Apparatus, Compositions, and Methods for Assessment of Chronic Obstructive Pulmonary Disease Progression Among Rapid and Slow Decline Conditions

Abstract

Methods are disclosed for generating and isolating an informative content repository of respiratory related biomarkers to accurately determine whether an individual has normal or abnormal pulmonary function. Specifically, methods are directed to determination of whether individuals have chronic obstructive pulmonary disease, and if so, whether the affected individuals experience rapid long decline or slow lung decline as a result of COPD. Also disclosed is an informative content repository of chronic obstructive pulmonary disease biomarkers, which when linked with other informative content provides a powerful tool for diagnosis, study, therapeutic discovery and development, condition management, health maintenance, and linking chronic obstructive pulmonary disease through pattern of life style, environmental exposure, and genetic susceptibility and inheritance. Disclosed herein is a chronic obstructive pulmonary disease biomarker informative content repository comprising at least one COPD biomarker, apparatus and methods to diagnose, assess, address, and ameliorate related conditions.

Description

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 11/770,074 filed on Jun. 28, 2007 now U.S. Pat. No. 9,885,085, which claims the Paris Convention Priority and fully incorporates by reference U.S. Provisional Application No. 60/817,316 entitled “Apparatus, Compositions, and Methods for Assessment of Chronic Obstructive Pulmonary Disease Progression Among Rapid and Slow Decline Conditions” filed on 28 Jun. 2006, each of which applications is hereby incorporated by reference as if fully disclosed herein.

INCORPORATION OF SEQUENCE LISTING

This application contains a sequence listing submitted electronically via EFS-web, which serves as both the paper copy and the computer readable form (CRF) and consists of a file entitled “001881-8004US02_seqlist.txt”, which was created on Apr. 30, 2018, which is 569,344 bytes in size, and which is herein incorporated by reference in its entirety.

GOVERNMENTAL INSPECTION

The present disclosure is subject to a right of inspection by the Department of Energy.

BACKGROUND

The present disclosure relates to the study of respiratory functions and conditions, specifically chronic obstructive pulmonary disease, biomarkers related to respiratory functions and conditions, specifically biomarkers related to chronic obstructive pulmonary disease, and the creation of an informative content repository of biomarkers related to chronic obstructive pulmonary disease (COPD).

SUMMARY

Methods are disclosed for generating and isolating biomarkers related to pulmonary functions and conditions, specifically biomarkers related to chronic obstructive pulmonary disease, and an informative content repository of respiratory related biomarkers to accurately determine whether an individual has normal or abnormal pulmonary function. Specifically, methods are directed to determination of whether individuals have chronic obstructive pulmonary disease, and if so, whether the affected individuals experience rapid lung decline or slow lung decline as a result of COPD. Also disclosed is an informative content repository of chronic obstructive pulmonary disease biomarkers, which when linked with other informative content provides a powerful tool for diagnosis, study, therapeutic discovery and development, condition management, health maintenance, and linking chronic obstructive pulmonary disease through pattern of life style, environmental exposure, and genetic susceptibility and inheritance. Disclosed herein are at least one biomarker related to COPD and a chronic obstructive pulmonary disease biomarker informative content repository comprising at least one COPD biomarker, apparatus and methods to diagnose, assess, address, and ameliorate related conditions.

According to a feature of the present disclosure, a respiratory condition informative content repository is disclosed comprising at least one respiratory condition-related biomarker.

According to a feature of the present disclosure, a process is disclosed comprising identification of a respiratory related condition to study, use of an informative content repository containing at least one first set of data useful in the selection of at least one individual having or predisposed to a respiratory related condition, identification of at least one biomarker from samples taken from the at least one individual; and populating a biomarker informative content repository with the at least one biomarker.

According to a feature of the present disclosure, a process is disclosed comprising, obtaining a sample from a patient, using a chronic obstructive pulmonary disease (COPD) biomarker diagnostic tool in conjunction the sample to obtain data, and using the data to decide whether the patient is a rapid decliner or a slow decliner.

According to a feature of the present disclosure, an informative content repository is disclosed comprising at least the amino acid sequences of SEQ ID NO:1 to SEQ ID NO:266.

According to a feature of the present disclosure, an informative content repository of proteins is disclosed comprising at least a set of data comprising the proteins of Table 2.

DRAWINGS

The above-mentioned features and objects of the present disclosure will become more apparent with reference to the following description taken in conjunction with the accompanying drawings wherein like reference numerals denote like elements and in which:

FIG. 1 is a graph illustrating the relative progression of the loss of pulmonary function between non-smokers, chronic pulmonary obstructive disorder (COPD) slow decliners, and COPD fast decliners;

FIG. 2A is a block diagram of an embodiment of an experimental design for determination of biomarkers contributing to COPD and biomarkers distinguishing between COPD rapid and slow decliners;

FIG. 2B is a graph illustrating how rapid decliner and slow decliner subjects are selected from a group of known COPD patients;

FIG. 3 is a block diagram of an embodiment of an experimental design for identification of the biomarkers of COPD using μLC-MS/MS analysis and μLC-FTICR-MS analysis to identify and correlate candidate proteins;

FIG. 4 is a block diagram of an embodiment of an experimental design illustrating a method for determining biomarkers from μLC-MS/MS analysis; and

FIGS. 5A and 5B are graphs of an embodiment of an experimental design illustrating statistical analysis of identified proteins.

DETAILED DESCRIPTION

In the following detailed description of embodiments of the invention, reference is made to the accompanying drawings in which like references indicate similar elements, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that logical, mechanical, biological, electrical, functional, and other changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims. As used in the present disclosure, the term “or” shall be understood to be defined as a logical disjunction and shall not indicate an exclusive disjunction unless expressly indicated as such or notated as “xor.”

As used in this application, the term “biomarker” means any characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention, including biological measurements that provide information regarding progression, pharmacology, or safety of conditions that can be used as a basis for decision-making in drug development and therapeutic administration decisions.

As used in this application, the term “function” and “condition” means normal physiological and pathophysiological states, including diseases and disorders. As used herein, the terms “function” and “condition” include normal physiological as well as acute and chronic pathophysiological states, such as diseases and disorders.

As used in this application, the term “disease” or “disorder” means any condition in humans or animals deemed to be abnormal as compared to the majority of humans and animals respectively.

As used in this application, the term “informative content repository” means a collection of at least one set of respiratory condition related biomarker data, optionally indexed together with other ancillary data, and stored in a suitable data structure. Examples of suitable data structures include databases, gene chips, protein chips, and filing cabinets.

As used in this application, the term “decline” refers to the rate in which a condition or conditions worsens over time, and the term “decliner” refers to an individual affected with a respiratory condition in whom the condition worsens over time.

As used in this application, the term “related” refers to causing or being associated with a function or condition.

As used in this application, the terms “COPD related biomarker(s)” or “COPD biomarker(s)” refer to one or more biomarkers that are associated with COPD.

Mapping condition indicators by use of integrated phenotypic and genotypic data from humans is a longstanding need, which only serves to underscore or highlight prior attempts to effectively do so with significant informative condition end-points.

Harnessing the power of the computer to manage large volumes of data in conjunction with the volume of information contained in the human genome, proteome, metabalome, regulome, functome, phenome, and textome proved critical for projects such as the human genome project to high-throughput devices such as DNA and protein microarrays. Naturally, the scientific community recognized the brute force power of computers for management of otherwise impossibly large volumes of information. Since then numerous bioinformatic and computational biology applications now exist. However, in most cases, the data sets are created as byproducts of experimental protocols. Other databases grew as researchers obtained experimental data and populated the databases with their findings for later reference.

Functions and conditions of the respiratory system, including lung conditions may be suitable targets for study using the instant techniques. Despite advances in practice, lung conditions continue to afflict millions of people worldwide. Many lung conditions such as emphysema, asthma, and COPD develop, at least in part, due to genetic predispositions or are directly linked with life-style choices and environmental exposure. Still others are caused by infections, such as tuberculosis.

Nonmalignant respiratory conditions are typically characterized as obstructive, restrictive, infectious, or vascular in nature. Obstructive respiratory conditions are those that impede the rate that air can flow into and out of the respiratory system, including the lungs. They include emphysema, bronchitis, asthma, and COPD. Similarly, restrictive lung conditions are characterized by a reduction of the functional volume of the lungs. Examples of restrictive lung conditions are sarcoidosis, pleural effusion, fibrosis, and alveolar effusion. Infectious lung conditions include tuberculosis, pneumonia, upper respiratory tract infections, and lower respiratory tract infections. Vascular lung conditions include pulmonary edema, pulmonary embolism, and pulmonary hypertension. Lung cancer alone causes of 3 million deaths each year.

Of the respiratory conditions, COPD is a condition especially suited to the instant techniques. COPD is a condition of the respiratory tract characterized by permanent airway obstruction. It constitutes an abnormal inflammatory response triggered by foreign particles and gasses. COPD victims experience a chronic inflammation of the bronchi, which leads to airway obstruction. Other causes of COPD may include α1-antitrypsin deficiency, byssinosis, genetic susceptibility, and idiopathic disease.

Researchers currently believe that smoking is that main risk factor associated with the development of COPD. Indeed, nearly one-fifth of all smokers will develop the condition. Nevertheless, other risk factors exist such as the prolonged breathing of dust, for example in coal-mines. Women comprise the majority of non-smoking victims. Greater susceptibility in women appears to be related to decreased estrogen levels. Additionally, it is estimated that up to 15% to 20% of all COPD cases are non-smoking related, thus highlighting the heterogeneous nature and genetic susceptibility of COPD.

COPD is a progressive condition that worsens over time and with prolonged contact to smoke or other irritants. Spirometry changes, as illustrated in FIG. 1, and decreased diffusion capacity are commonly seen prior to diagnosis of COPD. As COPD progresses, patients experience shortness of breath, coughs, and recurrent respiratory infections. Progression of COPD is marked by increased severity and duration of symptoms, until, during advanced stages, the patient experiences constant wheezing and shortness of breath, with a severe cough even while at rest. COPD advances either rapidly or slowly; rapid decliners, as shown in FIG. 1, experience a more pronounced deterioration in lung capacity compared to slow decliners.

Currently, doctors diagnose COPD by observing a patient's symptoms. Doctors evaluate life-style choices, such as smoking and occupation, perform physical examinations on patients, and conduct spirometry tests to measure patient's airflow. Generally and turning again to FIG. 1, FEV₁, to FVC ratio is decreased in a COPD patient. Often, the COPD patient cannot expire 80% of their vital capacity in one second, which is a measurement of normal airflow. Moreover, doctors may observe a residual volume or hyperinflation of lung capacity in COPD patients. Despite these indicia, no existing molecular factors or definitive tests currently exists to positively identify patients with COPD, nor are there existing molecular factors or definitive tests that can differentiate between affected patients that may experience rapid lung decline or slow lung decline as a result of COPD.

To that end, the present disclosure presents a novel way to discover or isolate respiratory system related biomarkers, specifically COPD related biomarkers. Once biomarkers are identified for a condition, diagnostic tools, for example genomic or protein chips, may be manufactured and used to positively determine whether a patient has developed the condition, and how a patient's condition may progress (i.e., rapid lung decline vs. slow lung decline). Moreover, isolating the biomarkers informs researchers as to the causative factors and pathways that eventually lead to COPD and how COPD progresses. Better understanding of the causes and pathways of COPD allow researchers to focus on discovering better treatments for COPD, including targeted therapeutics, combination of diagnostics and therapeutics, and holistic type treatments.

The present inventors have discovered embodiments of the present disclosure that contemplate COPD related biomarkers and repositories of biomarker content useful for diagnoses, treatment, research, and other uses suitable to such an informative content repository. Methods for the generation and use of informative content repositories are naturally contemplated as well. Specifically, the present disclosure relates to the development and use of COPD related biomarkers or an informative content repository of COPD biomarkers.

The COPD related biomarkers and the informative content repository disclosed in the present disclosure may be used for diagnosis or determination of predisposition of conditions in humans, plants, and animals or for the treatment of the condition. For example, the information contained in an informative content repository may be used to design personalized treatments. The informative content repository is also useful for basic research activities, health care decision-making, for forensic applications, or for genetic counseling.

The informative content repository contains at least one set of biomarkers. Biomarker informative content may include molecular factors comprising one or more sets of genes, proteins, or metabolites. In embodiments, the informative content is easily accessible, sortable, and indexed. Additionally, the informative content repository may be linked to other informative content repositories, which increases the correlative power of the informative content sets comprising the informative content repository.

In an embodiment, the data structure of the informative content repository is a computerized database, for example a MySQL or Oracle database. Informative content is easily accessible using MySQL or Oracle tables and may be manipulated in ways common to a person of ordinary skill in the art. Using computer databases as the data structure for the informative content repository is beneficial because it provides for easy searching, organization, and correlation to data in other informative content repositories.

Among the uses of COPD related biomarkers and the informative content repository are as diagnostic tools. An informative content repository with at least one respiratory related biomarker or at least one set of respiratory related biomarker informative content, for example, is useful in the diagnosis of respiratory related conditions. It is also useful for diagnosis of such conditions or predisposition to such conditions. As previously discussed, COPD diagnosis is accomplished by assessing symptoms because there does not exist definitive tests (blood-based or molecular-based) to diagnose COPD. Thus, COPD related biomarkers or an informative content repository of biomarkers that includes COPD biomarker informative content could be potentially used to positively diagnose whether an individual is affected with COPD and whether a COPD patient is a rapid decliner or a slow decliner. For example, for sets of protein biomarkers, protein chips may be used to screen a patient's blood (serum or plasma) for the presence of, or absence of, a pattern of proteins indicative of COPD or COPD progression.

Similarly, COPD related biomarkers or an informative content repository of respiratory related biomarkers are useful tools for prediction of predispositions to respiratory related conditions. Genes, proteins, metabolites, and other molecular or non-molecular indicia may give healthcare providers and researchers clues as to individuals or populations of individuals susceptible to specific respiratory related conditions. For example, in a subject with susceptibility to COPD, a healthcare provider (including pharmacists) could screen blood samples of each smoking subject using a diagnostic (i.e., genomic or proteomic) chip. Healthcare providers could then use the positive informative content as additional content in individualized healthcare regimens for their subjects.

COPD related biomarkers or an informative content repository of respiratory related biomarkers are also useful in the development of treatments for the conditions predicted by the biomarkers in the informative content repository. Using such an informative content repository, researchers can access sets of data useful in development of compounds for treatment of respiratory related conditions. For example, with COPD, COPD related biomarkers and informative content obtained from the informative content repository, such as proteins, genes, or metabolites can be used to target compounds against the specific proteins, genes, or metabolites. Compounds may also be used to induce or artificially introduce proteins, genes, or metabolites that characteristically are absent in rapid decline conditions. Consequently, the use of compound combinations based on clues provided by respiratory related biomarkers for a respiratory related condition gives researchers and healthcare providers the power to design optimized and personalized regimens of compounds targeted specifically towards maintaining or modulating the condition.

In addition to traditionally administered compounds, COPD related biomarkers or an informative content repository of related biomarkers are useful tools for creating or administering therapies. For example, contemplated in the present disclosure is use of an informative content repository of respiratory related biomarkers useful for the administration or development of inhaled substances including drug therapies.

An informative content repository of respiratory related biomarkers is also useful in developing more individualized or personalized drug treatments for patients. At doctor visits, patients may donate a sample to the doctor, which may then be analyzed and compared against informative content in the respiratory related informative content repository. Using the correlation between the informative content in the informative content repository and the patient's personal genetic, proteomic, and metabonomic make-up, the doctor can prescribe optimal drug regimens for each individual patient.

Naturally, the research applications of such COPD related biomarkers and informative content repositories are broader than simply for use in personalized medical applications. Researchers may also use the informative content repository of respiratory related biomarkers in the pursuit and development of compounds to treat respiratory related conditions in humans and animals. As previously discussed, COPD related biomarkers and biomarker informative content in informative content repositories gives researchers clues as to potential targets for newly developed compounds.

Moreover, the absence of healthy biomarkers in a condition may give healthcare providers clues about ways to induce resurgence of healthy proteins, genes, or metabolites that will restore normal function or eradicate a respiratory related condition. Similarly, information regarding healthy biomarkers gives healthcare providers health maintenance type tools. Healthcare providers may use these type of tools to help maintain and improve otherwise healthy states and lifestyles, in addition to helping patients prevent pathophysiological conditions. Indeed, the ability of healthcare providers to positively assert the presence of a healthy condition or an abnormal condition is a tool in the medical field that has utility in mapping lifestyles, habits, and genes that promote good health or healing.

Nevertheless, COPD related biomarkers or an informative content repository of respiratory related biomarkers are not only useful for research and development of new diagnostics, drugs, or medical devices. The COPD related biomarkers and informative content repository may also be used for more general research purposes. Using subject-specific informative content in an informative content repository, researchers can target individuals who may be susceptible to a particular condition for long-term studies before the condition develops to monitor physiological, phenotypic, and genetic changes in the subject. Similarly, COPD related biomarkers or an informative content repository gives researchers clues as to where they can find potential subjects for studies already expressing a respiratory related condition. Likewise, an informative content repository gives researchers another tool to study pathways, development, and expression of respiratory related conditions over time.

An informative content repository of respiratory related biomarkers may also be used in various decision-making processes. As previously alluded to, healthcare providers may use the correlation between informative content in the informative content repository and the biomarkers expressed in an individual to advise the patient regarding specific treatment decisions and lifestyle choices. Insurance companies, health maintenance organizations, pharmacies, pharmacy benefit managers, hospitals, and other healthcare related organizations may use such informative content to reduce costs by using information learned from correlation between patients and informative content repositories. For example, referencing a particular patient's informative content may reduce the need for expensive testing regimens by correlating with each patient profile with predispositions and other indicia useful in streamlining medical services. Moreover, informative content may be used to pre-approve patients for available treatments, visits to specialists with merely a phone call to a service center that correlates a set of symptoms, life-style choices, and predispositions with the informative content in an informative content repository.

Similarly, optimal prescriptions may be prescribed using COPD related biomarkers or an informative content repository of respiratory related biomarkers that will reduce costs for insurance companies, hospitals, pharmacy benefit managers, health management organizations, and other healthcare related organizations by accurate diagnoses, prescribing, dispensing, or administering optimal drugs for increased efficacy or for reducing the number of adverse reactions, for example. Finally, the correlation of patient information and information contained in an informative content repository of respiratory related biomarkers may be used for automated treatment decisions or insurance reimbursement decisions for both private insurance companies and for federal government insurance programs such as Medicare and Medicaid.

Furthermore, COPD related biomarkers or an informative content repository of respiratory related biomarkers may be used as a method of tracking family data over generations. In embodiments, conditions may be studied using genealogical correlations for each subject or tracked through generations to study condition evolution and inheritance in humans, plants, and animals. The informative content repository is also useful for tracking phenotypic data. Additionally, COPD related biomarkers or the informative content repository may be used for genetic analysis and counseling. The respiratory related data in an informative content repository is an invaluable tool for genetic counselors that allows them to streamline gathering, studying, and disseminating genetic information to clients. Clients could learn the consequences and risk factors for themselves and their children, now and in the foreseeable future.

According to an embodiment, an informative content repository contains data relevant to lung treatment and more broadly to the use of the respiratory system for treatment regimens, specifically inhalation-type conditions, and inhaled administration of therapeutic ingredients. The COPD related biomarkers and an informative content repository COPD related biomarkers and a set of biomarkers contained in the informative content repository would be specifically relevant to upper and lower respiratory system function and conditions. In an embodiment, the informative content repository contains biomarkers relevant to the progression of COPD. Specifically, the biomarkers would indicate differences between the progression of COPD rapid decline conditions and slow decline conditions. In another embodiment, the biomarker informative content would identify individuals with a greater or lesser degree of pulmonary function, thereby indicating or selecting individuals as candidates for inhaled drug therapy.

Example 1

An embodiment of the present disclosure describes the informative content repository of COPD markers. Clinical histories, pulmonary function tests, and related data were obtained from 100 subjects who had never smoked and unaffected with COPD, 100 smokers who were disease free, and 200 smokers with COPD, as illustrated in FIG. 2A. The COPD subjects were divided into quintiles depending on the decline rate, as shown in FIG. 2B. Subjects in the first quintile and fifth quintile representing the slowest decliners and the most rapid decliners were selected for the purposes of determining a set of biomarkers differentiating between slow and fast decliners. Plasma samples were taken from subjects and a subset of these samples were analyzed as a source of COPD biomarkers. The COPD informative content repository in an embodiment is designed to be a set of peptides or proteins. Approximately forty smokers with COPD were selected for the study herein disclosed.

The twelve top most abundant plasma proteins were depleted using GenWay Seppro 12 spin-columns (GenWay Biotech, Inc., San Diego, Calif., now ProteomeLab™ IgY-12, Beckman Coulter, Inc.). The removal of abundant proteins was monitored by SDS-PAGE.

Protein depletion has been used for some years to remove most of the albumin or IgG from biofluids such as plasma and serum prior to analysis, but it is clear that this alone is insufficient to enable progress to be made in biomarker discovery. The presence of highly abundant proteins significantly complicates the discovery process by masking the presence and limiting the detection of low abundance species. ProteomeLab IgY partitioning addresses this issue by reversibly capturing 12 of the more abundant proteins from human biofluids such as plasma and serum, yielding an enriched pool of low abundance proteins for further study. The captured proteins can also be easily recovered for investigation if required.

As shown in FIG. 3, after the abundant serum proteins were removed from the samples, the first series of mass spectrometry is μLC-MS/MS mass spectrometry in operation 300. μLC-MS-MS mass spectrometry was used to identify peptide fragments from trypsin-digested proteins. Proteins, after being run through the IgY-12 columns in operation 302 were trypsin digested in operation 304.

Trichloroacetic acid-precipitated protein from the depleted serum samples was denatured by addition of urea to 8 M, thiourea to 2 M, DTT to 5 mM, and heating to 60° C. for 30 minutes. The sample was then diluted 4-fold with 100 mM ammonium bicarbonate and CaCl2 was added to a concentration 1 mM. Methylated, sequencing-grade tryp sin (Promega, Madison, Wis.) was added at a substrate-to-enzyme ratio of 50:1 (mass:mass) and incubated at 37° C. for 15 hours. Sample cleanup was achieved using a 1-mL SPE C18 column (Supelco, Bellefonte, Pa.). The peptides were eluted from each column with 1 mL of methanol and concentrated via SpeedVac. The samples were reconstituted to 10 μg/μL with 25 mM ammonium bicarbonate and frozen at −20° C. until analyzed.

Selected plasma samples (corresponding to experimental sample numbers: 54110, 54128, 54207, 54112, 54154, 54118) were depleted of abundant proteins, trypsin digested as detailed previously, and pooled. Strong cation exchange chromatography was performed on the pooled peptide sample utilizing a Synchropak S 300, 100×2 mm chromatographic column (Thermo Hypersil-Keystone, Bellefonte, Pa.). A 1 h gradient was utilized at a flow rate of 200 μl/min with fractions collected every 2 minutes. The beginning solvent system was 25% acetonitrile, 75% water containing 10 mM HCOONH₄, pH 3.0, adjusted with formic acid, and the ending solvent system was 25% acetonitrile, 75% water containing 200 mM HCOONH₄, pH 8.0. The peptide mixture was resuspended in 25% acetonitrile, 75% water containing 10 mM HCOONH₄, pH 3.0 with formic acid prior to injection. Fractions were lyophilized and stored at −20° C. until mass spectrometer analysis.

The fractionated peptide samples were analyzed by tandem mass spectrometry to identify the peptides for a mass and time tag database in operation 306. Peptide samples were analyzed by reversed phase microcapillary LC coupled directly with electrospray tandem mass spectrometers (Thermo Finnigan, models LCQ Duo and DecaXP). Chromatography was performed on a 60-cm, 150 μm i.d.×360 μm o.d capillary column (Polymicro Technologies, Phoenix, Ariz.) packed with Jupiter C18 5-um-diameter particles (Phenomenex, Torrance, Calif.). A solvent gradient was used to elute the peptides using 0.1% formic acid in water (Solvent A) and 0.1% formic acid in acetonitrile (Solvent B). The gradient was linear from 0 to 5% solvent B in 20 minutes, followed by 5 to 70% solvent B in 80 minutes, and then 70-85% solvent B in 45 minutes. Solvent flow rate was 1.8 μl/min.

The capillary LC system was coupled to a LCQ ion trap mass spectrometer (Thermo Finnigan, San Jose, Calif.). The temperature of heated capillary and electrospray voltage was 200° C. and 3.0 kV, respectively. Samples were analyzed using the data-dependent MS/MS mode over the m/z range of 300-2000. The three most abundant ions detected in each MS scan were selected for collision-induced dissociation.

Peptide sequences (see operation 309 in FIG. 4) were obtained by analysis of MS/MS spectra using the SEQUEST algorithm against the human.fasta from the National Center for Biotechnology Information (RefSeq release 10, March, 2005) in operation 308 of FIG. 3. Peptide identifications were accepted using a conservative criteria set developed by Yates and coworkers (Link et al, 1999; Washburn et al, 2001) in operation 316. Briefly, all accepted SEQUEST results had a delta Cn of 0.1 or greater. Peptides with a +1 charge state were accepted if they were fully tryptic and had a cross correlation (Xcorr) of at least 1.9. Peptides with a +2 charge state were accepted if they were fully tryptic or partially tryptic and had an Xcorr of at least 2.2. Peptides with +2 or +3 charge states with an Xcorr of at least 3.0 or 3.75, respectively, were accepted regardless of their tryptic state.

The peptide identifications and elution times from analysis of the pooled samples were used to establish the mass and time tag database and combined with identifications of plasma protein peptides from previous multidimensional analyses done previously (Qian et al, 2005) in operation 318. The raw LC-MS/MS data from the pooled sample described above and from the previous multidimensional analysis were reanalyzed to populate the PMT database that was subsequently used for generating the AMT tag results. The PMT database was derived using a PMT quality score of 1.0 (requires a minimum cross correlation score of 2) and a discriminant score of 0.5 (Stritmatter et al, 2005).

Turning still to FIG. 3 and according to embodiments, the second round of mass spectrometry was done using microcapillary liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS) in operation 310 after sample preparation in operation 312. A modified and enhanced Bruker Daltonics 9.4 tesla FTICR mass spectrometer was employed for the high-throughput proteomics as described by Belov et al (2004). Briefly, the FTICR mass spectrometer was combined with the capillary liquid chromatography system and modified for concurrent internal mass calibration and auto-sampling in operation 314. Tryptic peptides were resuspended in mobile phase A (0.1% TFA) and analyzed using reversed phase capillary LC coupled to an electrospray ionization interface with a FTICR mass spectrometer as described by Smith et al.

Analysis of the LC-FTICR experiments was performed using in-house software tools (Kiebel et al. 2006) to identify MS features, deisotope, normalize elution times, and match features to peptides. These tools are incorporated into the Proteomics Research Information Storage and Management system (PRISM). The result yielded a set of peptides.

A discriminant program was used to determine peptide confidence probabilities. The results of an exemplary embodiment are shown in FIGS. 5A and 5B. The discriminant score takes advantage of elution time information and tryptic cleavage information, which enhances peptide confidence. Protein identifications from the list of peptides (see operation 318 in FIG. 4) were accomplished by using the ProteinProphet program and only peptides having a discriminant score greater than 0.5 were considered. The result was a set of proteins in operation 320.

Abundances of the individual peptides were computed by summing the intensity of the ions from a single scan or multiple scans that matched each peptide. Peptides from each protein that were in the top 66% in peak abundance for that protein were averaged to compute protein abundance. In general the integrated, averaged peptide intensities correlate with the relative protein mass.

Missing values were replaced using approximately one-half the minimum detectable peak (0.004). Data was preprocessed using a log₁₀ transformation and quantile normalization to make the distribution of ion currents for each mass spectrometry run in the experiment the same. Normalized technical replicates were averaged for each subject. For each of the over 525 proteins identified, a separate linear model accounting for phenotype and gender were used to assess the ion current values. A large-scale simultaneous testing approach was then used for the statistical analysis of the normalized data.

Once the proteins were identified between the sets of subjects with rapid declining pulmonary function versus slow declining pulmonary function, a statistical analysis was used to determine the relevant biomarkers. The statistical analysis compared the biomarkers of rapid decline condition subjects against the biomarkers of slow decline condition subjects to determine proteins either present or absent in rapid decline conditions versus slow decline conditions. Several statistical methods were used to determine the absence or presence of proteins in the rapid decline condition, including QC, filtering the data, transformation of the data, and normalization of the data, as would be common to a person of ordinary skill in the art.

As demonstrated in the current study of COPD biomarkers, 267 peptides leading to 78 proteins distinguished slow decline conditions from rapid decline conditions. Table 1 lists the proteins determined to distinguish slow decline conditions from rapid decline conditions:

TABLE 1
			Number of
			Unique	Present in
	SEQ. ID.		Peptides/	PLS analysis
Reference	NO.	Protein Description	Protein	of Proteins
gi|4501987|ref|NP_001124.1|	267	afamin precursor; alpha-albumin	1
		[Homo sapiens]
gi|4502027|ref|NP_000468.1|	268	albumin precursor; PRO0883 protein	8
		[Homo sapiens]
gi|21071030|ref|NP_570602.2|	269	alpha 1B-glycoprotein [Homo	6
		sapiens]
gi|4501843|ref|NP_001076.1|	270	alpha-1-antichymotrypsin, precursor;	2
		alpha-1-antichymotrypsin;
		antichymotrypsin [Homo sapiens]
gi|4557225|ref|NP_000005.1|	271	alpha-2-macroglobulin precursor	6
		[Homo sapiens]
gi|11386143|ref|NP_000925.1|	272	alpha-2-plasmin inhibitor; alpha-2-	7
		antiplasmin [Homo sapiens]
gi|4557287|ref|NP_000020.1|	273	angiotensinogen precursor;	1
		angiotensin II precursor; pre-
		angiotensinogen; angiotensin I
		[Homo sapiens]
gi|4557321|ref|NP000030.1|	274	apolipoprotein A-I precursor [Homo	2
		sapiens]
gi|4502149|ref|NP001634.1|	275	apolipoprotein A-II precursor	2
		[Homo sapiens]
gi|4502151|ref|NP000473.1|	276	apolipoprotein A-IV precursor	7
		[Homo sapiens]
gi|4502153|ref|NP_000375.1|	277	apolipoprotein B precursor; apoB-	25
		100; apoB-48 [Homo sapiens]
gi|4502157|ref|NP_001636.1|	278	apolipoprotein C-I precursor [Homo	1
		sapiens]
gi|4557325|ref|NP_000032.1|	279	apolipoprotein E precursor;	1
		apolipoprotein E3 [Homo sapiens]
gi|4557327|ref|NP_000033.1|	280	beta-2-glycoprotein I precursor	1
		[Homo sapiens]
gi|4557373|ref|NP_000051.1|	281	biotinidase precursor [Homo sapiens]	1
gi|4502517|ref|NP_001729.1|	282	carbonic anhydrase I; carbonic	1
		dehydratase [Homo sapiens]
gi|4503011|ref|NP_001299.1|	283	carboxypeptidase N, polypeptide 1,	2
		50 kD precursor [Homo sapiens]
gi|4557485|ref|NP_000087.1|	284	ceruloplasmin (ferroxidase);	6
		Ceruloplasmin [Homo sapiens]
gi|42716297|ref|NP_001822.2|	285	clusterin isoform 1; complement-	1
		associated protein SP-40 [Homo
		sapiens]
gi|4503635|ref|NP_000497.1|	286	coagulation factor II precursor;	4
		prothrombin [Homo sapiens]
gi|4503625|ref|NP_000495.1|	287	coagulation factor X precursor;	1
		prothrombinase; factor Xa [Homo
		sapiens]
gi|4557379|ref|NP_000053.1|	288	complement component 1 inhibitor	2
		precursor [Homo sapiens]
gi|4502493|ref|NP_001724.1|	289	complement component 1, r	1
		subcomponent [Homo sapiens]
gi|7706083|ref|NP_057630.1|	290	complement component 1, r	1
		subcomponent-like precursor;
		complement C1r-like proteinase; C1r-
		like serine protease analog [Homo
		sapiens]
gi|4502495|ref|NP_001725.1|	291	complement component 1, s	1
		subcomponent [Homo sapiens]
gi|14550407|ref|NP_000054.2|	292	complement component 2 precursor;	2
		C3/C5 convertase [Homo sapiens]
gi|4557385|ref|NP_000055.1|	293	complement component 3 precursor;	21
		acylation-stimulating protein cleavage
		product [Homo sapiens]
gi|4502503|ref|NP_000706.1|	294	complement component 4 binding	1	yes
		protein, alpha; Complement
		component 4-binding protein, alpha
		polypeptide; complement component
		4-binding protein, alpha [Homo
		sapiens]
gi|50345296|ref|NP_001002029.1|	295	complement component 4B	12
		preproprotein; Chido form of C4;
		basic C4; C4A anaphylatoxin [Homo
		sapiens]
gi|38016947|ref|NP_001726.2|	296	complement component 5 [Homo	4
		sapiens]
gi|4559406|ref|NP_000056.1|	297	Complement component 6 precursor	3
		[Homo sapiens]
gi|45580688|ref|NP_000578.2|	298	complement component 7 precursor	2
		[Homo sapiens]
gi|4557389|ref|NP_000553.1|	299	complement component 8, alpha	1
		polypeptide precursor [Homo
		sapiens]
gi|4502511|ref|NP_001728.1|	300	complement component 9 [Homo	2
		sapiens]
gi|4502397|ref|NP_001701.1|	301	complement factor B preproprotein;	7
		C3 proactivator; C3 proaccelerator;
		glycine-rich beta-glycoprotein; C3/C5
		convertase [Homo sapiens]
gi|4504375|ref|NP_000177.1|	302	complement factor H; H factor-1	3
		(complement); factor H-like 1; H
		factor 2 (complement); H factor 1
		(complement) [Homo sapiens]
gi|11761629|ref|NP_068657.1|	303	fibrinogen, alpha chain isoform alpha	7
		preproprotein [Homo sapiens]
gi|11761631|ref|NP_005132.1|	304	fibrinogen, beta chain preproprotein	9
		[Homo sapiens]
gi|4503715|ref|NP_000500.1|	305	fibrinogen, gamma chain isoform	5
		gamma-A precursor [Homo sapiens]
gi|47132557|ref|NP_997647.1|	306	fibronectin 1 isoform 1	3
		preproprotein; cold-insoluble
		globulin; migration-stimulating factor
		[Homo sapiens]
gi|4504165|ref|NP_000168.1|	307	gelsolin isoform a [Homo sapiens]	7
gi|11321561|ref|NP_000604.1|	308	hemopexin [Homo sapiens]	4
gi|4504355|ref|NP_000176.1|	309	heparin cofactor II [Homo sapiens]	3
gi|4504579|ref|NP_000195.1|	310	I factor (complement) [Homo	1
		sapiens]
gi|21489959|ref|NP_653247.1|	311	immunoglobulin J chain [Homo	1
		sapiens]
gi|4504781|ref|NP_002206.1|	312	inter-alpha (globulin) inhibitor H1;	6
		inter-alpha (globulin) inhibitor, H1
		polypeptide [Homo sapiens]
gi|4504783|ref|NP_002207.1|	313	inter-alpha (globulin) inhibitor H2;	10
		inter-alpha (globulin) inhibitor, H2
		polypeptide [Homo sapiens]
gi|10092579|ref|NP_002208.1|	314	inter-alpha (globulin) inhibitor H3;	2
		Inter-alpha (globulin) inhibitor, H3
		polypeptide; pre-alpha (globulin)
		inhibitor, H3 polypeptide [Homo
		sapiens]
gi|31542984|ref|NP_002209.2|	315	inter-alpha (globulin) inhibitor H4	11
		(plasma Kallikrein-sensitive
		glycoprotein); Inter-alpha (globulin)
		inhibitor, H4 polypeptide; inter-alpha
		(globulin) inhibitor, H polypeptide-
		like 1 [Homo sapiens]
gi|10835141|ref|NP_000563.1|	316	interleukin 10 precursor; cytokine	2
		synthesis inhibitory factor [Homo
		sapiens]
gi|4504893|ref|NP_000884.1|	317	kininogen 1; alpha-2-thiol proteinase	2
		inhibitor; bradykinin [Homo sapiens]
gi|4505047|ref|NP_002336.1|	318	lumican [Homo sapiens]	2
gi|33188445|ref|NP_036222.3|	319	microfilament and actin filament	1
		cross-linker protein isoform a; actin
		cross-linking factor; 620 kDa actin
		binding protein; macrophin 1;
		trabeculin-alpha; actin cross-linking
		family protein 7 [Homo sapiens]
gi|19923106|ref|NP_000437.3|	320	paraoxonase 1; Paraoxonase [Homo	1
		sapiens]
gi|21361845|ref|NP_443122.2|	321	peptidoglycan recognition protein L	2
		precursor [Homo sapiens]
gi|4504877|ref|NP_000883.1|	322	plasma kallikrein B1 precursor;	1
		kallikrein 3, plasma; Kallikrein,
		plasma; kallikrein B plasma; Fletcher
		factor [Homo sapiens]
gi|4505881|ref|NP_000292.1|	323	plasminogen [Homo sapiens]	2
gi|151465432|ref|XP_376519.2|	324	PREDICTED: ankyrin repeat	1	yes
		domain 6 [Homo sapiens]
gi|42662334|ref|XP_375941.1|	325	PREDICTED: FLJ45139 protein	1
		[Homo sapiens]
gi|42656986|ref|XP_098238.8|	326	PREDICTED: SH3 domain protein	1
		D19 [Homo sapiens]
gi|51464068|ref|XP_209550.4|	327	PREDICTED: similar to	1
		Carboxypeptidase N 83 kDa chain
		(Carboxypeptidase N regulatory
		subunit) [Homo sapiens]
gi|51458647|ref|XP_497680.1|	328	PREDICTED: similar to prohibitin	1
		[Homo sapiens]
gi|51460685|ref|XP_497833.1|	329	PREDICTED: similar to SULT6B1	1
		[Homo sapiens]
gi|4506117|ref|NP_000304.1|	330	protein S (alpha); Protein S, alpha	2
		[Homo sapiens]
gi|13325075|ref|NP_002817.2|	331	quiescin Q6 [Homo sapiens]	1
gi|5803139|ref|NP_006735.1|	332	RBP4 gene product [Homo sapiens]	1
gi|21361198|ref|NP_000286.2|	333	serine (or cysteine) proteinase	4
		inhibitor, clade A (alpha-1
		antiproteinase, antitrypsin), member
		1; protease inhibitor 1 (anti-elastase),
		alpha-1-antitrypsin [Homo sapiens]
gi|4507377|ref|NP_000345.1|	334	serine (or cysteine) proteinase	1
		inhibitor, clade A (alpha-1
		antiproteinase, antitrypsin), member
		7; thyroxine-binding globulin;
		thyroxin-binding globulin [Homo
		sapiens]
gi|4502261|ref|NP_000479.1|	335	serine (or cysteine) proteinase	7
		inhibitor, clade C (antithrombin),
		member 1; antithrombin III [Homo
		sapiens]
gi|39725934|ref|NP_002606.3|	336	serine (or cysteine) proteinase	2
		inhibitor, Glade F (alpha-2
		antiplasmin, pigment epithelium
		derived factor), member 1; pigment
		epithelium-derived factor [Homo
		sapiens]
gi|4502133|ref|NP_001630.1|	337	serum amyloid P component	2
		precursor; pentaxin-related; 9.5S
		alpha-1-glycoprotein [Homo sapiens]
gi|7382460|ref|NP_001031.2|	338	sex hormone-binding globulin; Sex	1	yes
		hormone-binding globulin (androgen
		binding protein) [Homo sapiens]
gi|4557739|ref|NP_000233.1|	339	soluble mannose-binding lectin	1
		precursor; Mannose-binding lectin 2,
		soluble (opsonic defect); mannose
		binding protein [Homo sapiens]
gi|4507659|ref|NP_003283.1|	340	translocated promoter region (to	1
		activated MET oncogene); Tumor
		potentiating region (translocated
		promoter region) [Homo sapiens]
gi|46195765|ref|NP_954712.1|	341	unc-13 homolog D [Homo sapiens]	1	yes
gi|32483410|ref|NP_000574.2|	342	vitamin D-binding protein precursor;	2
		vitamin D-binding alpha-globulin
		[Homo sapiens]
gi|18201911|ref|NP_000629.2|	343	vitronectin precursor; serum	4
		spreading factor; somatomedin B;
		complement S-protein; epibolin
		[Homo sapiens]

Data was determined using partial least squares-discriminant analysis (PLS-DA). Individual protein abundances were compared between slow decline condition and rapid decline condition populations to assess which proteins are differentially abundant between the two populations. If the protein was not identified in all the samples, an abundance value of 0.004 was assigned to those samples in which the protein was not detected. The value of 0.004 represents one-half of the minimum ion current or abundance value observed. The abundances from each peptide identified from slow decline conditions or rapid decline conditions were then averaged. The ratio between the two populations for each protein was then determined. Table 2 shows those proteins having a two-fold or greater difference in abundance between slow decline conditions and rapid decline conditions:

TABLE 2
			Anti-Log	Average Rapid
			(Rapid decline	decline conditions		Number of
NCBI	SEQ.		conditions vs. Slow	to Slow decline	Standard	Significant
Reference	ID. NO.	Protein Description	decline conditions	conditions Ratio	Deviation	Peptides
4501843	270	Antichymotrypsin	0.87	0.83	0.05	2
4557225	271	Alpha-2-macroglobulin	0.88	1.05	0.48	4
4502153	277	Apolipoprotein B	1.48	1.22	0.21	17
4557485	284	Ceruloplasmin	0.97	0.82	0.34	5
4557385	293	Complement component 3	0.64	0.71	0.22	15
11761629	303	Fibrogen, alpha chain isoform	1.24	1.29	0.37	6
11761631	304	Fibrogen, beta chain	1.41	1.23	0.30	5
4503715	305	Fibrogen, gamma chain isoform	1.21	1.24	0.50	5
47132557	306	Fibronectin 1 isoform 1	0.75	0.63	0.17	2
4504165	307	Gelsolin isoform a	0.76	0.85	0.30	4
4504893	317	Kininogen 1; bradykinin	1.20	1.27	0.10	2
4504877	322	Plasma kallikrein B1; kallikrein	0.46			1
		3, plasma
21361198	333	Serine (or cysteine) proteinase	1.60	1.37	0.15	4
		inhibitor; alpha-1-antitrypsin
4502133	337	Serum amyloid P component	1.26	1.23	0.04	2
32483410	342	Vitamin D-binding protein	0.86			1

The results of the mass spectrometry experiments yielded an average of 1,407 peptide fragments per subject, leading to 207 identified proteins per subject. Of the proteins identified in aggregate, 532 proteins occurred in more than 10 subjects. Of those 532 proteins, 21 were proteases, 16 were cytokines and chemokines, 26 were hypothetical proteins, and one was cytochrome P450.

According to embodiments and referring again to FIG. 3, the biomarker informative content repository 330 was created using data sets generated from the COPD protein study. According to embodiments, the COPD informative content repository is a MySQL database populated with protein, peptide, and metabolite data. The COPD informative content repository database resides on a server, according to embodiments, and may be accessed using various protocols such as http, ssh, ftp, and odbc.

The COPD informative content repository MySQL tables are organized and sortable by subject, sample name, peptide sequence, demographic information, and protein. Various tables are used to link the data, as well as to link other informative content repositories with the COPD informative content repository. The COPD informative content repository is linked to other databases for correlation of clinical data, genealogical data, and demographic data. The informative content repositories are maintained independently of each other and a firewall is employed to maintain independence of each respective informative content repository.

According to embodiments, the informative content repository of the present disclosure comprises high throughput screening devices, such as gene and protein chips, for rapid determination of predisposition to rapid COPD decline or slow COPD decline.

While the apparatus and method have been described in terms of what are presently considered to be the most practical and preferred embodiments, it is to be understood that the disclosure need not be limited to the disclosed embodiments. It is intended to cover various modifications and similar arrangements included within the spirit and scope of the claims, the scope of which should be accorded the broadest interpretation so as to encompass all such modifications and similar structures. The present disclosure includes any and all embodiments of the following claims.

REFERENCES

The following references are hereby incorporated by reference as if fully disclosed herein.

• Belov, M. E., Anderson, G. A., Wingerd, M. A., Udseth, H. R., Tang, K., Prior, D. C., Swanson, K. R. et al., 2004. J. Am Soc. Mass Spectrom. 15, 212-232.
• Bolstad, B. M., Irizarry, R. A., Astrand, M. and Speed, T. A. (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19(2), pp. 185-193.
• Efron, B. (2004) Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. J. Am. Stat. Assoc., 99(465), pp. 96-104.
• Eng, J. K., McCormack, A. L., Yates, J. R. 1994. J. Am Soc. Mass Spectrom. 5: 976-989.
• Gary R. Kiebel, Ken J. Auberry, Navdeep Jaitly, David A. Clark, Matthew E. Monroe, Elena S. Peterson, Nikola Toli, Gordon A. Anderson, Richard D. Smith. PROTEOMICS. 6:1783-1790. 2006.
• Link, A. J., Eng, J., Schieltz, D. M., Carmack, E., Mize, G. J., Morris, D. R., Garvik, B. M., Yates III, J. R. 1999. Nat. Biotechnol. 17: 676-682.
• Smith, R. D., Anderson, G. A., Lipton, M. S., Pasa-Tolic, L., Shen, Y., Conrads, T. P., Veenstra, T. D., and H. R. Udseth. 2002. Proteomics 2, 513-523.
• Smith, R. D., Anderson, G. A., Lipton, M. S., Pasa-Tolic, L., Shen, Y., Conrads, T. P., Veenstra, T. D., and H. R. Udseth. 2002. Proteomics 2, 513-523.
• Tang, H.; Wang, Y; Nicholson, J; and Lindon, J. “Use of relaxation-edited one-dimensional and two dimensional nuclear magnetic resonance spectroscopy to improve detection of small metabolites in blood plasma.” Analytical Biochemistry 325:260-272. 2004.
• Washburn, M. P., Wolters, D., Yates III, J. R. 2001. Nat. Biotechnol. 19: 242-247.
• Wei-Jun Qian, Jon M. Jacobi, David G. Camp III, Matthew E. Monroe, Ronald J. Moore, Marina A. Gritsenko, Steve E. Calvano, Stephen F. Lowry, Wenzhong Xiao, Lyle L. Moldawer, Ronald W. Davis, Ronald G. Tompkins and Richard D. Smith, 2005. Proteomics 5:572-584.

Claims

1.-31. (canceled)

32. A diagnostic tool comprising:

tryptic peptides of at least three of the respiratory condition biomarkers in Table 1, wherein the respiratory condition biomarkers are indexed with their abundance; with the proviso that the diagnostic tool does not comprise tryptic peptides of at least one of the proteins in Table 1.

33. The diagnostic tool of claim 32, comprising at least four of the respiratory condition biomarkers in Table 1.

34. The diagnostic tool of claim 33, further comprising a mass spectrometer, liquid chromatograph, size separation column, electrophoretic gel or protein chip.

35. The diagnostic tool of claim 32, comprising at least three of the respiratory condition biomarkers in Table 2.

36. The diagnostic tool of claim 32, comprising at least four of the respiratory condition biomarkers in Table 2.

37. The diagnostic tool of claim 36, further comprising a mass spectrometer, liquid chromatograph, size separation column, electrophoretic gel or protein chip.

38. The diagnostic tool of claim 32, wherein the diagnostic tool comprises a protein chip for determining three or more proteins, or peptides of three or more proteins, set forth in Table 1, wherein said proteins, or the peptides of the proteins, bind to the surface of the chip.

39. The diagnostic tool of claim 38, wherein the diagnostic tool comprises a protein chip for determining four or more proteins, or peptides of four or more proteins, set forth in Table 1, wherein said proteins, or the peptides of the proteins, bind to the surface of the chip.

40. The diagnostic tool of claim 38, wherein the diagnostic tool comprises a protein chip for determining five or more proteins, or peptides of five or more proteins, set forth in Table 1, wherein said proteins, or the peptides of the proteins, bind to the surface of the chip.

41. A diagnostic tool comprising:

a gene chip that specifically binds expressed nucleic acids for the respiratory condition biomarkers of three or more proteins in Table 1; wherein the expressed nucleic acids for the respiratory condition biomarkers specifically bind to the surface of the gene chip; and wherein said gene chip does not specifically bind an expressed nucleic acid for each of the respiratory condition biomarkers set forth in Table 1.

42. A process comprising:

obtaining a sample from a patient having or suspected of having a chronic obstructive pulmonary disease (COPD);

analyzing the sample using the diagnostic tool of claim 32 to obtain data; and

using the data to determine whether the patient will undergo a rapid or a slow decline in respiratory condition.