WO2008057966A2 - Panel de biomarqueurs de maladie d'artères périphériques - Google Patents

Panel de biomarqueurs de maladie d'artères périphériques Download PDF

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WO2008057966A2
WO2008057966A2 PCT/US2007/083377 US2007083377W WO2008057966A2 WO 2008057966 A2 WO2008057966 A2 WO 2008057966A2 US 2007083377 W US2007083377 W US 2007083377W WO 2008057966 A2 WO2008057966 A2 WO 2008057966A2
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cystatin
risk
biomarkers
microglobulin
artery disease
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PCT/US2007/083377
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WO2008057966A3 (fr
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Eric T. Fung
John Cooke
Fujun Zhang
Andrew Wilson
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Vermillion, Inc.
The Board Of Trustees Of The Leland Stanford Jr. University
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Publication of WO2008057966A2 publication Critical patent/WO2008057966A2/fr
Publication of WO2008057966A3 publication Critical patent/WO2008057966A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/66—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood sugars, e.g. galactose
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/72—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood pigments, e.g. haemoglobin, bilirubin or other porphyrins; involving occult blood
    • G01N33/721—Haemoglobin
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/72—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood pigments, e.g. haemoglobin, bilirubin or other porphyrins; involving occult blood
    • G01N33/721—Haemoglobin
    • G01N33/723—Glycosylated haemoglobin
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/46—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from vertebrates
    • G01N2333/47—Assays involving proteins of known structure or function as defined in the subgroups
    • G01N2333/4701—Details
    • G01N2333/4737—C-reactive protein
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/705—Assays involving receptors, cell surface antigens or cell surface determinants
    • G01N2333/70503—Immunoglobulin superfamily, e.g. VCAMs, PECAM, LFA-3
    • G01N2333/70539—MHC-molecules, e.g. HLA-molecules
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/81—Protease inhibitors
    • G01N2333/8107—Endopeptidase (E.C. 3.4.21-99) inhibitors
    • G01N2333/8139—Cysteine protease (E.C. 3.4.22) inhibitors, e.g. cystatin
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/32—Cardiovascular disorders

Definitions

  • Peripheral arterial disease affects 8 to 12 million individuals in the United States and is also prevalent in Europe and Asia (Allison et al., Am JPrev Med 2007; 32:328- 33; Hasimu et al., Hypertens Res 2006; 29:23-8; Brevetti et al., J Cardiovasc Med (Hagerstown) 2006; 7:608-13; Hayoz et al., J Intern Med 2005; 258:238-43; Criqui et al., Circulation 2005; 112:2703-7).
  • PID Peripheral arterial disease
  • PAD causes limb fatigue or pain brought on by exertion and relieved by rest, Le, intermittent claudication, and reduces functional capacity and quality of life (McDermott et al., Jama 2004; 292:453-61). It is frequently associated with coronary artery disease (CAD) and cerebral disease (McDermott et al., Jama 2004; 292:453-61; Steg et al., Jama 2007; 297:1197-206).
  • CAD coronary artery disease
  • the invention provides a blood test that increases the clinical index of suspicion and allows practitioners to identify patients that merit greater scrutiny for PAD, even where those practitioners lack immediate access to the specialized equipment and trained personnel to perform measurements of limb blood flow and pressure ⁇ e.g., the ankle- brachial index or ABI).
  • Patients with elevated scores can be referred to vascular specialists who could provide further evaluation and appropriate management.
  • Intensive risk factor modification confers longevity in these patients and extends freedom from major adverse cardiovascular events (Beckman et al., Circulation 2006; 114:861-6).
  • the blood tests of the invention can distinguish those at greater risk of having PAD and allow for those individually to be selectively triaged for vascular testing (rather than sending all patients for expensive vascular testing).
  • the invention provides a method for diagnosing peripheral artery disease in a subject, comprising obtaining measurements of the levels of beta-2- microglobulin (also referred to as "B2M,” herein), cystatin C, hsCRP, and at least one biomarker selected from the group consisting of glucose, hemoglobin AIc and glycated hemoglobin; comparing said measurements to a standard, wherein increases in said levels of beta-2-microglobulin, cystatin C, hsCRP, and glucose and/or hemoglobin AIc and/or glycated hemoglobin relative to said standard is associated with an increased risk of peripheral artery disease in a subject; and reporting or recording the results of said comparison.
  • beta-2- microglobulin also referred to as "B2M,” herein
  • cystatin C cystatin C
  • hsCRP hsCRP
  • at least one biomarker selected from the group consisting of glucose, hemoglobin AIc and glycated hemoglobin comparing said measurements
  • the method further comprises determining the risk of coronary artery disease ("CAD") in said subject using, e.g., a score sheet.
  • the score sheet could be that recommended by the American Heart Association for determining the risk of CAD.
  • the risk of CAD in the subject of interest is determined to be low, medium or high.
  • the subject is affirmatively diagnosed with coronary artery disease.
  • the subject is being treated for PAD and the method is used to monitor the course of treatment.
  • the invention provides a method for diagnosing peripheral artery disease in a subject, comprising obtaining measurements of the levels of beta-2- microglobulin, cystatin C, hsCRP, and at least one biomarker selected from the group consisting of glucose, hemoglobin AIc and glycated hemoglobin; combining the measured levels to derive an index score; comparing said index score with a standard, wherein said comparison identifies a risk of peripheral artery disease in a subject; and reporting or recording the results of said comparison.
  • the method further comprises determining the risk of coronary artery disease in said subject.
  • the method further comprises determining the risk of coronary artery disease ("CAD") in said subject using, e.g., a score sheet.
  • the score sheet could be that recommended by the American Heart Association for determining the risk of CAD.
  • the risk of CAD in the subject of interest is determined to be low, medium or high.
  • the subject is affirmatively diagnosed with coronary artery disease.
  • the risk of CAD in the subject is determined to be high.
  • the subject is being treated for PAD and the method is used to monitor the course of treatment.
  • the invention provides a method for diagnosing peripheral artery disease in a patient previously diagnosed with coronary artery disease or a risk thereof, comprising obtaining measurements of the levels of at least one biomarker selected from the group consisting of glucose, hemoglobin AIc and glycated hemoglobin, and at least one additional biomarker selected from the group consisting of ⁇ -2 -microglobulin, cystatin C, and hsCRP; comparing said measurements to a standard, wherein an increase in said levels of glucose and said at least one additional biomarker relative to said standard is associated with an increased risk of peripheral artery disease in a subject; and reporting or recording the results of said comparison.
  • the additional biomarkers are ⁇ -2- microglobulin and cystatin C. In another related embodiment, the additional biomarkers are cystatin C and hsCRP. In still another related embodiment, the additional biomarkers are ⁇ - 2-microglobulin and hsCRP. In yet another related embodiment, only one additional biomarker is selected from the group consisting of ⁇ -2 -microglobulin, cystatin C, and hsCRP. In still other related embodiments, other suitable biomarkers (e.g., lysozyme) are utilized in addition to the above-mentioned combinations of biomarkers. [0009] In another related embodiment, a computer algorithm is utilized to calculate the index score for assessing risk of peripheral artery disease. In yet another related embodiment, the index score is identified as falling into one of at least three categories of increasing risk. In yet another related embodiment, the index score is categorized as falling into a tertile corresponding to low, medium or high risk of PAD.
  • the levels of beta-2-microglobulin, cystatin C, hsCRP, hemoglobin AIc and/or glycated hemoglobin are measured by antibody, activity assays, mass spectrometry, and/or other methods known to those skilled in the art for measuring proteins found in human blood or serum.
  • the invention provides a kit useful for determining the risk of PAD in a subject, wherein the kit comprises a solid support comprising a capture reagent that binds beta-2-microglobulin; a solid support comprising a capture reagent that binds C reactive protein (CRP); a solid support comprising a capture reagent that binds Cystatin C; and instructions for using the solid support to detect beta-2-microglobulin, Cystatin C and CRP.
  • the kit comprises a capture reagent that binds beta 2- microglobulin, CRP and Cystatin C.
  • the kit further comprises reagents for measuring glucose levels, hemoglobin AIc levels and/or glycated hemoglobin levels in a serum sample.
  • the kit comprises reference standards for beta 2-microglobulin, CRP, and Cystatin C.
  • the kit comprises reference standards for hemoglobin AIc or glycated hemoglobin.
  • the invention provides a software product comprising code that accesses data attributed to a sample, the data comprising levels of at least four biomarkers in the sample, wherein the at least four biomarkers include beta 2-microglobulin, CRP, Cystatin C, and at least one biomarker selected from the group consisting glucose, hemoglobin AIc or glycated hemoglobin; and code that executes a classification algorithm that classifies the peripheral artery disease status of the sample as a function of said levels.
  • the invention provides the methods, kits and software products provided above, further incorporating materials and methods for the detection of levels of lysozyme C in a subject and correlating those measurements with PAD status.
  • Figure 1 shows a receiver operated curve (ROC) analysis of a four marker index comparing NHSD (no hemodynamically significant disease, e.g. , atherosclerosis) vs CAD+PAD and CAD vs CAD+PAD subjects.
  • NHSD no hemodynamically significant disease, e.g. , atherosclerosis
  • Figure 2 presents the odds ratio of CAD+PAD status by AHA (American Heart Association) risk score and by biomarker panel score.
  • AHA American Heart Association
  • biomarker panel score There is a positive interaction between the two assessments of disease risk. Individuals were assigned an AHA risk score using the traditional cardiovascular risk factors as described by Wilson et al., Circulation 1998; 97:1837-47.
  • the tertile cutoffs of the biomarker panel score were used to determine the risk level: low ( ⁇ .991), medium (.991-1.033), and high (>1.033).
  • Figure 3 shows an exemplary "score sheet" for determining the risk of coronary artery disease (CAD) in a subject.
  • CAD coronary artery disease
  • the subject is a female and LDL cholesterol levels are measured.
  • American Heart Association score sheets for determining the risk of CAD are well-known in the art.
  • This invention provides panels of multiple biomarkers that are useful for diagnosing peripheral arterial disease.
  • a four biomarker panel comprising ⁇ 2M, cystatin C, hsCRP, and glucose is presented that allows clinicians to evaluate the relative risk of PAD even in the background of traditional risk factors (e.g., diabetes). Individuals in the top quartile of the four biomarker panel score had a 7-fold greater risk of PAD.
  • Such a biomarker panel can be used to alert a clinician to the possibility of PAD in patients who might otherwise go undiagnosed.
  • a biomarker is an organic biomolecule which is differentially present in a sample taken from a subject of one phenotypic status (e.g., having a disease) as compared with another phenotypic status (e.g., not having the disease).
  • a biomarker is differentially present between different phenotypic statuses if the mean or median expression level of the biomarker in the different groups is calculated to be statistically significant. Common tests for statistical significance include, among others, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann- Whitney and odds ratio.
  • Biomarkers, alone or in combination provide measures of relative risk that a subject belongs to one phenotypic status or another. As such, they are useful as markers for disease (diagnostics), therapeutic effectiveness of a drug (theranostics) and of drug toxicity.
  • the biomarker of this invention were discovered, in part, using SELDI (Surface- enhanced laser desorption ionization). Accordingly, they can be characterized, in part, by their mass-to-charge ratio, the shape of the peak in a mass spectrum and their binding characteristics. These characteristics represent inherent characteristics of the biomolecule and not process limitations in the manner in which the biomolecule is discriminated. [0020]
  • the mass-to-charge ratio of some of the protein biomarkers are provided herein.
  • a particular molecular marker designated, for example, as "Ml 1.7K” has a measured mass-to- charge ratio of 1 1.7 kD.
  • the mass-to-charge ratios were determined from mass spectra generated on a Ciphergen Biosystems, Inc.
  • the PBS II is instrument has a mass accuracy of about +/- 0.15 percent. Additionally, the instrument has a mass resolution of about 400 to 1000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peak height.
  • the PCS4000 instrument has a mass accuracy of about +/- 0.12 % raw data with an expected externally calibrated mass accuracy of 0.1% and internally calibrated mass accuracy of 0.01%. Additionally, the instrument has a mass resolution of about 1000 to 2000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peak height.
  • the mass-to-charge ratio of the biomarkers was determined using Biomarker Wizard" 11 software (Ciphergen Biosystems, Inc.). Biomarker Wizard assigns a mass-to-charge ratio to a biomarker by clustering the mass-to-charge ratios of the same peaks from all the spectra analyzed, as determined by the PBSII or PCS4000, taking the maximum and minimum mass-to-charge-ratio in the cluster, and dividing by two. Accordingly, the masses provided reflect these specifications. [0021]
  • the biomarkers of this invention may be further characterized by the shape of their spectral peak in time-of-flight mass spectrometry.
  • biomarkers of this invention are also characterized by their binding characteristics to adsorbent surfaces.
  • the binding characteristics of the biomarkers are also described herein.
  • Useful protein biomarkers for peripheral artery disease include ⁇ 2-microglobulin, Cystatin C, hsCRP and lysozyme C.
  • ⁇ 2-microglobulin is a 99 amino acid protein derived from a 119 amino acid precursor (GI:179318; SwissProt Accession No. P61769) and is recognized by antibodies available from, e.g., Abeam (catalog AB759) (www.abcam.com, Cambridge, MA). Levels of ⁇ 2-microglobulin less than 1.85 mg/ml are considered within normal limits.
  • Various other features of the biomarkers are described in the Tables presented herein.
  • Lysozyme i.e., lysozyme C
  • the fractions referred to in the last column of Table 1, above, are the fractions in which the biomarkers elute from a QHyper DF column (BioSepra, Cergy, France).
  • the QHyperDF column can be used to purify the biomarkers from plasma, as described in, e.g., U.S. Pat. App. 11/685,146.
  • IMAC-Cu + * and CMlO refer to commercially available ProteinChips comprising metal chelating and cation exchange adsorbents, respectively.
  • C-reactive protein is a homopentameric oligoprotein composed of monomelic subunits that are each about 21 kD.
  • the human CRP molecule has a relative molecular weight of about 115 kDa (115,135 Da), and is composed of five identical non-glycosylated polypeptide subunits, each having a relative molecular weight of about 23 kDa (23,027 Da), and each containing 206 amino acid residues (Hirschfield and Pepys Q J Med 2003; 96: 793-807). Serum levels of hsCRP are elevated in individuals at risk for peripheral artery disease.
  • hsCRP be used to "detect enhanced absolute risk in persons in whom multiple risk factor scoring (based on the Framingham Heart Study global risk scoring system) projects a 10-year CHD risk in the range of 10% to 20%.”
  • risk factor scoring based on the Framingham Heart Study global risk scoring system
  • hsCRP can be used to determine those at lower or greater risk. Risk would be relatively “low” with hsCRP levels of less than lmg/L; "average” at 1-3 mg/L; and “high” at levels greater than 3mg/L.
  • one skilled in the art would know how to generate or obtain antibodies for the purpose of measuring CRP in human serum.
  • Cystatin C (sometimes referred to as cystatin 3) is a cysteine protease inhibitor found in serum that is sometimes used as a biomarker for kidney function. Antibodies useful for detecting cystatin C are readily available. The range of Cystatin C in human serum is between 0.5 and 0.99 mg/dl (see, e.g., Uhlmann EJ er a/., Clin Chem. 2001;47(l l):2031- 2033).
  • the protein biomarkers cystatin C, hsCRP and/or beta 2-microglobulin levels in serum are measured in addition to glucose levels.
  • Methods for measuring glucose levels in humans are well-known in the art. Blood glucose is typically measured after fasting (e.g., collected after an 8 to 10 hour fast), and/or as part of an oral glucose tolerance test (OGTT / GTT). Normal fasting levels of glucose are below 100mg/dl.
  • hemoglobin AIc and/or glycated hemoglobin whose levels may be correlated with glucose levels can also be measured and used in the context of the biomarker panel for PAD described herein.
  • Healthy persons typically have levels of hemoglobin AIc from 4-5.9%. Because higher levels of hemoglobin AIc are associated with higher levels of blood glucose (see, e.g., Koenig RJ et ⁇ /.(1976) N. Engl. J. Med. 295 (8):417-20; Larsen et al. (1990). N. Engl. J. Med.
  • the detection of higher levels of hemoglobin AIc is a useful indicator of increased risk of PAD in a subject according to the diagnostic methods described herein.
  • a variety of kits and methods for the detection of AIc are available and well-known to those of ordinary skill in the art.
  • Pre-translational modified forms include allelic variants, splice variants and RNA editing forms.
  • Post- translationally modified forms include forms resulting from proteolytic cleavage (e.g., fragments of a parent protein), glycosylation, phosphorylation, lipidation, oxidation, methylation, cysteinylation, sulphonation and acetylation.
  • an immunoassay using a monoclonal antibody will detect all forms of a protein containing the epitope and will not distinguish between them.
  • a sandwich immunoassay that uses two antibodies directed against different epitopes on a protein will detect all forms of the protein that contain both epitopes and will not detect those forms that contain only one of the epitopes.
  • the inability to distinguish different forms of a protein has little impact when the forms detected by the particular method used are equally good biomarkers as any particular form.
  • Mass spectrometry is a particularly powerful methodology to resolve different forms of a protein because the different forms typically have different masses that can be resolved by mass spectrometry. Accordingly, if one form of a protein is a superior biomarker for a disease than another form of the biomarker, mass spectrometry may be able to specifically detect and measure the useful form where a traditional immunoassay fails to distinguish the forms and fails to specifically detect the useful biomarker.
  • a biospecific capture reagent ⁇ e.g., an antibody, aptamer or Affibody that recognizes the biomarker and other forms of it
  • the biospecific capture reagent is bound to a solid phase, such as a bead, a plate, a membrane or a chip.
  • a solid phase such as a bead, a plate, a membrane or a chip.
  • the captured analytes are detected and/or measured by mass spectrometry.
  • This method also will also result in the capture of protein interactors that are bound to the proteins or that are otherwise recognized by antibodies and that, themselves, can be biomarkers.
  • Various forms of mass spectrometry are useful for dectecting the protein forms, including laser desorption approaches, such as traditional MALDI or SELDI, and electrospray ionization.
  • the step of "measuring beta-2- microglobulin” includes measuring beta-2-microglobulin by means that do not differentiate between various forms of the protein (e.g., certain immunoassays) as well as by means that differentiate some forms from other forms or that measure a specific form of the protein.
  • the particular form (or forms) is specified.
  • “measuring beta-2-microglobulin (Ml 1.7K)” means measuring beta-2- microglobulin Ml 1.7K in a way that distinguishes it from other forms of beta-2- microglobulin.
  • the ⁇ 2-microglobulin, cystatin C, hsCRP and glucose (or hemoglobin AIc) biomarkers of the present invention can be detected by any suitable method.
  • Detection paradigms include optical methods, electrochemical methods (voltametry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g., multipolar resonance spectroscopy.
  • Illustrative of optical methods in addition to microscopy, both confocal and non-confocal, are detection of fluorescence, luminescence, chemi luminescence, absorbance, reflectance, transmittance, and birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry).
  • detection of fluorescence, luminescence, chemi luminescence, absorbance, reflectance, transmittance, and birefringence or refractive index e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry.
  • a sample is analyzed by means of a biochip.
  • a biochip generally comprises a solid substrate having a substantially planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached.
  • a capture reagent also called an adsorbent or affinity reagent
  • the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there.
  • Protein biochips are biochips adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems, Inc. (Fremont, CA), Zyomyx (Hayward, CA), Invitrogen (Carlsbad, CA), Biacore (Uppsala, Sweden) and Procognia (Berkshire, UK). Examples of such protein biochips are described in the following patents or published patent applications: U.S. Patent No. 6,225,047 (Hutchens & Yip); U.S. Patent No. 6,537,749 (Kuimelis and Wagner); U.S. Patent No.
  • the biomarkers of this invention are detected by mass spectrometry, a method that employs a mass spectrometer to detect gas phase ions.
  • mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these.
  • a laser desorption mass spectrometer can be used which employs laser energy, typically from an ultraviolet laser, but also from an infrared laser, to desorb analytes from a surface, to volatilize and ionize them and make them available to the ion optics of the mass spectrometer.
  • the analysis of proteins by LDI can take the form of MALDI or of SELDI, described, for example, in U.S. Patents No. 5,719,060 and No. 6,225,047, both to Hutchens and Yip.
  • one can capture the biomarkers with a solid-phase bound immuno-adsorbent that has antibodies that bind the biomarkers.
  • the biomarkers After washing the adsorbent to remove unbound material, the biomarkers are eluted from the solid phase and detected by applying to a chip (e.g., a SELDI chip) that binds the biomarkers and analyzing by SELDI.
  • a chip e.g., a SELDI chip
  • the biomarkers bound to the substrates are detected in a gas phase ion spectrometer such as a time-of-flight mass spectrometer.
  • the biomarkers are ionized by an ionization source such as a laser, the generated ions are collected by an ion optic assembly, and then a mass analyzer disperses and analyzes the passing ions.
  • the detector then translates information of the detected ions into mass-to-charge ratios. Detection of a biomarker typically will involve detection of signal intensity. Thus, both the quantity and mass of the biomarker can be determined.
  • SEND Surface-Enhanced Neat Desorption
  • SEPAR Surface-Enhanced Photolabile Attachment and Release
  • the biomarkers of the invention are measured by a method other than mass spectrometry, or any other method that requires determining the mass of the biomarker.
  • the biomarkers are measured by immunoassay.
  • Immunoassay requires biospecific capture reagents, such as antibodies, to bind or capture the biomarkers.
  • Antibodies can be produced by methods well known in the art, e.g. , by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well known in the art.
  • Antibodies and methods for detecting beta 2-microgIobuIin using standard assays are described in the art, e.g., Hilgert et al. ⁇ Folia Biol (Praha) (1984) 30:369-76). Examples of the use of these antibodies to detect increased levels of beta 2-microglobulin in PAD patients relative to normal patients are provided herein. Similar methods for the immunoassay detection of cystatin C, hsCRP, and other protein biomarkers are also known in the art. Enzyme-linked immunoabsorbent assays (ELISA' s) directed at these biomarkers are commercially available from a wide variety of sources, including Dade-Behring and BioCheck.
  • ELISA' s Enzyme-linked immunoabsorbent assays directed at these biomarkers are commercially available from a wide variety of sources, including Dade-Behring and BioCheck.
  • This invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, other enzyme immunoassays and western blot.
  • Nephelometry is an assay done in liquid phase, in which antibodies are in solution. Binding of the antigen to the antibody results in changes in absorbance, which is measured.
  • a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, such as a pre-activated ProteinChip array. The biomarker is then specifically captured on the biochip through this reagent, and the captured biomarker is detected by mass spectrometry.
  • Measurements of levels of glucose or hemoglobin Alc/glycated hemoglobin in human serum or plasma can be achieved using any of a variety of well-known assays, including commercial kits.
  • glucose test kits are available from a wide variety of vendors, including Abbott and Olympus.
  • peripheral artery disease status includes any distinguishable manifestation of the disease, including non-disease.
  • peripheral artery disease status includes, without limitation, the presence or absence of disease ⁇ e.g., peripheral artery disease v. non-peripheral artery disease), the risk of developing disease, the stage of the disease, the progression of disease ⁇ e.g. , progress of disease or remission of disease over time), the severity of the disease, and the effectiveness or response to treatment of disease.
  • the correlation of test results with peripheral artery disease status involves applying a classification algorithm of some kind to the results to generate the status.
  • the classification algorithm may be as simple as determining whether or not the amount of beta-2- microglobulin measured is above or below a particular cut-off number.
  • the classification algorithm may be a linear regression formula.
  • the classification algorithm may be the product of a learning algorithm.
  • One way to translate biomarker measurements into an assessment of disease risk is to devise a scoring sheet, such as that shown in Figure 3 (showing how LDL cholesterol levels may be used to determine the likelihood of coronary artery disease).
  • This type of scoring sheet provides cut-off values against which measurements are compared in order to determine how many "points" are assigned to the measurement. For example, a measurement of 170 mg/dL LDL-cholesterol merits 2 points in the scoring sheet (see Figure 3, Step 2).
  • the correlation of the results of tests using, e.g., a combined beta 2-microglobulin, CRP, cystatin C and glucose (or hemoglobin AIc) biomarker panel with PAD status generally involves applying a classification algorithm.
  • such an algorithm may be as simple as determining whether the levels of a particular biomarker are above or below a "cut-off or "threshold” or value. Levels above threshold are considered “high” and may be assigned a higher score value in the context of the algorithm. Levels below the threshold may be considered “low” and may be assigned a lower score value. [0046]
  • the "threshold" value for a biomarker in the context of the present method for assessing PAD status refers to a median value of a range of biomarker levels ⁇ e.g., amounts) for a selected subject population.
  • a median value for a selected subject population is suitable for evaluating high and low levels of each of the biomarkers in a panel (e.g., CRP/B2M/CystatinC/glucose) in view of the left skewed distributions of those biomarkers across a population.
  • a “high” biomarker level is a level of biomarker that is greater than this threshold value; a “low” biomarker level is lower than this threshold value.
  • a threshold value is a median value such that a test value above, usually significantly above, the median value is classified as "high” and a test value below, usually significantly below, the median value is classified as "low”.
  • a median biomarker level can be used to define a cut-off value between a upper middle quartile values and lower middle quartile values, with the upper quartile and upper middle quartile representing roughly equal numbers of biomarker values from the subject population that are above the median, and the lower middle quartile and lower quartile representing roughly equal numbers of biomarker values below the median value.
  • Evaluation of PAD risk can be assessed by, for example, assigning a subject to a quartile/quintile according to assessed biomarkers levels, where the quartile/quintile associated with the highest B2M, CRP, Cystatin C and glucose (and/or hemoglobin AIc) levels represents the highest risk of PAD.
  • the points may be added up or transformed by an algorithm into a value that correlates with, e.g., low risk of disease, moderate risk of disease, or high risk of disease.
  • a computer e.g., a. programmable digital computer.
  • the power of a diagnostic test to correctly predict status is commonly measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic ("ROC") curve.
  • Sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative.
  • An ROC curve provides the sensitivity of a test as a function of 1 - specificity. The greater the area under the ROC curve, the more powerful the predictive value of the test.
  • Other useful measures of the utility of a test are positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative.
  • a test that has a high sensitivity but a low specificity may still be useful if its negative predictive value is high enough to exclude a diagnosis of PAD.
  • An example of a clinically very useful test that has high sensitivity but low specificity is the ventilation-perfusion scan.
  • a negative test virtually excludes pulmonary embolism since the negative predictive value is over 95%.
  • Such a test result can reduce the need for further and more expensive testing.
  • ⁇ 2-microglobulin, hsCRP, cystatin C and glucose are differentially present in subjects with peripheral artery disease. While individual biomarkers are useful diagnostic biomarkers, it has been found that a combination of biomarkers can provide greater predictive value of a particular status than single biomarkers alone.
  • the detection of a plurality of biomarkers in a sample can increase the sensitivity and/or specificity of the test.
  • a combination of at least two biomarkers is sometimes referred to as a "biomarker profile,” “biomarker fingerprint,” or “biomarker panel.”
  • a biomarker panel comprising ⁇ 2-microglobulin, hsCRP, cy statin C and glucose (or hemoglobin AIc) can be combined with other biomarkers for peripheral artery disease to improve the sensitivity and/or specificity of the diagnostic test.
  • biomarker panel comprising ⁇ 2-microglobulin, hsCRP, cy statin C and glucose (or hemoglobin AIc) can be combined with other biomarkers for peripheral artery disease to improve the sensitivity and/or specificity of the diagnostic test. Examples of other biomarkers useful for screening for PAD are found in the PCT Application US2005/018728 (Inter. Pub. No. WO2005/121758), filed May 26, 2005.
  • this invention provides methods for assessing the risk of peripheral artery disease in a subject (status: peripheral artery disease v. non-peripheral artery disease).
  • the risk of peripheral artery disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level.
  • this invention provides methods for determining the risk of developing peripheral artery disease in a subject.
  • Biomarker amounts or patterns are characteristic of various risk states, e.g., high, medium or low.
  • the risk of developing a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level
  • this invention provides methods for determining the severity of peripheral artery disease in a subject.
  • a subject with peripheral artery disease will have a characteristic pattern of biomarker levels depending on the severity of the disease.
  • Disease severity may be determined by measuring the relevant biomarkers and then submitting the measured amounts to a classification algorithm, or measuring the relevant biomarkers and then comparing them with a reference amount and/or pattern of biomarkers associated with a degree of severity of the disease. For example, one can classify between mild, moderate, and severe peripheral artery disease, as well as non-peripheral artery disease.
  • An index of severity based on the biomarker panel of the invention can likewise be used to predict the likelihood of future cardiovascular events such as stroke, myocardial infarction, and loss of limb. Determining Course (Progression/Remission) of Disease
  • this invention provides methods for determining the course of disease in a subject.
  • Disease course refers to changes in disease status over time, including disease progression (worsening) and disease regression (improvement).
  • the amounts or relative amounts (e.g., the pattern) of the biomarkers changes. For example, high beta-2-microglobulin levels, high cystatin C levels, high hsCRP levels, and high glucose (and/or hemoglobin AIc) levels are correlated with PAD. Therefore, the trend of these markers, either increased or decreased over time toward diseased or non-diseased, can be used to monitor the course of the disease.
  • this method involves measuring one or more biomarkers in a subject for at least two different time points, e.g., a first time and a second time, and comparing the change in amounts, if any. The course of disease is determined based on these comparisons. Reporting the Status
  • Additional embodiments of the invention relate to the communication of assay results or diagnoses or both to technicians, physicians or patients, for example.
  • computers will be used to communicate assay results or diagnoses or both to interested parties, e.g., physicians and their patients.
  • the assays will be performed or the assay results analyzed in a country or jurisdiction which differs from the country or jurisdiction to which the results or diagnoses are communicated.
  • a diagnosis based on measurements of ⁇ 2-microglobulin, cystatin C, hsCRP and glucose (or, in some instances, hemoglobin Alc/glycated hemoglobin) in a test subject is communicated to the subject after the diagnosis is obtained.
  • the diagnosis may be communicated to the subject by the subject's treating physician. Alternatively, the diagnosis may be sent to a test subject by email or communicated to the subject by phone. A computer may be used to communicate the diagnosis by email or phone.
  • the message containing results of a diagnostic test may be generated and delivered automatically to the subject using a combination of computer hardware and software which will be familiar to artisans skilled in telecommunications.
  • a healthcare-oriented communications system is described in U.S. Patent Number 6,283,761; however, the present invention is not limited to methods which utilize this particular communications system.
  • all or some of the method steps, including the assaying of samples, diagnosing of diseases, and communicating of assay results or diagnoses may be carried out in diverse (e.g., foreign) jurisdictions.
  • the methods further comprise managing subject treatment based on the status.
  • Such management includes the actions of the physician or clinician subsequent to determining peripheral artery disease status. For example, if a physician makes a diagnosis of peripheral artery disease, then a certain regimen of treatment may follow.
  • a suitable regimen of treatment may include, without limitation, a supervised exercise program; control of blood pressure, sugar intake, and/or lipid levels; cessation of smoking, including any necessary counseling and nicotine replacement; and drug therapies including the administration of aspirin (with or without dipyridamole), clopidogrel, cilostazol, and/or pentoxifylline; and/or an angiotensin converting enzyme inhibitor; and/or a beta-adrenergic antagonist (medications which have been shown to prolong the lives of individuals with PAD include anti-platelet agents, statins, ACE inhibitors and beta-adrenergic antagonists).
  • a diagnosis of PAD might be followed by further testing to determine whether a patient is suffering from a specific form of PAD, or whether the patient is suffering from related diseases such as coronary artery disease. Also, if the diagnostic test gives an inconclusive result on PAD status, further tests may be called for.
  • data derived from the spectra e.g., mass spectra or time-of- flight spectra
  • samples such as "known samples”
  • a "known sample” is a sample that has been pre-classified.
  • the data that are derived from the spectra and are used to form the classification model can be referred to as a "training data set.”
  • the classification model can recognize patterns in data derived from spectra generated using unknown samples.
  • the classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased).
  • the training data set that is used to form the classification model may comprise raw data or pre-processed data.
  • raw data can be obtained directly from time-of-flight spectra or mass spectra, and then may be optionally "pre-processed" as described above.
  • Classification models can be formed using any suitable statistical classification (or "learning") method that attempts to segregate bodies of data into classes based on objective parameters present in the data.
  • Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000, the teachings of which are incorporated by reference.
  • supervised classification training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships.
  • supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART - classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).
  • linear regression processes e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)
  • binary decision trees e.g., recursive partitioning processes such as CART - classification and regression trees
  • artificial neural networks such as back propagation networks
  • discriminant analyses e.g.,
  • a preferred supervised classification method is a recursive partitioning process.
  • Recursive partitioning processes use recursive partitioning trees to classify spectra derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. Patent 6,675,104 (Paulse et al., "Method for analyzing mass spectra").
  • the classification models that are created can be formed using unsupervised learning methods. Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived. Unsupervised learning methods include cluster analyses.
  • a cluster analysis attempts to divide the data into "clusters" or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other.
  • Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self-Organizing Map algorithm. [0065] Learning algorithms asserted for use in classifying biological information are described, for example, in PCT International Publication No. WO 01/31580 (Barnhill et al, "Methods and devices for identifying patterns in biological systems and methods of use thereof), U.S. Patent Application No.
  • the classification models can be formed on and used on any suitable digital computer.
  • Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system, such as a Unix, WindowsTM or LinuxTM based operating system.
  • the digital computer that is used may be physically separate from the mass spectrometer that is used to create the spectra of interest, or it may be coupled to the mass spectrometer.
  • the training data set and the classification models according to embodiments of the invention can be embodied by computer code that is executed or used by a digital computer.
  • the computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including C, C++, visual basic, etc.
  • the learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, or for finding new biomarkers for peripheral artery disease.
  • the classification algorithms form the base for diagnostic tests by providing diagnostic values ⁇ e.g., cut-off points) for biomarkers used singly or in combination.
  • this invention provides compositions of matter based on the biomarker panel of this invention, e.g., ⁇ 2-microglobulin, cystatin C, hsCRP, glucose (and/or hemoglobin AIc or an equivalent proxy for glucose levels).
  • compositions of matter based on the biomarker panel of this invention, e.g., ⁇ 2-microglobulin, cystatin C, hsCRP, glucose (and/or hemoglobin AIc or an equivalent proxy for glucose levels).
  • this invention provides the biomarker of this invention in purified form.
  • Purified biomarkers have utility as antigens to raise antibodies.
  • Purified biomarkers also have utility as standards in assay procedures.
  • a "purified biomarker” is a biomarker that has been isolated from other proteins and peptides, and/or other material from the biological sample in which the biomarker is found.
  • the biomarkers can be isolated from biological fluids, such as urine or serum. Biomarkers may be purified using any method known in the art, including, but not limited to, mechanical separation (e.g., centrifugation), ammonium sulphate precipitation, dialysis (including size-exclusion dialysis), electrophoresis (e.g.
  • acrylamide gel electrophoresis size-exclusion chromatography, affinity chromatography, anion-exchange chromatography, cation-exchange chromatography, and metal-chelate chromatography.
  • Such methods may be performed at any appropriate scale, for example, in a chromatography column, or on a biochip.
  • this invention provides a biospecific capture reagent, optionally in purified form, that specifically binds a biomarker of this invention.
  • the biospecific capture reagent is an antibody.
  • Such compositions are useful for detecting the biomarker in a detection assay, e.g., for diagnostics.
  • this invention provides an article comprising a biospecific capture reagent that binds a biomarker of this invention, wherein the reagent is bound to a solid phase.
  • this invention contemplates a device comprising bead, chip, membrane, monolith or microtiter plate derivatized with the biospecific capture reagent. Such articles are useful in biomarker detection assays.
  • this invention provides a composition
  • a biospecific capture reagent such as an antibody
  • a biomarker of this invention the composition optionally being in purified form.
  • Such compositions are useful for purifying the biomarker or in assays for detecting the biomarker.
  • this invention provides an article comprising a solid substrate to which is attached an adsorbent, e.g., a chromatographic adsorbent or a biospecific capture reagent, to which is further bound a biomarker of this invention.
  • the article is a biochip or a probe for mass spectrometry, e.g., a SELDI probe.
  • Such articles are useful for purifying the biomarker or detecting the biomarker.
  • kits for qualifying peripheral artery disease status which kits are used to detect biomarkers according to the invention.
  • the kit comprises a solid support, such as a chip, a microtiter plate or a bead or resin having a capture reagent attached thereon, wherein the capture reagent binds a biomarker of the invention.
  • the kits of the present invention can comprise mass spectrometry probes for SELDI, such as ProteinChip ® arrays.
  • the kit can comprise a solid support with a reactive surface, and a container comprising the biospecific capture reagent (e.g., an antibody that recognizes beta2- microglobulin, hsCRP or cystatin C).
  • the biospecific capture reagent e.g., an antibody that recognizes beta2- microglobulin, hsCRP or cystatin C.
  • the kit can also comprise a washing solution or instructions for making a washing solution, in which the combination of the capture reagent and the washing solution allows capture of the biomarker or biomarkers on the solid support for subsequent detection by, e.g., mass spectrometry.
  • the kit may include more than type of adsorbent, each present on a different solid support.
  • the kit can also comprise antibodies against one or more of the biomarkers, or reagents for detecting the activity or presence of the biomarker.
  • such a kit can comprise instructions for suitable operational parameters in the form of a label or separate insert. For example, the instructions may inform a consumer about how to collect the sample, how to wash the probe or the particular biomarkers to be detected.
  • the kit can comprise one or more containers with biomarker samples, to be used as standard(s) for calibration.
  • this invention provides methods for determining the therapeutic efficacy of a pharmaceutical drug. These methods are useful in performing clinical trials of the drug, as well as monitoring the progress of a patient on the drug. Therapy or clinical trials involve administering the drug in a particular regimen. The regimen may involve a single dose of the drug or multiple doses of the drug over time. The doctor or clinical researcher monitors the effect of the drug on the patient or subject over the course of administration. If the drug has a pharmacological impact on PAD, the amounts or relative amounts (e.g., the pattern or profile) of ⁇ 2-microglobulin and/or cystatin C and/or hsCRP and/or glucose should be observed to change toward a non-disease profile.
  • the amounts or relative amounts e.g., the pattern or profile
  • this method involves measuring one or more biomarkers in a subject receiving drug therapy, and correlating the amounts of the biomarkers with the disease status of the subject.
  • One embodiment of this method involves determining the levels of the biomarkers for at least two different time points during a course of drug therapy, e.g., a first time and a second time, and comparing the change in amounts of the biomarkers, if any.
  • the biomarkers can be measured before and after drug administration or at two different time points during drug administration.
  • the effect of therapy is determined based on these comparisons. If a treatment is effective, then the biomarkers will trend toward normal, while if treatment is ineffective, the biomarkers will trend toward disease indications. If a treatment is effective, then the biomarkers will trend toward normal, while if treatment is ineffective, the biomarkers will trend toward disease indications.
  • the biomarkers can be used to screen for compounds that modulate the expression of the biomarkers in vitro or in vivo, which compounds in turn may be useful in treating or preventing peripheral artery disease in patients.
  • the biomarkers can be used to monitor the response to treatments for peripheral artery disease.
  • the biomarker panel can be used in heredity studies to determine if the subject is at risk for developing peripheral artery disease.
  • PAD biomarkers of the present invention were initially identified as such in a screening study using SELDI technology employing ProteinChip arrays from Ciphergen Biosystems, Inc. (Fremont, CA) ("Ciphergen").
  • the study set consisted of 45 patients with PAD and 43 patients without PAD.
  • Subjects placed in the PAD group were those with an ankle-brachial index of 0.9 or less.
  • Patients in the PAD group were slightly older and generally had higher frequencies of cardiovascular risk factors.
  • Plasma samples were obtained from subjects in a fasting state. Each plasma sample was subjected to fractionation on a QhyperDF column before analysis using Ciphergen's ProteinChips, as described in the detailed protocol below. After fractionation, selected fractions were analyzed using Ciphergen's IMAC30 or CMlO ProteinChips. The spectra of polypeptides in the samples were generated by time-of-flight mass spectrometry on a Ciphergen PBSII mass spectrometer. Peak intensity values (1619 peaks/sample) were analyzed by Statistical Analysis of Microarrays (SAM) software.
  • SAM Statistical Analysis of Microarrays
  • the present study differs from studies which purport to show a relationship between ⁇ 2 -microglobulin levels and symptoms such as arterial stiffness ⁇ see, e.g., Saijo et al., Hypertens. Res., 28(6):505-511 (2005)). Those studies excluded from their trials subjects diagnosed with PDA and patients with low ABI ( ⁇ 0.9). Also, the studies relied on a pulse wave velocity (PWV) assay for including/excluding patients. The PWV assay measures vascular compliance and not arterial disease per se.
  • PWV pulse wave velocity
  • Table 2 also includes the exact p values of linear regression analysis between ABI values and peak intensities for the selected differentially expressed proteins.
  • Western blots using anti-beta2 microglobulin antibody showed that higher beta 2- microglobulin concentrations were observed in samples from 4 patients with PAD compared to samples from 4 control subjects. This finding is consistent when using plasma fractionated at pH 5 or using whole, unfractionated plasma.
  • a confirmation study was conducted to confirm that the observed correlation was not confounded by other patient traits (e g., other cardiovascular risk factors, renal function, etc.).
  • plasma was obtained from 20 patients with PAD and 20 control subjects who had no clinical evidence of PAD or coronary disease.
  • the patients in the two comparison groups were similar in age and gender. However, as expected, the PAD group had higher frequencies of cardiovascular risk factors and a trend toward lower glomerular filtration rate.
  • Beta 2-microglobulin was measured using a commercially available ELISA kit. The measurements showed that plasma and serum beta 2-microglobulin levels were significantly higher in PAD patients than control subjects, using a Mann-Whitney nonparametric test. [0090] The results of a Spearman's Rank Correlation analysis showed a strong negative correlation (r ⁇ -0.5) between ⁇ 2-microglobulin levels and ABI. A relationship was also observed between peak intensity of ⁇ 2m and claudication time.
  • a linear regression analysis of the data showed that, among traditional risk factors for cardiovascular disease, a history of smoking, hyperlipidemia, and diabetes were statistically significant univariate predictors of ankle-brachial index.
  • glomerular filtration rate had a positive trend toward a correlation with ankle-brachial index.
  • ⁇ 2-microglobulin transformed logarithmically to reduce skewness, was strongly correlated with ankle-brachial index.
  • Coronary angiograms were reviewed by an experienced angiographer blinded to the subject's ABI.
  • a significant coronary lesion was defined as an angiographic stenosis of > 60% in any vessel.
  • ROC analysis was performed to test the predictive power of the biomarker panel score. All subjects were assigned a score using the AHA Framingham risk score charts based on data obtained at recruitment.
  • the odds ratio was calculated in the logistic regression analysis.
  • R was used in the linear regression analysis.
  • SAS was used for logistic regression analysis and odds ratio calculation.
  • Analyze-it was used for ROC analysis.
  • the NHSD group was younger than the CAD+PAD group.
  • the group with CAD+PAD had a significantly higher incidence of cardiovascular risk factors such as smoking, diabetes, and hypertension.
  • the data summarized in Table 3 shows that biochemical markers for cardiovascular risk were higher in the CAD+PAD group than in the NHSD group.
  • the panel score comprising all four markers had the highest odds ratio when comparing the highest quartile vs the lowest quartile, and its significance was still apparent even after adjusting for traditional risk factors of age, diabetes, and smoking.
  • Table 5 shows that after adjusting for the traditional risk factors, individuals in the top quartile of the four marker index had a 7-fold greater chance of having PAD.
  • ROC analysis was performed to determine the diagnostic accuracy of the individual markers and marker combinations.
  • the marker panel that encompassed all four markers i.e., ⁇ 2M, cystatin C 5 hsCRP, and glucose
  • the AUC for the four marker panel was 0.747 (95% confidence interval .702-.79I). As shown in Figure 1, a cutoff corresponding to the 75th percentile, the index had a sensitivity of 90.4% and specificity of 36.6%.
  • the combination marker score was able to identify a group with an odds ratio greater than seven for PAD, in a population of patients referred for coronary angiography.
  • clinical assessments of risk factor burden such as the AHA risk score
  • CV risk factors are used to predict risk of future events.
  • the predictive power of the biomarker panel was compared to the accepted AHA risk score. We found a positive interaction between the biomarker panel and the AHA risk score. Subjects at highest risk were those with both a high AHA score, and a high biomarker panel score.
  • biomarkers and, in particular, the four biomarker panel disclosed herein were identifiable as subjects with a high risk of PAD.
  • one use for the biomarkers and, in particular, the four biomarker panel disclosed herein is to identify a group of patients who are high risk for PAD that would otherwise be missed.
  • Another use for the ⁇ 2-microbulin, hsCRP, Cystatin C and/or glucose/hemoglobin AIc biomarkers described herein and, in particular, the four biomarker panel is to further stratify the risk of PAD in patients known to be at risk of CAD. Such a stratification can be used to further inform a physician's diagnosis and/or enable the physician to treat a subject with the most effective and/or least harmful therapeutics.
  • Body mass 28.3 (24.7-33.1) 27.9 (25.1-31.6) 28.5 (25.9-31.8) 0.3 0.32 index, (kg/m2)
  • HsCRP (mg/1) 1.5 (0.6-3.7) 2.2 (0.9-6.3) 1.4 (0.7-3.9) ⁇ 0.001 0.031
  • Triglycerides 88 (63-131) 105 (70.2-145) 91.1 (68.9-130) 0.007 0.166 (mg/dl)
  • HDL 42 (34-51) 38 (30-45) 37 (30-46) 0.003 0.91 cholesterol (mg/dl)
  • Body mass index kg/m2 0.017 -0.069, 0.102 0.694
  • Cystatin C mg/L -0.302 -0.378, -0.222 ⁇ 0.001 hsCRP, mg/L -0.180 -0.261, -0.096 ⁇ 0.001
  • Triglycerides mg/dL -0.110 -0.194, -0.025 0.009
  • Model 3 Adjusted for diabetes status
  • Model 5 Adjusted for age, diabetes, smoking
  • Cystatin C 0.704 (0.655, 0.752) 0.593 (0.524, 0.662) hsCRP 0.593 (0.54, 0.645) 0.583(0.511,0.655)
  • Glucose 0.637 (0.585, 0.69) 0.563 (0.492, 0.633) ⁇ 2M + HsCRP 0.617(0.565,0.668) 0.600(0.529,0.671) ⁇ 2M + Cystatin C 0.690(0.641,0.74) 0.557 (0.486, 0.627) ⁇ 2M + Glucose 0.677 (0.627, 0.726) 0.606 (0.536, 0.675) hsCRP + Cystatin C 0.669(0.62,0.718) 0.612 (0.542, 0.682) hsCRP + Glucose 0.683 (0.632, 0.733) 0.627 (0.559, 0.696)

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Abstract

L'invention concerne des biomarqueurs comprenant de la ß-2-microglobuline, de la cystatine C, hsCRP et du glucose ainsi que les procédés d'utilisation de biomarqueurs pour le diagnostic et/ou l'évaluation du risque de maladie des artères périphériques chez un sujet. Dans certains modes de réalisation, le sujet testé peut souffrir de ou être prédisposé à d'autres maladies du système circulatoire, notamment de la maladie des artères coronaires. L'hémoglobine A1c ou d'autres mandataires pour la mesure des taux de glucose peuvent être substitués pour ou mesurés en plus du glucose dans le contexte de la présente invention.
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US8377646B2 (en) 2006-03-11 2013-02-19 Vermillion, Inc. Beta-2 microglobulin as a biomarker for peripheral artery disease
US8709735B2 (en) 2006-03-11 2014-04-29 Vermillion, Inc. β-2 microglobulin as a biomarker for peripheral artery disease
EP2167961A1 (fr) * 2007-06-27 2010-03-31 The Board of Trustees of The Leland Stanford Junior University Bêta-2-microglobuline et protéine c réactive (crp) en tant que biomarqueurs pour une maladie artérielle périphérique
EP2167961A4 (fr) * 2007-06-27 2010-07-21 Univ Leland Stanford Junior Bêta-2-microglobuline et protéine c réactive (crp) en tant que biomarqueurs pour une maladie artérielle périphérique
US8227201B2 (en) 2007-06-27 2012-07-24 Board Of Trustees Of The Leland Stanford Junior University BETA2-microglobulin and C reactive protein (CRP) as biomarkers for peripheral artery disease
EP3194981B1 (fr) * 2015-06-05 2018-08-22 Beckman Coulter, Inc. Groupe de biomarqueurs d'apnée obstructive du sommeil (osa)

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