Patent Application
20220187312
The present invention pertains to the field of diagnostics. In particular, it relates to protein biomarkers for the screening, diagnosis and/or monitoring of subclinical atherosclerosis and methods and kits using thereof.
One of the challenges associated to the clinical management of atherosclerotic disease is that it is often diagnosed too late, usually when the condition is very advanced and lesions are already irreversible, or when it has caused clinical signs or events in organs or territories vascularized by the diseased arteries.
Primary prevention is currently based on the evaluation of modifiable risk factors according to standardized recommendations. However, it has been found that at every level of risk factor exposure, there is substantial variation in the amount of atherosclerosis (Fernandez-Ortiz A et al., Am Heart J 2013; 166:990-8).
Non-invasive measurement by imaging methods of the subclinical atherosclerotic burden in middle-aged people has the potential to improve assessment of cardiovascular risk and might contribute to a more effective prevention of cardiovascular events. Nevertheless, the most recent guidelines (Perk J. et al., Eur Heart J 2012; 33:1635-701) only recommend these imaging tests in asymptomatic adults considered at moderate risk (Fernandez-Ortiz A et al. 2013).
The Progression and Early detection of Subclinical Atherosclerosis (PESA) study is a longitudinal cohort study aimed at characterizing the prevalence of subclinical atherosclerosis and the determinants associated with the presence and progression of the disease in a middle-aged asymptomatic population.
Interestingly, a substantial proportion of asymptomatic individuals classified as low risk on the basis of traditional cardiovascular risk scores (i.e., FHS-10 year) were shown in the PESA study to have extensive atherosclerosis (Fernandez-Friera L et al., Circulation. 2015; 131:2104-2113).
The authors performed an analysis aiming to identify differences between participants with subclinical atherosclerosis in a single territory (focal disease) and those with multiple territories (intermediated or generalized disease). The results of this analysis evidenced a statistically significant association between extent of atherosclerosis and cardiovascular risk (CVR) scores and most CVR factors. Fernandez-Friera L et al. 2015 further discloses that most individuals classified as high risk by the traditional risk factor scores scales had subclinical atherosclerosis, with a high proportion having intermediate or generalized disease.
However, subclinical atherosclerosis was also present in 60% of the individuals classified as low risk, suggesting an association of atherosclerosis with characteristics not considered in standard risk scales. This was a striking observation since individuals presenting multi-territorial subclinical atherosclerosis, despite being classified as low risk, will be more likely to develop atherosclerotic cardiovascular diseases (ASCVD) events.
Fernandez-Friera L et al. 2017 (J Am Coll Cardiol. 2017; 70(24):2979-2991) also relates to the PESA study and reports that many middle-aged individuals without CVR factors present atherosclerosis. This study has found that LDL-cholesterol (LDL-C) levels, even at levels currently considered normal, is independently associated with the presence and extent of subclinical atherosclerosis. Thus, pointing to a reduction of LDL-C as a preventive measure to be adopted even in individuals considered to be at low risk.
Identification of biomarkers of atherosclerosis in the subclinical setting is technically more challenging than in a context of well-developed CVD or acute CV events, where the disease is expected to have a clear impact on plasma protein levels. Very few studies have assessed potential plasma protein biomarkers of subclinical atherosclerosis. Immunoassays have been used to quantify alterations in the specific plasma proteins adiponectin (Saarikoski L A et al., 2010) and TIMP4 (Oikonen M et al., 2012), showing that these proteins are decreased in individuals with asymptomatic subclinical carotid atherosclerosis in the Cardiovascular Risk in Young Finns cohort. A discovery proteomics analysis of the Cardiovascular Risk in Young Finns cohort found an association between FBLN1C plasma levels and plaque presence in middle-aged individuals (Bhosale S D et al., 2018), and the result was confirmed by targeted proteomics in the same population. However, in our study FBLN1C did not pass the stringent filtering criteria used.
Yin X et al., 2014 (Arteriosclerosis, thrombosis, and vascular biology 2014, 34:939-945) is concerned with the identification of new plasma protein biomarkers that individually or in aggregate predict risk of ASCVD. This document revealed an association between PIGR and myocardial infarction in single marker analysis.
Ngo D et al. 2016 (Circulation 2016, 134:270-285) aims to detect associations between plasma protein concentrations and Framingham risk score components in the Framingham Heart Study (FHS). In particular, it mentions an association between levels of PIGR in plasma and smoking. Smoking is a well stablished cardiac risk factor and is one of the variables in the FHS risk assessment algorithm. PIGR is further reported to be associated with the coronary heart disease FHS score.
Accordingly, there is a need to find new biomarkers for the screening, diagnosis and/or monitoring of individuals presenting subclinical atherosclerosis. Moreover, it is particularly desirable to identify markers which are independent of traditional CVR factors and scores, in order to be able to detect those subjects presenting subclinical atherosclerosis from those classified as having low cardiovascular risk.
The present application provides the results of what is, to our knowledge, the deepest and largest mass spectrometry-based plasma proteomics analysis to date in the search for atherosclerosis-related biomarkers. The inventors used a proteomics platform capable of quantifying more than 1000 proteins from undepleted plasma samples in a cohort of 444 individuals from the PESA study. This analysis was possible by combining the quantitative accuracy and robustness provided by multiplexed isobaric labeling with well-validated and automated statistical workflows (Navarro P and Vazquez J, 2014; Garcia-Marques F et al, 2016; Trevisan-Herraz M et al., 2018). A second analysis of plasma samples obtained from the same individuals at 3-year follow-up validated the results and identified a set of putative biomarker proteins whose association with atherosclerosis remained stable over time. This validation in the second visit was essential to reduce error sources in the discovery phase, to discard proteins with marked biological variability, and to concentrate the efforts on a robust set of biomarkers useful for the detection of subclinical atherosclerosis.
The levels of PIGR (Polymeric immunoglobulin receptor), APOA (Apolipoprotein(a)), HPT (haptoglobin), HEP2 (heparin cofactor 2), C5 (complement component 5), ITIH1 (Inter-alpha-trypsin inhibitor 1) and IGHA2 (immunoglobulin heavy constant alpha 2) in plasma as determined by liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis have been found by the inventors to be associated to subclinical atherosclerotic disease independently of each other, and of established cardiovascular risk factors (e.g., age, smoking) and cardiovascular risk scales (e.g. FHS risk score).
Moreover, the accumulation of these proteins in the media layer and mainly in the intima layer (plaque) has been shown to be higher as the plaque evolves from the fatty streak-type to the fibrolipid lesion-type (see
On this basis, these biomarkers, and preferably combinations thereof, are proposed by the inventors for use in a method for the screening, diagnosis and/or monitoring of subclinical atherosclerosis as described herein.
Accordingly, the first aspect of the invention relates to a method for the screening, diagnosis and/or monitoring of subclinical atherosclerosis in a subject, which comprises determining the levels of one or more protein markers selected from the group consisting of PIGR, APOA, HPT, HEP2, C5, ITIH1 and IGHA2 in a biological sample isolated from a subject, wherein said method comprises:
In a second aspect, the present invention provides a method for treating a subject having subclinical atherosclerosis, wherein said method comprises:
In a further aspect, the present invention refers to a kit for determining the levels of one or more of the protein markers as described herein in a biological sample isolated from a subject. The kit may also contain instructions indicating how the materials within the kit may be used.
In still an additional aspect, the invention relates to the use of a kit of the preceding aspect in a method for the screening, diagnosis and/or monitoring of subclinical atherosclerosis as described herein.
The network was constructed by analyzing the correlation of each one of the continuous risk factors with the plasma levels of each one of the proteins quantified by proteomics. Statistically significant correlations were selected by a Bonferroni-corrected p-value<0.05 in both PESA-V1 and PESA-V2, and Pearson's correlation coefficients were used as weights to construct a correlation network using Cytoscape. The thickness of the lines is proportional to the weights (dark grey: positive correlation; light grey: negative correlation). Correlations between the risk factors are also shown. DBP: diastolic blood pressure; SBP: systolic blood pressure; LDL: low density lipoprotein; Ch: cholesterol; HDL: high density lipoprotein; APOB: apolipoprotein B-100; PRG4: proteoglycan 4; PCYOX1: prenylcysteine oxidase 1; P06309: Immunoglobulin kappa variable 2D-28 (GeneName IGKV2D-28); APOM: apolipoprotein M; APOE: apolipoprotein E; APOC3: apolipoprotein C-III; APOC2: apolipoprotein C-II; THRB: prothrombin; PLTP: phospholipid transfer protein; APOF: apolipoprotein F; PON3: Serum paraoxonase/lactonase 3; CFB: complement factor B; APOA1: apolipoprotein A-I; APOA2: apolipoprotein A-II.
(A) Correlation of relative plasma protein levels with plaque thickness (Pearson's correlation coefficients) obtained in PESA-V1 are compared with those obtained in PESA-V2. Dot sizes are indicative of protein abundances in plasma (in number of quantified peptides per protein). Inset shows the behavior of proteins related to humoral immune response, and of other proteins yielding a significant correlation.
(B) Correlation of relative plasma levels with CACS in PESA-V1 and in PESA-V2. Data are shown as in (A).
Odds ratios refer to relative protein values determined by proteomics and expressed in units of standard deviation, using univariate logistic regression models (Unadjusted), or multivariate models adjusted by common Risk Scores (FHS 10-year, BEWAT or ICHS). Error bars indicate 95% confidence intervals.
The five proteins were subjected to absolute quantitation by proteomics and their levels expressed relative to the total protein amount of each sample. The levels were measured in samples from the media layer (obtained from healthy aortas, or from aortas showing early plaques (with fibrolipidic lesions or with fatty streaks)), or from the intima layer (with fibrolipidic lesions or with fatty streaks). Indicated statistical significances of protein abundance changes with respect to those of healthy samples is calculated using Student's t-test.
Odds ratios refer to protein values as determined by turbidimetry and expressed in units of standard deviation, using univariate logistic regression models (Unadjusted), or multivariate models adjusted by common Risk Scores (FHS 10-year, BEWAT or ICHS). Error bars indicate 95% confidence intervals.
The terms “subject”, or “individual’” are used herein interchangeably to refer to all the animals classified as mammals and includes but is not limited to domestic and farm animals, primates and humans, for example, human beings, non-human primates, cows, horses, pigs, sheep, goats, dogs, cats, or rodents. Preferably, the subject is a male or female human being of any age or race.
The term “diagnosis”, as used herein, refers both to the process of attempting to determine and/or identify a possible disease in a subject, i.e. the diagnostic procedure, and to the opinion reached by this process, i.e. the diagnostic opinion.
The term “screening” is understood herein as the examination or testing of a group of asymptomatic individuals pertaining to the general population, or of a group of individuals having one or more risk factors (i.e., a subject suspected of developing or at risk of developing a disease), with the objective of discriminating healthy individuals from those who have or are suspected of having a disease. A method of screening is generally used for the “early detection” of a disease. The expression “early detection” refers to detection before the presence of clinical signs.
The term “monitoring” as used herein refers to determining the evolution of the disease and/or the efficacy of a therapy, for example determining whether there is a remission of the disease; or on the contrary whether there is disease progression or a relapse.
The term “biomarker” as used herein refers to markers of disease which are typically substances found in a bodily sample that can be easily measured. The measured amount can correlate to underlying disease pathophysiology, such as presence or absence of subclinical atherosclerosis, or with its prognosis (i.e., likelihood of overcoming the underlying disease). In patients receiving treatment for their condition the measured amount may also correlate with responsiveness to therapy.
The term “therapeutically effective amount” as used herein refers to an amount that is effective, upon single or multiple dose administration to a subject (such as a human patient) in the prophylactic or therapeutic treatment of a disease, disorder or pathological condition.
The term “substantially identical” sequence as used herein refers to a sequence which is at least about 95%, preferably at least about 96%, 97%, 98%, or 99% identical to a reference sequence.
Identity percentage between the two sequences can be determined by any means known in the art, for example the Needleman and Wunsch global alignment algorithm.
The term “affinity reagent” may refer to a ligand (e.g., antibody, peptide, protein, nucleic acid or small molecule) that selectively captures (binds to) a target molecule through specific molecular recognition, typically with a binding affinity in the nanomolar to sub-nanomolar range. For example, the affinity reagent may be an aptamer, antibody or antibody-mimetic.
The term “affinity” as used herein may refer to the equilibrium constant for the dissociation of an antigen with an antigen-binding molecule (KD), and is considered a measure for the binding strength between an antigenic determinant and an antigen-binding site on the antigen-binding molecule: the lesser the value of the KD, the stronger the binding strength between an antigenic determinant and the antigen-binding molecule (alternatively, the affinity can also be expressed as the association constant (KA), which is 1/KD). It will be clear to the skilled person that the dissociation constant may be the actual or apparent dissociation constant.
The term “aptamer” or “nucleic acid aptamer” as used herein may refer to an isolated or purified single-stranded nucleic acid (RNA or DNA) that binds with high specificity and affinity to a target through interactions other than Watson-Crick base pairing. An aptamer has a three dimensional structure that provides chemical contacts to specifically bind to a target. Unlike traditional nucleic acid binding, aptamer binding is not dependent upon a conserved linear base sequence, but rather a particular secondary or tertiary structure. That is, the nucleic acid sequences of aptamers are non-coding sequences. Any coding potential that an aptamer may possess is entirely fortuitous and plays no role whatsoever in the binding of an aptamer to a target. A typical minimized aptamer is 5-15 kDa in size (15-45 nucleotides), binds to a target with nanomolar to sub-nanomolar affinity, and discriminates against closely related targets (e.g., aptamers will typically not bind to other proteins from the same gene or functional family).
The term “antibody” as used herein may refer to an immunoglobulin or an antigen-binding fragment thereof. Unless otherwise specified, the term includes, but is not limited to, polyclonal, monoclonal, monospecific, multispecific, humanized, human, chimeric, synthetic, recombinant, hybrid, mutated, grafted, and in vitro generated antibodies. The antibody can include a constant region, or a portion thereof, such as the kappa, lambda, alpha, gamma, delta, epsilon and mu constant region genes. For example, heavy chain constant regions of the various isotypes can be used, including: IgG1, IgG2, IgG3, IgG4, IgM, IgA1, IgA2, IgD, and IgE. By way of example, the light chain constant region can be kappa or lambda. In certain embodiments, the term “antibody” may also refer to antibody derivatives, such as antibody-based fusion proteins (e.g. including a region equivalent to the Fc region of an immunoglobulin) or antibodies further modified to contain additional non-proteinaceous moieties, such as water soluble polymers, e.g. polyethylene glycol (PEG).
The terms “antigen-binding domain” and “antigen-binding fragment” refer to a part of an antibody molecule that comprises amino acids responsible for the specific binding between antibody and antigen. For certain antigens, the antigen-binding domain or antigen-binding fragment may only bind to a part of the antigen. The part of the antigen that is specifically recognized and bound by the antibody is referred to as the “epitope” or “antigenic determinant.” Antigen-binding domains and antigen-binding fragments include Fab; a F(ab′)2 fragment (a bivalent fragment having two Fab fragments linked by a disulfide bridge at the hinge region); a Fv fragment; a single chain Fv fragment (scFv); a Fd fragment (having the two VH and CH1 domains); single domain antibodies (sdAbs; consisting of a single VH domain), and other antibody fragments that retain antigen-binding function. The Fab fragment has VH-CH1 and VL-CL domains covalently linked by a disulfide bond between the constant regions. The Fv fragment is smaller and has VH and VL domains non-covalently linked. The scFv contains a flexible polypeptide that links (1) the C-terminus of VH to the N-terminus of VL, or (2) the C-terminus of VL to the N-terminus of VH. The sdAbs include heavy chain antibodies naturally devoid of light chains and single-domain antibodies derived from conventional four chain antibodies. These antigen-binding domains and fragments are obtained using conventional techniques known to those with skill in the art, and are evaluated for function in the same manner as are intact immunoglobulins.
The term “recombinant antibody” as used herein refers to an antibody produced or expressed using a recombinant expression vector, where the expression vector comprises a nucleic acid encoding the recombinant antibody, such that introduction of the expression vector into an appropriate host cell results in the production or expression of the recombinant antibody. Recombinant antibodies may be chimeric or humanized antibodies, mono- or multi-specific antibodies.
The term “antibody mimetic” as used herein may refer to protein-based scaffolds which have been engineered to bind therapeutic targets with affinity and specificity that match that of natural antibodies. Antibody mimetics have been developed utilizing an immunoglobulin-like fold, for example, fibronectin type III, NCAM and CTLA-4. Other mimetics scaffolds bearing no similarity to immunoglobulin folds have also been obtained. Non-limiting examples of said scaffolds are DARPins, anticalins, affibodies, adnectins, fynomers, etc. (see for instance, Weidle et al., 2013, Cancer Genomics & Proteomics, 10, 1-18; Lofblom, J. et al., 2011, Curr. Opin. Biotechnol., 22, 843-848; Banta, S. et al., 2010, Annu. Rev. Biomed. Eng., 15, 93-113).
In a first aspect, the present invention provides a method for the screening, diagnosis and/or monitoring of subclinical atherosclerosis in a subject, which comprises determining the levels of one or more protein markers selected from the group consisting of PIGR, APOA, HPT (haptoglobin), HEP2 (heparin cofactor 2), C5, ITIH1 and IGHA2 in a biological sample isolated from a subject, wherein said method comprises:
Moreover, the accumulation of PIGR, IGHA2, APOA, HPT, HEP2, ITIH1 and C5 in plaque has been associated by the inventors to the degree of advancement of atherosclerosis (see
The term “subclinical atherosclerosis” as used herein refers to atherosclerosis in asymptomatic individuals. The expression “asymptomatic individuals” refers to those subjects which have not previously experienced clinical signs of cardiovascular disease, including for instance myocardial infarction, angina pectoris or stroke.
Preferably, “subclinical atherosclerosis” refers to the presence of atherosclerotic plaques in the carotid, aortic, or iliofemoral territories or CACS ≥1 in asymptomatic individuals. The multiterritorial “extent” of subclinical atherosclerosis is defined according to the number of vascular sites affected (right carotid, left carotid, abdominal aorta, right iliofemoral, left iliofemoral, and coronary arteries). For instance, as detailed in the Examples, in the PESA study participants were classified in four distinct categories according to the results of the imaging assays (“PESA score”), namely as disease free (0 vascular sites affected) or as having focal (1 site), intermediate (2-3 sites), or generalized (4-6 sites) atherosclerosis.
The “presence of atherosclerotic plaques” may be assessed by cross-sectional sweep of carotids, infrarenal abdominal aorta, and iliofemoral arteries. The term “plaque” typically refers to a focal protrusion into the arterial lumen of thickness >0.5 mm or >50% of the surrounding intima-media thickness or a diffuse thickness >1.5 mm measured between the media-adventitia and intima-lumen interfaces.
Step (a) of the method under the first aspect of the invention comprises determining in said biological sample the expression levels of one or more protein markers as defined above.
PIGR (Polymeric Immunoglobulin Receptor) binds polymeric IgA and IgM molecules at the basolateral surface of epithelial cells; the complex is then transported across the cell to be secreted at the apical surface. During this process a cleavage occurs that separates the extracellular (known as the secretory component) from the transmembrane segment. The canonical sequence of human PIGR is referred as SEQ ID NO:1 (UniProtKB Accession Number P01833-1; this is version 4 of the sequence of Jun. 26, 2007):
The term “PIGR” as used herein refers to human PIGR protein with SEQ ID NO:1 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:1.
The canonical sequence of human Immunoglobulin heavy constant alpha 2 (IGHA2) is referred as SEQ ID NO:2 (UniProtKB Accession Number P01877-1; this is version 4 of the sequence of Mar. 15, 2017):
The term “IGHA2” as used herein refers to human IGHA2 protein with SEQ ID NO:2 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:2.
The presence of PIGR and IGHA2 in early atherosclerotic lesions is a new finding. PIGR and IGHA2 are functionally related and are known to be involved in the humoral immune response. IgA immunoglobulins are thought to be the first line of antigen-specific immune protection at mucosal surfaces, while PIGR is a receptor that binds circulating polymeric IgA and IgM at the basolateral surface of intestinal epithelial cells and transports them across the cell to be secreted at the apical surface into intestinal lumen (Kaetzel C S, 2005; Wines B D and Hogarth P M, 2006). While atherosclerosis is today considered a chronic immune-inflammatory disease with a strong autoimmune component and a clear contribution from both innate and adaptive immunity (Hansson G K and Hermansson A, 2011), there is little information about the role of IgA antibodies in CV diseases (Tsiantoulas D et al., 2014). PIGR levels have been reported to increase in sputum and blood of smokers and chronic obstructive pulmonary disease patients (Ohlmeier S et al., 2012); moreover, PIGR was linked to smoking in a cohort from the Framingham Offspring Study, although PIGR levels were not associated with CV disease in that cohort (Ngo D et al., 2016). The only link we are aware of between PIGR and atherosclerosis is the elevated level of this protein in extracellular vesicles from individuals with acute coronary syndrome, although inclusion of this parameter did not improve disease detection over conventional risk factors or troponin I (de Hoog V C et al., 2013). No association between the IgA isoform IGHA2 and atherosclerosis has been reported before; however, elevated total serum IgA (which includes IGHA1, the most abundant isoform, and IGHA2) has been reported in relation to advanced atherosclerosis or end-stage CV events; for example, in patients with severe atherosclerosis (Muscari A et al., 1988) or with previous myocardial infarction or other major ischemic events (Muscari A et al., 1993). Levels of IgA, together with those of IgE and IgG, but not IgM, also correlate with myocardial infarction and cardiac death in dyslipidemic men (Kovanen P T et al., 1998).
Apolipoprotein(a) (APOA) is the main constituent of lipoprotein(a) (LPA). APOA is known to be proteolytically cleaved and fragments accumulate in atherosclerotic lesions. APOA is well-known to accumulate into the intima; this protein contains lysine-binding sites that allows it to bind tightly to exposed surfaces on denuded endothelium, enter, and accumulate into subintimal spaces (Tsimikas S, 2017). There is abundant evidence linking APOA to atherosclerosis. While the physiological role of Lp(a) in human is still not fully elucidated (Orso E and Schmitz G, 2017), the particle has been identified as independent predictor of coronary artery calcification (Greif M et al., 2013), and epidemiology supports a strong association between elevated Lp(a) and atherosclerotic CV disease outcomes (Ellis K L et al., 2017), suggesting that Lp(a) plays a causal role in the disease (Ellis K L and Watts G F, 2018). There are no treatments that specifically lower Lp(a), and hence the potential effect of lowering Lp(a) on CVD risk has not been demonstrated; nevertheless, some authorities recommend testing Lp(a) levels in certain clinical situations, for example in individuals with a personal or family history of premature CAD or at intermediate/high risk of CAD (Ellis K L and Watts G F, 2018).
Our results provide the first demonstration of the potential of APOA protein levels as an independent predictor of atherosclerosis in the subclinical phase, complementing the information provided not only by known risk factors but also by IGAH2 and HPT.
The canonical sequence of human APOA is referred as SEQ ID NO:3 (UniProtKB Accession Number P08519; this is version 1 of the sequence of Aug. 1, 1988):
The term “APOA” as used herein refers to human APOA protein with SEQ ID NO:3 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:3.
Inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1) may act as a carrier of hyaluronan in serum or as a binding protein between hyaluronan and other matrix protein, including those on cell surfaces in tissues to regulate the localization, synthesis and degradation of hyaluronan which are essential to cells undergoing biological processes. The canonical sequence of human ITIH1 is referred as SEQ ID NO:4 (UniProtKB Accession Number P19827; this is version 3 of the sequence of Jul. 15, 1998):
The term “ITIH1” as used herein refers to human ITIH1 protein with SEQ ID NO:4 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:4.
Human complement C5 activation by a C5 convertase initiates the spontaneous assembly of the late complement components, C5-C9, into the membrane attack complex. The canonical sequence of human C5 is referred as SEQ ID NO:5 (UniProtKB Accession Number P01031-1; this is version 4 of the sequence of Feb. 5, 2008):
The term “C5” as used herein refers to human C5 protein with SEQ ID NO:5 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:5.
The presence and accumulation of HPT in the intimal layer is consistent with its antioxidant role in promoting clearance of free hemoglobin released from red blood cells (RBC); however, the accumulation of HPT in the earliest phases of plaque formation has not been documented before. In this regard, the presence of redox-active iron, hemoglobin and glycophorin A in early atherosclerotic plaques has been recently revealed, implicating intimal RBC infiltration as one of the initial triggers for intimal oxidation (Delbosc S et al., 2017).
Several human studies have indicated that the HPT 2-2 phenotype may be associated with CVD in type 2 diabetes mellitus (Levy A P et al., 2002; Levy A P et al., 2004). Increased HPT levels have been previously observed in CAD patients (Lee C W et al., 2013) and were predictive of CV events (Holme I et al., 2009). HPT is considered an acute phase protein, and, like other proteins of this class such as CRP, fibrinogen, and serum-amyloid protein, has been used as a marker of inflammation. These markers, alone or in combination, improve the prediction of major CV events such as acute myocardial infarction and ischemic stroke (Holme I et al., 2009; Engstrom G et al., 2002; Holme I et al., 2010; Brea D et al., 2009; Sabatine M S et al., 2007). However, these proteins also detect other inflammation-associated diseases; for instance, similar increases in HPT and fibrinogen occur in patients with renal dysfunction or advanced CV disease, reflecting shared inflammatory events is these conditions (Luczak M et al., 2011). Moreover, a recent study of associations between acute phase proteins and CV outcome in 581 patients found no association between HPT and coronary plaque characteristics or clinical events (Battes L C et al., 2014). In a discovery proteomics study, the inflammatory markers SAA-1 and CRP were found to be increased in patients with stable atherosclerosis or acute coronary syndrome, but no changes were detected in HPT levels (Kristensen L P et al., 2014). Our present results showed no association of these acute phase proteins with plaque thickness or CACS. These results suggest that HPT plasma levels do not strictly track the behavior of inflammatory markers.
The canonical sequence of human haptoglobin is referred as SEQ ID NO:9 (UniProtKB Accession Number P00738; this is version 1 of the sequence of Jul. 21, 1986):
The term “HPT” as used herein refers to human haptoglobin protein with SEQ ID NO:9 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:9.
HEP2 has been previously found in the lipid-rich core of atheromas (Rau J C et al., 2009), but has not been described in early plaques. Reduced expression of HEP2 has been associated with aggravated atherosclerosis (Kanagawa Y et al., 2001; Takamori N et al., 2004), due presumably to its role as inhibitor of thrombin. Similarly, plasma HEP2 activity has been inversely associated to the prevalence of arterial disease (Aihara K et al., 2009), individuals with high levels of HEP2 have been shown to have less atherosclerosis (Aihara K et al., 2004) and HEP2 is thought to play a protective role against vascular and cardiac remodeling (Aihara K et al., 2009; Ikeda Y et al., 2012). Hence, our finding that HEP2 is increased in subclinical atherosclerosis was not expected from previous studies.
The canonical sequence of human heparin cofactor 2 (HEP2) is referred as SEQ ID NO:10 (UniProtKB Accession Number P05546; this is version 3 of the sequence of Nov. 1, 1991):
The term “HEP2” as used herein refers to human HEP2 protein with SEQ ID NO:10 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:10.
In a particular embodiment, the method for the screening, diagnosis and/or monitoring of the invention comprises determining in step a) the protein expression levels of one or more biomarkers comprising or consisting of:
Preferably, step a) comprises determining the expression levels of PIGR and/or IGHA2 and one, two or three biomarkers selected from APOA, HPT, HEP2, ITIH1 and C5. Accordingly, in a preferred embodiment, optionally in combination with one or more of the features or embodiments described herein, said one or more biomarkers in step a) is a plurality of biomarkers comprising or consisting of:
In a particular embodiment, optionally in combination with one or more of the features or embodiments described herein, said one or more biomarkers in step a) is a plurality of biomarkers comprising or consisting of:
More preferably, said plurality of biomarkers in a) comprises PIGR and/or IGHA2; and further comprises two biomarkers which are APOA; and at least one of HPT, HEP2, ITIH1 or C5. Accordingly, in another embodiment, optionally in combination with any of the embodiments or features described herein, said plurality of biomarkers comprises or consists of:
Even more preferably, said plurality of biomarkers in a) comprises or consists of:
In a further embodiment, said plurality of biomarkers in a) comprises or consists of PIGR, IGHA2, APOA, and at least one of HPT, HEP2, ITIH1 or C5. Preferably, said plurality of biomarkers in a) comprises or consists of a plurality of biomarkers selected from the group consisting of:
Alternatives embodiments comprise determining in step a) the expression levels of one or more biomarkers comprising or consisting of:
Preferably, said one or more biomarkers in a) is a plurality of biomarkers comprising ITIH1 and one or two biomarkers selected from APOA, HPT, HEP2, C5, PIGR and IGHA2. Accordingly, in a preferred embodiment, optionally in combination with one or more of the features or embodiments described herein, said plurality of biomarkers comprises or consists of:
More preferably, said plurality of biomarkers in a) comprises ITIH1 and two biomarkers consisting of (i) APOA; and (ii) at least one of PIGR, IGHA2, HPT, HEP2 or C5.
Additional alternatives embodiments comprise determining in step a) the expression levels of one or more biomarkers comprising or consisting of:
Preferably, said plurality of biomarkers in a) comprises HPT and one or two biomarkers selected from APOA, HEP2, C5, ITIH1, PIGR and IGHA2. Accordingly, in a preferred embodiment, optionally in combination with one or more of the features or embodiments described herein, said plurality of biomarkers comprises or consists of:
More preferably, said plurality of biomarkers in a) comprises HPT and two biomarkers consisting of (i) APOA; and (ii) at least one of PIGR, HEP2, IGHA2, C5 or ITIH1.
Still additional alternative embodiments comprise determining in step a) the expression levels of one or more biomarkers comprising or consisting of:
Preferably, said one or more biomarkers in a) is a plurality of biomarkers comprising APOA and one or two biomarkers selected from ITIH1, HPT, HEP2, C5, PIGR and IGHA2. Accordingly, in a preferred embodiment, optionally in combination with one or more of the features or embodiments described herein, said plurality of biomarkers comprises or consists of:
More preferably, said plurality of biomarkers in a) comprises APOA and two biomarkers consisting of (i) HPT; and (ii) at least one of PIGR, IGHA2, HEP2, ITIH1 or C5.
Still further alternatives embodiments comprise determining in step a) the expression levels of one or more biomarkers comprising or consisting of:
Preferably, said plurality of biomarkers in a) comprises HEP2 and one or two biomarkers selected from APOA, HPT, C5, ITIH1, PIGR and IGHA2. Accordingly, in a preferred embodiment, optionally in combination with one or more of the features or embodiments described herein, said plurality of biomarkers comprises or consists of:
More preferably, said plurality of biomarkers in a) comprises HEP2 and two biomarkers consisting of (i) APOA; and (ii) at least one of PIGR, HPT, IGHA2, C5 or ITIH1.
The term “sample” or “biological sample”, as used herein, refers to biological material isolated from a subject. The biological sample may contain any biological material suitable for detecting the desired biomarker and may comprise cellular and/or non-cellular material from the subject. The sample can be isolated from any suitable biological tissue or fluid such as, for example, blood, blood plasma, serum, cerebral spinal fluid (CSF), urine, amniotic fluid, lymph fluids, external secretions of the respiratory, intestinal, genitourinary tracts, tears, saliva, white blood cells. Preferably, the samples used for the determination of the level(s) of the protein markers in the methods of the invention are samples which can be obtained using minimally invasive procedures. In a preferred embodiment, the samples are blood, plasma or serum samples. Preferably, this biological sample is a blood plasma.
These types of samples are routinely used in the clinical practice and a person skilled in the art will know how to identify the most appropriate means for their obtaining and preservation. Once a sample has been obtained, it may be used fresh, it may be frozen, lyophilized or preserved using appropriate means.
The expression “determining the levels of the marker”, as used herein, refers to ascertaining the absolute or relative amount or concentration of the biomarker in the sample. Techniques to assay levels of individual biomarkers from test samples are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed.
Methods for quantifying protein expression are well known in the art. Suitable methods for determining the levels of a given protein include, without limitation, those described herein below. Preferred methods for determining the protein expression levels in the methods of the present invention are immunoassays. Various types of immunoassays are known to one skilled in the art for the quantitation of proteins of interest. These methods are based on the use of affinity reagents, which may be any antibody or other ligand specifically binding to the target protein or to a fragment thereof, wherein said affinity reagent is preferably labeled. For instance, the affinity reagent may be enzymatically labelled, or labeled with a radioactive isotope or with a fluorescent agent.
Affinity reagents may be any antibody or ligand specifically binding to the target protein or to a fragment thereof. Affinity ligands may include proteins, peptides, nucleic acid or peptide aptamers, and other target specific protein scaffolds, like antibody-mimetics. Specific antibodies against the protein markers used in the methods of the invention may be produced for example by immunizing a host with a protein of the present invention or a fragment thereof. Likewise, peptides specific against the protein markers used in the methods of the invention may be produced by screening synthetic peptide libraries.
Western blot or immunoblotting techniques allow comparison of relative abundance of proteins separated by an electrophoretic gel (e.g., native proteins by 3-D structure or denatured proteins by the length of the polypeptide). Immunoblotting techniques use antibodies (or other specific ligands in related techniques) to identify target proteins among a number of unrelated protein species. They involve identification of protein target via antigen-antibody (or protein-ligand) specific reactions. Proteins are typically separated by electrophoresis and transferred onto a sheet of polymeric material (generally nitrocellulose, nylon, or polyvinylidene difluoride). Dot and slot blots are simplified procedures in which protein samples are not separated by electrophoresis but immobilized directly onto a membrane.
Traditionally, quantification of proteins in solution has been carried out by immunoassays on a solid support. Said immunoassay may be for example an enzyme-linked immunosorbent assay (ELISA), a fluorescent immunosorbent assay (FIA), a chemiluminescence immunoassay (CIA), or a radioimmunoassay (RIA), an enzyme multiplied immunoassay, a solid phase radioimmunoassay (SPROA), a fluorescence polarization (FP) assay, a fluorescence resonance energy transfer (FRET) assay, a time-resolved fluorescence resonance energy transfer (TR-FRET) assay, a surface plasmon resonance (SPR) assay. Multiplex and any next generation versions of any of the above, are specifically encompassed. In a particular embodiment, said immunoassay is an ELISA assay or any multiplex version thereof.
Other methods that can be used for quantification of proteins in solution are techniques based on mass spectrometry (MS), also called mass spectroscopy. The term “mass spectrometry (MS)-based methods” as used herein refers to mass spectrometry alone or coupled to other detection or separation methods, including gas chromatography combined with mass spectroscopy, liquid chromatography combined with mass spectroscopy, supercritical fluid chromatography combined with mass spectroscopy, ultra-performance liquid chromatography combined with mass spectrometry, MALDI combined with mass spectroscopy, ion spray spectroscopy combined with mass spectroscopy, capillary electrophoresis combined with mass spectrometry, NMR combined with mass spectrometry and IR combined with mass spectrometry. These MS-based methods may include single MS or tandem MS. In another particular embodiment, protein expression levels are determined by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) analysis.
Mass spectrometers operate by converting the analyte molecules to a charged (ionized) state, with subsequent analysis of the ions and any fragment ions that are produced during the ionization process, on the basis of their mass to charge ratio (m/z). Several different technologies are available for both ionization and ion analysis, resulting in many different types of mass spectrometers with different combinations of these two processes. On the one hand, examples of ion sources include electrospray ionization source, atmospheric pressure chemical ionization source, atmospheric pressure photo-ionization or matrix-assisted laser desorption ionization (MALDI). On the other hand, mass spectrometers analyzers may be, but are not limited to, quadrupole analyzers, time-of-flight (TOF) analyzers, ion trap analyzers, orbitrap analyzers or hybrid analyzers, such as hybrid quadrupole time-of-flight (QTOF) analyzers, hybrid quadrupole-orbitrap analyzers, hybrid ion trap-orbitrap analyzers, hybrid triple quadrupole linear ion trap analyzers or trihybrid quadrupole-ion trap-orbitrap analyzers. In a preferred embodiment, the levels of the protein markers are determined by using a hybrid quadrupole-orbitrap analyzer.
Internal standards may be used in the MS analysis as it enables to correct for any losses or inefficiencies in the sample preparation process or for alterations in ionization efficiency, for instance those due to ion suppression. Stable or isobaric isotope versions of the analyte are ideal internal standards as they have almost identical chemical properties but are easily distinguished during MS. In a preferred embodiment, optionally in combination with one or more of the embodiments described above or below, the determination of the levels of the protein marker is conducted by an MS-based method using isotope/isobaric-labeled versions of the protein marker as internal standards. In addition, or alternatively, the extraction solvent may be spiked with compounds not detected in unspiked biological samples.
As shown in the Examples (see Tables 5 and 6), IGHG1 and IGHG2 are also independent predictors of subclinical atherosclerosis. In particular, these have been shown to be decreased in the presence of the disease. Accordingly, the method of the invention may further comprise.
In addition, the IGHA2 levels may be expressed relative to the levels of any of immunoglobulin heavy constant alpha 1 (IGHA1), immunoglobulin heavy constant gamma 1 (IGHG1), immunoglobulin heavy constant gamma 2 (IGHG2) or a combination thereof. Preferably, IGHA2 levels are expressed relative to the levels of IGHA1 or the average levels of IGHG1 and IGHG2.
The canonical sequence of human immunoglobulin heavy constant alpha 1 (IGHA1) is referred as SEQ ID NO:6 (UniProtKB Accession Number P01876-1; this is version 2 of the sequence of Feb. 1, 1991):
The term “IGHA1” as used herein refers to human IGHA1 protein with SEQ ID NO:6 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:6.
The canonical sequence of human immunoglobulin heavy constant gamma 1 (IGHG1) is referred as SEQ ID NO:7 (UniProtKB Accession Number P01857-1; this is version 1 of the sequence of Jul. 21, 1986):
The term “IGHG1” as used herein refers to human IGHG1 protein with SEQ ID NO:7 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:7.
The canonical sequence of human immunoglobulin heavy constant gamma 2 (IGHG2) is referred as SEQ ID NO:8 (UniProtKB Accession Number P01859-1; this is version 2 of the sequence of Dec. 16, 2008):
The term “IGHG2” as used herein refers to human IGHG2 protein with SEQ ID NO:8 and to sequences substantially identical thereto. Preferably, said sequence is SEQ ID NO:8.
The inventors also found that IGHG1, IGKC and IGLC2 decreased with plaque thickness in PESA-V1, whereas IGHA1, IGHG3, IGHG4 and IGHM were unaffected, remaining these changes detectable in PESA-V2 (
In step (b), the screening and/or diagnostic method of the invention comprises comparing the level(s) of the protein marker(s) with a reference value.
The term “reference value”, as used herein, relates to a predetermined criteria used as a reference for evaluating the values or data obtained from the samples collected from a subject. This “reference value” may also be referred as “cut-off value” or “threshold value”.
The reference value or reference level can be an absolute value, a relative value, a value that has an upper or a lower limit, a range of values, an average value, a median value, a mean value, or a value as compared to a particular control or baseline value. A reference value can be based on an individual sample value or can be based on a large number of samples, such as from population of subjects of the chronological age matched group, or based on a pool of samples including or excluding the sample to be tested.
The expression levels of the protein markers can be used for calculating a combined score to be compared with a reference value. The combined score is a value obtained according to a given mathematical formula or algorithm wherein the expression values of each of the protein markers used in the methods of the invention are variables of said mathematical algorithm. In a particular embodiment, when calculating the combined score, this is proportional to the expression levels of one or more of PIGR, IGHA2, APOA, HPT, HEP2, ITIH1 and C5, wherein the higher the score, the higher the likelihood of subclinical atherosclerosis.
Furthermore, step (c) the method under the first aspect of the invention comprises classifying the subject as having or presenting a high likelihood of having subclinical atherosclerosis according to the comparison with the reference value, wherein when the levels in the subject's sample of PIGR, IGHA2, APOA, HPT, HEP2, ITIH1 and/or C5 are increased in the subject sample with respect to the corresponding reference value is indicative of subclinical atherosclerosis.
The biomarkers levels are considered “increased” when its value is higher than a reference value. Preferably, the biomarker levels (or the combined score) are considered to be higher than a reference value when it is at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more higher than the reference value.
Likewise, the biomarker levels are considered “decreased” when its value is lower than the reference value. Preferably, the biomarker levels (or the combined score) are considered to be lower than a reference value when it is at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more lower than the reference value.
Alternatively or in addition, subjects having more than about 1.1, 1.2, 1.3, 1.4, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 or 20 fold levels deviation with respect to the reference value as described herein.
The method of the invention, as it is understood by a person skilled in the art, does not claim to be correct in 100% of the analyzed samples. However, it requires that a statistically significant amount of the analyzed samples are classified correctly. The amount that is statistically significant can be established by a person skilled in the art by means of using different statistical significance measures obtained by statistical tests; illustrative, non-limiting examples of said statistical significance measures include determining confidence intervals, determining the p-value, etc. Preferred confidence intervals are at least 90%, at least 95%, at least 97%, at least 98%, at least 99%. The p-values are, preferably less than 0.1, less than 0.05, less than 0.01, less than 0.005 or less than 0.0001. The teachings of the present invention preferably allow correctly classifying at least 60%, at least 70%, at least 80%, or at least 90% of the subjects of a determining group or population analyzed.
It is further noted that the accuracy of the method of the invention can be further increased by additionally considering biochemical parameters and/or clinical characteristics of the patients (e.g. age, sex, tobacco and/or other cardiovascular risk factors), such as included in traditional cardiovascular risk scores.
In a particular embodiment, optionally in combination with one or more of the embodiments or features as described herein, the method according to the first aspect of the invention further comprises conducting a traditional cardiovascular risk score. Typically, risk scores comprise biochemical and physiological determinations, as well as other clinical and/or lifestyle characteristics of the subject. Preferably said traditional cardiovascular risk score is selected from the group consisting of 10-y FHS or 30-y FHS (Kannel et al., 1979; Splansky et al., 2007), ICHS (Fernández-Alvira et al., 2017) and the BEWAT (Fernández-Alvira et al., 2017) scores, more preferably wherein said cardiovascular risk score is 10-y FHS.
Said risk score may be combined with the protein measures of PIGR, IGHA2, APOA, HPT, HEP2, ITIH1 and/or C5 (including any combination thereof as described herein above) using appropriate mathematical combinations, preferably using multivariate logistic regression models.
In a particular embodiment, optionally in combination with any of the embodiments or features as described herein, said method is preferably a method for the screening and/or diagnosis of subclinical atherosclerosis and the biomarker measurement is performed in a sample isolated from a subject classified as low risk when conducting a traditional cardiovascular risk score. For instance, coronary heart disease (CHD) risk at 10 years in percent can be calculated with the help of the Framingham Risk Score (FHS-10 y). Individuals with low risk have 10% or less CHD risk at 10 years, with intermediate risk 10-20%, and with high risk 20% or more. However it should be remembered that these categorizations are arbitrary.
Moreover, the method under the first aspect of the invention can comprise additionally conducting in vivo imaging studies. This may include but are not limited to ⅔-dimensional vascular ultrasound analysis of presence and morphology of atherosclerosis plaques and computed tomography analysis of coronary artery calcification. Preferably, additional in vivo imaging studies are conducted in those subjects selected for presenting in step c) biomarker levels indicative of atherosclerosis.
The methods of the present invention or any of the steps thereof might be implemented by a computer. Therefore, a further aspect of the invention refers to a computer implemented method, wherein the method is any of the methods disclosed herein or any combination thereof.
It is noted that any computer program capable of implementing any of the methods of the present invention or used to implement any of these methods or any combination thereof, also forms part of the present invention. This computer program is typically directly loadable into the internal memory of a digital computer, comprising software code portions for performing the steps of comparing the levels of the protein markers as described in the invention, from the one or more biological samples of a subject, with a reference value and determining the presence or likelihood of having subclinical atherosclerosis, when said product is run on a computer.
It is also noted that any device or apparatus comprising means for carrying out the steps of any of the methods of the present invention or any combination thereof, or carrying a computer program capable of, or for implementing any of the methods of the present invention or any combination thereof, is included as forming part of the present specification.
The methods of the invention may also comprise the storing of the method results in a data carrier, preferably wherein said data carrier is a computer readable medium. The present invention further relates to a computer-readable storage medium having stored thereon a computer program of the invention or the results of any of the methods of the invention. As used herein, “a computer readable medium” can be any apparatus that may include, store, communicate, propagate, or transport the results of the determination of the method of the invention. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium.
In a second aspect, the present invention provides a method for treating a subject having subclinical atherosclerosis, wherein said method comprises:
These may include treatments for reducing the levels of HDL cholesterol or total triglyceride levels; and/or increasing HDL cholesterol levels in plasma. Statins are currently among the most therapeutically effective drugs available for reducing the level of LDL in blood. Statins are also known to raise HDL cholesterol levels and decrease total triglyceride levels. It is believed that statins disrupt the biosynthesis of cholesterol and other sterols in the liver by competitively inhibiting the 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase enzyme (“HMG-CoA reductase”). HMG-CoA reductase catalyzes the conversion of HMG-CoA to mevalonate, which is the rate determining step in the biosynthesis of cholesterol. Consequently, its inhibition leads to a reduction in the rate of formation of cholesterol in the liver. Statins are well known in the art, and these include for instance pravastatin, simvastatin, lovastatin, fluvastatin, atorvastatin and rosuvastatin.
In a further aspect, the present invention refers to a kit for determining the levels of one or more of the protein markers as described herein in a biological sample isolated from a subject. The kit may also contain instructions indicating how the materials within the kit may be used.
The term “kit” or “testing kit” denotes combinations of reagents and adjuvants required for an analysis. Although a test kit consists in most cases of several units, one-piece analysis elements are also available, which must likewise be regarded as testing kits.
In a particular embodiment, the kit according to the invention comprises reagents adequate for the determination of the protein expression levels of one or more of the markers selected from the group consisting of PIGR, IGHA2, APOA, HPT, HEP2, ITIH1 and C5.
These reagents may be useful for determining the expression levels of the target protein marker(s) using any suitable method as described herein above. For instance, the determination of the levels of said protein marker(s) may be carried out by using an affinity reagent, (e.g., by an immunoassay) as described under the first aspect of the invention.
In a particular embodiment, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In a preferred embodiment, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In an alternative embodiment, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In a preferred embodiment, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In a further alternative embodiment, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In a preferred embodiment, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In still additional alternative embodiments, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In a preferred embodiment, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In still further alternative embodiments, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
In a preferred embodiment, optionally in combination with one or more of the features or embodiments as described herein, said kit comprises:
Preferred combinations of biomarkers to be measured and corresponding reagents (e.g. affinity reagents) are as described for the first aspect of the invention herein above.
Moreover, any of these embodiments, may further comprise one or more of the following:
The term “an affinity reagent for” as used herein refers to an affinity reagent capable of specifically binding to the recited target protein. The various affinity reagents may be labelled with the same or different tags. Preferably, these will be labelled with different tags for multiplex analysis.
In a particular embodiment, optionally in combination with one or more of the embodiments or features described herein, said affinity reagent is an antibody, preferably a monoclonal antibody. The affinity reagent may bind to any linear or conformational region (e.g. epitope) specific for the target protein.
Illustrative/non-liming examples of commercially available antibodies specifically binding to the proteins as described herein, include the following:
Possible immunoassays and affinity reagents have been described herein. In a particular embodiment, said immunoassay is an ELISA assay.
In some embodiments, the kit includes the above-mentioned affinity reagents for the target protein(s) and one or more ancillary reagents. A primary affinity reagent (e.g. a primary antibody) may be used for capturing purposes and a secondary affinity reagent (e.g. secondary antibody) for detection purposes.
The primary or capturing affinity reagent is typically a monoclonal antibody specific for the target protein. Secondary or detection affinity reagents can be monoclonal or polyclonal antibodies. Also, these can be derived from any mammalian organism, including mice, rats, hamsters, goats, camels, chicken, rabbit, and others.
The detection affinity reagent may be labeled. A tag or label can be any composition which is detectable. Any analytical means known in the art can be used for determining or detecting the secondary affinity reagent. These means include the use of spectroscopy, chemistry, photochemistry, biochemistry, immunochemistry, or optics. The label can be, for example, an enzyme (e.g., horseradish peroxidase (HRP), alkaline phosphatase, beta-galactosidase, and others commonly used in an ELISA), a radiolabel (e.g., 3H, 125I, 35S, 14C, or 32P), a chemiluminescent compound (e.g. luciferin, and 2,3-dihydrophthalazinediones, luminol, etc.), a fluorescent dye (e.g., fluorescein isothiocyanate, Texas red, rhodamine, etc.), or any other dye known in the art.
The label may be coupled directly or indirectly (e.g., via binding pairs such as biotin and avidin) to the detection affinity reagent according to methods well known in the art. As indicated above, a wide variety of labels may be used. The choice of label may depend on sensitivity required, ease of conjugation with the compound, stability requirements, available instrumentation, or disposal provisions.
Preferably, said kit comprises a solid support or surface which is coated with the primary affinity reagent. The solid support can include any support known in the art on which a protein of this disclosure can be immobilized. In some embodiments, said solid supports are microtiter well plates, slides (e.g., glass slides), chips (e.g., protein chips, biosensor chips, such as Biacore chips), microfluidic cartridges, cuvettes, beads (e.g., magnetic beads, xMAP® beads) or resins.
Ancillary reagents typically used in an immunoassay, such as a solid support immunoassay, can include, e.g., an immobilization buffer, an immobilization reagent, a dilution buffer, a detection reagent, a blocking buffer, a washing buffer, a detection buffer, a stop solution, a system rinse buffer, and a system cleaning solution which are well known by a person skilled in the art.
Preferred features and embodiments of the kit of the invention are as defined above for other aspects of the invention.
In addition, the determination of the levels of said protein marker(s) may be carried out by a mass spectrometry (MS)-based method, and said kit may comprise said marker unlabelled and/or said marker stably labelled for detection by a mass spectrometry (MS)-based method, preferably wherein the marker is labelled with a tag which comprises one or more stable isotope. Isotopic atoms which may be incorporated into the tag are heavy atoms for example 13C, 15N, 18O and/or 34S, which can be distinguished by MS.
In still an additional aspect, the invention relates to the use of a kit of the preceding aspect in a method for the screening, diagnosis and/or monitoring of subclinical atherosclerosis as described herein.
Preferred features and embodiments are as defined above for other aspects of the invention.
It is contemplated that any features described herein can optionally be combined with any of the embodiments of any method, kit, use of a kit, or computer program of the invention; and any embodiment discussed in this specification can be implemented with respect to any of these. It will be understood that particular embodiments described herein are shown by way of illustration and not as limitations of the invention. The principal features of this invention can be employed in various embodiments without departing from the scope of the invention.
All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.
The use of the word “a” or “an” may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one”. The use of the term “another” may also refer to one or more. The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive.
As used in this specification and claim(s), the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps. The term “comprises” also encompasses and expressly discloses the terms “consists of” and “consists essentially of”. As used herein, the phrase “consisting essentially of” limits the scope of a claim to the specified materials or steps and those that do not materially affect the basic and novel characteristic(s) of the claimed invention. As used herein, the phrase “consisting of” excludes any element, step, or ingredient not specified in the claim except for, e.g., impurities ordinarily associated with the element or limitation.
The term “or combinations thereof” as used herein refers to all permutations and combinations of the listed items preceding the term. For example, “A, B, C, or combinations thereof” is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context.
As used herein, words of approximation such as, without limitation, “about”, “around”, “approximately” refers to a condition that when so modified is understood to not necessarily be absolute or perfect but would be considered close enough to those of ordinary skill in the art to warrant designating the condition as being present. The extent to which the description may vary will depend on how great a change can be instituted and still have one of ordinary skilled in the art recognize the modified feature as still having the required characteristics and capabilities of the unmodified feature. In general, but subject to the preceding discussion, a numerical value herein that is modified by a word of approximation such as “about” may vary from the stated value by ±1, 2, 3, 4, 5, 6, 7, 8, 9, or 10%. Accordingly, the term “about” may mean the indicated value ±5% of its value, preferably the indicated value ±2% of its value, most preferably the term “about” means exactly the indicated value (±0%).
The following examples serve to illustrate the present invention and should not be construed as limiting the scope thereof.
PESA subcohort: the proteomics analysis are performed on plasma from 476 individuals. The samples are paired according to sex, age and clinical history. All PESA individuals have been subjected to extensive imaging and biochemical analysis as described previously (Fernandez-Friera et al., 2015; Fernandez-Ortiz et al., 2013). According to the extent of subclinical atherosclerosis, individuals were classified into four groups using the PESA score (Fernandez-Friera et al., 2015): no disease, focalized, intermediate and generalized disease.
Quantitative proteomics by multiplexed isobaric labeling and quantitative analysis of the data are performed following detailed protocols set up in our laboratory and already described (Burillo et al., 2016; Garcia-Marques et al., 2016; Gomez-Serrano et al., 2016; Latorre-Pellicer et al., 2016; Martin-Alonso et al., 2015). Identification, quantification and statistical and systems biology analysis are done using models developed in our laboratory and fully described before (Bonzon-Kulichenko et al., 2015; Garcia-Marques et al., 2016; Jorge et al., 2009; Navarro et al., 2014; Navarro and Vazquez, 2009; Trevisan-Herraz et al., 2018, in the press). Mass spectrometry analysis are performed in a hybrid quadrupole-orbitrap machine (HF Orbitrap, ThermoFisher).
The epidemiological analysis are performed as follows. The selection of a panel of potential biomarkers in the PESA subcohort is performed by analyzing quantitative protein values, determined as described above, using the SPSS package by constructing multivariate linear or logistic regression models to predict disease (in terms of plaque number, thickness, number of affected territories or PESA score), after adjustment by all other known risk factors (e.g., cholesterol, blood pressure, gender, age, LDL and HDL levels). ROC analysis is then performed to measure the increase in predictive power of the biomarker panel over existing methods to predict cardiovascular risk (for instance FHS 10 year, BEWAT or ICHS scores).
1.2.—Analysis of the Association of PIGR, APOA and C5 Levels in Plasma with the Extent of Atherosclerotic Lesions in Asymptomatic Individuals
The table shows how the plasma levels of PIGR, APOA and C5 have a strong correlation with either of two independent measurements of the extent of atherosclerotic lesions even after adjustment by conventional risk factors. Besides, the three proteins remain significantly associated to plaque thickness when the three are taken together in a multivariate model including risk factors. This result means that the three proteins are associated with plaque independently from each other.
The table shows how the levels of PIGR, APOA and C5, taken together in a multivariate model, maintain their correlation with the extent of atherosclerotic lesions even in the subpopulation of individuals with low-risk according to FHS10y. These results indicate that these proteins may independently and effectively trace the presence of atherosclerosis lesions in cases where there is no evidence of cardiovascular risk according to classical risk factors.
1.3.—Analysis of the Protein Levels of PIGR, APOA, ITIH1, C5 and IGHA2 in Media and Intima Layers from Human Aorta without or with Atherosclerotic Lesions
The data shown in
These data show that each one of PIGR, APOA, ITIH1 and C5 levels in plasma is an independent predictor of generalized subclinical atherosclerosis even when known risk factors are included in the logistic model. In addition, PIGR, APOA and ITIH1, together, are also independent predictors in the presence of known risk scores such as 10y-FHS, 30y-FHS, BEWAT or ICHS. Without willing to be bound by theory, in the regression analysis we observed that C5 and ITIH1 have strong covariance, so that they appear to be dependent. The observed covariance would suggest that C5 and ITIH1 can be interchanged (if ITIH1 is not considered in the multivariate model, then C5 behaves as independent predictor together with PIGR and APOA and any of the known risk factors)
Table 4 shows that PIGR, APOA and ITIH1 maintain their ability of being independent predictors of generalized disease even in the subpopulation of individuals with low-risk according to 10yFHS. The table also shows that PIGR and ITIH1 maintain their ability of being independent predictors of generalized disease even in the subpopulation of individuals with low-risk according to 30yFHS. These results indicate that these proteins may effectively predict the presence of atherosclerosis lesions in cases where there is no evidence of cardiovascular risk according to classical risk factors.
1.5.—Analysis of the Association with Atherosclerosis and of the Power to Predict Generalized Atherosclerosis of IGHA2 Levels in Plasma
The table shows that plaque thickness correlates with increased abundance of IGHA2 (but not IGHA1) and with decreased abundance of IGHG1 or IGHG2, independently of 10yFHS. The correlation with IGHA2 is maintained even in the subpopulation of individuals with low-risk according to FHS10y. The ratio IGHA2/IGHG1 have an even stronger correlation than IGHA2 alone. The ratio IGHA2/average(IGHG1,IGHG2) is even better. These results suggest that plaque thickness correlates with Ig isotype switching.
The table shows that the IGHA2 level in plasma (corrected or not by other Igs), but not those of IGHA1, is an independent predictor of generalized subclinical atherosclerosis even when the risk score 10yFHS is included in the logistic model. This effect is maintained in the population with low risk.
The table shows how the three risk scores, as well as individual risk factors, are able to produce a statistically significant discrimination between no diseased individuals and those with generalized disease.
The table shows that each one of the individual proteins are able to produce a statistically significant discrimination between no diseased individuals and those with generalized disease.
When compared with the data in Table 7, this table shows that it is possible to use combinations of three proteins that are able to discriminate between no diseased individuals and those with generalized disease with a performance similar or better than that of widely-used risk scores.
The table shows that the addition to 10yFHS of panels containing several proteins from the group PIGR, APOA, ITIH1, C5 and IGHA2 produces a discriminatory performance significantly higher than that of 10yFHS alone (as deduced from the 95% confidence intervals of AUC values). These results indicate that the protein panel may serve to significantly improve the prediction of subclinical atherosclerosis in comparison with current standards.
The table shows that the addition to ICHS of panels containing several proteins from the group PIGR, APOA, ITIH1, C5 and IGHA2 produces a discriminatory performance significantly higher than that of ICHS alone (as deduced from the 95% confidence intervals of AUC values). These results indicate that the protein panel may serve to significantly improve the prediction of subclinical atherosclerosis in comparison with current standards.
The table shows that the addition to BEWAT score of panels containing several proteins from the group PIGR, APOA, ITIH1, C5 and IGHA2 produces a discriminatory performance significantly higher than that of BEWAT alone (as deduced from the 95% confidence intervals of AUC values). These results indicate that the protein panel may serve to significantly improve the prediction of subclinical atherosclerosis in comparison with current standards.
Plasma samples. Plasma samples were collected from PESA study cohort (Fernandez-Friera L et al., 2015) and AWHS cohort. (Casasnovas J A et al., 2012) For the discovery phase, a nested case-control study within the prospective PESA cohort was designed (PESA-V1). To account for potential cofounding and to increase efficiency, the study were restricted to men, and controls were matched for CV risk factors. Firstly, 222 case subjects were selected among participants with extensive subclinical atherosclerosis, defined as subjects with 3 or more vascular territories affected. Control subjects were selected in a 1:1 fashion, among participants with non-extensive subclinical atherosclerosis, defined as subjects with none or 1 vascular territory affected. The control subjects were matched with case subjects based on age (caliper: 3 years), hypertension, dyslipdemia and diabetes. Plasma samples from the same individuals were also collected three years later (PESA-V2), except two cases and their matched controls that did not renew their consent for the ‘omics’ analysis. The validation set was designed within the AWHS cohort following the same methodology. A nested case-control, restricted to men and matched by age, hypertension, dyslipdemia and diabetes was performed. Two hundred and twenty case subjects were selected among participants with extensive subclinical atherosclerosis, defined as subjects with 3 or more vascular territories affected. The same number of control subjects were selected from control participants with non-extensive subclinical atherosclerosis, defined as subjects with 2 or less vascular territories affected.
Assessment of CV risk factors and subclinical atherosclerosis. In the PESA study, CV risk factors were prospectively collected through questionnaires (smoking, family history) or objective quantification (hypertension, diabetes, dyslipidemia) as previously described. (Fernandez-Friera L et al., 2015) Two-dimensional vascular ultrasound and noncontrast cardiac computed tomography were performed in all participants as previously described. (Fernandez-Friera L et al., 2015; Fernandez-Friera L et al., 2017) The presence of atherosclerotic plaques by ultrasound was assessed by cross-sectional sweep of carotids, infrarenal abdominal aorta, and iliofemoral arteries. The identification of plaques in both carotid and femoral arteries and the clinical characteristics in the AWHS study were determined as described (Laclaustra M et al., 2016).
Tissue samples. Aortic tissue samples from the media and intima layers were obtained from dead organ donors with the authorization of the French Biomedicine Agency (PFS 09-007, BRIF BB-0033-00029; AoS BBMRI-EU/infrastructure BIOBANQUE; No. Access: 2, Last: Apr. 15, 2014. [BIORESOURCE]). Some of these samples were macroscopically normal (AoS) and devoid of early atheromatous lesions and were used as healthy controls (9 individuals) for comparison with those with samples with fatty streaks (FS) (7 individuals for media layer and 6 in the case of the intima layer) or fibrolipidic (FL) plaques (11 individuals in the case of media layer and 12 in the case of intima layer). Tissue (100 mg of FL, FS or AoS) was homogenized at low temperature, and lysates were resuspended in buffer (50 mM iodoacetamide (Sigma), 1% SDS, 1 mM EDTA, 100 mM Tris-HCL, pH 8.5) for protein extraction or in TRIZOL for mRNA isolation. Proteins were extracted by vortexing samples 4 times with 15 min intervals on ice. Proteins in the supernatant were measured by the BCA method and stored at −80° C. until proteomics analysis.
Proteomic analysis. Plasma and tissue protein samples were subjected to filter-aided digestion with trypsin according to manufacture's instructions (Nanosep Centrifugal Devices with Omega Membrane-10K, PALL), and the resulting peptides to multiplexed isobaric labeling with TMT reagents (Thermo Fisher Scientific). (Baldan-Martin M et al., 2018; Bagwan N et al., 2018) Two of the 10 channels were reserved for internal reference standard samples created by pooling the samples. Plasma peptides were separated into five fractions using a high pH reversed-phase peptide fractionation kit (Thermo Fisher Scientific), and tissue peptides were separated into eight fractions using OASIS MCX cartridges. Each plasma fraction was analyzed by LC-MS/MS using an Ultimate 3000 HPLC system (Thermo Fisher Scientific) coupled via a nanoelectrospray ion source (Thermo Fisher Scientific, Bremen, Germany) to a Q Exactive HF mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). (Bagwan N et al., 2018) Tissue fractions were analyzed on an Easy nLC 1000 liquid chromatography system coupled to an Orbitrap Fusion mass spectrometer (Thermo Scientific). (Baldan-Martin M et al., 2018)
Immunoturbidimetric analysis. Plasma levels of IGHA2, HPT and APOA were measured by immunoturbidimetric assays (LK088.OPT, NK058.OPT and LK098.OPT, respectively) using the Binding Site Optilite analyzer. The analysis was performed in a blinded manner by a technician from The Binding Site company.
Statistical analysis. Peptide identification, quantification and statistical and systems biology analysis were performed using the models developed in our laboratory (Navarro P et al., 2014; Navarro P and Vazquez J, 2008; Jorge I et al., 2009; Garcia-Marques F et al., 2016; Bonzon-Kulichenko E et al., 2016; Martinez-Bartolome S et al., 2008). Quantitative information was extracted from the MS/MS spectra of TMT-labeled peptides. Peptide quantification was analyzed using the WSPP model, which uses raw quantifications as input data and computes the protein log 2-fold changes for each individual with respect to the mean value of the two reference internal standard samples. In this model, protein log 2-ratios are expressed as standardized variables in units of standard deviation according to their estimated variances (Zq values).
Adjusted linear and logistic regression models were used to analyze the association of proteins with the presence and extent of atherosclerosis using SPSS software (IBM, Armonk, N.Y.). A multivariate analysis was conducted to explore candidate variables with a clinical association: glucose, LDL, HDL, systolic and diastolic blood pressure, smoking, and age. Associations were expressed as odds ratios (ORs) with 95% confidence intervals (CI). Differences were considered significant at a p-value<0.05 was considered statistically significant. The C-statistic or area under the receiver operating characteristic (ROC) curve (AUC) were calculated for each model as a measure of the discriminatory power of each biomarker protein panel. Comparison of the C-index for models including and not including information provided by the biomarker panels was performed according to the method of DeLong (DeLong E R et al., 1988). To evaluate whether the biomarker panels helped to correctly classify individuals according to presence and extent of subclinical atherosclerosis, we calculated the categorical net reclassification index using four discrete risk categories (NRI0.25), and the category-free net reclassification index (NRI>0), also called continuous net reclassification index (Kerr K F et al., 2014, Pepe M S et al., 2015; Leening M J et al., 2014), which does not depend on the arbitrary choice of categories, but deems any change in predicted risk in the correct direction as appropriate in the low-risk subpopulation we evaluated whether the biomarker panels helped to classify individuals according to the presence of subclinical atherosclerosis using two risk categories (risk/no risk) (NRI0.5).
Study Design
The current report examines a subcohort of the PESA study. (Fernandez-Ortiz A et al., 2013) The first cohort, used for the discovery phase (PESA-V1), included 444 men with a mean age of 48.5 years, organized as 222 pairs of individuals with no clinical history of atherosclerosis. Complete clinical characteristics of the PESA-V1 cohort are detailed in Table 13. Plasma samples from PESA-V1 were analyzed by deep quantitative proteomics.
The stability of the detected protein abundance changes over time was confirmed by repeating the proteomics analysis with plasma collected from the same individuals three years later (PESA-V2) (except for the four individuals that did not renew their consent for the ‘omics’ analysis); the clinical characteristics of the PESA-V2 individuals are depicted in Supplementary Table 1. The proteomics quantified a mean of 1093 proteins per individual, and 454 proteins could be quantified in more than 80% of the individuals. The analysis of the 884 plasma samples required a total of 560 LC-MS runs.
For the validation phase, we examined a third cohort of 350 men from the Aragon Workers Health Study. This cohort had a mean age of 50.3 years and was organized as 175 pairs of individuals with no history of clinical CV disease and with clinical characteristics similar to those of the PESA subcohort (Table 15). (Casasnovas J A et al., 2012; Laclaustra M et al., 2016) Plasma samples from the AWHS cohort were analyzed by turbidimetry using commercially available antibodies against selected proteins.
2.1 Association of Plasma Proteins with Traditional Risk Factors
A correlation network was constructed including the proteins whose levels were significantly correlated with traditional continuous CV risk factors in both the PESA-V1 and the PESA-V2 sample sets, the CV risk factors considered were glucose, age, systolic and diastolic blood pressure, cholesterol, LDL and HDL (
2.2 Association of Plasma Proteins with Plaque Thickness and Calcium Score
Association between plasma protein levels and plaque thickness was assessed by univariate linear regression analysis corrected by multiple hypothesis testing. This analysis revealed a list of plasma proteins showing significant correlation (FDR<5%) with plaque thickness in PESA-V1 (Table 16). Most proteins maintained the correlation with plaque thickness with a FDR below 15% at 3-year follow-up (PESA-V2) (Table 16). Most of these proteins were related to the humoral immune response, including polymeric immunoglobulin receptor (PIGR) and IGHA2, which were increased, and immunoglobulin heavy constant gamma 2 (IGHG2), immunoglobulin kappa constant (IGKC), immunoglobulin heavy constant gamma 1 (IGHG1) and immunoglobulin lambda constant 2 (IGLC2), which were decreased. HPT, C4-binding protein (C4BP), heparin cofactor 2 (HEP2), APOA, complement component 9 (CO9) and gelsolin (GELS) also maintained their correlation with plaque thickness three years later (Table 16). We also noticed that vascular adhesion protein 1 (VCAM1) and von Willebrand factor (VWF), two proteins considered to be biomarkers of endothelial dysfunction, decreased their levels with plaque thickness in PESA-V1 (Table 16). Although this tendency was contrary to the expected, this observation was not reproduced in PESA-V2.
In general the correlations with plaque thickness in PESA-V1 were reproduced in PESA-V2 (
Among the proteins that correlated with plaque thickness, we judged that IGHA2, PIGR, HEP2, HPT, APOA, GELS, IGKC, IGLC2 and IGHG1 were the proteins that better maintained the correlation in both PESA-V1 and V2 after adjustment by individual risk factors using multivariate linear regression analysis (Table 17).
Since these results were obtained after the analysis of a large number of proteins by proteomics, we studied whether some of them could be reproduced using other approaches. Turbidimetry analysis of the plasma levels of two representative proteins (IGHA2 and APOA) for which there were commercially available antibodies confirmed their independent correlation with plaque thickness (Table 18).
A similar analysis was performed with the proteins that correlated with coronary artery calcium score (CACS). Some of the proteins associated to plaque thickness, including HPT, APOA and GELS, also showed a significant correlation with CACS in PESA-V1 which was maintained in V2 (Table 19 and
However, among the proteins related to humoral immune response, only IGKC maintained its correlation with CACS (Table 19 and
We also analyzed the behavior of representative acute-phase proteins (including C-reactive protein (CRP), serum amyloid A-1 (SAA1) and alpha-1-acid glycoprotein 1 (ORM1)) and of other proteins that have been previously described to associate with subclinical atherosclerosis (Table 21). While CRP correlated with plaque thickness in PESA-V2, none of the acute phase proteins maintained a stable correlation in the two PESA visits. Among proteins previously proposed to associate with subclinical atherosclerosis, (Saarikoski L A et al., 2010; Bhosale S D et al., 2018) cadherin-13 (CDH13) showed significant correlation with plaque thickness in PESA-V1, but the result was not confirmed in PESA-V2. No associations were detected with CACS in any of the two visits.
A logistic regression analysis was conducted to determine the effect of plasma protein levels on the likelihood that participants have subclinical atherosclerotic disease. Increased plasma levels of PIGR, IGHA2, APOA, HPT and HEP2 were significantly associated with increased likelihood of disease and the associations remained significant after adjustment by FHS 10-year score (Table 22 and
To select a biomarker panel, several protein combinations were tested using multivariate logistic regression models. IGHG1 did not reach statistical significance when other proteins were included in the same panel, while PIGR, IGHA2, APOA, HPT and HEP2 could be combined to form several three-protein panels, where PIGR could be replaced by IGHA2 and HEP2 by HPT (Table 23). The individual association of these five proteins with subclinical atherosclerosis remained significant after adjustment by BEWAT or ICHS risk scores (
2.4 the Plasma Proteins that Associate with Subclinical Atherosclerosis Accumulate in Early Atherosclerotic Lesions
We determined the abundance of the five selected proteins in the media and intima layers of early human atherosclerosis lesions (fatty streak and fibrolipidic plaques). Quantitative proteomics analysis revealed markedly elevated absolute abundances of PIGR, IGHA2, APOA, HPT and HEP2 in the intimal relative to the medial layer (
2.5 Validation in a Cohort from the Aragon Worker Health Study (AWHS)
For the validation phase, we selected the panel composed by IGHA2, APOA and HPT, since these proteins could be measured using commercially available antibody-based kits used in the clinic. The validation study was conducted with a set of 350 plasma samples obtained from the AWHS cohort. Multivariate logistic regression analysis revealed that increased plasma concentration of each of the three proteins was significantly associated with an increased likelihood of subclinical atherosclerosis (
The ability of the three proteins, included as a biomarker panel, to improve subclinical atherosclerosis prediction by risk scores alone was assessed by receiver operating characteristic (ROC) analysis. In the PESA-V1 cohort, a panel composed by the three proteins and FHS 10-year score produced an AUC (0.76:0.71-0.81 95% CI) significantly better than the FHS 10-year score alone (0.71:0.66-0.77, p<0.05, DeLong test) (
To assess whether these proteins provide useful information about individuals at low risk of CV events according to risk scores, we selected a subpopulation of participants with a FHS 10-year score <0.1 (Fernandez-Friera L et al., 2015; Pencina M J et al., 2009; Ford E S et al., 2004). In this subpopulation, the 3-protein biomarker panel efficiently predicted subclinical disease in PESA-V1 (AUC=0.62:0.56-0.68, p<0.001 vs AUC=0.5) (
| Number | Date | Country | Kind |
|---|---|---|---|
| 18382778.1 | Oct 2018 | EP | regional |
| 19382594.0 | Jul 2019 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2019/079623 | 10/30/2019 | WO | 00 |
