WO2018204509A1 - Predictive factors for acute respiratory distress syndrome - Google Patents

Predictive factors for acute respiratory distress syndrome Download PDF

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Publication number
WO2018204509A1
WO2018204509A1 PCT/US2018/030675 US2018030675W WO2018204509A1 WO 2018204509 A1 WO2018204509 A1 WO 2018204509A1 US 2018030675 W US2018030675 W US 2018030675W WO 2018204509 A1 WO2018204509 A1 WO 2018204509A1
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biomarker profile
csf
ards
ifn
mcp1
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PCT/US2018/030675
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French (fr)
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Joseph GUTIERREZ
Seth SCHOBEL-MCHUGH
Eric Elster
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The Henry M. Jackson Foundation For The Advancement Of Military Medicine, Inc.
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Publication of WO2018204509A1 publication Critical patent/WO2018204509A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P11/00—Drugs for disorders of the respiratory system
    • 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
    • G01N33/6863—Cytokines, i.e. immune system proteins modifying a biological response such as cell growth proliferation or differentiation, e.g. TNF, CNF, GM-CSF, lymphotoxin, MIF or their receptors
    • G01N33/6869—Interleukin
    • 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
    • G01N2333/475—Assays involving growth factors
    • G01N2333/4753—Hepatocyte growth factor; Scatter factor; Tumor cytotoxic factor II
    • 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
    • G01N2333/475—Assays involving growth factors
    • G01N2333/49—Platelet-derived growth factor [PDGF]
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    • 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
    • G01N2333/475—Assays involving growth factors
    • G01N2333/50—Fibroblast growth factors [FGF]
    • G01N2333/503—Fibroblast growth factors [FGF] basic FGF [bFGF]
    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/521—Chemokines
    • G01N2333/522—Alpha-chemokines, e.g. NAP-2, ENA-78, GRO-alpha/MGSA/NAP-3, GRO-beta/MIP-2alpha, GRO-gamma/MIP-2beta, IP-10, GCP-2, MIG, PBSF, PF-4 or KC
    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/521—Chemokines
    • G01N2333/523—Beta-chemokines, e.g. RANTES, I-309/TCA-3, MIP-1alpha, MIP-1beta/ACT-2/LD78/SCIF, MCP-1/MCAF, MCP-2, MCP-3, LDCF-1or LDCF-2
    • G—PHYSICS
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    • G01N2333/53—Colony-stimulating factor [CSF]
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    • G01N2333/52—Assays involving cytokines
    • G01N2333/53—Colony-stimulating factor [CSF]
    • G01N2333/535—Granulocyte CSF; Granulocyte-macrophage CSF
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    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/54—Interleukins [IL]
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    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/54—Interleukins [IL]
    • G01N2333/5421—IL-8
    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/54—Interleukins [IL]
    • G01N2333/5425—IL-9
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    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/54—Interleukins [IL]
    • G01N2333/5428—IL-10
    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/555—Interferons [IFN]
    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/555—Interferons [IFN]
    • G01N2333/56—IFN-alpha
    • 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
    • G01N2333/52—Assays involving cytokines
    • G01N2333/555—Interferons [IFN]
    • G01N2333/57—IFN-gamma
    • 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
    • G01N2333/715—Assays involving receptors, cell surface antigens or cell surface determinants for cytokines; for lymphokines; for interferons
    • G01N2333/7155—Assays involving receptors, cell surface antigens or cell surface determinants for cytokines; for lymphokines; for interferons for interleukins [IL]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/12—Pulmonary diseases
    • G01N2800/125—Adult respiratory distress syndrome
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • the present invention relates to methods of determining if a subject has an increased risk of developing acute respiratory distress syndrome (ARDS) prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject’s biomarker profile and comparing the value of the subject’s biomarker profile with the value of a normal biomarker profile. A change in the value of the subject’s biomarker profile, over or under normal values is indicative that the subject has an increased risk of developing ARDS prior to the onset of any detectable symptoms thereof.
  • ARDS acute respiratory distress syndrome
  • the present invention relates to methods of determining if a human subject has an increased risk of developing ARDS prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject’s biomarker profile and comparing the value of the subject’s biomarker profile with the value of a normal biomarker profile. A change in the value of the subject’s biomarker profile, over or under normal values is indicative that the subject has an increased risk of developing or developing symptoms associated with ARDS prior to the onset of any detectable symptoms thereof.
  • the present invention relates to a method of determining if a human subject has an increased risk of developing acute respiratory distress syndrome (ARDS) prior to the onset of detectable symptoms thereof, the method comprising (a) obtaining a biological sample from the human subject, and (b) measuring the levels of one or more of basic fibroblast growth factor (FGF ⁇ basic), granulocyte colony ⁇ stimulating factor (G ⁇ CSF), granulocyte ⁇ macrophage colony ⁇ stimulating factor (GM ⁇ CSF), hepatocyte growth factor (HGF), interferon alpha (IFN ⁇ ), interferon gamma (IFN ⁇ ⁇ ), interleukin ⁇ 1 alpha (IL ⁇ 1 ⁇ ), interleukin ⁇ 1 beta (IL ⁇ 1 ⁇ ), interleukin ⁇ 1 receptor agonist (IL ⁇ 1RA), interleukin ⁇ 6 (IL ⁇ 6), interleukin ⁇ 8 (IL ⁇ 8), interleukin ⁇ 9 (IL ⁇ 9), interleukin ⁇ 10 (IL ⁇ 10), monocyte chemoat
  • the biomarker profile further includes interferon gamma ⁇ induced protein 10 (IP ⁇ 10) and/or interleukin ⁇ 15 (IL ⁇ 15) and a decrease in the level of these biomarkers compared to a normal biomarker profile is indicative that the human subject has an increased risk of developing ARDS compared to individuals with a normal biomarker profile.
  • IP ⁇ 10 interferon gamma ⁇ induced protein 10
  • IL ⁇ 15 interleukin ⁇ 15
  • the human subject is a trauma patient and the biomarker profile comprises serum levels of FGF ⁇ basic, G ⁇ CSF, IFN ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1RA, IL ⁇ 6, IL ⁇ 8, IL ⁇ 10, MCP1, and MIP1 ⁇ .
  • the human subject is not a trauma patient and the biomarker profile comprises serum levels of MCP1 and MIG.
  • the human subject is not a trauma patient and the biomarker profile comprises serum levels of eotaxin, GM ⁇ CSF, IL ⁇ 8, IL ⁇ 12, IL ⁇ 13, IP ⁇ 10, MCP1 and RANTES.
  • the biomarker profile comprises serum levels of G ⁇ CSF, GM ⁇ CSF, IFN ⁇ ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1RA, IL ⁇ 6, IL ⁇ 8, IL ⁇ 10, IL ⁇ 15, MCP1, MIG, and VEGF.
  • the invention relates to a method of detecting elevated levels of biomarkers in a human subject, the method comprising measuring serum levels of two or more of FGF ⁇ basic, G ⁇ CSF, GM ⁇ CSF, HGF, IFN ⁇ , IFN ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1RA, IL ⁇ 6, IL ⁇ 8, IL ⁇ 9, IL ⁇ 10, MCP1, MIG, MIP1 ⁇ , and VEGF in a serum sample obtained from the human subject.
  • the human subject is a trauma patient and the serum levels of FGF ⁇ basic, G ⁇ CSF, IFN ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1RA, IL ⁇ 6, IL ⁇ 8, IL ⁇ 10, MCP1, and MIP1 ⁇ are measured.
  • the human subject is not a trauma patient and the serum levels of MCP1 and MIG are measured.
  • the levels of eotaxin, GM ⁇ CSF, IL ⁇ 8, IL ⁇ 12, IL ⁇ 13, IP ⁇ 10, MCP1 and RANTES are measured.
  • the invention relates to a method of treating a human subject for acute respiratory distress syndrome (ARDS), the method comprising (a) producing a biomarker profile comprising measuring serum levels of two or more of FGF ⁇ basic, G ⁇ CSF, GM ⁇ CSF, HGF, IFN ⁇ , IFN ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1RA, IL ⁇ 6, IL ⁇ 8, IL ⁇ 9, IL ⁇ 10, MCP1, MIG, MIP1 ⁇ , and VEGF, and (b) administering a treatment for ARDS to the human subject when the biomarker profile for the subject is greater than the biomarker profile of a normal subject.
  • ARDS acute respiratory distress syndrome
  • the treatment is administered to the human subject prior to the onset of any detectable symptoms of the subject exhibiting ARDS.
  • the human subject is a trauma patient and the biomarker profile comprises serum levels of FGF ⁇ basic, G ⁇ CSF, IFN ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1RA, IL ⁇ 6, IL ⁇ 8, IL ⁇ 10, MCP1, and MIP1 ⁇ .
  • the human subject is not a trauma patient and the biomarker profile comprises serum levels of MCP1 and MIG.
  • the biomarker profile comprises serum levels of eotaxin, GM ⁇ CSF, IL ⁇ 8, IL ⁇ 12, IL ⁇ 13, IP ⁇ 10, MCP1 and RANTES.
  • the biological sample is a blood sample.
  • the sample is a serum sample.
  • the sample is a plasma sample.
  • FIG. 1 Quantification of cytokine biomarkers in ARDS and Non ⁇ ARDS patients upon initial presentation.
  • the levels of GM ⁇ CSF, IL ⁇ 08, IL ⁇ 12, IL ⁇ 13, IP ⁇ 10, MCP1, and RANTES were significantly different between ARDS and Non ⁇ ARDS patients according to a Wilcoxon Rank Sum test (p ⁇ 0.05).
  • the ends of the box mark the upper and lower quartiles.
  • the median is marked by a vertical line inside the box.
  • the whiskers extend to the highest and lowest observations.
  • Figure 2. Quantification of cytokine biomarkers in ARDS and Non ⁇ ARDS patients 0 days after initial presentation.
  • the present invention relates to methods of determining if a human subject has an increased risk of developing ARDS prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject’s biomarker profile and comparing the value of the subject’s biomarker profile with the value of a normal biomarker profile. A change in the value of the subject’s biomarker profile, over or under normal values is indicative that the subject has an increased risk of developing or developing symptoms associated with ARDS prior to the onset of any detectable symptoms thereof (i.e. a risk profile for ARDS).
  • the term “subject” or “test subject” indicates a human, in particular a human who is hospitalized.
  • the biomarker profile comprises serum levels of at least one of eotaxin, granulocyte ⁇ macrophage colony ⁇ stimulating factor (GM ⁇ CSF), interleukin ⁇ 8 (IL ⁇ 8), interleukin ⁇ 12 (IL ⁇ 12), interleukin ⁇ 13 (IL ⁇ 13), interferon gamma induced protein 10 (IP ⁇ 10), monocyte chemoattractant protein 1 (MCP ⁇ 1), RANTES, interferon gamma (IFN ⁇ ), interleukin ⁇ 1a (IL ⁇ 1a), interleukin ⁇ 2 (IL ⁇ 2), interleukin ⁇ 17 (IL ⁇ 17), interleukin ⁇ 1RA (IL ⁇ 1RA), interleukin ⁇ 4 (IL ⁇ 4), interleukin ⁇ 10 (IL ⁇ 10), macrophage inflammatory protein ⁇ 1b (MIP1b) and vascular end
  • the human subject is a trauma patient. In another embodiment, the subject is not a trauma patient. In still another embodiment, if the subject is a trauma patient the biomarker profile comprises serum levels of eotaxin, GM ⁇ CSF, IFN ⁇ , IL ⁇ 1a, IL ⁇ 2, IL ⁇ 17 and MCP1. In still another embodiment, if the subject is not a trauma patient, the biomarker profile comprises serum levels of IL ⁇ 1RA, IL ⁇ 4, IL ⁇ 10, MIP1b and VEGF.
  • the status of the patient i.e., trauma or non ⁇ trauma
  • the biomarker profile comprises serum levels of eotaxin, GM ⁇ CSF, IL ⁇ 8, IL ⁇ 12, IL ⁇ 13, IP ⁇ 10, MCP1 and RANTES.
  • the present invention also relates to methods of detecting elevated levels of a specific collection of analytes in one or more samples obtained from a subject.
  • the collection of analytes comprises serum levels of at least one of eotaxin, granulocyte ⁇ macrophage colony ⁇ stimulating factor (GM ⁇ CSF), interleukin ⁇ 8 (IL ⁇ 8), interleukin ⁇ 12 (IL ⁇ 12), interleukin ⁇ 13 (IL ⁇ 13), interferon gamma induced protein 10 (IP ⁇ 10), monocyte chemoattractant protein 1 (MCP ⁇ 1), RANTES, interferon gamma (IFN ⁇ ), interleukin ⁇ 1a (IL ⁇ 1a), interleukin ⁇ 2 (IL ⁇ 2), interleukin ⁇ 17 (IL ⁇ 17), interleukin ⁇ 1RA (IL ⁇ 1RA), interleukin ⁇ 4 (IL ⁇ 4), interleukin ⁇ 10 (IL ⁇ 10), macrophage inflammatory protein ⁇ 1b (MIP1b) and vascular endothelial growth factor (VEGF).
  • GM ⁇ CSF granulocyte ⁇ macrophage colony ⁇ stimulating factor
  • IL ⁇ 8
  • the human subject is a trauma patient. In another embodiment, the subject is not a trauma patient. In still another embodiment, if the subject is a trauma patient the biomarker profile comprises serum levels of eotaxin, GM ⁇ CSF, IFN ⁇ , IL ⁇ 1a, IL ⁇ 2, IL ⁇ 17 and MCP1. In still another embodiment, if the subject is not a trauma patient, the biomarker profile comprises serum levels of IL ⁇ 1RA, IL ⁇ 4, IL ⁇ 10, MIP1b and VEGF.
  • the status of the patient i.e., trauma or non ⁇ trauma
  • the biomarker profile comprises serum levels of eotaxin, GM ⁇ CSF, IL ⁇ 8, IL ⁇ 12, IL ⁇ 13, IP ⁇ 10, MCP1 and RANTES.
  • ARDS acute respiratory distress syndrome
  • the term acute respiratory distress syndrome, or ARDS is used herein to mean a subject with a PaO 2 /FiO 2 of ⁇ 300 mmHg.
  • the term means “increased risk” is used to mean that the test subject has an increased chance of developing ARDS compared to a normal individual. The increased risk may be relative or absolute and may be expressed qualitatively or quantitatively.
  • an increased risk may be expressed as simply determining the subject’s biomarker profile and placing the patient in an “increased risk” category, based upon previous population studies.
  • a numerical expression of the subject’s increased risk may be determined based upon the biomarker profile.
  • examples of expressions of an increased risk include but are not limited to, odds, probability, odds ratio, p ⁇ values, attributable risk, biomarker index score, relative frequency, positive predictive value, negative predictive value, and relative risk.
  • the correlation between a subject’s biomarker profile and the likelihood of developing ARDS may be measured by an odds ratio (OR) and by the relative risk (RR).
  • the attributable risk can also be used to express an increased risk.
  • the AR describes the proportion of individuals in a population exhibiting ARDS to a specific member of the biomarker profile. AR may also be important in quantifying the role of individual components (specific member) in condition etiology and in terms of the public health impact of the individual risk factor.
  • the public health relevance of the AR measurement lies in estimating the proportion of cases of ARDS in a population of subjects that could be prevented if the profile or individual factor were absent.
  • the increased risk of a human subject can be determined from p ⁇ values that are derived from association studies. Specifically, associations with specific profiles can be performed using regression analysis by regressing the risk profile with the presence or absence of ARDS. In addition, the regression may or may not be corrected or adjusted for one or more factors.
  • the factors for which the analyses may be adjusted include, but are not limited to age, sex, weight, ethnicity, type of wound if present, number of wounds if present, trauma, number of days from injury, geographic location, fasting state, state of pregnancy or post ⁇ pregnancy, menstrual cycle, general health of the subject, alcohol or drug consumption, caffeine or nicotine intake and circadian rhythms, to name a few.
  • Increased risk can also be determined from p ⁇ values that are derived using logistic regression. Binomial (or binary) logistic regression is a form of regression which is used when the dependent is a dichotomy and the independents are of any type.
  • Logistic regression can be used to predict a dependent variable on the basis of continuous or categorical or both (continuous and categorical) independents and to determine the percent of variance in the dependent variable explained by the independents; to rank the relative importance of independents; to assess interaction effects; and to understand the impact of covariate control variables.
  • Logistic regression applies maximum likelihood estimation after transforming the dependent into a “logit” variable (the natural log of the odds of the dependent occurring or not). In this way, logistic regression estimates the probability of a certain event occurring.
  • select embodiments of the present invention comprise the use of a computer comprising a processor and the computer is configured or programmed to generate one or more risk profiles and/or to determine statistical risk from a biomarker profile.
  • the methods may also comprise displaying the one or risk profiles on a screen that is communicatively connected to the computer.
  • two different computers can be used: one computer configured or programmed to generate one or more risk profiles and a second computer configured or programmed to determine statistical risk. Each of these separate computers can be
  • risk profile means the combination of a subject’s risk factors analyzed or observed from a biomarker profile.
  • factor and/or “component” are used to mean the individual constituents that are assessed when generating the profile.
  • the risk profile is a collection of measurements, such as but not limited to a quantity or concentration, for individual factors taken from a test sample of the subject.
  • test samples or sources of components for the risk profile include, but are not limited to, biological fluids, which can be tested by the methods of the present invention described herein, and include but are not limited to whole blood, such as but not limited to peripheral blood, serum, plasma, cerebrospinal fluid, urine, amniotic fluid, lymph fluids, various external secretions of the respiratory, intestinal and genitourinary tracts, tears, saliva, white blood cells, myelomas and the like.
  • the risk profile can include a “biological effector” or aspect and/or a non ⁇ biological effector aspect.
  • biological effector is used to mean a molecule, such as but not limited to, a protein, peptide, a carbohydrate, a fatty acid, a nucleic acid, a glycoprotein, a proteoglycan, etc. that can be assayed.
  • biological effectors can include, cytokines, growth factors, antibodies, hormones, cell surface receptors, cell surface proteins, carbohydrates, etc.
  • ILs interleukins
  • IL ⁇ 1RA interleukins
  • IL ⁇ 2R IL ⁇ 3, IL ⁇ 4, IL ⁇ 5, IL ⁇ 6, IL ⁇ 7, IL ⁇ 8, IL ⁇ 10, IL ⁇ 12, IL ⁇ 13, IL ⁇ 15, IL ⁇ 17
  • growth factors such as tumor necrosis factor alpha (TNF ⁇ ), granulocyte colony stimulating factor (G ⁇ CSF), granulocyte macrophage colony stimulating factor (GM ⁇ CSF), interferon alpha (INF ⁇ ), interferon gamma (IFN ⁇ ), epithelial growth factor (EGF), basic endothelial growth factor (bEGF), hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF), and chemokines such as monocyte chemoattractant protein ⁇ 1 (CCL2/MC
  • levels of individual components of the biomarker profile are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed.
  • levels of the individual factors in the serum of the biomarker profile are assessed using mass spectrometry in conjunction with ultra ⁇ performance liquid chromatography (UPLC), high ⁇ performance liquid chromatography (HPLC), gas chromatography (GC), gas chromatography/mass spectroscopy (GC/MS), and UPLC to name a few.
  • Other methods of assessing levels of some of the individual components include biological methods, such as but not limited to ELISA assays, Western Blot and multiplexed immunoassays etc.
  • Other techniques may include using quantitative arrays, PCR, Northern Blot analysis.
  • determining levels of, for example, a fragment of protein being analyzed may be sufficient to conclude or assess that an individual component of the biomarker profile being analyzed is increased or decreased.
  • arrays or blots are used to determine component levels, the
  • non ⁇ biological effector is a component that is generally considered not to be a specific molecule. Although not a specific molecule, a non ⁇ biological effector may nonetheless still be quantifiable, either through routine measurements or through measurements that stratify the data being assessed. For example, number or concentrate of red blood cells, white blood cells, platelets, coagulation time, blood oxygen content, etc. would be a non ⁇ biological effector component of the biomarker profile. All of these components are measureable or quantifiable using routine methods and equipment.
  • the mechanism of injury is included in the biomarker profile.
  • the phrase “mechanism of injury” means the manner in which the subject received an injury.
  • the mechanism of injury may include trauma and may be described as a gunshot wound, a vehicle accident, laceration, etc.
  • data regarding injury type is included in the biomarker profile.
  • data on the occurrence of multiple wounds is included in the biomarker profile.
  • biomarker profile data on the number of days from injury is included in the biomarker profile [0050]
  • routine statistical methods can be employed. For example, rfImpute from the randomForest R package can be used to impute missing data. Up ⁇ sampling and predictor rank transformations can be performed on the data set only for variable selection to accommodate class imbalance and non ⁇ normality in the data.
  • variable selection the constraint ⁇ based algorithms fast.iamb, iamb and gs and the constraint ⁇ based local discovery learning algorithms mmpc and si.hiton.pc from the “bnlearn” R package can be used to search the input dataset for nodes of Bayesian networks.
  • the nodes can be chosen as the reduced variable sets.
  • the variables Before running the data through variable selection and binary classification algorithms, the variables may or may not randomly re ⁇ ordered.
  • the data can be run through the variable selection and binary classification algorithms more than once, for example, 10, 20, 30, 40, 50 or even more times.
  • each variable set can be pulled from the raw data and run in sundry binary classification algorithms using the train function from the R caret package: linear discriminant analysis (lda), classification and regression trees (cart), k ⁇ nearest neighbors (knn), support vector machine (svm), logistic regression (glm), random forest (rf), generalized linear models (glmnet) and na ⁇ ve Bayes (nb).
  • Lda linear discriminant analysis
  • cart classification and regression trees
  • knnn k ⁇ nearest neighbors
  • svm logistic regression
  • rf random forest
  • generalized linear models glmnet
  • na ⁇ ve Bayes nb
  • Model performance can be further assessed using the plot.
  • roc command to compute the Receiver Operator Characteristic Curves (ROC) and area under curve (AUC).
  • AUC Area under curve
  • DCA Decision Curve Analysis
  • a Wilcoxon rank ⁇ sum test can be used to identify which biomarkers from specific patient groups are were associated with a specific indication.
  • the assessment of the levels of the individual components of the biomarker profile can be expressed as absolute or relative values and may or may not be expressed in relation to another component, a standard an internal standard or another molecule of compound known to be in the sample.
  • the standard may be added to the test sample prior to, during or after sample processing.
  • a sample may be taken from the subject.
  • the sample may or may not processed prior assaying levels of the components of the biomarker profile.
  • whole blood may be taken from an individual and the blood sample may be processed, e.g., centrifuged, to isolate plasma or serum from the blood.
  • the sample may or may not be stored, e.g., frozen, prior to processing or analysis.
  • the individual levels of each of the risk factors are higher than those compared to normal levels.
  • one, two, three, four, five, six or seven of the levels of each of the factor are higher than normal levels while others, if any, are lower than or the same as normal levels.
  • the levels of depletion of the factors or components compared to normal levels can vary.
  • the levels of any one or more of the factors or components is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 higher than normal levels (where, for sake of clarity, a marker a level of “1” would indicate that the component is at the same level in both the subject and normal samples).
  • the number of “times” the levels of a factor are higher over normal can be a relative or absolute number of times.
  • the levels of the factors or components may be normalized to a standard and these normalized levels can then be compared to one another to determine if a factor or component is lower, higher or about the same.
  • the biomarker profile comprises at least one, two, three, four, five, six, seven or eight of the factors or components for the prediction of ARDS. If one factor or component of the biological effector aspect of the biomarker profile is used in generating the biomarker profile for the prediction of ARDS, then any one of the listed factors or components can be used to generate the profile.
  • any combination of the two listed above can be used. If three factors or components of the biological effector aspect of the biomarker profile are used in generating the biomarker profile for the prediction of ARDS, any combination of three of the factors or components listed above can be used. If four factors or components of the biological effector aspect of the biomarker profile are used in generating the biomarker profile for the prediction of ARDS, any combination of four of the factors or components listed above can be used.
  • any combination of five of the factors or components listed above can be used. If six factors or components of the biological effector aspect of the biomarker profile are used in generating the biomarker profile for the prediction of ARDS, any combination of six of the factors or components listed above can be used. If seven factors or components of the biological effector aspect of the biomarker profile are used in generating the biomarker profile for the prediction of ARDS, any combination of seven of the factors or components listed above can be used. Of course all members of the biological effector aspect of each biomarker profile panel can be used to generate a biomarker profile for the prediction of ARDS.
  • the subject’s biomarker profile is compared to the profile that is deemed to be a normal biomarker profile.
  • an individual or group of individuals may be first assessed to ensure they have no signs, symptoms or diagnostic indicators of ARDS. Once established, the biomarker profile of the individual or group of individuals can then be determined to establish a “normal biomarker profile.”
  • a normal biomarker profile can be ascertained from the same subject when the subject is deemed as healthy with no signs, symptoms or diagnostic indicators of ARDS.
  • a biomarker profile from a “normal subject” e.g., a “normal biomarker profile” is a human subject that does not exhibit or display ARDS, but may still may not be considered as healthy.
  • a “normal” biomarker profile is assessed in the same subject from whom the sample is taken prior to the onset of any signs, symptoms or diagnostic indicators that they may exhibit ARDS. That is, the term “normal” with respect to a biomarker profile can be used to mean the subject’s baseline biomarker profile prior to the onset of any signs, symptoms or diagnostic indicators of potential ARDS. The biomarker profile can then be reassessed periodically and compared to the subject’s baseline biomarker profile.
  • the present invention also includes methods of monitoring the progression of ARDS in a subject, with the methods comprising determining the subject’s biomarker profile at more than one time point.
  • some embodiments of the methods of the present invention will comprise determining the subject’s biomarker profile at two, three, four, five, six, seven, eight, nine, 10 or even more time points over a period of time, such as a week or more, two weeks or more, three weeks or more, four weeks or more, a month or more, two months or more, three months or more, four months or more, five months or more, six months or more, seven months or more, eight months or more, nine months or more, ten months or more, 11 months or more, a year or more or even two years.
  • the methods of monitoring a subject’s risk of developing ARDS would also include embodiments in which the subject’s biomarker profile is assessed before and/or during and/or after treatment of ARDS.
  • the present invention also includes methods of monitoring the efficacy of treatment of ARDS by assessing the subject’s biomarker profile over the course of the treatment and after the treatment.
  • the methods of monitoring the efficacy of treatment of ARDS comprise determining the subject’s biomarker profile at at least one, two, three, four, five, six, seven, eight, nine or 10 or more different time points prior to the receipt of treatment for ARDS and subsequently determining the subject’s biomarker profile at at least one, two, three, four, five, six, seven, eight, nine or 10 or more different time points after beginning of treatment for ARDS, and determining the changes, if any, in the biomarker profile of the subject.
  • the treatment may be any treatment designed to cure, remove or diminish the likelihood of developing ARDS.
  • a normal biomarker profile is assessed in a sample from a different subject or patient (from the subject being analyzed) and this different subject does not have or is not suspected of developing ARDS.
  • the normal biomarker profile is assessed in a population of healthy individuals, the constituents of which display no signs, symptoms or diagnostic indicators that they may have or will develop ARDS.
  • the subject’s biomarker profile can be compared to a normal biomarker profile generated from a single normal sample or a biomarker profile generated from more than one normal sample.
  • measurements of the individual components e.g., concentration, ratio, log ratios etc.
  • concentration, ratio, log ratios etc. of the normal biomarker profile
  • values that do not fall within this “normal range” are said to be outside the normal range.
  • These measurements may or may not be converted to a value, number, factor or score as compared to measurements in the “normal range.”
  • a measurement for a specific factor or component that is below the normal range may be assigned a value or ⁇ 1, ⁇ 2, ⁇ 3, etc., depending on the scoring system devised.
  • the measurements of the individual components themselves are used in the risk profile, and these levels can be used to provide a “binary” value to each component, e.g., “elevated” or “not elevated.” Each of the binary values can be converted to a number, e.g., “1” or “0,” respectively.
  • the “risk profile value” can be a single value, number, factor or score given as an overall collective value to the individual components of the biomarker profile. For example, if each component is assigned a value, such as above, the component value may simply be the overall score of each individual or categorical value.
  • the risk profile in this example would be +8, with a normal value being, for example, “0.”
  • the biomarker profile value could be a useful single number or score, the actual value or magnitude of which could be an indication of the actual risk of developing ARDS, e.g., the “more positive” the value, the greater the risk of developing ARDS.
  • the “risk profile value” can be a series of values, numbers, factors or scores given to the individual components of the overall biomarker profile.
  • the “risk profile value” may be a combination of values, numbers, factors or scores given to individual components of the profile as well as values, numbers, factors or scores collectively given to a group of components, such as a biological effector portion.
  • the risk profile value may comprise or consist of individual values, number or scores for specific component as well as values, numbers or scores for a group of components.
  • individual values from the biomarker profile and/or the mechanism of injury can be used to develop a single score, such as a “combined risk index,” which may utilize weighted scores from the individual component values reduced to a diagnostic number value.
  • the combined risk index may also be generated using non ⁇ weighted scores from the individual component values.
  • the threshold value would be or could be set by the combined risk index from one or more normal subjects.
  • the value of the biomarker profile can be the collection of data from the individual measurements and need not be converted to a scoring system, such that the “risk profile value” is a collection of the individual measurements of the individual components of the biomarker profile.
  • a human subject is diagnosed of having an increased risk of suffering from ARDS if the subject’s eight, seven, six, five, four, three, two or even one of the components or factors herein are at abnormal levels.
  • the attending health care provider may subsequently prescribe or institute a treatment program.
  • the present invention also provides for methods of treating individuals for ARDS.
  • the attending healthcare worker may begin treatment, based on the subject’s biomarker profile, before there are perceivable, noticeable or measurable signs of ARDS in the individual.
  • the invention provides methods of treating ARDS in a subject in need thereof.
  • the treatment methods include obtaining a subject’s biomarker profile as defined herein and prescribing a treatment regimen to the subject if the biomarker profile indicates that the subject is at risk of developing ARDS.
  • the methods of treatment also include methods of monitoring the effectiveness of a treatment for ARDS. Once a treatment regimen has been established, with or without the use of the methods of the present invention to assist in a diagnosis of a risk of developing ARDS, the methods of monitoring a subject’s biomarker profile over time can be used to assess the effectiveness of treatments for ARDS. Specifically, the subject’s biomarker profile can be assessed over time, including before, during and after treatments for ARDS.
  • kits that can be used in the methods of the present invention. Specifically, the present invention provides kits for assessing the increased risk of developing ARDS, with the kits comprising one or more sets of antibodies that are immobilized onto a solid substrate and specifically bind to at least one of the factors or components listed herein. In specific embodiments, the kits comprise at least two, three, four, five, six or seven sets of antibodies immobilized onto a solid substrate, with each set corresponding to a factor. [0074] The antibodies that are immobilized onto the substrate may or may not be labeled.
  • the antibodies may be labeled, e.g., bound to a labeled protein, in such a manner that binding of the specific protein may displace the label and the presence of the marker in the sample is marked by the absence of a signal.
  • the antibodies that are immobilized onto the substrate may be directly or indirectly immobilized onto the surface. Methods for immobilizing proteins, including antibodies, are well ⁇ known in the art, and such methods may be used to immobilize a target protein, e.g., IL ⁇ 12, or another antibody onto the surface of the substrate to which the antibody directed to the specific factor can then be specifically bound. In this manner, the antibody directed to the specific biomarker is immobilized onto the surface of the substrate for the purposes of the present invention.
  • kits of the present invention may or may not include containers for collecting samples from the subject and one or more reagents, e.g., purified target biomarker for preparing a calibration curve.
  • the kits may or may not include additional reagents such as wash buffers, labeling reagents and reagents that are used to detect the presence (or absence) of the label.
  • additional reagents such as wash buffers, labeling reagents and reagents that are used to detect the presence (or absence) of the label.
  • Example 1 This prospective cohort study enrolled a total of 226 patients ages 18 years and older with injury or illness requiring surgical care or treatment in a critical care or emergency setting being cared for at Surgical Critical Care Initiative (SC2i) member sites (Walter Reed National Military Medical Center, Emory University Hospital, Grady Memorial Hospital, Duke University School of Medicine) between 2014 and 2017. ARDS patients were diagnosed according to the Berlin Definition with a PaO 2 /FiO 2 of ⁇ 300mmHg and samples were collected according to our Tissue and Data Acquisition Protocol (TDAP). [0079] Of the 226 patients studied, 14 (6.1%) developed ARDS during hospitalization.
  • SC2i Surgical Critical Care Initiative
  • TDAP Tissue and Data Acquisition Protocol
  • Inflammatory cytokine biomarker levels were quantified in a multiplexed assay on a LuminexTM instrument using sandwich immunoassays with a capture antibody conjugated to a colored bead, and a detection antibody conjugated to a fluorophore.
  • the combination of colored bead and fluorophore intensity gives a concentration that derived from a calibrated to a standard curve of known analyte concentrations.
  • Analysis of the biomarker data with a Wilcoxon Rank Sum test showed eotaxin, GM ⁇ CSF, IL ⁇ 8, IL ⁇ 12, IL ⁇ 13, IP ⁇ 10, MCP1 and RANTES (p ⁇ 0.05) to be significantly different between ARDS and non ⁇ ARDS patients.
  • eotaxin, GM ⁇ CSF, IFN ⁇ , IL ⁇ 1a, IL ⁇ 2, IL ⁇ 17, and MCP1 were significantly different among trauma patients with and without ARDS.
  • IL ⁇ 1RA, IL ⁇ 4, IL ⁇ 10, MIP1b, and VEGF were significantly different between ARDS and non ⁇ ARDS patients.
  • Table 1 Demographic characteristics of patients with acute respiratory distress syndrome versus without. ARDS: acute respiratory distress syndrome.
  • Table 2 Demographic characteristic of patients living and deceased patients with and without acute respiratory distress syndrome.
  • ARDS acute respiratory distress syndrome.
  • Borderline elevated levels (0.05 ⁇ p ⁇ 0.10 in a two ⁇ sided Wilcoxon Rank Sum test) were observed for FGF basic, HGF, IFN ⁇ alpha, IL ⁇ 9, and MIP ⁇ 1 beta (aka CCL4). Borderline reduced levels were observed for IP ⁇ 10/CXCL10.
  • biomarkers were statistically different between ARDS/No ARDS groups (10 higher in ARDS, 0 lower in ARDS). Elevated levels were observed for FGF basic, G ⁇ CSF, IFN ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1 ⁇ , IL ⁇ 1 ra/IL ⁇ 1F3, IL ⁇ 6, IL ⁇ 8, IL ⁇ 10, and MCP ⁇ 1.

Abstract

The present invention relates to methods of determining if a subject has an increased risk of developing acute respiratory distress syndrome (ARDS) prior to the onset of any detectable symptoms thereof. The methods comprise analyzing at least one sample from the subject to determine a value of the subject's biomarker profile and comparing the value of the subject's biomarker profile with the value of a normal biomarker profile. A change in the value of the subject's biomarker profile, over or under normal values is indicative that the subject has an increased risk of having or developing symptoms associated with ARDS prior to the onset of any detectable symptoms thereof.

Description

  PREDICTIVE FACTORS FOR ACUTE RESPIRATORY DISTRESS SYNDROME  Statement Regarding Federally Sponsored Research or Development 
[0001] This invention was made with government support under HT9404‐13‐1 and HU0001‐15‐2‐ 0001 awarded by The Department of Defense.  The government has certain rights in the invention.    Background of the Invention 
Field of the Invention 
[0002] The present invention relates to methods of determining if a subject has an increased risk of  developing acute respiratory distress syndrome (ARDS) prior to the onset of any detectable  symptoms thereof.  The methods comprise analyzing at least one sample from the subject to  determine a value of the subject’s biomarker profile and comparing the value of the subject’s  biomarker profile with the value of a normal biomarker profile.  A change in the value of the  subject’s biomarker profile, over or under normal values is indicative that the subject has an  increased risk of developing ARDS prior to the onset of any detectable symptoms thereof.    Background of the Invention 
[0003] Each year in the United States there are approximately 190,600 cases of acute respiratory  distress syndrome (ARDS), which are associated with 74,500 deaths and 3.6 million hospital days.   Despite recent advancements in the treatment of ARDS, effective treatment options remain limited  and there is a need for the development of prevention strategies and methods of early detection to  better guide clinical practice.  Given that alveolar inflammation is the major underlying mechanism  of ARDS, several serum inflammatory biomarkers have been identified among ARDS patients with  the goal of developing predictive models for ARDS in the future.  Summary of the Invention 
[0004] The present invention relates to methods of determining if a human subject has an  increased risk of developing ARDS prior to the onset of any detectable symptoms thereof.  The  methods comprise analyzing at least one sample from the subject to determine a value of the  subject’s biomarker profile and comparing the value of the subject’s biomarker profile with the value  of a normal biomarker profile.  A change in the value of the subject’s biomarker profile, over or  under normal values is indicative that the subject has an increased risk of developing or developing  symptoms associated with ARDS prior to the onset of any detectable symptoms thereof.    [0005] The present invention relates to a method of determining if a human subject has an  increased risk of developing acute respiratory distress syndrome (ARDS) prior to the onset of    detectable symptoms thereof, the method comprising (a) obtaining a biological sample from the  human subject, and (b) measuring the levels of one or more of basic fibroblast growth factor (FGF‐ basic), granulocyte colony‐stimulating factor (G‐CSF), granulocyte‐macrophage colony‐stimulating  factor (GM‐CSF), hepatocyte growth factor (HGF), interferon alpha (IFN‐α), interferon gamma (IFN‐ γ), interleukin‐1 alpha (IL‐1α), interleukin‐1 beta (IL‐1β), interleukin‐1 receptor agonist (IL‐1RA),  interleukin‐6 (IL‐6), interleukin‐8 (IL‐8), interleukin‐9 (IL‐9), interleukin‐10 (IL‐10), monocyte  chemoattractant protein 1 (MCP1), monokine induced by gamma interferon (MIG), macrophage  inflammatory protein‐1beta (MIP1β), and vascular endothelial growth factor (VEGF) in the biological  sample to create a biomarker profile, wherein an increase in the biomarker profile compared with a  normal biomarker profile is indicative that the human subject has an increased risk of developing  ARDS compared to individuals with a normal biomarker profile.  [0006] In some embodiments, the biomarker profile further includes interferon gamma‐induced  protein 10 (IP‐10) and/or interleukin‐15 (IL‐15) and a decrease in the level of these biomarkers  compared to a normal biomarker profile is indicative that the human subject has an increased risk of  developing ARDS compared to individuals with a normal biomarker profile.  [0007] In some embodiments, the normal biomarker profile comprises levels of one or more of  basic fibroblast growth factor (FGF‐basic), granulocyte colony‐stimulating factor (G‐CSF),  granulocyte‐macrophage colony‐stimulating factor (GM‐CSF), hepatocyte growth factor (HGF),  interferon alpha (IFN‐α), interferon gamma (IFN‐γ), interleukin‐1 alpha (IL‐1α), interleukin‐1 beta (IL‐ 1β), interleukin‐1 receptor agonist (IL‐1RA), interleukin‐6 (IL‐6), interleukin‐8 (IL‐8), interleukin‐9 (IL‐ 9), interleukin‐10 (IL‐10), monocyte chemoattractant protein 1 (MCP1), monokine induced by  gamma interferon (MIG), macrophage inflammatory protein‐1beta (MIP1β), and vascular endothelial  growth factor (VEGF)  generated from a population of human subjects that did not exhibit ARDS.    [0008] In some embodiments, the normal biomarker profile comprises levels of one or more of  basic fibroblast growth factor (FGF‐basic), granulocyte colony‐stimulating factor (G‐CSF),  granulocyte‐macrophage colony‐stimulating factor (GM‐CSF), hepatocyte growth factor (HGF),  interferon alpha (IFN‐α), interferon gamma (IFN‐γ), interleukin‐1 alpha (IL‐1α), interleukin‐1 beta (IL‐ 1β), interleukin‐1 receptor agonist (IL‐1RA), interleukin‐6 (IL‐6), interleukin‐8 (IL‐8), interleukin‐9 (IL‐ 9), interleukin‐10 (IL‐10), monocyte chemoattractant protein 1 (MCP1), monokine induced by  gamma interferon (MIG), macrophage inflammatory protein‐1beta (MIP1β), and vascular endothelial  growth factor (VEGF) generated from a population of human subjects that were trauma patients.    [0009] In some embodiments, the human subject is a trauma patient and the biomarker profile  comprises serum levels of FGF‐basic, G‐CSF, IFN‐α, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐10, MCP1, and  MIP1β.    [0010] In some embodiments, the human subject is not a trauma patient and the biomarker profile  comprises serum levels of MCP1 and MIG.   [0011] In some embodiments, the human subject is not a trauma patient and the biomarker profile  comprises serum levels of eotaxin, GM‐CSF, IL‐8, IL‐12, IL‐13, IP‐10, MCP1 and RANTES.    [0012] In some embodiments, the biomarker profile comprises serum levels of G‐CSF, GM‐CSF, IFN‐ γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐10, IL‐15, MCP1, MIG, and VEGF.  [0013] The invention relates to a method of detecting elevated levels of biomarkers in a human  subject, the method comprising measuring serum levels of two or more of FGF‐basic, G‐CSF, GM‐ CSF, HGF, IFN‐α, IFN‐γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐9, IL‐10, MCP1, MIG, MIP1β, and VEGF in a  serum sample obtained from the human subject.   [0014] In some embodiments, the human subject is a trauma patient and the serum levels of FGF‐ basic, G‐CSF, IFN‐α, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐10, MCP1, and MIP1β are measured.    [0015] In some embodiments, the human subject is not a trauma patient and the serum levels of  MCP1 and MIG are measured.    [0016] In some embodiments, the levels of eotaxin, GM‐CSF, IL‐8, IL‐12, IL‐13, IP‐10, MCP1 and  RANTES are measured.    [0017] The invention relates to a method of treating a human subject for acute respiratory distress  syndrome (ARDS), the method comprising (a) producing a biomarker profile comprising measuring  serum levels of two or more of FGF‐basic, G‐CSF, GM‐CSF, HGF, IFN‐α, IFN‐γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6,  IL‐8, IL‐9, IL‐10, MCP1, MIG, MIP1β, and VEGF, and (b) administering a treatment for ARDS to the  human subject when the biomarker profile for the subject is greater than the biomarker profile of a  normal subject.  [0018] In some embodiments, the treatment is administered to the human subject prior to the  onset of any detectable symptoms of the subject exhibiting ARDS.  In some embodiments, the  human subject is a trauma patient and the biomarker profile comprises serum levels of FGF‐basic, G‐ CSF, IFN‐α, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐10, MCP1, and MIP1β.      [0019] In some embodiments, the human subject is not a trauma patient and the biomarker profile  comprises serum levels of MCP1 and MIG. In some embodiments, the biomarker profile comprises  serum levels of eotaxin, GM‐CSF, IL‐8, IL‐12, IL‐13, IP‐10, MCP1 and RANTES.  [0020] In some embodiments the biological sample is a blood sample. In some embodiments the  sample is a serum sample. In some embodiments the sample is a plasma sample.  Brief Description of the Drawings 
[0021] Figure 1. Quantification of cytokine biomarkers in ARDS and Non‐ARDS patients upon initial  presentation. The levels of GM‐CSF, IL‐08, IL‐12, IL‐13, IP‐10, MCP1, and RANTES were significantly  different between ARDS and Non‐ARDS patients according to a Wilcoxon Rank Sum test (p<0.05).  The ends of the box mark the upper and lower quartiles. The median is marked by a vertical line  inside the box. The whiskers extend to the highest and lowest observations.  [0022] Figure 2. Quantification of cytokine biomarkers in ARDS and Non‐ARDS patients 0 days after  initial presentation. The levels of IL‐1RA, IL‐4, IL‐10, MIP1b, and VEGF were significantly different  between ARDS and Non‐ARDS patients according to a Wilcoxon Rank Sum test (p<0.05).   [0023] Figure 3. Quantification of cytokine biomarkers in ARDS and Non‐ARDS patients 0 days after  initial presentation. The levels of eotaxin, GM‐CSF, IFN‐γ, IL‐1a, IL‐2, IL‐17, and MCP1 were  significantly different between ARDS and Non‐ARDS patients according to a Wilcoxon Rank Sum test  (p<0.05).   [0024] Figure 4. Quantification of cytokine biomarkers in all ARDS and Non‐ARDS patients.  [0025] Figure 5. Quantification of cytokine biomarkers in ARDS and Non‐ARDS patients in the  trauma cohort.  [0026] Figure 6. Quantification of cytokine biomarkers in ARDS and Non‐ARDS patients in the non‐ trauma cohort.  [0027] Figure 7. Quantification of cytokine biomarkers at the initial timepoint in all ARDS and Non‐ ARDS patients.  [0028] Figure 8. Quantification of cytokine biomarkers at the initial timepoint in ARDS and Non‐ ARDS patients in the trauma cohort.    [0029] Figure 9. Quantification of cytokine biomarkers at the initial timepoint in ARDS and Non‐ ARDS patients in the non‐trauma cohort.  Detailed Description of the Invention 
[0030] The present invention relates to methods of determining if a human subject has an  increased risk of developing ARDS prior to the onset of any detectable symptoms thereof.  The  methods comprise analyzing at least one sample from the subject to determine a value of the  subject’s biomarker profile and comparing the value of the subject’s biomarker profile with the value  of a normal biomarker profile.  A change in the value of the subject’s biomarker profile, over or  under normal values is indicative that the subject has an increased risk of developing or developing  symptoms associated with ARDS prior to the onset of any detectable symptoms thereof (i.e. a risk  profile for ARDS).    [0031] As used herein, the term “subject” or “test subject” indicates a human, in particular a human  who is hospitalized.  The test subject is in need of an assessment of susceptibility of ARDS.  For  example, the test subject may have no symptoms that ARDS may occur.    [0032] In one embodiment, the biomarker profile comprises serum levels of at least one of eotaxin,  granulocyte‐macrophage colony‐stimulating factor (GM‐CSF), interleukin‐8 (IL‐8), interleukin‐12 (IL‐ 12), interleukin‐13 (IL‐13), interferon gamma induced protein 10 (IP‐10), monocyte chemoattractant  protein 1 (MCP‐1), RANTES, interferon gamma (IFN‐γ), interleukin‐1a (IL‐1a), interleukin‐2 (IL‐2),  interleukin‐17 (IL‐17), interleukin‐1RA (IL‐1RA), interleukin‐4 (IL‐4), interleukin‐10 (IL‐10),  macrophage inflammatory protein‐1b (MIP1b) and vascular endothelial growth factor (VEGF).    [0033] In one embodiment, the human subject is a trauma patient.  In another embodiment, the  subject is not a trauma patient.  In still another embodiment, if the subject is a trauma patient the  biomarker profile comprises serum levels of eotaxin, GM‐CSF, IFN‐γ, IL‐1a, IL‐2, IL‐17 and MCP1.  In  still another embodiment, if the subject is not a trauma patient, the biomarker profile comprises  serum levels of IL‐1RA, IL‐4, IL‐10, MIP1b and VEGF.  In still another embodiment, the status of the  patient, i.e., trauma or non‐trauma, is irrelevant and the biomarker profile comprises serum levels of  eotaxin, GM‐CSF, IL‐8, IL‐12, IL‐13, IP‐10, MCP1 and RANTES.    [0034] The present invention also relates to methods of detecting elevated levels of a specific  collection of analytes in one or more samples obtained from a subject.  In one embodiment, the  collection of analytes comprises serum levels of at least one of eotaxin, granulocyte‐macrophage  colony‐stimulating factor (GM‐CSF), interleukin‐8 (IL‐8), interleukin‐12 (IL‐12), interleukin‐13 (IL‐13),    interferon gamma induced protein 10 (IP‐10), monocyte chemoattractant protein 1 (MCP‐1),  RANTES, interferon gamma (IFN‐γ), interleukin‐1a (IL‐1a), interleukin‐2 (IL‐2), interleukin‐17 (IL‐17),  interleukin‐1RA (IL‐1RA), interleukin‐4 (IL‐4), interleukin‐10 (IL‐10), macrophage inflammatory  protein‐1b (MIP1b) and vascular endothelial growth factor (VEGF).    [0035] In one embodiment, the human subject is a trauma patient.  In another embodiment, the  subject is not a trauma patient.  In still another embodiment, if the subject is a trauma patient the  biomarker profile comprises serum levels of eotaxin, GM‐CSF, IFN‐γ, IL‐1a, IL‐2, IL‐17 and MCP1.  In  still another embodiment, if the subject is not a trauma patient, the biomarker profile comprises  serum levels of IL‐1RA, IL‐4, IL‐10, MIP1b and VEGF.  In still another embodiment, the status of the  patient, i.e., trauma or non‐trauma, is irrelevant and the biomarker profile comprises serum levels of  eotaxin, GM‐CSF, IL‐8, IL‐12, IL‐13, IP‐10, MCP1 and RANTES.    [0036] The term acute respiratory distress syndrome, or ARDS, is used herein to mean a subject  with a PaO2/FiO2 of < 300 mmHg.    [0037] As used herein, the term means “increased risk” is used to mean that the test subject has an  increased chance of developing ARDS compared to a normal individual.  The increased risk may be  relative or absolute and may be expressed qualitatively or quantitatively.  For example, an increased  risk may be expressed as simply determining the subject’s biomarker profile and placing the patient  in an “increased risk” category, based upon previous population studies.  Alternatively, a numerical  expression of the subject’s increased risk may be determined based upon the biomarker profile.  As  used herein, examples of expressions of an increased risk include but are not limited to, odds,  probability, odds ratio, p‐values, attributable risk, biomarker index score, relative frequency, positive  predictive value, negative predictive value, and relative risk.    [0038] For example, the correlation between a subject’s biomarker profile and the likelihood of  developing ARDS may be measured by an odds ratio (OR) and by the relative risk (RR).  If P(R+) is the  probability of developing ARDS for individuals with the risk profile (R) and P(R‐) is the probability of  developing ARDS for individuals without the risk profile, then the relative risk is the ratio of the two  probabilities: RR=P(R+)/P(R‐).    [0039] In case‐control studies, however, direct measures of the relative risk often cannot be  obtained because of sampling design.  The odds ratio allows for an approximation of the relative risk  for low‐incidence diseases and can be calculated: OR=(F+/(1‐F+))/(F‐/(1‐F‐)), where F+ is the frequency  of a risk profile in cases studies and F‐ is the frequency of risk profile in controls.  F+ and F‐ can be  calculated using the risk profile frequencies of the study.     [0040] The attributable risk (AR) can also be used to express an increased risk.  The AR describes the  proportion of individuals in a population exhibiting ARDS to a specific member of the biomarker  profile.  AR may also be important in quantifying the role of individual components (specific  member) in condition etiology and in terms of the public health impact of the individual risk factor.   The public health relevance of the AR measurement lies in estimating the proportion of cases of  ARDS in a population of subjects that could be prevented if the profile or individual factor were  absent.  AR may be determined as follows: AR=PE(RR‐1)/(PE(RR‐1)+1), where AR is the risk  attributable to a profile or individual factor of the profile, and PE is the frequency of exposure to a  profile or individual component of the profile within the population at large.  RR is the relative risk,  which can be approximated with the odds ratio when the profile or individual factor of the profile  under study has a relatively low incidence in the general population.     [0041] In one embodiment, the increased risk of a human subject can be determined from p‐values  that are derived from association studies.  Specifically, associations with specific profiles can be  performed using regression analysis by regressing the risk profile with the presence or absence of  ARDS.  In addition, the regression may or may not be corrected or adjusted for one or more factors.   The factors for which the analyses may be adjusted include, but are not limited to age, sex, weight,  ethnicity, type of wound if present, number of wounds if present, trauma, number of days from  injury, geographic location, fasting state, state of pregnancy or post‐pregnancy, menstrual cycle,  general health of the subject, alcohol or drug consumption, caffeine or nicotine intake and circadian  rhythms, to name a few.   [0042] Increased risk can also be determined from p‐values that are derived using logistic  regression.  Binomial (or binary) logistic regression is a form of regression which is used when the  dependent is a dichotomy and the independents are of any type.  Logistic regression can be used to  predict a dependent variable on the basis of continuous or categorical or both (continuous and  categorical) independents and to determine the percent of variance in the dependent variable  explained by the independents; to rank the relative importance of independents; to assess  interaction effects; and to understand the impact of covariate control variables.  Logistic regression  applies maximum likelihood estimation after transforming the dependent into a “logit” variable (the  natural log of the odds of the dependent occurring or not).  In this way, logistic regression estimates  the probability of a certain event occurring. These analyses may be conducted with virtually any  statistics program, such as not limited to SAS, R package available through CRAN repository.  [0043] SAS (“statistical analysis software”) is a general purpose package (similar to Stata and SPSS)  created by Jim Goodnight and N.C. State University colleagues.  Ready‐to‐use procedures handle a    wide range of statistical analyses, including but not limited to, analysis of variance, regression,  categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster  analysis, and nonparametric analysis.  R package is free, general purpose package that complies with  and runs on a variety of UNIX platforms.    [0044] Accordingly, select embodiments of the present invention comprise the use of a computer  comprising a processor and the computer is configured or programmed to generate one or more risk  profiles and/or to determine statistical risk from a biomarker profile.  The methods may also  comprise displaying the one or risk profiles on a screen that is communicatively connected to the  computer.  In another embodiment, two different computers can be used:  one computer configured  or programmed to generate one or more risk profiles and a second computer configured or  programmed to determine statistical risk.  Each of these separate computers can be 
communicatively linked to its own display or to the same display.    [0045] As used herein, the phrase “risk profile” means the combination of a subject’s risk factors  analyzed or observed from a biomarker profile.  The terms “factor” and/or “component” are used to  mean the individual constituents that are assessed when generating the profile. The risk profile is a  collection of measurements, such as but not limited to a quantity or concentration, for individual  factors taken from a test sample of the subject.  Examples of test samples or sources of components  for the risk profile include, but are not limited to, biological fluids, which can be tested by the  methods of the present invention described herein, and include but are not limited to whole blood,  such as but not limited to peripheral blood, serum, plasma, cerebrospinal fluid, urine, amniotic fluid,  lymph fluids, various external secretions of the respiratory, intestinal and genitourinary tracts, tears,  saliva, white blood cells, myelomas and the like.    [0046] The risk profile can include a “biological effector” or aspect and/or a non‐biological effector  aspect.  As used herein, the term “biological effector” is used to mean a molecule, such as but not  limited to, a protein, peptide, a carbohydrate, a fatty acid, a nucleic acid, a glycoprotein, a  proteoglycan, etc. that can be assayed.  Specific examples of biological effectors can include,  cytokines, growth factors, antibodies, hormones, cell surface receptors, cell surface proteins,  carbohydrates, etc.  More specific examples of biological effectors include analytes such as  interleukins (ILs) such as IL‐1α, IL‐1β, IL‐1 receptor antagonist (IL‐1RA), IL‐2, IL‐2 receptor (IL‐2R), IL‐3,  IL‐4, IL‐5, IL‐6, IL‐7, IL‐8, IL‐10, IL‐12, IL‐13, IL‐15, IL‐17, as well as growth factors such as tumor  necrosis factor alpha (TNFα), granulocyte colony stimulating factor (G‐CSF), granulocyte macrophage  colony stimulating factor (GM‐CSF), interferon alpha (INF‐α), interferon gamma (IFN‐γ), epithelial  growth factor (EGF), basic endothelial growth factor (bEGF), hepatocyte growth factor (HGF),    vascular endothelial growth factor (VEGF), and chemokines such as monocyte chemoattractant  protein‐1 (CCL2/MCP‐1), macrophage inflammatory protein‐1 alpha (CCL3/MIP‐1α), macrophage  inflammatory protein‐1 beta (CCL4/MIP‐1β), CCL5/RANTES, CCL11/eotaxin, monokine induced by  gamma interferon (CXCL9/MIG) and interferon gamma‐induced protein‐10 (CXCL10/IP10).    [0047] Techniques to assay levels of individual components of the biomarker profile 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.  In one embodiment, levels of the individual factors in the  serum of the biomarker profile are assessed using mass spectrometry in conjunction with ultra‐ performance liquid chromatography (UPLC), high‐performance liquid chromatography (HPLC), gas  chromatography (GC), gas chromatography/mass spectroscopy (GC/MS), and UPLC to name a few.   Other methods of assessing levels of some of the individual components include biological methods,  such as but not limited to ELISA assays, Western Blot and multiplexed immunoassays etc.  Other  techniques may include using quantitative arrays, PCR, Northern Blot analysis.  To determine levels  of components or factors, it is not necessary that an entire component, e.g., a full length protein or  an entire RNA transcript, be present or fully sequenced.  In other words, determining levels of, for  example, a fragment of protein being analyzed may be sufficient to conclude or assess that an  individual component of the biomarker profile being analyzed is increased or decreased.  Similarly,  if, for example, arrays or blots are used to determine component levels, the 
presence/absence/strength of a detectable signal may be sufficient to assess levels of components.    [0048] As used herein, the term non‐biological effector is a component that is generally considered  not to be a specific molecule.  Although not a specific molecule, a non‐biological effector may  nonetheless still be quantifiable, either through routine measurements or through measurements  that stratify the data being assessed.  For example, number or concentrate of red blood cells, white  blood cells, platelets, coagulation time, blood oxygen content, etc. would be a non‐biological  effector component of the biomarker profile.  All of these components are measureable or  quantifiable using routine methods and equipment.  Other non‐biological components include data  that may not be readily or routinely quantifiable or that may require a practitioner’s judgment or  opinion.    [0049] In one embodiment, the mechanism of injury is included in the biomarker profile.  As used  herein, the phrase “mechanism of injury” means the manner in which the subject received an injury.   For example, the mechanism of injury may include trauma and may be described as a gunshot  wound, a vehicle accident, laceration, etc.  In another embodiment, data regarding injury type is  included in the biomarker profile.  In another embodiment, data on the occurrence of multiple    wounds is included in the biomarker profile.  In another embodiment, data on the number of days  from injury is included in the biomarker profile   [0050] To determine which of the biological effector or non‐biological effector components may be  critical in the subjects’ biomarker profiles, routine statistical methods can be employed.  For  example, rfImpute from the randomForest R package can be used to impute missing data.  Up‐ sampling and predictor rank transformations can be performed on the data set only for variable  selection to accommodate class imbalance and non‐normality in the data.    [0051] For variable selection, the constraint‐based algorithms fast.iamb, iamb and gs and the  constraint‐based local discovery learning algorithms mmpc and si.hiton.pc from the “bnlearn” R  package can be used to search the input dataset for nodes of Bayesian networks.  The nodes can be  chosen as the reduced variable sets.  Before running the data through variable selection and binary  classification algorithms, the variables may or may not randomly re‐ordered.  The data can be run  through the variable selection and binary classification algorithms more than once, for example, 10,  20, 30, 40, 50 or even more times.    [0052] For binary classification and model selection, each variable set can be pulled from the raw  data and run in sundry binary classification algorithms using the train function from the R caret  package: linear discriminant analysis (lda), classification and regression trees (cart), k‐nearest  neighbors (knn), support vector machine (svm), logistic regression (glm), random forest (rf),  generalized linear models (glmnet) and naïve Bayes (nb).  The best variable set and binary  classification algorithm combination that first produces the highest kappa and then the highest  sensitivity with reasonable specificity can then be chosen.    [0053] The resultant models are then examined using accuracy, no information rate, positive  predictive value and negative predictive value.  Model performance can be further assessed using  the plot.roc command to compute the Receiver Operator Characteristic Curves (ROC) and area  under curve (AUC).  The dca R command from the Memorial Sloan Kettering Cancer Center website,  www.mskcc.org, can be used to compute the Decision Curve Analysis (DCA).   [0054] Finally, for univariate analysis, a Wilcoxon rank‐sum test can be used to identify which  biomarkers from specific patient groups are were associated with a specific indication.    [0055] The assessment of the levels of the individual components of the biomarker profile can be  expressed as absolute or relative values and may or may not be expressed in relation to another  component, a standard an internal standard or another molecule of compound known to be in the    sample.  If the levels are assessed as relative to a standard or internal standard, the standard may be  added to the test sample prior to, during or after sample processing.    [0056] To assess levels of the individual components of the biomarker profile, a sample may be  taken from the subject.  The sample may or may not processed prior assaying levels of the  components of the biomarker profile.  For example, whole blood may be taken from an individual  and the blood sample may be processed, e.g., centrifuged, to isolate plasma or serum from the  blood.  The sample may or may not be stored, e.g., frozen, prior to processing or analysis.    [0057] In one embodiment, the individual levels of each of the risk factors are higher than those  compared to normal levels.  In another embodiment, one, two, three, four, five, six or seven of the  levels of each of the factor are higher than normal levels while others, if any, are lower than or the  same as normal levels.    [0058] The levels of depletion of the factors or components compared to normal levels can vary.  In  one embodiment, the levels of any one or more of the factors or components is at least 1.05, 1.1,  1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 higher  than normal levels (where, for sake of clarity, a marker a level of “1” would indicate that the  component is at the same level in both the subject and normal samples).  For the purposes of the  present invention, the number of “times” the levels of a factor are higher over normal can be a  relative or absolute number of times.  In the alternative, the levels of the factors or components may  be normalized to a standard and these normalized levels can then be compared to one another to  determine if a factor or component is lower, higher or about the same.    [0059] For the purposes of the present invention the biomarker profile comprises at least one, two,  three, four, five, six, seven or eight of the factors or components for the prediction of ARDS.  If one  factor or component of the biological effector aspect of the biomarker profile is used in generating  the biomarker profile for the prediction of ARDS, then any one of the listed factors or components  can be used to generate the profile.  If two factors or components of the biological effector aspect of  the biomarker profile are used in generating the biomarker profile for the prediction of ARDS, any  combination of the two listed above can be used.  If three factors or components of the biological  effector aspect of the biomarker profile are used in generating the biomarker profile for the  prediction of ARDS, any combination of three of the factors or components listed above can be used.   If four factors or components of the biological effector aspect of the biomarker profile are used in  generating the biomarker profile for the prediction of ARDS, any combination of four of the factors  or components listed above can be used.  If five factors or components of the biological effector    aspect of the biomarker profile are used in generating the biomarker profile for the prediction of  ARDS, any combination of five of the factors or components listed above can be used.  If six factors  or components of the biological effector aspect of the biomarker profile are used in generating the  biomarker profile for the prediction of ARDS, any combination of six of the factors or components  listed above can be used.  If seven factors or components of the biological effector aspect of the  biomarker profile are used in generating the biomarker profile for the prediction of ARDS, any  combination of seven of the factors or components listed above can be used.  Of course all members  of the biological effector aspect of each biomarker profile panel can be used to generate a  biomarker profile for the prediction of ARDS.    [0060] The subject’s biomarker profile is compared to the profile that is deemed to be a normal  biomarker profile.  To establish the biomarker profile of a normal individual, an individual or group  of individuals may be first assessed to ensure they have no signs, symptoms or diagnostic indicators  of ARDS.  Once established, the biomarker profile of the individual or group of individuals can then  be determined to establish a “normal biomarker profile.”  In one embodiment, a normal biomarker  profile can be ascertained from the same subject when the subject is deemed as healthy with no  signs, symptoms or diagnostic indicators of ARDS.  In one embodiment, a biomarker profile from a  “normal subject” e.g., a “normal biomarker profile” is a human subject that does not exhibit or  display ARDS, but may still may not be considered as healthy.    [0061] In one embodiment, a “normal” biomarker profile is assessed in the same subject from  whom the sample is taken prior to the onset of any signs, symptoms or diagnostic indicators that  they may exhibit ARDS.  That is, the term “normal” with respect to a biomarker profile can be used  to mean the subject’s baseline biomarker profile prior to the onset of any signs, symptoms or  diagnostic indicators of potential ARDS.  The biomarker profile can then be reassessed periodically  and compared to the subject’s baseline biomarker profile.  Thus, the present invention also includes  methods of monitoring the progression of ARDS in a subject, with the methods comprising  determining the subject’s biomarker profile at more than one time point.  For example, some  embodiments of the methods of the present invention will comprise determining the subject’s  biomarker profile at two, three, four, five, six, seven, eight, nine, 10 or even more time points over a  period of time, such as a week or more, two weeks or more, three weeks or more, four weeks or  more, a month or more, two months or more, three months or more, four months or more, five  months or more, six months or more, seven months or more, eight months or more, nine months or  more, ten months or more, 11 months or more, a year or more or even two years.  The methods of  monitoring a subject’s risk of developing ARDS would also include embodiments in which the    subject’s biomarker profile is assessed before and/or during and/or after treatment of ARDS.  In  other words, the present invention also includes methods of monitoring the efficacy of treatment of  ARDS by assessing the subject’s biomarker profile over the course of the treatment and after the  treatment.  In specific embodiments, the methods of monitoring the efficacy of treatment of ARDS  comprise determining the subject’s biomarker profile at at least one, two, three, four, five, six,  seven, eight, nine or 10 or more different time points prior to the receipt of treatment for ARDS and  subsequently determining the subject’s biomarker profile at at least one, two, three, four, five, six,  seven, eight, nine or 10 or more different time points after beginning of treatment for ARDS, and  determining the changes, if any, in the biomarker profile of the subject.  The treatment may be any  treatment designed to cure, remove or diminish the likelihood of developing ARDS.    [0062] In another embodiment, a normal biomarker profile is assessed in a sample from a different  subject or patient (from the subject being analyzed) and this different subject does not have or is not  suspected of developing ARDS.  In still another embodiment, the normal biomarker profile is  assessed in a population of healthy individuals, the constituents of which display no signs, symptoms  or diagnostic indicators that they may have or will develop ARDS.  Thus, the subject’s biomarker  profile can be compared to a normal biomarker profile generated from a single normal sample or a  biomarker profile generated from more than one normal sample.    [0063] Of course, measurements of the individual components, e.g., concentration, ratio, log ratios  etc., of the normal biomarker profile can fall within a range of values, and values that do not fall  within this “normal range” are said to be outside the normal range.  These measurements may or  may not be converted to a value, number, factor or score as compared to measurements in the  “normal range.”  For example, a measurement for a specific factor or component that is below the  normal range, may be assigned a value or ‐1, ‐2, ‐3, etc., depending on the scoring system devised.    [0064] In another embodiment, the measurements of the individual components themselves are  used in the risk profile, and these levels can be used to provide a “binary” value to each component,  e.g., “elevated” or “not elevated.”  Each of the binary values can be converted to a number, e.g., “1”  or “0,” respectively.    [0065] In one embodiment, the “risk profile value” can be a single value, number, factor or score  given as an overall collective value to the individual components of the biomarker profile.  For  example, if each component is assigned a value, such as above, the component value may simply be  the overall score of each individual or categorical value.  For example, if five components of the  biomarker profile for predicting ARDS are used and three of those components are assigned values    of “+2” and two are assigned values of “+1,” the risk profile in this example would be +8, with a  normal value being, for example, “0.”  In this manner, the biomarker profile value could be a useful  single number or score, the actual value or magnitude of which could be an indication of the actual  risk of developing ARDS, e.g., the “more positive” the value, the greater the risk of developing ARDS.    [0066] In another embodiment the “risk profile value” can be a series of values, numbers, factors or  scores given to the individual components of the overall biomarker profile.  In another embodiment,  the “risk profile value” may be a combination of values, numbers, factors or scores given to  individual components of the profile as well as values, numbers, factors or scores collectively given  to a group of components, such as a biological effector portion.  In another example, the risk profile  value may comprise or consist of individual values, number or scores for specific component as well  as values, numbers or scores for a group of components.    [0067] In another embodiment individual values from the biomarker profile and/or the mechanism  of injury can be used to develop a single score, such as a “combined risk index,” which may utilize  weighted scores from the individual component values reduced to a diagnostic number value.  The  combined risk index may also be generated using non‐weighted scores from the individual  component values.  When the “combined risk index” exceeds a specific threshold level, determined  by a range of values developed similarly from control (normal) subjects, the individual has a high  risk, or higher than normal risk, of developing ARDS, whereas maintaining a normal range value of  the “combined risk index” would indicate a low or minimal risk of developing ARDS.  In this  embodiment, the threshold value would be or could be set by the combined risk index from one or  more normal subjects.    [0068] In another embodiment, the value of the biomarker profile can be the collection of data  from the individual measurements and need not be converted to a scoring system, such that the  “risk profile value” is a collection of the individual measurements of the individual components of  the biomarker profile.    [0069] In specific embodiments, a human subject is diagnosed of having an increased risk of  suffering from ARDS if the subject’s eight, seven, six, five, four, three, two or even one of the  components or factors herein are at abnormal levels.    [0070] If it is determined that a human subject has an increased risk of developing ARDS, the  attending health care provider may subsequently prescribe or institute a treatment program.  In this  manner, the present invention also provides for methods of treating individuals for ARDS.  The    attending healthcare worker may begin treatment, based on the subject’s biomarker profile, before  there are perceivable, noticeable or measurable signs of ARDS in the individual.    [0071] Accordingly, the invention provides methods of treating ARDS in a subject in need thereof.   The treatment methods include obtaining a subject’s biomarker profile as defined herein and  prescribing a treatment regimen to the subject if the biomarker profile indicates that the subject is  at risk of developing ARDS.    [0072] The methods of treatment also include methods of monitoring the effectiveness of a  treatment for ARDS.  Once a treatment regimen has been established, with or without the use of the  methods of the present invention to assist in a diagnosis of a risk of developing ARDS, the methods  of monitoring a subject’s biomarker profile over time can be used to assess the effectiveness of  treatments for ARDS.  Specifically, the subject’s biomarker profile can be assessed over time,  including before, during and after treatments for ARDS.  The biomarker profile can be monitored,  with, for example, the normalization or decline in the values of the profile over time being indicative  that the treatment may be showing efficacy of treatment.   [0073] The present invention also provides kits that can be used in the methods of the present  invention.  Specifically, the present invention provides kits for assessing the increased risk of  developing ARDS, with the kits comprising one or more sets of antibodies that are immobilized onto  a solid substrate and specifically bind to at least one of the factors or components listed herein.  In  specific embodiments, the kits comprise at least two, three, four, five, six or seven sets of antibodies  immobilized onto a solid substrate, with each set corresponding to a factor.    [0074] The antibodies that are immobilized onto the substrate may or may not be labeled.  For  example, the antibodies may be labeled, e.g., bound to a labeled protein, in such a manner that  binding of the specific protein may displace the label and the presence of the marker in the sample is  marked by the absence of a signal.  In addition, the antibodies that are immobilized onto the  substrate may be directly or indirectly immobilized onto the surface.  Methods for immobilizing  proteins, including antibodies, are well‐known in the art, and such methods may be used to  immobilize a target protein, e.g., IL‐12, or another antibody onto the surface of the substrate to  which the antibody directed to the specific factor can then be specifically bound.  In this manner, the  antibody directed to the specific biomarker is immobilized onto the surface of the substrate for the  purposes of the present invention.    [0075] The kits of the present invention may or may not include containers for collecting samples  from the subject and one or more reagents, e.g., purified target biomarker for preparing a    calibration curve.  The kits may or may not include additional reagents such as wash buffers, labeling  reagents and reagents that are used to detect the presence (or absence) of the label.    [0076] All patents and publications cited herein are incorporated by reference to the same extent  as if each individual publication was specifically and individually indicated as having been  incorporated by reference in its entirety.    Examples 
[0077] Example 1  [0078] This prospective cohort study enrolled a total of 226 patients ages 18 years and older with  injury or illness requiring surgical care or treatment in a critical care or emergency setting being  cared for at Surgical Critical Care Initiative (SC2i) member sites (Walter Reed National Military  Medical Center, Emory University Hospital, Grady Memorial Hospital, Duke University School of  Medicine) between 2014 and 2017.  ARDS patients were diagnosed according to the Berlin Definition  with a PaO2/FiO2 of <300mmHg and samples were collected according to our Tissue and Data  Acquisition Protocol (TDAP).    [0079] Of the 226 patients studied, 14 (6.1%) developed ARDS during hospitalization.  Of the 160  trauma patients, 11 (10.1%) developed ARDS compared to 2 (3.1%) of the 65 non‐trauma patients.   Neither the presence or absence of trauma (p=0.43) nor blunt versus penetrating trauma (p=0.88)  were found to be significant factors in the development of ARDS.  The overall hospital mortality rate  was 4% compared to the ARDS hospital mortality rate of 30.8%.  Of the 9 patients in the cohort who  died during hospitalization, 4 (45.4%) were diagnosed with ARDS.  Complication rates were  comparable between ARDS and non‐ARDS patients.    [0080] Serum samples were collected upon initial presentation and at days 0 and 0. Inflammatory  cytokine biomarker levels were quantified in a multiplexed assay on a Luminex™ instrument using  sandwich immunoassays with a capture antibody conjugated to a colored bead, and a detection  antibody conjugated to a fluorophore. The combination of colored bead and fluorophore intensity  gives a concentration that derived from a calibrated to a standard curve of known analyte  concentrations. Analysis of the biomarker data with a Wilcoxon Rank Sum test showed eotaxin, GM‐ CSF, IL‐8, IL‐12, IL‐13, IP‐10, MCP1 and RANTES (p<0.05) to be significantly different between ARDS  and non‐ARDS patients.  Additionally, eotaxin, GM‐CSF, IFN‐γ, IL‐1a, IL‐2, IL‐17, and MCP1 (p<0.05)  were significantly different among trauma patients with and without ARDS.  Among non‐trauma    patients, IL‐ 1RA, IL‐4, IL‐10, MIP1b, and VEGF (p<0.05) were significantly different between ARDS  and non‐ARDS patients.   Table 1:  Demographic characteristics of patients with acute respiratory distress syndrome versus  without. ARDS: acute respiratory distress syndrome.   
Figure imgf000018_0001
Table 2:  Demographic characteristic of patients living and deceased patients with and without acute  respiratory distress syndrome. ARDS: acute respiratory distress syndrome.   
Figure imgf000018_0002
[0081] Example 2  [0082] 186 additional patients were enrolled in the prospective study for a total of 389 patients. 74  of these patients were diagnosed with ARDS as some point during their in hospital recovery with a  total of 918 serum samples. Wilcoxon Rank Sum tests were performed on all serum biomarker data  (n=918, 389 patients, see Figure 4), all serum biomarker data in the trauma cohort (n=597, 264  patients, see Figure 5), all serum biomarker data in the non‐trauma cohort (n=321, 102 patients, see  Figure 6), all initial serum biomarker data (n=232, 232 patients, see Figure 7), all initial serum  biomarker data in the trauma cohort (n=161, 161 patients, see Figure 8), and all initial serum  biomarker data in the non‐trauma cohort (n=71, 71 patients, see Figure 9). Directional inferences  were made using one‐sided tests, while tests for difference in means were made with a two‐sided  test. Table 3.  [0083] In the all serum dataset 28 (2 borderline) biomarkers were statistically different (p < 0.05)  between ARDS/No ARDS groups (27 higher in ARDS, 3 lower in ARDS). [0084] In the all serum trauma dataset 22 (1 borderline – 0.05 > p > 0.10) biomarkers were  statistically different between ARDS/No ARDS groups (22 higher in ARDS, 1 lower in ARDS).  [0085] In the all serum non‐trauma dataset 20 (1 borderline) biomarkers were statistically different  between ARDS/No ARDS groups (17 higher in ARDS, 4 lower in ARDS).    [0086] In the initial time point serum dataset 12 (6 borderline) biomarkers were statistically  different between ARDS/No ARDS groups (16 higher in ARDS, 2 lower in ARDS). Elevated levels (p <  0.05 in a two‐sided Wilcoxon Rank Sum test) were observed for G‐CSF, GM‐CSF, IFN‐γ, IL‐1α, IL‐1β,  IL‐1RA (aka IL‐1F3), IL‐6, IL‐8 (aka CXCL8), IL‐10, MCP‐1 (aka CCL2), and VEGF‐A. Reduced levels (p <  0.05) were observed for IL‐15. Borderline elevated levels (0.05 < p < 0.10 in a two‐sided Wilcoxon  Rank Sum test) were observed for FGF basic, HGF, IFN‐alpha, IL‐9, and MIP‐1 beta (aka CCL4).  Borderline reduced levels were observed for IP‐10/CXCL10.  [0087] In the initial time point trauma serum dataset 10 biomarkers were statistically different  between ARDS/No ARDS groups (10 higher in ARDS, 0 lower in ARDS). Elevated levels were observed  for FGF basic, G‐CSF, IFNα, IL‐1α, IL‐1β, IL‐1 ra/IL‐1F3, IL‐6, IL‐8, IL‐10, and MCP‐1.  [0088] In the initial time point non‐trauma serum dataset 0 (2 borderline) biomarkers were  statistically different between ARDS/No ARDS groups (2 higher in ARDS, 0 lower in ARDS). Borderline  elevated levels were observed for MCP‐1/CCL2 and MIG.   
                                       
                                     
                                     
                                                                           
                                   
   
                                   
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Claims

  Claims 
1. A method of determining if a human subject has an increased risk of developing acute 
respiratory distress syndrome (ARDS) prior to the onset of detectable symptoms thereof, the  method comprising (a) obtaining a biological sample from the human subject, and (b)  measuring the levels of one or more of basic fibroblast growth factor (FGF‐basic),  granulocyte colony‐stimulating factor (G‐CSF), granulocyte‐macrophage colony‐stimulating  factor (GM‐CSF), hepatocyte growth factor (HGF), interferon alpha (IFN‐α), interferon  gamma (IFN‐γ), interleukin‐1 alpha (IL‐1α), interleukin‐1 beta (IL‐1β), interleukin‐1 receptor  agonist (IL‐1RA), interleukin‐6 (IL‐6), interleukin‐8 (IL‐8), interleukin‐9 (IL‐9), interleukin‐10  (IL‐10), monocyte chemoattractant protein 1 (MCP1), monokine induced by gamma  interferon (MIG), macrophage inflammatory protein‐1 beta (MIP1β), and vascular  endothelial growth factor (VEGF) in the biological sample to create a biomarker profile,  wherein an increase in the biomarker profile compared with a normal biomarker profile is  indicative that the human subject has an increased risk of developing ARDS compared to  individuals with a normal biomarker profile. 
2. The method of claim 1, wherein the normal biomarker profile comprises levels of one or  more of FGF‐basic, G‐CSF, GM‐CSF, HGF, IFN‐α, IFN‐γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐9, IL‐ 10, MCP1, MIG, MIP1β, and VEGF generated from a population of human subjects that did  not exhibit ARDS.   
3. The method of claim 2, wherein the normal biomarker profile comprises levels of one or  more of FGF‐basic, G‐CSF, GM‐CSF, HGF, IFN‐α, IFN‐γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐9, IL‐ 10, MCP1, MIG, MIP1β, and VEGF generated from a population of human subjects that were  trauma patients. 
4. The method of claim 1, wherein the human subject is a trauma patient.  
5. The method of claim 4, wherein the biomarker profile comprises serum levels of FGF‐basic,  G‐CSF, IFN‐α, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐10, MCP1, and MIP1β.   
6. The method of claims 1 or 2, wherein the human subject is not a trauma patient.   
7. The method of claim 6, wherein the biomarker profile comprises serum levels of MCP1 and  MIG.    
8. The method of claims 1 ‐ 3, wherein the biomarker profile comprises serum levels of G‐CSF,  GM‐CSF, IFN‐γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐10, IL‐15, MCP1, MIG, and VEGF.   
9. A method of detecting elevated levels of biomarkers in a human subject, the method 
comprising measuring serum levels of two or more of FGF‐basic, G‐CSF, GM‐CSF, HGF, IFN‐α,  IFN‐γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐9, IL‐10, MCP1, MIG, MIP1β, and VEGF in a serum  sample obtained from the human subject.  
10. A method of detecting changed elevated levels of biomarkers in a human subject, the 
method comprising measuring serum levels of two or more of FGF‐basic, G‐CSF, GM‐CSF,  HGF, IFN‐α, IFN‐γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐9, IL‐10, IP‐10, IL‐15, MCP1, MIG, MIP1β,  and VEGF in a serum sample obtained from the human subject.  
11. The method of claim 9, wherein the human subject is a trauma patient.  
12. The method of claim 11, wherein the serum levels of FGF‐basic, G‐CSF, IFN‐α, IL‐1α, IL‐1β, IL‐ 1RA, IL‐6, IL‐8, IL‐10, MCP1, and MIP1β are measured.   
13. The method of claim 9, wherein the human subject is not a trauma patient.   
14. The method of claim 13, wherein the serum levels of MCP1 and MIG are measured.  
15. The method of claims 9, wherein the levels of eotaxin, GM‐CSF, IL‐8, IL‐12, IL‐13, MCP1 and  RANTES are measured. 
16. A method of treating a human subject for acute respiratory distress syndrome (ARDS), the  method comprising (a) producing a biomarker profile comprising measuring serum levels of  two or more of FGF‐basic, G‐CSF, GM‐CSF, HGF, IFN‐α, IFN‐γ, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8,  IL‐9, IL‐10, MCP1, MIG, MIP1β, and VEGF, and (b) administering a treatment for ARDS to the  human subject when the biomarker profile for the subject is greater than the biomarker  profile of a normal subject. 
17. The method of claim 16, wherein the treatment is administered to the human subject prior  to the onset of any detectable symptoms of the subject exhibiting ARDS. 
18. The method of claims 16 or 17, wherein the human subject is a trauma patient.  
19. The method of claim 18, wherein the biomarker profile comprises serum levels of FGF‐basic,  G‐CSF, IFN‐α, IL‐1α, IL‐1β, IL‐1RA, IL‐6, IL‐8, IL‐10, MCP1, and MIP1β.     
20. The method of claims 16 or 17, wherein the human subject is not a trauma patient.   
21. The method of claim 19, wherein the biomarker profile comprises serum levels of MCP1 and  MIG.  
22. The method of claims 16 or 17, wherein the biomarker profile comprises serum levels of  eotaxin, GM‐CSF, IL‐8, IL‐12, IL‐13, MCP1 and RANTES.      
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150377885A1 (en) * 2013-02-14 2015-12-31 Turun Patenttitoimisto Oy A method for determining acute respiratory distress syndrome (ards) related biomarkers, a method to monitor the development and treatment of ards in a patient
US20160312285A1 (en) * 2015-04-27 2016-10-27 National Jewish Health Methods of identifying and treating subjects having acute respiratory distress syndrome

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150377885A1 (en) * 2013-02-14 2015-12-31 Turun Patenttitoimisto Oy A method for determining acute respiratory distress syndrome (ards) related biomarkers, a method to monitor the development and treatment of ards in a patient
US20160312285A1 (en) * 2015-04-27 2016-10-27 National Jewish Health Methods of identifying and treating subjects having acute respiratory distress syndrome

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