015 Apr 29.

33. Moreau R, Claria J, Aguilar F, Fenaille F, Lozano JJ, Junot C, et al. Blood metabolomics uncovers inflammation-associated mitochondrial dysfunction as a potential mechanism underlying ACLF. J Hepatol. 2020 Apr;72(4):688-701. doi:

10.1016/j .jhep.2019.11.009. Epub 2019 Nov 25. Erratum in: J Hepatol. 2020 Jun;72(6): 1218- 1220. 34. Boudah S, Olivier M-F, Aros-Calt S, Oliveira L, Fenaille F, Tabet J-C, et al. Annotation of the human serum metabolome by coupling three liquid chromatography methods to high-resolution mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2014 Sep l;966:34-47. doi: 10.1016/j.jchromb.2014.04.025. Epub 2014 Apr 21.

35. Giacomoni F, Le Corguille G, Monsoor M, Landi M, Pericard P, Petera M, et al. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinforma Oxf Engl. 2015 May 1 ;31(9): 1493- 5. doi: 10.1093/bioinformatics/btu813. Epub 2014 Dec 19.

36. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinforma Oxf Engl. 2013 Jan 1 ;29(1): 15— 21. doi: 10.1093/bioinformatics/bts635. Epub 2012 Oct 25.

37. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinforma Oxf Engl. 2014 Apr l;30(7):923- 30. doi: 10.1093/bioinformatics/btt656. Epub 2013 Nov 13.

38. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000 Jan l;28(l):27-30. doi: 10.1093/nar/28.1.27.

39. Ashbumer M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000 May;25(l):25-9. doi: 10.1038/75556.

40. Martens M, Ammar A, Riutta A, Waagmeester A, Slenter DN, Hanspers K, et al. WikiPathways: connecting communities. Nucleic Acids Res. 2021 Jan 8;49(D1):D613-21. doi: 10.1093/nar/gkaal024.

41. Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer; 2016. 266 p.

42. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016 Sep 15;32(18):2847-9. doi: 10.1093/bioinformatics/btw313. Epub 2016 May 20.

43. Cardoso CRL, Leite NC, Salles GF. Importance of hematological parameters for micro- and macrovascular outcomes in patients with type 2 diabetes: the Rio de Janeiro type 2 diabetes cohort study. Cardiovasc Diabetol. 2021 Jul 6;20(l):133. doi: 10.1186/sl2933-021 - 01324-4. PMID: 34229668; PMCID: PMC8261940.

44. Choi SH, Kim JH, Lim S, Lim JY, Kim KW, Park KS, Jang HC. Monocyte count as a predictor of cardiovascular mortality in older Korean people. Age Ageing. 2017 May l;46(3):433-438. doi: 10.1093/ageing/afw226. PMID: 27932363. 45. Kato A, Takita T, Furuhashi M, Maruyama Y, Kumagai H, Hishida A. Blood monocyte count is a predictor of total and cardiovascular mortality in hemodialysis patients. Nephron Clin Pract. 2008;110(4):c235-43. doi: 10.1159/000167871. Epub 2008 Oct 31. PMID: 18974655.

46. Oh ES, You Z, Nowak KL, Jovanovich AJ. Association of Monocyte Count and Monocyte/Lymphocyte Ratio with the Risk of Cardiovascular Outcomes in Patients with CKD. Kidney360. 2022 Feb 3;3(4):657-665. doi: 10.34067/KID.0007922021. PMID: 35721602; PMCID: PMC9136887.

47. Home BD, Anderson JL, John JM, Weaver A, Bair TL, Jensen KR, Renlund DG, Muhlestein JB; Intermountain Heart Collaborative Study Group. Which white blood cell subtypes predict increased cardiovascular risk? J Am Coll Cardiol. 2005 May 17;45(10): 1638- 43. doi: 10.1016/j.jacc.2005.02.054. Epub 2005 Apr 25. PMID: 15893180.

48. Rahman MS, Murphy AJ, Woollard KJ. Effects of dyslipidaemia on monocyte production and function in cardiovascular disease. Nat Rev Cardiol. 2017 Jul;14(7):387-400. doi: 10.1038/nrcardio.2017.34. Epub 2017 Mar 16. PMID: 28300081.

49. Siedlinski M, Jozefczuk E, Xu X, Teumer A, Evangelou E, Schnabel RB, Welsh P, Maffia P, Erdmann J, Tomaszewski M, Caulfield MJ, Sattar N, Holmes MV, Guzik TJ. White Blood Cells and Blood Pressure: A Mendelian Randomization Study. Circulation. 2020 Apr 21;141(16):1307-1317. doi: 10.1161/CIRCULATIONAHA.119.045102. Epub 2020 Mar 9. PMID: 32148083; PMCID: PMC7176352.

50. Delgado GE, Kramer BK, Marz W, Hellstem P, Kleber ME, Leipe J. Immune Status and Mortality in Smokers, Ex-smokers, and Never-Smokers: The Ludwigshafen Risk and Cardiovascular Health Study. Nicotine Tob Res. 2021 Jun 8;23(7): 1191-1198. doi: 10.1093/ntr/ntab011. PMID: 33460442.

51. Lo SC, Lee WJ, Chen CY, Lee BC. Intermediate CD14++CD16+ monocyte predicts severe coronary stenosis and extensive plaque involvement in asymptomatic individuals. Int J Cardiovasc Imaging. 2017 Aug;33(8): 1223-1236. doi: 10.1007/sl0554-017-1097-z. Epub 2017 Feb 26. PMID: 28239800.

52. Cappellari R, D'Anna M, Bonora BM, Rigato M, Cignarella A, Avogaro A, Fadini GP. Shift of monocyte subsets along their continuum predicts cardiovascular outcomes. Atherosclerosis. 2017 Nov;266:95-102. doi: 10.1016/j. atherosclerosis.2017.09.032. Epub 2017 Sep 29. PMID: 29017104.

53. Rogacev KS, Cremers B, Zawada AM, Seiler S, Binder N, Ege P, GroBe-Dunker G, Heisel I, Hornof F, Jeken J, Rebling NM, Ulrich C, Scheller B, Bohm M, Fliser D, Heine GH. CD14++CD16+ monocytes independently predict cardiovascular events: a cohort study of 951 patients referred for elective coronary angiography. J Am Coll Cardiol. 2012 Oct 16;60(16): 1512-20. doi: 10.1016/j.jacc.2012.07.019. Epub 2012 Sep 19. PMID: 22999728.

54. Williams H, Mack CD, Li SCH, Fletcher JP, Medbury HJ. Nature versus Number: Monocytes in Cardiovascular Disease. Int J Mol Sci. 2021 Aug 24;22(17):9119. doi: 10.3390/ijms22179119. PMID: 34502027; PMCID: PMC8430468.

55. Nagareddy PR, Murphy AJ, Stirzaker RA, Hu Y, Yu S, Miller RG, Ramkhelawon B, Distel E, Westerterp M, Huang LS, Schmidt AM, Orchard TJ, Fisher EA, Tall AR, Goldberg IJ. Hyperglycemia promotes myelopoiesis and impairs the resolution of atherosclerosis. Cell Metab. 2013 May 7;17(5):695-708. doi: 10.1016/j.cmet.2013.04.001. PMID: 23663738; PMCID: PMC3992275.

56. Yvan-Charvet L, Pagler T, Gautier EL, Avagyan S, Siry RL, Han S, Welch CL, Wang N, Randolph GJ, Snoeck HW, Tall AR. ATP -binding cassette transporters and HDL suppress hematopoietic stem cell proliferation. Science. 2010 Jun 25;328(5986):1689-93. doi: 10.1126/science.1189731. Epub 2010 May 20. PMID: 20488992; PMCID: PMC3032591.

57. Vlacil AK, Schuett J, Schieffer B, Grote K. Variety matters: Diverse functions of monocyte subtypes in vascular inflammation and atherogenesis. Vascul Pharmacol. 2019 Feb;l 13:9-19. doi: 10.1016/j.vph.2018.12.002. Epub 2018 Dec 12. PMID: 30553027.

58. Zawada AM, Rogacev KS, Schirmer SH, Sester M, Bohm M, Fliser D, Heine GH. Monocyte heterogeneity in human cardiovascular disease. Immunobiology. 2012 Dec;217(12):1273-84. doi: 10.1016/j.imbio.2012.07.001. Epub 2012 Jul 25. PMID: 22898391.

59. Hilgendorf I, Swirski FK. Making a difference: monocyte heterogeneity in cardiovascular disease. Curr Atheroscler Rep. 2012 Oct; 14(5):450-9. doi: 10.1007/sl 1883-012- 0274-8. PMID: 22847772; PMCID: PMC3436972.

60. Tak T, Drylewicz J, Conemans L, de Boer RJ, Koenderman L, Borghans JAM, Tesselaar K. Circulatory and maturation kinetics of human monocyte subsets in vivo. Blood. 2017 Sep 21;130(12): 1474-1477. doi: 10.1182/blood-2017-03 -771261. Epub 2017 Jul 25. PMID: 28743715.

61. Patel AA, Zhang Y, Fullerton JN, Boelen L, Rongvaux A, Maini AA, Bigley V, Flavell RA, Gilroy DW, Asquith B, Macallan D, Yona S. The fate and lifespan of human monocyte subsets in steady state and systemic inflammation. J Exp Med. 2017 Jul 3;214(7): 1913-1923. doi: 10.1084/jem.20170355. Epub 2017 Jun 12. PMID: 28606987; PMCID: PMC5502436. 62. Quintero-Gonzalez DC, Munoz-Urbano M, Vasquez G. Mitochondria as a key player in systemic lupus erythematosus. Autoimmunity. 2022 Dec;55(8):497-505. doi: 10.1080/08916934.2022.2112181. Epub 2022 Aug 17. PMID: 35978536.

63. Becker YLC, Duvvuri B, Fortin PR, Lood C, Boilard E. The role of mitochondria in rheumatic diseases. Nat Rev Rheumatol. 2022 Nov;18(l l):621-640. doi: 10.1038/s41584-022- 00834-z. Epub 2022 Sep 29. PMID: 36175664.

64.. Huismans M, Van DoorenE, HolvoetP. Mitochondrial reactive oxygen species and risk of atherosclerosis. Curr Atheroscler Rep. 2012 Jun;14(3):264-76. doi: 10.1007/sl 1883- 012-0237-0. PMID: 22350585.

65. Cosentino F, Eto M, De Paolis P, van der Loo B, Bachschmid M, Ullrich V, Kouroedov A, Delli Gatti C, Joch H, Volpe M, Luscher TF. High glucose causes upregulation of cyclooxygenase-2 and alters prostanoid profile in human endothelial cells: role of protein kinase C and reactive oxygen species. Circulation. 2003 Feb 25;107(7):1017-23. doi: 10.1161/01. cir.0000051367.92927.07. PMID: 12600916.

66. Wang Y, Tabas I. Emerging roles of mitochondria ROS in atherosclerotic lesions: causation or association? J Atheroscler Thromb. 2014;21(5):381-90. doi: 10.555 l/jat.23929. Epub 2014 Apr 8. PMID: 24717761; PMCID: PMC4174275.

67. Victor VM, Apostolova N, Herance R, Hemandez-Mijares A, Rocha M. Oxidative stress and mitochondrial dysfunction in atherosclerosis: mitochondria-targeted antioxidants as potential therapy. Curr Med Chem. 2009;16(35):4654-67. doi: 10.2174/092986709789878265. PMID: 19903143.

68. Batty M, Bennett MR, Yu E. The Role of Oxidative Stress in Atherosclerosis. Cells. 2022 Nov 30;l l(23):3843. doi: 10.3390/cellsl 1233843. PMID: 36497101; PMCID: PMC9735601.

69. Li X, Fang P, Li Y, Kuo YM, Andrews AJ, Nanayakkara G, Johnson C, Fu H, Shan H, Du F, Hoffman NE, Yu D, Eguchi S, Madesh M, Koch WJ, Sun J, Jiang X, Wang H, Yang X. Mitochondrial Reactive Oxygen Species Mediate Lysophosphatidylcholine-Induced Endothelial Cell Activation. Arterioscler Thromb Vase Biol. 2016 Jun;36(6): 1090-100. doi: 10.1161/ATVBAHA.115.306964. Epub 2016 Apr 28. PMID: 27127201; PMCID: PMC4882253.

70. Naik E, Dixit VM. Mitochondrial reactive oxygen species drive proinflammatory cytokine production. J Exp Med. 2011 Mar 14;208(3):417-20. doi: 10.1084/jem.20110367. Epub 2011 Feb 28. PMID: 21357740; PMCID: PMC3058577. 71. Ciccarelli G, Conte S, Cimmino G, Maiorano P, Morri one A, Giordano A. Mitochondrial Dysfunction: The Hidden Player in the Pathogenesis of Atherosclerosis? Int J Mol Sci. 2023 Jan 6;24(2):1086. doi: 10.3390/ijms24021086. PMID: 36674602; PMCID: PMC9861427.

72. Puhm F, Afonyushkin T, Resch U, Obermayer G, Rohde M, Penz T, Schuster M, Wagner G, Rendeiro AF, Melki I, Kaun C, Wojta J, Bock C, Jilma B, Mackman N, Boilard E, Binder CJ. Mitochondria Are a Subset of Extracellular Vesicles Released by Activated Monocytes and Induce Type I IFN and TNF Responses in Endothelial Cells. Circ Res. 2019 Jun 21;125(l):43-52. doi: 10.1161/CIRCRESAHA.118.314601. Epub 2019 May 8. Erratum in: Circ Res. 2019 Oct 25;125(10):e93. PMID: 31219742.

73. Madireddy S, Madireddy S. Therapeutic Interventions to Mitigate Mitochondrial Dysfunction and Oxidative Stress-Induced Damage in Patients with Bipolar Disorder. Int J Mol Sci. 2022 Feb 6;23(3):1844. doi: 10.3390/ijms23031844. PMID: 35163764; PMCID: PMC8836876.

74. Park JS, Davis RL, Sue CM. Mitochondrial Dysfunction in Parkinson's Disease: New Mechanistic Insights and Therapeutic Perspectives. Curr Neurol Neurosci Rep. 2018 Apr 3;18(5):21. doi: 10.1007/sl l910-018-0829-3. PMID: 29616350; PMCID: PMC5882770.

75. Xu J, Yang Y. Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients. Front Cardiovasc Med. 2021;8:736497. doi: 10.3389/fcvm.2021.736497.

76. Starr AE, Bellac CL, Dufour A, Goebeler V, Overall CM. Biochemical characterization and N-terminomics analysis of leukolysin, the membrane-type 6 matrix metalloprotease (MMP25): chemokine and vimentin cleavages enhance cell migration and macrophage phagocytic activities. J Biol Chem. 2012 Apr 13;287(16): 13382-95. doi: 10.1074/jbc.Ml l l.314179. Epub 2012 Feb 24.

Claims

CLAIMS:

1. A method of determining whether a patient with a type 2 diabetes (TD2) is at a risk of developing a cardiovascular event comprising the steps of: i) determining in a sample obtained from the patient the quantity of monocytes ii) comparing said quantity determined at step i) with a predetermined reference value and iii) providing that a high quantity of monocytes compared to said predetermined reference value is indicative of whether a patient is at a risk of developing a cardiovascular event and that a low quantity of monocytes compared to said predetermined reference value is indicative of whether a patient is not at a risk of developing a cardiovascular event.

2. The method according to the claim 1 wherein the cardiovascular event is a major adverse cardiovascular event (MACE).

3. The method according to the claims 1 or 2 wherein the quantity of classical monocytes is determined to perform the method of the invention.

4. The method according to the claims 1 to 3 wherein the sample is blood or peripheralblood.

5. The method according to the claims 1 to 4 wherein the predetermined reference value is 0.5 x 10⁹ cells/L.

6. The method according to the claims 1 to 5 wherein the cardiovascular event included but is not limited to ischemic heart disease, atherosclerotic cardiovascular (CV) disease like atherosclerosis, coronary artery disease, peripheral arterial disease, cardiomyopathy, hypertensive heart disease, heart failure, pulmonary heart disease or myocarditis.

7. A method for treating a cardiovascular event in a patient with a risk of cardiovascular event above comprising the administration to said patient of an anti-cardiovascular disease treatment.