Biomarkers for Amyotrophic Lateral Sclerosis and Methods Using the Same

Abstract
The disclosure provides biomarkers of amyotrophic lateral sclerosis (ALS). The disclosure also provides various methods of using the biomarkers, including methods for diagnosis of ALS, methods of determining predisposition to ALS, methods of monitoring progression/regression of ALS, methods of assessing efficacy of compositions for treating ALS, methods of screening compositions for activity in modulating biomarkers of ALS, methods of treating ALS, as well as other methods based on biomarkers of ALS.
Description
SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted in ASCII format via EFS-Web and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Oct. 12, 2012, is named 13778109.txt and is 2,037 bytes in size.


FIELD

The invention generally relates to biomarkers for amyotrophic lateral sclerosis and methods based on the same biomarkers.


BACKGROUND

Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease, is a fatal neurological disease that rapidly attacks and destroys the nerve cells that are responsible for voluntary movement. The destruction of the neurons in the brain and spinal cord that control movement eventually progresses to the point that all voluntary motor control is lost. Death typically occurs from respiratory failure within 3-5 years of disease onset.


Approximately 20,000 people in the United States have ALS, and 5,000 people are diagnosed with ALS each year. ALS is common worldwide, affecting people of all races and ethnic backgrounds. The average age of onset of ALS is between 40 and 60 years of age, but ALS can strike both younger and older men and women. In 90-95% of ALS cases, the disease is apparently random (known as sporadic ALS (SALS)). In such SALS cases, there is no family history of the disease and no clearly associated risk factors. In 5-10% of ALS cases there is an inherited genetic link (known as familial ALS (FALS)).


Currently definitive diagnosis is delayed on average 15 months from symptom onset. While identification of certain of the genetic mutations underlying FALS is available, there is no single ALS screening test available for SALS. The ALS diagnosis is arrived at by evidence of clinical progression, neurological examination, electrodiagnostic testing and blood and urine tests to screen for illnesses that mimic ALS. Currently the Revised ALS Functional Rating Scale (ALSFRS-R), a self-reported functionality scale, is used in the clinic for ALS diagnosis and for monitoring progression of disability in ALS subjects. A low score is indicative of more severe disease and a high score is indicative of less severe disease. However, a major limitation of the ALSFRS-R is its subjective nature.


Metabolic biomarkers that could definitively identify the presence or absence of ALS in a symptomatic patient are urgently needed. Further, there are no known effective pharmacologic treatments. Diagnostic biomarkers would enable drug development efforts, enable enrolment of ALS subjects in clinical trials earlier and provide surrogate markers of disease progression or regression. In addition, biomarkers for drug targets, drug screens and therapeutic agents are needed.


SUMMARY

In one embodiment, a method of diagnosing whether a subject has amyotrophic lateral sclerosis (ALS) is provided. The method comprises: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the one or more biomarkers are selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19, and combinations thereof; and comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to diagnose whether the subject has amyotrophic lateral sclerosis.


In yet another embodiment, a method of monitoring progression/regression of amyotrophic lateral sclerosis (ALS) in a subject is provided. The method comprises: analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the first sample is obtained from the one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19, and combinations thereof and; analyzing a second biological sample from a subject to determine the level(s) of the one or more biomarkers, wherein the second sample is obtained from the subject at a second time point; and comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to monitor the progression/regression of ALS in the subject.


In yet a further embodiment, a method of distinguishing whether a subject has amyotrophic lateral sclerosis (ALS) or has a neurological disorder with symptoms that mimic ALS (a symptom mimic disorder) is provided. The method comprises: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the one or more biomarkers comprise one or more biomarkers selected from Tables 5, 9, and 17; and comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether a subject has ALS or a neurodegenerative disease with symptoms that mimic ALS. Exemplary biomarkers include tryptophan betaine and indolepropionate.


In yet another embodiment, a method of distinguishing whether a subject has amyotrophic lateral sclerosis (ALS) or has a non-ALS motor neuron disorder (non-ALS MND) such as, for example, pure lower motor neuron (LMN) disease or pure upper motor neuron (UMN), disease is provided. The method comprises: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the one or more biomarkers comprise one or more biomarkers selected from Tables 11, 12 and 18; and comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether a subject has ALS or has a non-ALS motor neuron disorder (non-ALS MND).


In yet another embodiment, a method of distinguishing whether a subject has MND or has a disease with symptoms that mimic MND but is not MND, that is, neurological diseases that cause symptoms that appear clinically similar to MND (e.g., multi-focal motor neuropathy, spinal muscular atrophy, Kennedy's disease, multiple sclerosis) is provided. The method comprises: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for MND in the sample, wherein the one or more biomarkers comprise one or more biomarkers selected from Table 13; and comparing the level(s) of the one or more biomarkers in the sample to MND-positive and/or MND-negative reference levels of the one or more biomarkers in order to determine whether a subject has MND or has a disease with symptoms that mimic MND.


In a further embodiment, a method of assessing the efficacy of a composition for treating amyotrophic lateral sclerosis (ALS) is provided. The method comprises: analyzing, from a subject having amyotrophic lateral sclerosis and currently or previously being treated with a composition, a biological sample to determine the level(s) of one or more biomarkers for ALS selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof; and comparing the level(s) of the one or more biomarkers in the sample to (a) levels of the one or more biomarkers in a previously-taken biological sample from the subject, wherein the previously-taken biological sample was obtained from the subject before being treated with the composition, (b) ALS-positive reference levels of the one or more biomarkers, (c) ALS-negative reference levels of the one or more biomarkers, (d) ALS-progression-positive reference levels of the one or more biomarkers, and/or (e) ALS-regression-positive reference levels of the one or more biomarkers.


In yet a further embodiment, a method for assessing the efficacy of a composition in treating amyotrophic lateral sclerosis (ALS) is provided comprising: analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers for ALS, the first sample obtained from the subject at a first time point wherein the one or more biomarkers are selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof; administering the composition to the subject; analyzing a second biological sample from the subject to determine the level(s) of the one or more biomarkers, the second sample obtained from the subject at a second time point after administration of the composition; comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the efficacy of the composition for treating amyotrophic lateral sclerosis.


In another embodiment, a method of assessing the relative efficacy of two or more compositions for treating amyotrophic lateral sclerosis (ALS) is provided. The method comprises: analyzing, from a first subject having ALS and currently or previously being treated with a first composition, a first biological sample to determine the level(s) of one or more biomarkers selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof; analyzing, from a second subject having ALS and currently or previously being treated with a second composition, a second biological sample to determine the level(s) of the one or more biomarkers; and comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the relative efficacy of the first and second compositions for treating amyotrophic lateral sclerosis.


In yet another embodiment, a method for screening a composition for activity in modulating one or more biomarkers of amyotrophic lateral sclerosis is provided comprising: contacting one or more cells with a composition; analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level(s) of one or more biomarkers of amyotrophic lateral sclerosis selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19, and combinations thereof; and comparing the level(s) of the one or more biomarkers with predetermined standard levels for the biomarkers to determine whether the composition modulated the level(s) of the one or more biomarkers.


In a further embodiment, a method for identifying a potential drug target for amyotrophic lateral sclerosis (ALS) is provided. The method comprises: identifying one or more biochemical pathways associated with one or more biomarkers for ALS, wherein the one or more biomarkers are selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19, and combinations thereof; and identifying a protein affecting at least one of the one or more identified biochemical pathways, the protein being a potential drug target for amyotrophic lateral sclerosis.


In another embodiment, a method for treating a subject having amyotrophic lateral sclerosis (ALS) is provided. The method comprises administering to the subject an effective amount of one or more biomarkers selected from Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19, such as indolepropionate and/or tryptophan betaine, that are decreased in subjects having ALS as compared to subjects not having ALS.


In another embodiment, a method of determining whether a subject is predisposed to developing amyotrophic lateral sclerosis (ALS) is provided, comprising:


analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the one or more biomarkers are selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 16, 17 and 18, and combinations thereof; and comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether the subject is predisposed to developing amyotrophic lateral sclerosis.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is an importance plot of biomarkers that distinguish subjects having ALS from Healthy Subjects. The importance plot was generated using a Random Forest analysis as discussed in Example 1. Peptide “HWESASXX” disclosed as SEQ ID NO: 1.



FIG. 2 is a receiver operating characteristic (ROC) curve generated using the Random Forest analysis discussed in Example 1.



FIG. 3 is an importance plot of biomarkers that distinguish subjects having ALS from subjects having symptom mimic diseases. The importance plot was generated using a Random Forest analysis as discussed in Example 2.



FIG. 4 is a ROC curve generated using the Random Forest analysis discussed in Example 2 below.



FIG. 5 is a ROC curve generated using a Lasso analysis as discussed in Example 2.



FIG. 6 is a ROC curve generated using a LASSO/SVM predictive model to analyze the biomarkers identified in Table 9, which markers distinguish subjects having ALS from subjects having symptom mimic diseases. The LASSO/SVM analysis is discussed in Example 3.



FIG. 7 is a ROC curve generated using a Bayesian factor analysis approach. The analysis was used to aid in determining biomarkers that distinguish subjects having ALS from subjects having non-ALS motor neuron diseases, as discussed in Example 4.



FIG. 8 is a ROC curve generated using a SVM predictive model to analyze biomarkers for distinguishing subjects having an MND from subjects having a symptom mimic disease, as described in Example 5.



FIG. 9 is a ROC curve generated using a LASSO predictive model to analyze biomarkers for distinguishing subjects having MND from subjects having a symptom mimic disease, as described in Example 6.





DETAILED DESCRIPTION

The present invention relates to biomarkers of amyotrophic lateral sclerosis, methods for diagnosis of and/or aiding in diagnosis of ALS (including distinguishing ALS from neurological disorders that mimic ALS and from non-ALS motor neuron disorders), methods of determining predisposition to ALS, methods of monitoring progression/regression of ALS, methods of assessing efficacy of compositions for treating ALS, methods of screening compositions for activity in modulating biomarkers of ALS, methods of treating ALS, as well as other methods based on biomarkers of ALS. Prior to describing this invention in further detail, however, the following terms will first be defined.


DEFINITIONS

“Biomarker” means a compound, preferably a xenobiotic or a metabolite, that is differentially present (i.e., increased or decreased) in a biological sample from a subject or a group of subjects having a first phenotype (e.g., having a disease) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having the disease). A biomarker may be differentially present at any level, but is generally present at a level that is increased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more; or is generally present at a level that is decreased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, or by at least 100% or more (i.e., absent). A biomarker is preferably differentially present at a level that is statistically significant (e.g., a p-value less than 0.05 and/or a false discovery rate of less than 0.2 as determined using either Welch's T-test or Wilcoxon's rank-sum Test).


The “level” of one or more biomarkers means the absolute or relative amount or concentration of the biomarker in the sample.


“Sample” or “biological sample” means biological material isolated from a subject. The biological sample may contain any biological material suitable for detecting the desired biomarkers, and may comprise cellular and/or non-cellular material from the subject. The sample can be isolated from any suitable biological tissue or fluid such as, for example, blood, blood plasma, urine, or cerebral spinal fluid (CSF). The sample may include isolated cells, such as, for example neuronal cells, motor neurons, astocytes, dendrocytes and the like as well as cell secretions.


“Subject” means any animal, but is preferably a mammal, such as, for example, a human, monkey, non-human primate, rat, mouse, cow, dog, cat, pig, horse, or rabbit.


A “reference level” of a biomarker means a level of the biomarker that is indicative of a particular disease state, phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or lack thereof. A “positive” reference level of a biomarker means a level that is indicative of a particular disease state or phenotype. A “negative” reference level of a biomarker means a level that is indicative of a lack of a particular disease state or phenotype. For example, an “ALS-positive reference level” of a biomarker means a level of a biomarker that is indicative of a positive diagnosis of ALS in a subject, and an “ALS-negative reference level” of a biomarker means a level of a biomarker that is indicative of a negative diagnosis of ALS in a subject. As another example, an “ALS-progression-positive reference level” of a biomarker means a level of a biomarker that is indicative of progression of ALS in a subject, and an “ALS-regression-positive reference level” of a biomarker means a level of a biomarker that is indicative of regression of ALS in a subject. A “reference level” of a biomarker may be an absolute or relative amount or concentration of the biomarker, a presence or absence of the biomarker, a range of amount or concentration of the biomarker, a minimum and/or maximum amount or concentration of the biomarker, a mean amount or concentration of the biomarker, and/or a median amount or concentration of the biomarker; and, in addition, “reference levels” of combinations of biomarkers may also be ratios of absolute or relative amounts or concentrations of two or more biomarkers with respect to each other. Appropriate positive and negative reference levels of biomarkers for a particular disease state, phenotype, or lack thereof may be determined by measuring levels of desired biomarkers in one or more appropriate subjects, and such reference levels may be tailored to specific populations of subjects (e.g., a reference level may be age-matched so that comparisons may be made between biomarker levels in samples from subjects of a certain age and reference levels for a particular disease state, phenotype, or lack thereof in a certain age group). Such reference levels may also be tailored to specific techniques that are used to measure levels of biomarkers in biological samples (e.g., LC-MS, GC-MS, etc.), where the levels of biomarkers may differ based on the specific technique that is used.


“Metabolite”, or “small molecule”, means organic and inorganic molecules which are present in a cell. The term does not include large macromolecules, such as large proteins (e.g., proteins with molecular weights over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), large nucleic acids (e.g., nucleic acids with molecular weights of over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), or large polysaccharides (e.g., polysaccharides with a molecular weights of over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000). The small molecules of the cell are generally found free in solution in the cytoplasm or in other organelles, such as the mitochondria, where they form a pool of intermediates which can be metabolized further or used to generate large molecules, called macromolecules. The term “small molecules” includes signaling molecules and intermediates in the chemical reactions that transform energy derived from food into usable forms. Examples of small molecules include sugars, fatty acids, amino acids, nucleotides, intermediates formed during cellular processes, and other small molecules found within the cell.


“Xenobiotic” means a chemical foreign to a given organism (i.e., not produced in vivo). Xenobiotics include, but are not limited to, drugs, pesticides, and carcinogens. The metabolism of xenobiotics occurs in two phases. Phase I enzymes include Cytochrome P450 enzymes and Phase II enzymes include UDP-glucuronosyltransferases and glutathione S-transferases.


“Metabolic profile”, or “small molecule profile”, means a complete or partial inventory of small molecules within a targeted cell, tissue, organ, organism, or fraction thereof (e.g., cellular compartment). The inventory may include the quantity and/or type of small molecules present. The “small molecule profile” may be determined using a single technique or multiple different techniques.


“Non-biomarker compound” means a compound that is not differentially present in a biological sample from a subject or a group of subjects having a first phenotype (e.g., having a first disease) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having the first disease). Such non-biomarker compounds may, however, be biomarkers in a biological sample from a subject or a group of subjects having a third phenotype (e.g., having a second disease) as compared to the first phenotype (e.g., having the first disease) or the second phenotype (e.g., not having the first disease).


“Metabolome” means all of the small molecules present in a given organism.


“Neurological diseases or disorders” are disorders or diseases affecting the brain, spinal cord or nerves. There are more than 600 neurologic diseases. Examples include but are not limited to, for example injuries to the brain or spinal cord, infections such as meningitis, diseases with a genetic basis such as Huntington's disease or muscular dystrophy, developmental diseases such as spinal bifida, seizure disorders such as epilepsy, diseases to blood vessels supplying the brain such as stroke, and brain tumors including cancer.


“Neurodegenerative diseases” are a subset of neurological diseases that result in the loss of structure or function of neurons, including death of neurons. Neurodegenerative diseases include, but are not limited to, amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS), progressive muscular atrophy (PMA), pseudobulbar palsy, progressive bulbar palsy, multiple sclerosis, Huntington's Disease, Alzheimer's Disease, Parkinson's Disease, neural demyelination disorders (e.g., progressive multifocal leukoencephalopathy, PML), Motor Neuron disorders, (MND), Upper Motor Neuron (UMN) disorder, and Lower Motor Neuron (LMN) disorders. Some neurodegenerative diseases may involve motor neurons while other neurodegenerative diseases involve other types of neurons (e.g., sensory neurons). Treatment for various kinds of neurodegenerative diseases varies. Thus, identifying whether and the type of neurodegenerative disease (e.g., motor neuron disease vs. non-motor neuron disease) that a subject has is valuable in determining a course of treatment for the subject.


“Amyotrophic Lateral Sclerosis” or “ALS” is a neurodegenerative disease characterized by motor neuron loss and resulting in progressive muscle wasting and weakness.


For purposes of this application, a “symptom mimic disease” or “disease symptom mimic” refers to a neurological disease that presents with symptoms similar to ALS but is not ALS. Some symptom mimic diseases are MNDs and some symptom mimic diseases are not MNDs. Examples include cervical myelopathy, multiple sclerosis, hereditary spastic paraparesis, autoimmune motor neuropathy, spinal muscular atrophy, Kennedy's disease. Symptom mimic diseases may be treated differently from MNDs, including differently from ALS. Thus, it is valuable for a clinician to be able to distinguish between a symptom mimic disease and a motor neuron disease, whether it be ALS or another MND.


“Motor Neuron Disease (MND)” refers to a neurological disorder that affects motor neurons. The tenth International Statistical Classification of Diseases and Related Health Problems (ICD-10) published in 1992 recognized 5 subtypes of MNDs including, ALS, two pure Upper Motor Neuron (UMN) degeneration (primary lateral sclerosis, pseudobulbar palsy) and two pure Lower Motor Neuron (LMN) degeneration (progressive muscular atrophy, progressive bulbar palsy) motor neuron diseases. Thus, the universe of patients having MNDs is larger than that of just those having ALS. Further, not all MNDs are treated the same as ALS is. Thus, it is valuable for a clinician to be able to distinguish between ALS and other MNDs.


“Non-ALS motor neuron disease (non-ALS MND)” as used herein refers to a neurological disorder that affects motor neurons in a pathologically distinct way from ALS and has a different course of treatment than ALS. Non-ALS MNDs include pure Upper Motor Neuron (UMN) and pure Lower Motor Neuron (LMN) motor neuron diseases. Examples include primary lateral sclerosis, pseudobulbar palsy, progressive bulbar palsy, progressive muscular atrophy. As used herein, non-ALS MND refers to motor neuron diseases that are not ALS.


“ALS Status Score” as used herein refers to a determined value that indicates ALS severity and can be used to monitor ALS progression and/or regression in a subject. The ALS Status Score may be determined using an algorithm or mathematical model.


“ALS Probability Score” as used herein refers to a determined value that is used for diagnosis. That is, the probability that a subject has ALS and not a disease that has symptoms that mimic ALS. The ALS Probability Score may be determined using an algorithm or mathematical model.


I. Biomarkers

The ALS biomarkers described herein were discovered using metabolomic profiling techniques. Such metabolomic profiling techniques are described in more detail in the Examples set forth below as well as in U.S. Pat. No. 7,005,255, U.S. Pat. No. 7,329,489; U.S. Pat. No. 7,550,258; U.S. Pat. No. 7,550,260; U.S. Pat. No. 7,553,616; U.S. Pat. No. 7,635,556; U.S. Pat. No. 7,682,783; U.S. Pat. No. 7,682,784; U.S. Pat. No. 7,910,301 and U.S. Pat. No. 7,947,453, the entire contents of which are hereby incorporated herein by reference.


Generally, metabolic profiles were determined for biological samples from human subjects diagnosed with ALS as well as from one or more other groups of human subjects (e.g., healthy control subjects not diagnosed with ALS, symptom mimic disease subjects, non-ALS MND subjects). The metabolic profile for ALS was compared to the metabolic profile for biological samples from the one or more other groups of subjects. Those molecules differentially present, including those molecules differentially present at a level that is statistically significant, in the metabolic profile of ALS samples as compared to another group (e.g., healthy control subjects not diagnosed with ALS, symptom mimic disease subjects, non-ALS MND subjects) were identified as biomarkers to distinguish those groups.


The biomarkers are discussed in more detail herein. The biomarkers that were discovered correspond with the following group(s):


Biomarkers for distinguishing ALS vs. healthy control subjects not diagnosed with ALS (see Tables 1 & 16);


Biomarkers for distinguishing subjects having ALS vs. subjects having neurological diseases with symptoms that mimic ALS (see Tables 5, 9, and 17);


Biomarkers for distinguishing subjects having ALS vs. subjects having non-ALS MND (see Tables 11, 12 and 18);


Biomarkers for distinguishing subjects having MND vs. subjects having non-MND (i.e., diseases with symptoms that mimic MND) (see Table 13)


Biomarkers for distinguishing early stage ALS vs. later stages of ALS (i.e., biomarkers for distinguishing progression/regression of ALS) (see Tables 14, 15 and 19);


II. Diagnosis of ALS

The identification of biomarkers for ALS allows for the diagnosis of (or for aiding in the diagnosis of) ALS in subjects presenting with one or more symptoms of ALS. A method of diagnosing (or aiding in diagnosing) whether a subject has ALS comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of amyotrophic lateral sclerosis in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to diagnose (or aid in the diagnosis of) whether the subject has amyotrophic lateral sclerosis. The one or more biomarkers that are used are selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19, and combinations thereof. When such a method is used to aid in the diagnosis of ALS, the results of the method may be used along with other methods (or the results thereof) useful in the clinical determination of whether a subject has ALS.


Any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.


The levels of one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19, and combinations thereof may be determined in the methods of diagnosing and methods of aiding in diagnosing whether a subject has ALS. For example, one or more of the following biomarkers may be used alone or in combination to diagnose or aid in diagnosing ALS: creatine, pro-hydroxy-pro, tryptophan betaine, theophylline, cortisone, paraxantine, n1-methyladenosine, 1-palmtoleoylglycerophosphocholine, indolepropionate, caffeine, quinate, levulinate-4-oxovalerate, 1-heptadecanoylglycerophosphocholine, 1,3-7-trimethylurate, cortisol, theobromine, catechol sulfate, pseudouridine, biliverdin, bradykinin, 4-vinylphenol sulfate, 10-undecenoate (11:1n1), citrate, HWESASXX (SEQ ID NO:1), alpha-ketobutyrate, C-glycosyltryptophan, histidine, oleoylcarnitine, phosphate, creatine, iminodiacetate (IDA), palmitoyl sphingomyelin, 3-dehydrocarnitine, serine, hexadecanedioate (C16), 2-hydroxybutyrate (AHB), pyroglutamine, 3-methylxanthine, delta-tocopherol, 5,6-dihydrouracil, octadecanedioate (C18), 7-methylxanthine, urate, 1,2-propanediol, cysteine, proline, 1-methylurate, dodecanedioate (C12), cholesterol, creatinine, 1-stearoyl-GPI (18:0), arachidonate (20:4n6) and glutamine. In a further example, the biomarkers indolepropionate and tryptophan-betaine may be used alone or in combination with one another or any other biomarkers to diagnose or aid in diagnosing ALS. Additionally, for example, the level(s) of one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers, etc., including a combination of all of the biomarkers selected from Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 or any fraction thereof, may be determined and used in such methods. Determining levels of combinations of the biomarkers may allow greater sensitivity and specificity in diagnosing ALS and aiding in the diagnosis of ALS, and may allow better differentiation of ALS from other neurodegenerative diseases that may have similar or overlapping biomarkers to ALS (as compared to a subject not having a neurodegenerative disease). For example, ratios of the levels of certain biomarkers (and non-biomarker compounds) in biological samples may allow greater sensitivity and specificity in diagnosing ALS and aiding in the diagnosis of ALS, and may allow better differentiation of ALS from other neurodegenerative diseases that may have similar or overlapping biomarkers to ALS (as compared to a subject not having a neurodegenerative disease).


One or more biomarkers that are specific for diagnosing ALS (or aiding in diagnosing ALS) in a certain type of sample (e.g., CSF sample or blood plasma sample) may also be used. For example, when the biological sample is cerebral spinal fluid, one or more biomarkers listed in Tables 16, 17, and 18, or any combination thereof, may be used to diagnose (or aid in diagnosing) whether a subject has ALS. When the sample is blood plasma, one or more biomarkers selected from Tables 1, 5, 9, 11 and/or 12 may be used to diagnose (or aid in diagnosing) whether a subject has ALS.


After the level(s) of the one or more biomarkers in the sample are determined, the level(s) are compared to ALS-positive and/or ALS-negative reference levels to aid in diagnosing or to diagnose whether the subject has ALS. Levels of the one or more biomarkers in a sample corresponding to the ALS-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of ALS in the subject. Levels of the one or more biomarkers in a sample corresponding to the ALS-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of no ALS in the subject. In addition, levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to ALS-negative reference levels are indicative of a diagnosis of ALS in the subject. Levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to ALS-positive reference levels are indicative of a diagnosis of no ALS in the subject.


The level(s) of the one or more biomarkers may be compared to ALS-positive and/or ALS-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more biomarkers in the biological sample to ALS-positive and/or ALS-negative reference levels. The level(s) of the one or more biomarkers in the biological sample may also be compared to ALS-positive and/or ALS-negative reference levels using one or more statistical analyses (e.g., t-test, Welch's T-test, Wilcoxon's rank sum test, Random Forest, T-score, Z-score) and/or using a mathematical model (e.g., algorithm, statistical model).


For example, a mathematical model comprising a single algorithm or multiple algorithms may be used to determine whether a subject has a motor neuron disease, and if so, whether the MND is ALS. A mathematical model may also be used to distinguish between a symptom mimic disease and a MND (including ALS and non-ALS MNDs) in a subject presenting with symptoms. An exemplary mathematical model may use the measured levels of any number of biomarkers (for example, 2, 3, 5, 7, 9, etc.) from a subject to determine, using an algorithm or a series of algorithms based on mathematical relationships between the levels of the measured biomarkers, whether a subject has ALS, whether ALS is progressing or regressing in a subject, whether a subject has a non-ALS MND, whether a subject has a symptom mimic disease, etc.


In addition, the biological samples may be analyzed to determine the level(s) of one or more non-biomarker compounds. The level(s) of such non-biomarker compounds may also allow differentiation of ALS from other neurodegenerative diseases that may have similar or overlapping biomarkers to ALS (as compared to a subject not having a neurodegenerative disease). For example, a known non-biomarker compound present in biological samples of subjects having ALS and subjects not having ALS could be monitored to verify a diagnosis of ALS as compared to a diagnosis of another neurodegenerative disease when biological samples from subjects having the other neurodegenerative disease do not have the non-biomarker compound. For example, one or more of the following biomarkers may be used alone or in any combination to distinguish ALS from neurological diseases having symptoms that mimic ALS: hexadecanedioate, creatine, 2-hydroxybutyrate (AHB), arachidonate (20:4n6), iminodiacetate (IDA), 10-undecenoate (11:1n1), cortisone, phosphate, palmitoyl sphingomyelin, serine, glutamine, 3-dehydrocarnitine, pyroglutamine, 4-vinylphenol sulfate, and theobromine. In another example, one or more of the following biomarkers may be used alone or in any combination to distinguish ALS from non-ALS MND: 2-palmitoylglycerophosphocholine, 13-HODE+9-HODE, 2-aminobutyrate, 3-(4-hydroxyphenyl)lactate, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF), 3-hydroxyisobutyrate, 5alpha-androstan-3alpha,17beta-diol disulfate, acetoacetate, alpha-hydroxyisovalerate, arachidonate (20:4n6), asparagine, bilirubin (Z,Z), bradykinin, C-glycosyltryptophan, caproate (6:0), cysteine, cystine, erythronate, gamma-glutamylalanine, gamma-glutamylisoleucine, gamma-glutamylleucine, gamma-glutamylmethionine, gamma-glutamyiphenylalanine, gamma-glutamyltyrosine, gamma-glutamylvaline, glutamate, glutamine, glutaroyl carnitine, glycerate, histidine, HWESASXX (SEQ ID NO:1), isovalerylcarnitine, methylglutaroylcarnitine, pyroglutamine, tryptophan betaine, and urate.


After the level(s) of the one or more biomarker(s) is determined, the level(s) may be compared to disease or condition reference level(s) of the one or more biomarker(s) to determine a rating for each of the one or more biomarker(s) in the sample. The rating(s) may be aggregated using any algorithm or mathematical (statistical) model to create a score, for example, an ALS status score or an ALS probability score, for the subject. The algorithm may take into account any factors relating to ALS, including the number of biomarkers, the correlation of the biomarkers to the disease or condition, or severity of the disease or condition, clinical parameters, ALSFRS-R Score, etc.


An ALS Status Score can be used to place the subject in an ALS disease severity range from normal (i.e., no ALS) to high. An ALS Status Score can be used in multiple ways: for example, disease progression or regression can be monitored by periodic determination and monitoring of the ALS Status Score; response to therapeutic intervention can be determined by monitoring the ALS Status Score; and drug efficacy can be evaluated using the ALS Status Score.


In another example an ALS Probability Score may be used to direct therapeutic treatment or to direct clinical trial enrollment. For example if an ALS Probability Score of less than 19 is indicative of a high probability that a patient has ALS, then said patient may be treated for ALS or enrolled in a clinical trial for an ALS therapeutic. Conversely, if an ALS Probability Score of greater than 30 is indicative of a low probability of a patient having ALS, then said patient would not be treated for ALS or would not be suitable for enrollment in a clinical trial for an ALS therapeutic.


In one example, the subject's ALS Probability score may be correlated to any index indicative of motor neuron function, from normal motor neuron function to ALS. For example, a subject having a motor neuron function score of 40 may indicate that the subject has normal motor neuron function and the ALS Probability Score would be <10%; a score between 30 and 20 may indicate that the subject has impaired motor neuron function and the ALS Probability Score would be >50%; a score lower than 19 may indicate that the subject has ALS and the ALS Probability Score would be >90%.


III. Methods for Distinguishing ALS from Other Neurological Diseases


The identification of biomarkers for ALS allows for distinguishing whether a subject has amyotrophic lateral sclerosis or has another neurological disease with symptoms similar to ALS (mimic disease). A method of distinguishing whether a subject has ALS or has a mimic disease (e.g., multi-focal motor neuropathy, spinal muscular atrophy, Kennedy's disease, multiple sclerosis) comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of amyotrophic lateral sclerosis in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether the subject has amyotrophic lateral sclerosis or a symptom mimic disease (e.g., multi-focal motor neuropathy, spinal muscular atrophy, Kennedy's disease, multiple sclerosis). The ALS-positive and/or ALS-negative reference levels may be levels that are specific for comparison with another particular neurological disease (e.g., reference levels of biomarkers for ALS that distinguish between symptom mimic diseases).


The one or more biomarkers that are used are selected from the biomarkers listed in Tables 5, 9 and 17, and any combination thereof. For example, in another embodiment, a method of distinguishing whether a subject has amyotrophic lateral sclerosis (ALS) or has a symptom mimic disease comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the one or more biomarkers comprise the biomarkers listed in Tables 5, 9 and 17, and any combination thereof; and (2) comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether a subject has ALS or has a symptom mimic disease.


Any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.


After the level(s) of the one or more biomarkers in the sample are determined, the level(s) are compared to ALS-positive and/or ALS-negative reference levels to distinguish whether the subject has ALS or has another disease (e.g., multi-focal motor neuropathy, spinal muscular atrophy, Kennedy's disease, multiple sclerosis) with symptoms similar to ALS. Levels of the one or more biomarkers in a sample corresponding to the ALS-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of ALS in the subject. Levels of the one or more biomarkers in a sample corresponding to the ALS-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of no ALS in the subject. In addition, levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to ALS-negative reference levels are indicative of a diagnosis of ALS in the subject. Levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to ALS-positive reference levels are indicative of a diagnosis of no ALS in the subject.


The level(s) of the one or more biomarkers may be compared to ALS-positive and/or ALS-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more biomarkers in the biological sample to ALS-positive and/or ALS-negative reference levels. The level(s) of the one or more biomarkers in the biological sample may also be compared to ALS-positive and/or ALS-negative reference levels using one or more statistical analyses (e.g., t-test, Welch's T-test, Wilcoxon's rank sum test, Random Forest, T-score, Z-score) or using a mathematical model (e.g., algorithm, statistical model).


IV. Methods for Distinguishing ALS from Non-ALS Motor Neuron Disease (Non-ALS MND)


The identification of biomarkers for ALS allows for distinguishing whether a subject has amyotrophic lateral sclerosis or has non-ALS MND (i.e., either a pure upper motor neuron (UMN) disease or a pure lower motor neuron (LMN) disease). A method of distinguishing whether a subject has ALS or has non-ALS MND comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of amyotrophic lateral sclerosis in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether the subject has amyotrophic lateral sclerosis or non-ALS MND. The ALS-positive and/or ALS-negative reference levels may be levels that are specific for comparison with non-ALS MND (e.g., reference levels of biomarkers for ALS that distinguish between non-ALS MND).


The one or more biomarkers that are used are selected from the biomarkers listed in Tables 11, 12 and 18, and any combination thereof. For example, in another embodiment, a method of distinguishing whether a subject has amyotrophic lateral sclerosis (ALS) or has non-ALS MND comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the one or more biomarkers comprise the biomarkers listed in Tables 11, 12 and 18, and any combination thereof; and (2) comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether a subject has ALS or has non-ALS MND.


Any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.


After the level(s) of the one or more biomarkers in the sample are determined, the level(s) are compared to ALS-positive and/or ALS-negative reference levels to distinguish whether the subject has ALS or has non-ALS MND. Levels of the one or more biomarkers in a sample corresponding to the ALS-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of ALS in the subject. Levels of the one or more biomarkers in a sample corresponding to the ALS-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of no ALS in the subject. In addition, levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to ALS-negative reference levels are indicative of a diagnosis of ALS in the subject. Levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to ALS-positive reference levels are indicative of a diagnosis of no ALS in the subject. The level(s) of the one or more biomarkers may be compared to ALS-positive and/or ALS-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more biomarkers in the biological sample to ALS-positive and/or ALS-negative reference levels. The level(s) of the one or more biomarkers in the biological sample may also be compared to ALS-positive and/or ALS-negative reference levels using one or more statistical analyses (e.g., t-test, Welch's T-test, Wilcoxon's rank sum test, Random Forest, T-score, Z-score) or using a mathematical model (e.g., algorithm, statistical model).


V. Methods for Distinguishing Motor Neuron Disease (MND) from Non-MND Neurological Disorders


The identification of biomarkers for MND allows for distinguishing whether a subject has MND or has a disease with symptoms that mimic MND but is not MND, that is, neurological diseases that cause symptoms that appear clinically similar to MND (e.g., multi-focal motor neuropathy, spinal muscular atrophy, Kennedy's disease, multiple sclerosis). A method of distinguishing whether a subject has MND or has a disease with symptoms that mimic MND comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of MND in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to MND-positive and/or MND-negative reference levels of the one or more biomarkers in order to determine whether the subject has MND or non-MND symptom mimic disease. The MND-positive and/or MND-negative reference levels may be levels that are specific for comparison with non-MND symptom mimic disease (e.g., reference levels of biomarkers for MND that distinguish between non-MND symptom mimic disease).


The one or more biomarkers that are used are selected from the biomarkers listed in Table 13, and any combination thereof. For example, in another embodiment, a method of distinguishing whether a subject has MND comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for MND in the sample, wherein the one or more biomarkers comprise the biomarkers listed in Table 13 and any combination thereof; and (2) comparing the level(s) of the one or more biomarkers in the sample to MND-positive and/or MND-negative reference levels of the one or more biomarkers in order to determine whether a subject has MND.


Any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.


After the level(s) of the one or more biomarkers in the sample are determined, the level(s) are compared to MND-positive and/or MND-negative reference levels to distinguish whether the subject has MND. Levels of the one or more biomarkers in a sample corresponding to the MND-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of MND in the subject. Levels of the one or more biomarkers in a sample corresponding to the MND-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of no MND in the subject. In addition, levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to MND-negative reference levels are indicative of a diagnosis of MND in the subject. Levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to MND-positive reference levels are indicative of a diagnosis of no MND in the subject. The level(s) of the one or more biomarkers may be compared to MND-positive and/or MND-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more biomarkers in the biological sample to MND-positive and/or MND-negative reference levels. The level(s) of the one or more biomarkers in the biological sample may also be compared to MND-positive and/or MND-negative reference levels using one or more statistical analyses (e.g., t-test, Welch's T-test, Wilcoxon's rank sum test, Random Forest, T-score, Z-score) or using a mathematical model (e.g., algorithm, statistical model).


VI. Methods of Monitoring Progression/Regression of ALS

The identification of biomarkers for ALS also allows for monitoring progression/regression of ALS in a subject. A method of monitoring the progression/regression of amyotrophic lateral sclerosis in a subject comprises (1) analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers for ALS selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof, the first sample obtained from the subject at a first time point, (2) analyzing a second biological sample from a subject to determine the level(s) of the one or more biomarkers, the second sample obtained from the subject at a second time point, and (3) comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to monitor the progression/regression of ALS in the subject. The results of the method are indicative of the course of ALS (i.e., progression or regression, if any change) in the subject.


The change (if any) in the level(s) of the one or more biomarkers over time may be indicative of progression or regression of ALS in the subject. In order to characterize the course of ALS in the subject, the level(s) of the one or more biomarkers in the first sample, the level(s) of the one or more biomarkers in the second sample, and/or the results of the comparison of the levels of the biomarkers in the first and second samples may be compared to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers. If the comparisons indicate that the level(s) of the one or more biomarkers are increasing or decreasing over time (e.g., in the second sample as compared to the first sample) to become more similar to the ALS-positive reference levels (or less similar to the ALS-negative reference levels), then the results are indicative of ALS progression. If the comparisons indicate that the level(s) of the one or more biomarkers are increasing or decreasing over time to become more similar to the ALS-negative reference levels (or less similar to the ALS-positive reference levels), then the results are indicative of ALS regression.


The course of ALS in the subject may also be characterized by comparing the level(s) of the one or more biomarkers in the first sample, the level(s) of the one or more biomarkers in the second sample, and/or the results of the comparison of the levels of the biomarkers in the first and second samples to ALS-progression-positive and/or ALS-regression-positive reference levels (e.g., Examples 7 and 11 below describe biomarkers for distinguishing early stage ALS vs. later stage ALS indicating whether certain biomarkers increase or decrease as ALS progresses; such trends and/or levels of biomarkers at a later stage of ALS versus an earlier stage of ALS are one example of ALS-progression positive reference levels). If the comparisons indicate that the level(s) of the one or more biomarkers are increasing or decreasing with decreasing ALSFRS-R score to become more similar to the ALS-progression-positive reference levels (or less similar to the ALS-regression-positive reference levels), then the results are indicative of ALS progression. If the comparisons indicate that the level(s) of the one or more biomarkers are increasing or decreasing with decreasing ALSFRS-R scores to become more similar to the ALS-regression-positive reference levels (or less similar to the ALS-progression-positive reference levels), then the results are indicative of ALS regression.


As with the other methods described herein, the comparisons made in the methods of monitoring progression/regression of ALS in a subject may be carried out using various techniques, including simple comparisons, one or more statistical analyses, mathematical models (algorithms) and combinations thereof.


The results of the method may be used along with other methods (or the results thereof) useful in the clinical monitoring of progression/regression of ALS in a subject.


As described above in connection with methods of diagnosing (or aiding in the diagnosis of) ALS, any suitable method may be used to analyze the biological samples in order to determine the level(s) of the one or more biomarkers in the samples. In addition, the level(s) one or more biomarkers, including selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof may be determined and used in methods of monitoring progression/regression of ALS in a subject.


In one example, the results of the method may be based on an ALS Status Score which is indicative of the presence or severity of ALS in the subject and which can be monitored over time. By comparing the ALS Status Score from a first time point sample to the ALS Status Score from at least a second time point sample the progression or regression of ALS can be determined. Such a method of monitoring the progression/regression of ALS in a subject comprises (1) analyzing a first biological sample from a subject to determine an ALS Status Score for the first sample obtained from the subject at a first time point, (2) analyzing a second biological sample from a subject to determine a second ALS Status Score, the second sample obtained from the subject at a second time point, and (3) comparing the ALS Status Score in the first sample to the ALS Score in the second sample in order to monitor the progression/regression of ALS in the subject.


Such methods could be conducted to monitor the course of ALS in subjects having ALS or could be used in subjects not having ALS (e.g., subjects suspected of being predisposed to developing ALS) in order to monitor levels of predisposition to ALS.


VII. Methods of Determining Predisposition to ALS

The identification of biomarkers for ALS also allows for the determination of whether a subject having no symptoms of ALS is predisposed to developing ALS. A method of determining whether a subject having no symptoms of ALS is predisposed to developing amyotrophic lateral sclerosis comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers selected from Tables 1, 5, 9, 11, 12, 13, 16, 17 and 18 in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether the subject is predisposed to developing amyotrophic lateral sclerosis. The results of the method may be used along with other methods (or the results thereof) useful in the clinical determination of whether a subject is predisposed to developing ALS.


As described above in connection with methods of diagnosing (or aiding in the diagnosis of) ALS, any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample.


As with the methods of diagnosing (or aiding in the diagnosis of) ALS described above, the level(s) of one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers, etc., including a combination of all of the biomarkers selected from Tables 1, 5, 9, 11, 12, 13, 16, 17 and 18 or any fraction thereof, may be determined and used in methods of determining whether a subject having no symptoms of ALS is predisposed to developing ALS.


After the level(s) of the one or more biomarkers in the sample are determined, the level(s) are compared to ALS-positive and/or ALS-negative reference levels in order to predict whether the subject is predisposed to developing amyotrophic lateral sclerosis. Levels of the one or more biomarkers in a sample corresponding to the ALS-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the subject being predisposed to developing ALS. Levels of the one or more biomarkers in a sample corresponding to the ALS-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the subject not being predisposed to developing ALS. In addition, levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to ALS-negative reference levels are indicative of the subject being predisposed to developing ALS. Levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to ALS-positive reference levels are indicative of the subject not being predisposed to developing ALS.


Furthermore, it may also be possible to determine reference levels specific to assessing whether or not a subject that does not have ALS is predisposed to developing ALS. For example, it may be possible to determine reference levels of the biomarkers for assessing different degrees of risk (e.g., low, medium, high) in a subject for developing ALS. Such reference levels could be used for comparison to the levels of the one or more biomarkers in a biological sample from a subject.


As with the methods described above, the level(s) of the one or more biomarkers may be compared to ALS-positive and/or ALS-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more biomarkers in the biological sample to ALS-positive and/or ALS-negative reference levels. The level(s) of the one or more biomarkers in the biological sample may also be compared to ALS-positive and/or ALS-negative reference levels using one or more statistical analyses (e.g., T-score, Z-score) or using a mathematical model (e.g., algorithm), and combinations thereof.


As with the methods of diagnosing (or aiding in diagnosing) whether a subject has ALS, the methods of determining whether a subject having no symptoms of ALS is predisposed to developing amyotrophic lateral sclerosis may further comprise analyzing the biological sample to determine the level(s) of one or more non-biomarker compounds.


VIII. Methods of Assessing Efficacy of Compositions for Treating ALS

The identification of biomarkers for ALS also allows for assessment of the efficacy of a composition for treating ALS as well as the assessment of the relative efficacy of two or more compositions for treating ALS. Such assessments may be used, for example, in efficacy studies as well as in lead selection of compositions for treating ALS.


A method of assessing the efficacy of a composition for treating amyotrophic lateral sclerosis comprises (1) analyzing, from a subject (or group of subjects) having amyotrophic lateral sclerosis and currently or previously being treated with a composition, a biological sample (or group of samples) to determine the level(s) of one or more biomarkers selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof, and (2) comparing the level(s) of the one or more biomarkers in the sample (or group of samples) to (a) level(s) of the one or more biomarkers in a previously-taken biological sample (or group of samples) from the subject (or group of subjects), wherein the previously-taken biological sample was obtained from the subject (or group of subjects) before being treated with the composition, (b) ALS-positive reference levels of the one or more biomarkers, (c) ALS-negative reference levels of the one or more biomarkers (d) ALS-progression-positive reference levels of the one or more biomarkers, and/or (e) ALS-regression-positive reference levels of the one or more biomarkers. The results of the comparison are indicative of the efficacy of the composition for treating ALS.


Thus, in order to characterize the efficacy of the composition for treating ALS, the level(s) of the one or more biomarkers in the biological sample are compared to (1) ALS-positive reference levels, (2) ALS-negative reference levels, (3) ALS-progression-positive reference levels, (4) ALS-regression-positive reference levels, and/or (5) previous levels of the one or more biomarkers in the subject (or group of subjects) before treatment with the composition.


When comparing the level(s) of the one or more biomarkers in the biological sample (from a subject or group of subjects having amyotrophic lateral sclerosis and currently or previously being treated with a composition) to ALS-positive reference levels, ALS-negative reference levels, ALS-progression-positive reference levels, and/or ALS-regression-positive reference levels, level(s) in the sample(s) corresponding to the ALS-negative reference levels or ALS-regression-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the composition having efficacy for treating ALS. Levels of the one or more biomarkers in the sample(s) corresponding to the ALS-positive reference levels or ALS-progression-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the composition not having efficacy for treating ALS. The comparisons may also indicate degrees of efficacy for treating ALS based on the level(s) of the one or more biomarkers.


When the level(s) of the one or more biomarkers in the biological sample (from a subject or group of subjects having ALS and currently or previously being treated with a composition) are compared to level(s) of the one or more biomarkers in a previously-taken biological sample(s) from the subject (or group of subjects) before treatment with the composition, any changes in the level(s) of the one or more biomarkers are indicative of the efficacy of the composition for treating ALS. That is, if the comparisons indicate that the level(s) of the one or more biomarkers have increased or decreased after treatment with the composition to become more similar to the ALS-negative or ALS-regression-positive reference levels (or less similar to the ALS-positive or ALS-progression positive reference levels), then the results are indicative of the composition having efficacy for treating ALS. If the comparisons indicate that the level(s) of the one or more biomarkers have not increased or decreased after treatment with the composition to become more similar to the ALS-negative or ALS-regression-positive reference levels (or less similar to the ALS-positive or ALS-progression-positive reference levels), then the results are indicative of the composition not having efficacy for treating ALS. The comparisons may also indicate degrees of efficacy for treating ALS based on the amount of changes observed in the level(s) of the one or more biomarkers after treatment. In order to help characterize such a comparison, the changes in the level(s) of the one or more biomarkers, the level(s) of the one or more biomarkers before treatment, and/or the level(s) of the one or more biomarkers in the subject currently or previously being treated with the composition may be compared to ALS-positive, ALS-negative, ALS-progression-positive, and/or ALS-regression-positive reference levels of the one or more biomarkers.


Another method for assessing the efficacy of a composition in treating amyotrophic lateral sclerosis (ALS) comprises (1) analyzing a first biological sample (or group of samples) from a subject (or group of subjects) to determine the level(s) of one or more biomarkers selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof, the first sample obtained from the subject at a first time point, (2) administering the composition to the subject, (3) analyzing a second biological sample from a subject to determine the level(s) of the one or more biomarkers, the second sample obtained from the subject at a second time point after administration of the composition, and (4) comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the efficacy of the composition for treating amyotrophic lateral sclerosis. As indicated above, if the comparison of the samples indicates that the level(s) of the one or more biomarkers have increased or decreased after administration of the composition to become more similar to the ALS-negative or ALS-regression-positive reference levels (or less similar to the ALS-positive or ALS-progression-positive reference levels), then the results are indicative of the composition having efficacy for treating ALS. If the comparison indicates that the level(s) of the one or more biomarkers have not increased or decreased after administration of the composition to become more similar to the ALS-negative or ALS-regression-positive reference levels (or less similar to the ALS-positive or ALS-progression-positive reference levels), then the results are indicative of the composition not having efficacy for treating ALS. The comparison may also indicate a degree of efficacy for treating ALS based on the amount of changes observed in the level(s) of the one or more biomarkers after administration of the composition. In order to help characterize such a comparison, the changes in the level(s) of the one or more biomarkers, the level(s) of the one or more biomarkers before administration of the composition, and/or the level(s) of the one or more biomarkers after administration of the composition may be compared to ALS-positive, ALS-negative, ALS-progression-positive, and/or ALS-regres sion-positive reference levels of the one or more biomarkers of the two compositions.


A method of assessing the relative efficacy of two or more compositions for treating amyotrophic lateral sclerosis comprises (1) analyzing, from a first subject having ALS and currently or previously being treated with a first composition, a first biological sample to determine the level(s) of one or more biomarkers selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof, (2) analyzing, from a second subject having ALS and currently or previously being treated with a second composition, a second biological sample to determine the level(s) of the one or more biomarkers, and (3) comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the relative efficacy of the first and second compositions for treating amyotrophic lateral sclerosis. The results are indicative of the relative efficacy of the two compositions, and the results (or the levels of the one or more biomarkers in the first sample and/or the level(s) of the one or more biomarkers in the second sample) may be compared to ALS-positive, ALS-negative, ALS-progression-positive, and/or ALS-regression-positive reference levels to aid in characterizing the relative efficacy.


Each of the methods of assessing efficacy may be conducted on one or more subjects or one or more groups of subjects (e.g., a first group being treated with a first composition and a second group being treated with a second composition).


As with the other methods described herein, the comparisons made in the methods of assessing efficacy (or relative efficacy) of compositions for treating ALS may be carried out using various techniques, including simple comparisons, one or more statistical analyses, a mathematical model or algorithm, an ALS status score, and combinations thereof. Any suitable method may be used to analyze the biological samples in order to determine the level(s) of the one or more biomarkers in the samples. In addition, the level(s) of one or more biomarkers, including a combination both of the biomarkers selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof, may be determined and used in methods of assessing efficacy (or relative efficacy) of compositions for treating ALS.


Finally, the methods of assessing efficacy (or relative efficacy) of one or more compositions for treating ALS may further comprise analyzing the biological sample to determine the level(s) of one or more non-biomarker compounds. The non-biomarker compounds may then be compared to reference levels of non-biomarker compounds for subjects having (or not having) ALS.


IX. Methods of Screening a Composition for Activity in Modulating Biomarkers Associated with ALS


The identification of biomarkers for ALS also allows for the screening of compositions for activity in modulating biomarkers associated with ALS, which may be useful in treating ALS. Methods of screening compositions useful for treatment of ALS comprise assaying test compositions for activity in modulating the levels of one or more biomarkers selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof. Such screening assays may be conducted in vitro and/or in vivo, and may be in any form known in the art useful for assaying modulation of such biomarkers in the presence of a test composition such as, for example, cell culture assays, organ culture assays, and in vivo assays (e.g., assays involving animal models).


In one embodiment, a method for screening a composition for activity in modulating one or more biomarkers of amyotrophic lateral sclerosis comprises (1) contacting one or more cells with a composition, (2) analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level(s) of one or more biomarkers of amyotrophic lateral sclerosis selected from one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and combinations thereof; and (3) comparing the level(s) of the one or more biomarkers with predetermined standard levels for the one or more biomarkers to determine whether the composition modulated the level(s) of the one or more biomarkers. As discussed above, the cells may be contacted with the composition in vitro and/or in vivo. The predetermined standard levels for the one or more biomarkers may be the levels of the one or more biomarkers in the one or more cells in the absence of the composition. The predetermined standard levels for the one or more biomarkers may also be the level(s) of the one or more biomarkers in control cells not contacted with the composition.


In addition, the methods may further comprise analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level(s) of one or more non-biomarker compounds of amyotrophic lateral sclerosis. The levels of the non-biomarker compounds may then be compared to predetermined standard levels of the one or more non-biomarker compounds.


Any suitable method may be used to analyze at least a portion of the one or more cells or a biological sample associated with the cells in order to determine the level(s) of the one or more biomarkers (or levels of non-biomarker compounds). Suitable methods include chromatography (e.g., HPLC, gas chromatograph, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), ELISA, antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers (or levels of non-biomarker compounds) may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) (or non-biomarker compounds) that are desired to be measured.


X. Method of Identifying Potential Drug Targets

The identification of biomarkers for ALS also allows for the identification of potential drug targets for ALS. A method for identifying a potential drug target for amyotrophic lateral sclerosis (ALS) comprises (1) identifying one or more biochemical pathways associated with one or more biomarkers for ALS selected from Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and (2) identifying a protein (e.g., an enzyme, a transporter protein) affecting at least one of the one or more identified biochemical pathways, the protein being a potential drug target for amyotrophic lateral sclerosis.


Another method for identifying a potential drug target for amyotrophic lateral sclerosis (ALS) comprises (1) identifying one or more biochemical pathways associated with one or more biomarkers for ALS selected from Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and one or more non-biomarker compounds of ALS selected from Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 and (2) identifying a protein affecting at least one of the one or more identified biochemical pathways, the protein being a potential drug target for amyotrophic lateral sclerosis.


One or more biochemical pathways (e.g., biosynthetic and/or metabolic (catabolic) pathway) are identified that are associated with one or more biomarkers (or non-biomarker compounds). After the biochemical pathways are identified, one or more proteins affecting at least one of the pathways are identified. Preferably, those proteins affecting more than one of the pathways are identified.


A build-up of one metabolite (e.g., a pathway intermediate) may indicate the presence of a ‘block’ downstream of the metabolite and the block may result in a low/absent level of a downstream metabolite (e.g. product of a biosynthetic pathway). In a similar manner, the absence of a metabolite could indicate the presence of a ‘block’ in the pathway upstream of the metabolite resulting from inactive or non-functional enzyme(s) or from unavailability of biochemical intermediates that are required substrates to produce the product. Alternatively, an increase in the level of a metabolite could indicate a genetic mutation that produces an aberrant protein which results in the over-production and/or accumulation of a metabolite which then leads to an alteration of other related biochemical pathways and result in dysregulation of the normal flux through the pathway; further, the build-up of the biochemical intermediate metabolite may be toxic or may compromise the production of a necessary intermediate for a related pathway. It is possible that the relationship between pathways is currently unknown and this data could reveal such a relationship.


For example, it has been proposed that high glutamate levels in ALS lead to hyper-excitability of the glutamate receptors, causing neurotoxicity. The drug Riluzole is thought to work by lowering glutamate levels by pre-synaptically inhibiting glutamate release in the central nervous system. This drug, however, does not lower the overall glutamate levels in the body. The identity of glutamate as a biomarker that is elevated in ALS as compared to a normal subject would suggest that potential drug targets may be in the pathways leading to glutamate production. A composition that would function by inhibiting the synthesis of glutamate may suppress the levels of glutamate. An example of such an enzyme is glutaminase 2, which converts glutamine to glutamate. Pathways leading to the production of any elevated biomarker would provide a number of potential targets for drug discovery.


The proteins identified as potential drug targets may then be used to identify compositions that may be potential candidates for treating ALS, including compositions for gene therapy.


XI. Methods of Treating ALS

The identification of biomarkers for ALS also allows for the treatment of ALS. For example, in order to treat a subject having ALS, an effective amount of one or more ALS biomarkers that are lowered in ALS as compared to a healthy subject not having ALS may be administered to the subject. The biomarkers that may be administered may comprise one or more of the biomarkers in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 that are decreased in ALS as compared to subjects not having ALS. Such biomarkers could be isolated based on the identity of the biomarker compound (i.e. compound name). In some embodiments, the biomarkers that are administered are one or more biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 that are decreased in ALS and that have a p-value less than 0.05 and/or a false discovery rate of less than 0.2. In other embodiments, the biomarkers that are administered are one or biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 that are decreased in ALS by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, or by at least 100% or more (i.e., absent).


Biomarkers listed in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 may be useful as therapeutic agents. Exemplary markers include tryptophan betaine, indolepropionate, and homocarnosine. Such metabolites are decreased in ALS relative to Healthy participants and symptom mimic disease participants, indicating that supplementing with the metabolite may be useful to treat ALS. Tryptophan betaine and indolepropionate are both particularly interesting in the pathogenesis of ALS. Tryptophan betaine, a quaternary amine, has significantly reduced levels in the plasma of ALS patients, possibly due to uptake from the circulation. This suggests that tryptophan betaine could be at higher levels in other parts of the body such as in the brain or spinal cord. Tryptophan betaine has no known metabolic fate in humans, and its presence could contribute to energy dysregulation or axonal disruption. Tryptophan betaine is produced by plants and ectomycorrhizal fungi and has been shown to induce actin reorganization in root hairs. The actin cytoskeleton is important in motor neuron formation and reorganization. Indolepropionate, a deamination product of tryptophan formed by symbiotic bacteria in the gastrointestinal tract of mammals and birds, has known antioxidant properties and functions as a free radical scavenger. Oxidative stress is associated with motor neuron death in ALS; increasing levels of antioxidants such as indolepropionate ameliorate oxidative stress.


XII. Other Methods

Other methods of using the biomarkers discussed herein are also contemplated. For example, the methods described in U.S. Pat. No. 7,005,255, U.S. Pat. No. 7,329,489; U.S. Pat. No. 7,550,258; U.S. Pat. No. 7,550,260; U.S. Pat. No. 7,553,616; U.S. Pat. No. 7,635,556; U.S. Pat. No. 7,682,783; U.S. Pat. No. 7,682,784; U.S. Pat. No. 7,910,301 and U.S. Pat. No. 7,947,453 may be conducted using a small molecule profile comprising one or more of the biomarkers disclosed herein and/or one or more of the non-biomarker compounds disclosed herein.


In any of the methods listed herein, the biomarkers that are used may be selected from those biomarkers in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 having p-values of less than 0.05 and/or those biomarkers in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 having q-values of less than 0.10. The biomarkers that are used in any of the methods described herein may also be selected from those biomarkers in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 that are decreased as compared to the control group (e.g., subjects not having ALS, subjects with a symptom mimic disease, subjects with non-ALS MND, subjects having an earlier stage of ALS, etc.) by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, or by at least 100% or more (i.e., absent); and/or those biomarkers in Tables 1, 5, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 19 that are increased as compared to the control group by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more.


EXAMPLES

The invention will be further explained by the following illustrative examples that are intended to be non-limiting.


I. General Methods

A. Identification of Metabolic Profiles for ALS


Each sample was analyzed to determine the concentration of several hundred metabolites. Analytical techniques such as GC-MS (gas chromatography-mass spectrometry) and LC-MS (liquid chromatography-mass spectrometry) were used to analyze the metabolites. Multiple aliquots were simultaneously, and in parallel, analyzed, and, after appropriate quality control (QC), the information derived from each analysis was recombined. Every sample was characterized according to several thousand characteristics, which ultimately amount to several hundred chemical species. The techniques used were able to identify novel and chemically unnamed compounds. The methods are described in at least U.S. Pat. No. 7,884,318; Evans et al., 2009, Analytical Chemistry 81: 6656-6667; and Lawton et al., 2008, Pharmacogenomics 9: 383-397.


B. Statistical Analysis


The metabolomic data was analyzed using several statistical methods to identify molecules (either known, named metabolites or unnamed metabolites) present at differential levels in a definable population or subpopulation (e.g., biomarkers for ALS biological samples compared to healthy control biological samples; biomarkers for ALS compared to other diseases having symptoms similar to ALS; biomarkers for ALS compared to non-ALS MND; biomarkers for MND compared to other diseases having symptoms similar to ALS) useful for distinguishing between the definable populations (e.g., ALS and control; ALS and symptom mimic disease patients; ALS and non-ALS MND; MND and other diseases with symptoms that mimic ALS). Other molecules (either known, named metabolites or unnamed metabolites) in the definable population or subpopulation can also be identified.


T-test comparisons were used to test if the means of two independent groups (e.g. ALS and control; ALS and symptom mimic disease s; ALS and non-ALS MND; MND and symptom mimic diseases), were equal. Two parameters are typically evaluated when considering statistical significance, namely the p-value and the q-value. The p-value relates the probability of obtaining a result as or more extreme than the observed data; a low p-value (p≦0.05) is generally accepted as strong evidence that the two means are different. The q-value describes the false discovery rate; a low q-value (q≦0.2) is an indication of high confidence in a result (because of the multiple testing occurring in the data sets produced by metabolomic studies, data is often evaluated for false positives).


While a higher q-value indicates diminished confidence, it does not necessarily rule out the significance of a result. Other lines of evidence may be taken into consideration when determining whether a result merits further scrutiny. Such evidence may include a) significance in another dimension of the study, b) inclusion in a common pathway with a highly significant compound, or c) residing in a similar functional biochemical family with other significant compounds.


Random Forest analysis was used for classification of samples into groups (e.g. disease or healthy). Random Forests give an estimate of how well individuals in a new data set can be classified into each group, in contrast to a t-test, which tests whether the unknown means for two populations are different or not. Random Forests create a set of classification trees based on continual sampling of the experimental units and compounds. Then each observation is classified based on the majority votes from all the classification trees. The results of the analysis are presented in a table termed the Confusion Matrix.


Based on the Random Forest Confusion Matrix, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the classification of the samples were calculated. Sensitivity is the ability to identify positives or the proportion of subjects classified as positive among all those that are truly positive. Specificity is the ability to identify negatives or the proportion of the subjects classified as negative among all those that are truly negative. PPV is the true positive rate or the proportion of subjects that are truly positive among all those classified as positive. NPV is the true negative rate or the proportion of the subjects that are truly negative among all those classified as negative. Using these data, a receiver operating characteristic (ROC) curve was generated. The ROC curve is a plot of the sensitivity vs. false positive rate (1−specificity). The area under the curve (AUC) from this curve is the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.


Regression analysis was performed using the Random Forest Regression method and the Univariate Correlation/Linear Regression method to build models that are useful to identify the biomarker compounds that are associated with disease or disease indicators obtained from the patient metadata (e.g. ALS-FRS score) and then to identify biomarker compounds useful to classify individuals. Biomarker compounds that are useful to predict disease or measures of disease (e.g. ALS) and that are positively or negatively correlated with disease or measures of disease (e.g. ALS) were identified in these analyses.


Least Absolute Shrinkage and Selection Operator penalized logistic regression (LASSO) and Support Vector Machine (SVM) predictions were used to classify the samples. LASSO regression, a standard approach for high dimensional data, uses L1 penalty, minimizing the residual sum of squares subject to the sum of the absolute value of the regression coefficient less than a constant. The tuning parameter is determined by cross validation using training data only within each cross validation fold. SVM is a powerful machine learning method based on the Vapnik-Chervonenkis theory; it has strong regularization properties and is applied to pattern recognition problems. With this method, the data input space is projected into feature space, and then an optimal hyperplane is constructed to maximize the separating margin of the two classification categories. Linear kernel was applied. Leave one out cross validation (LOO) was used to evaluate the predictive performance, and the area under the curve (AUC) was computed based on predictive probability.


Bayesian factor regression modeling (BFRM), a well-cited unsupervised approach for high dimensional data, was used for feature construction to uncover the underlying latent metabolomic signature which can be powerful in prediction. BFRM factorizes the data matrix X (i.e., normalized metabolomic data) as X=AΛ+Ψ where A is a factor loadings matrix, Λ is a factor scores matrix and Ψ is an error matrix. The number of factors is estimated statistically. With this approach, individual metabolites are grouped into meta metabolites (i.e., groups of metabolites) which reflect aggregate patterns associated with a pathway or network. Factor data was then used as the features for predictive models. The factor data is not high dimensional (p<n), and the correlation among factors is negligible, therefore a logistic model is used for prediction. Leave one out cross validation (LOO) was used to evaluate the predictive performance, and the area under the curve (AUC) was computed based on predictive probability.


The Wilcoxon signed-rank test and the Benjamini-Hochberg (BH) method for multiple test adjustment were used for biomarker candidate selection; metabolites with a false discovery rate <0.15 were selected as candidate biomarkers.


Recursive partitioning relates a ‘dependent’ variable (Y) to a collection of independent (‘predictor’) variables (X) in order to uncover or understand the relationship, Y=f(X). This analysis can be performed with the JMP program (SAS) to generate a decision tree. The statistical significance of the “split” of the data can be placed on a more quantitative footing by computing p-values, which discern the quality of a split relative to a random event. The significance level of each “split” of data into the nodes or branches of the tree can be computed as p-values, which discern the quality of the split relative to a random event. It is given as LogWorth, which is the negative log 10 of a raw p-value.


Bayesian factor regression modeling was performed using MATLAB and Version 2 of BFRM software. The “R” package “glmnet” was used for running LASSO. All other statistical analyses were performed with the program “R” available on the worldwide web at the website cran.r-project.org and in JMP 6.0.2 (SAS® Institute, Cary, N.C.).


C. Biomarker Identification


Various peaks identified in the analyses (e.g. GC-MS, LC-MS, MS-MS), including those identified as statistically significant, were subjected to a mass spectrometry based chemical identification process.


Example 1
Biomarkers that Distinguish ALS from Healthy Subjects in Plasma

In one example, biomarkers were discovered by (1) analyzing plasma samples from different groups of human subjects to determine the levels of metabolites in the samples and then (2) statistically analyzing the results to determine those metabolites that are differentially present in the two groups.


Four studies were carried out to identify biomarkers that distinguish ALS patients from Healthy Control subjects (i.e., individuals that have not been diagnosed with ALS or other neurological disorders). In study 1, plasma samples from 62 ALS subjects and 69 healthy control subjects not diagnosed with ALS were used for the analysis. In Study 2, plasma samples used for the analysis were from 172 ALS subjects and 50 healthy control subjects not diagnosed with ALS. In Study 3, the plasma samples used for the analysis were from 199 ALS subjects and 94 healthy control subjects not diagnosed with ALS. In Study 4, the plasma samples used for the analysis were from 62 ALS subjects and 62 healthy control subjects not diagnosed with ALS. After the levels of metabolites were determined, the data were analyzed using univariate T-tests (i.e., Welch's T-test) as described in the General Methods section.


Biomarkers

As listed below in Table 1, biomarkers were discovered that were differentially present between samples from ALS subjects and Healthy Control subjects not diagnosed with ALS.


Table 1 includes, for each biomarker, an indication of the percentage difference in the ALS mean as compared to the control mean (positive values represent an increase in ALS, and negative values represent a decrease in ALS), the p-value and the q-value (expressed in scientific notation), determined in the statistical analysis of the data concerning the biomarkers, and the study in which the biomarker was identified. CompID refers to the identifier for that biomarker in the internal chemical library database.









TABLE 1







ALS Biomarkers from plasma samples-T-test Analysis of ALS vs. Healthy Controls














%






Comp
Change


Biochemical Name
ID
in ALS
p-value
q-value
Study















tryptophan betaine
37097
−92% 
2.58E−06
5.23E−04
Study 2


indolepropionate
32405
−54% 
3.70E−03
4.49E−02
Study 2


4-vinylphenol sulfate
36098
−43% 
1.90E−03
3.04E−02
Study 2


pro-hydroxy-pro
35127
53%
9.25E−08
2.98E−06
Study 1


theophylline
18394
−96% 
1.11E−06
3.38E−04
Study 2


cortisone
1769
20%
5.93E−05
4.51E−03
Study 2


paraxanthine
18254
−69% 
4.95E−05
4.31E−03
Study 2


creatine
27718
57%
9.41E−07
3.38E−04
Study 2


N1-methyladenosine
15650
 8%
7.23E−04
1.92E−02
Study 2


1-palmitoyl-sn-glycero-3-
16138
−11% 
4.47E−01
6.71E−01
Study 4


phosphocholine


caffeine
569
−89% 
2.09E−05
2.54E−03
Study 2


quinate
18335
−79% 
1.03E−03
2.24E−02
Study 2


levulinate (4-oxovalerate)
22177
−27% 
2.70E−04
1.10E−02
Study 2


1-
33957
38%
1.19E−03
2.35E−02
Study 2


heptadecanoylglycerophosphocholine


1,3,7-trimethylurate
34404
−52% 
1.34E−04
8.17E−03
Study 2


cortisol
1712
26%
1.49E−03
2.60E−02
Study 2


theobromine
18392
−75% 
3.87E−05
3.92E−03
Study 2


catechol sulfate
35320
−43% 
2.16E−04
9.37E−03
Study 2


pseudouridine
33442
11%
3.10E−03
7.50E−03
Study 1


biliverdin
2137
27%
1.45E−02
1.20E−01
Study 2


creatinine
513
−27% 
1.58E−08
1.02E−06
Study 1


2-hydroxybutyrate (AHB)
21044
40%
1.63E−05
0.0001
Study 1


10-undecenoate (11:1n1)
32497
−25% 
2.72E−03
3.93E−02
Study 2


citric acid
6359
−40% 
2.50E−05
4.94E−05
Study 3


alpha-ketobutyrate
4968
33%
1.43E−03
2.60E−02
Study 2


C-glycosyltryptophan
32675
14%
7.26E−04
1.92E−02
Study 2


histidine
59
−8%
4.32E−03
5.37E−02
Study 2


oleoylcarnitine
35160
34%
1.49E−03
2.60E−02
Study 2


HWESASXX (SEQ ID NO: 1)
32836
 3%
2.59E−02
1.71E−01
Study 2


bradykinin
22154
−54% 
1.10E−03
2.31E−02
Study 2


bradykinin, des-arg(9)
34420
 8%
1.94E−02
1.48E−01
Study 2


bradykinin, hydroxy-pro(3)
33962
−61% 
9.78E−02
1.19E−01
Study 1


glutamyl-valine
11053
744% 
6.35E−24
3.13E−22
Study 3


2-isopropylmalic acid
8449
−46% 
4.47E−20
7.35E−19
Study 3


4-methyl-2-oxopentanoate
5808
−45% 
4.30E−18
6.36E−17
Study 3


margarate (17:0)
1121
61%
3.66E−07
9.43E−06
Study 1


deoxycarnitine
36747
−33% 
1.22E−06
2.61E−05
Study 1


TMS-pyrophosphate
9776
65%
1.49E−06
3.94E−06
Study 3


10-heptadecenoate (17:1n7)
33971
68%
2.05E−06
3.77E−05
Study 1


hypoxanthine
3127
43%
2.52E−06
4.05E−05
Study 1


choline
5702
47%
5.82E−06
1.41E−05
Study 3


mannose
584
28%
6.65E−06
0.0001
Study 1


palmitate (16:0)
1336
37%
1.13E−05
1.00E−04
Study 1


stearoyl sphingomyelin
19503
50%
1.51E−05
1.00E−04
Study 1


tiglyl carnitine
35428
−35% 
2.16E−05
2.00E−04
Study 1


uric acid
7925
−9%
3.20E−05
5.98E−05
Study 3


L-alpha-glycerophosphorylcholine
5563
20%
3.82E−05
7.07E−05
Study 3


stearate (18:0)
1358
34%
4.06E−05
3.00E−04
Study 1


pyroglutamine
32672
−43% 
1.00E−04
5.00E−04
Study 1


myristate (14:0)
1365
34%
1.00E−04
5.00E−04
Study 1


oleate (18:1n9)
1359
42%
1.00E−04
5.00E−04
Study 1


docosapentaenoate (n3 DPA; 22:5n3)
32504
62%
1.00E−04
5.00E−04
Study 1


linoleate (18:2n6)
1105
38%
3.00E−04
1.60E−03
Study 1


EDTA
32511
39%
3.00E−04
1.50E−03
Study 1


eicosenoate (20:1n9 or 11)
33587
49%
3.00E−04
1.40E−03
Study 1


n-acetyl-L-aspartic acid
7359
−44% 
3.04E−04
4.78E−04
Study 3


HXGXA (SEQ ID NO: 2)
6112
1236% 
3.23E−04
5.02E−04
Study 3


3-hydroxybutyrate (BHBA)
542
70%
4.00E−04
1.60E−03
Study 1


myristoleate (14:1n5)
32418
40%
6.00E−04
2.50E−03
Study 1


1-stearoylglycerol (1-monostearin)
21188
48%
6.00E−04
2.50E−03
Study 1


dihomo-linoleate (20:2n6)
17805
48%
6.00E−04
2.40E−03
Study 1


palmitoyl sphingomyelin
37506
22%
7.00E−04
2.60E−03
Study 1


adrenate (22:4n6)
32980
40%
7.00E−04
2.60E−03
Study 1


1-palmitoleoylglycerophosphocholine
33230
26%
7.94E−04
1.94E−02
Study 2


1-palmitoylglycerol (1-monopalmitin)
21127
29%
8.00E−04
2.70E−03
Study 1


1-pentadecanoylglycerophosphocholine
37418
40%
8.00E−04
2.70E−03
Study 1


piperine
33935
−64% 
8.29E−04
1.94E−02
Study 2


pyruvate
599
34%
0.001 
0.0032
Study 1


cyclo(leu-pro)
37104
−33% 
1.19E−03
2.35E−02
Study 2


linolenate [alpha or gamma; (18:3n3
34035
43%
1.70E−03
4.90E−03
Study 1


or 6)]


hydroquinone sulfate
35322
−30% 
2.20E−03
5.90E−03
Study 1


carnitine
15500
 8%
0.0022
0.0059
Study 1


eicosapentaenoate (EPA; 20:5n3)
18467
50%
2.60E−03
6.60E−03
Study 1


butyrylcarnitine
32412
24%
2.80E−03
7.10E−03
Study 1


threonine
12666
−20% 
3.10E−03
1.34E−01
Study 4


3-dehydrocarnitine
32654
−19% 
3.23E−03
4.19E−02
Study 2


15-methylpalmitate
38295
23%
3.30E−03
7.80E−03
Study 1


1,7-dimethylurate
34400
−23% 
3.46E−03
4.39E−02
Study 2


isovaleryl-, valeryl- and/or 2-
9491
−16% 
4.00E−03
5.19E−03
Study 3


methylbutytl-carnitine


N-acetylornithine
15630
−61% 
4.20E−03
9.50E−03
Study 1


3-phenylpropionate (hydrocinnamate)
15749
−28% 
4.81E−03
5.74E−02
Study 2


4-hydroxyhippurate
35527
−35% 
5.40E−03
1.18E−02
Study 1


propionylcarnitine
9130
30%
5.90E−03
1.54E−01
Study 4


5alpha-pregnan-3beta,20alpha-diol
37198
−82% 
6.39E−03
6.95E−02
Study 2


disulfate


kynurenine
15140
17%
7.10E−03
1.48E−02
Study 1


3-methoxytyrosine
12017
44%
7.52E−03
7.64E−02
Study 2


1-
33871
21%
7.53E−03
7.64E−02
Study 2


eicosadienoylglycerophosphocholine


1-docosapentaenoylglycerophospho
37231
24%
9.10E−03
1.80E−02
Study 1


choline


1-methylxanthine
34389
−22% 
9.14E−03
8.70E−02
Study 2


3-carboxy-4-methyl-5-propyl-2-
31787
−54% 
1.10E−02
9.72E−02
Study 2


furanpropanoate (CMPF)


stearidonate (18:4n3)
33969
56%
1.11E−02
2.14E−02
Study 1


DSGEGDFLAEGGGVR (SEQ ID
6208
776% 
1.14E−02
1.30E−02
Study 3


NO: 3)


2-hydroxypalmitate
35675
13%
1.22E−02
2.31E−02
Study 1


serine
12663
−15% 
1.34E−02
2.16E−01
Study 4


pyridoxic acid
6486
241% 
1.44E−02
1.54E−02
Study 3


2-stearoylglycerophosphocholine
35255
26%
1.47E−02
1.20E−01
Study 2


1-oleoylglycerophosphocholine
33960
14%
1.48E−02
1.20E−01
Study 2


2-palmitoylglycerophosphocholine
35253
17%
1.51E−02
1.21E−01
Study 2


dihomo-linolenate (20:3n3 or n6)
35718
23%
1.51E−02
2.79E−02
Study 1


oxalic acid
7639
−18% 
1.60E−02
1.67E−02
Study 3


nicotinamide
594
17%
1.75E−02
3.13E−02
Study 1


methionine
12726
−24% 
1.76E−02
2.32E−01
Study 4


3-methylxanthine
32445
−41% 
1.79E−02
3.16E−02
Study 1


azelate
15328
−29% 
1.88E−02
2.37E−01
Study 4


fumarate
1643
18%
1.95E−02
1.48E−01
Study 2


thymol sulfate
36095
−85% 
2.00E−02
1.49E−01
Study 2


7-methylxanthine
34390
−20% 
2.24E−02
1.55E−01
Study 2


lathosterol
33488
26%
2.30E−02
3.91E−02
Study 1


betaine
3141
−10% 
2.61E−02
4.24E−02
Study 1


glycerol 3-phosphate (G3P)
15365
10%
2.61E−02
4.24E−02
Study 1


5-dodecenoate (12:1n7)
33968
17%
2.65E−02
4.24E−02
Study 1


pantothenate
1508
30%
0.0266
0.0424
Study 1


docosadienoate (22:2n6)
32415
18%
2.78E−02
1.77E−01
Study 2


1-stearoylglycerophosphocholine
33961
20%
3.27E−02
1.96E−01
Study 2


3-methyl-2-oxobutyrate
21047
11%
3.27E−02
4.97E−02
Study 1


tetradecanedioate
35669
29%
3.28E−02
1.96E−01
Study 2


heptanoate (7:0)
1644
−22% 
3.31E−02
4.97E−02
Study 1


N-(2-furoyl)glycine
31536
−79% 
3.46E−02
5.12E−02
Study 1


phenyllactate (PLA)
22130
−25% 
3.59E−02
5.20E−02
Study 1


bilirubin (E, Z or Z, E)
34106
31%
3.72E−02
2.14E−01
Study 2


glutamate
12751
26%
3.81E−02
3.08E−01
Study 4


hexadecanedioate
35678
27%
3.95E−02
5.66E−02
Study 1


2-hydroxystearate
17945
17%
4.15E−02
5.88E−02
Study 1


undecanoate (11:0)
12067
−8%
4.68E−02
2.52E−01
Study 2


glycoursodeoxycholate
39379
51%
5.10E−02
2.70E−01
Study 2


ethylenediaminotetraacetate
12790
−15% 
5.36E−02
3.81E−01
Study 4


2-oleoylglycerophosphocholine
35254
10%
5.46E−02
7.49E−02
Study 1


acetylcarnitine
5697
−11% 
5.86E−02
4.93E−02
Study 3


laurate (12:0)
1645
−61% 
5.98E−02
2.96E−01
Study 2


10-nonadecenoate (19:1n9)
33972
25%
6.13E−02
2.96E−01
Study 2


homostachydrine
33009
−18% 
6.17E−02
2.96E−01
Study 2


arachidonate (20:4n6)
1110
12%
6.22E−02
2.96E−01
Study 2


4-acetominophen sulfate
10240
571% 
6.37E−02
4.03E−01
Study 4


glutaroyl carnitine
35439
−9%
6.47E−02
3.03E−01
Study 2


L-Norleucine
1968
−12% 
6.62E−02
5.41E−02
Study 3


uridine
606
−11% 
7.13E−02
3.24E−01
Study 2


N2,N2-dimethylguanosine
35137
 7%
7.49E−02
3.28E−01
Study 2


succinylcarnitine
37058
−12% 
8.20E−02
3.51E−01
Study 2


2-methylbutyroylcarnitine
35431
 6%
8.31E−02
1.07E−01
Study 1


isovalerate
34732
15%
8.31E−02
3.51E−01
Study 2


alpha-hydroxyisovalerate
33937
−49% 
8.60E−02
1.08E−01
Study 1


p-acetamidophenyl-beta-D-
11082
−8%
8.87E−02
4.46E−01
Study 4


Glucuronide


gamma-glutamylglutamate
36738
16%
9.13E−02
3.66E−01
Study 2


phenol sulfate
32553
−25% 
9.59E−02
3.77E−01
Study 2


2,3-dihydroxybenzoic acid
7447
−37% 
9.75E−02
7.35E−02
Study 3


gamma-glutamylphenylalanine
13214
−14% 
9.91E−02
4.70E−01
Study 4


1,3-dihydroxyacetone
35981
18%
1.01E−01
3.85E−01
Study 2


1-
34214
10%
1.03E−01
1.22E−01
Study 1


arachidonoylglycerophosphoinositol


2-octenoyl carnitine
35440
−11% 
1.04E−01
3.92E−01
Study 2


erythronate
33477
 8%
1.05E−01
1.22E−01
Study 1


trans-hydroxyproline
12673
26%
1.07E−01
4.77E−01
Study 4


erythritol
20699
−355% 
1.09E−01
3.97E−01
Study 2


heme
32593
33%
1.11E−01
1.28E−01
Study 1


1-
35186
10%
1.15E−01
1.31E−01
Study 1


arachidonoylglycerophosphoethanol


amine


4-Guanidinobutanoic acid
7670
18%
1.16E−01
8.32E−02
Study 3


caproate (6:0)
32489
−15% 
1.21E−01
1.34E−01
Study 1


1-
32635
−10% 
1.22E−01
4.15E−01
Study 2


linoleoylglycerophosphoethanolamine


tyrosine
12780
−13% 
1.25E−01
4.94E−01
Study 4


glutamine
12757
−9%
1.29E−01
4.94E−01
Study 4


cis-vaccenate (18:1n7)
33970
38%
1.34E−01
4.42E−01
Study 2


ethanolamine
34285
−25% 
1.36E−01
4.42E−01
Study 2


2-hydroxyoctanoate
22036
−12% 
1.37E−01
4.42E−01
Study 2


2-hydroxy butanoate
12543
21%
1.39E−01
4.94E−01
Study 4


ornithine
16511
14%
1.39E−01
4.94E−01
Study 4


docosapentaenoate (n6 DPA; 22:5n6)
37478
26%
1.39E−01
4.42E−01
Study 2


ergothioneine
37459
−18% 
1.44E−01
1.47E−01
Study 1


citrulline
2132
−10% 
1.44E−01
1.47E−01
Study 1


octadecanedioate
36754
15%
1.46E−01
4.46E−01
Study 2


N-acetylglycine
27710
22%
1.49E−01
4.53E−01
Study 2


proline
12650
−11% 
1.50E−01
4.94E−01
Study 4


DSGEGDFXAEGGGVR (SEQ ID
31548
206% 
1.54E−01
4.63E−01
Study 2


NO: 4)


phenylacetate
15958
14%
1.56E−01
4.66E−01
Study 2


laurylcarnitine
34534
−23% 
1.59E−01
4.68E−01
Study 2


3-(4-hydroxyphenyl)-1-(2,4,6-
38153
1495% 
1.62E−01
4.69E−01
Study 2


trihydroxyphenyl)-1-propanone


3-methyl-2-oxovalerate
15676
 7%
1.62E−01
1.58E−01
Study 1


17-methylstearate
38296
13%
1.64E−01
4.72E−01
Study 2


phenylacetylglutamine
35126
 7%
1.67E−01
4.72E−01
Study 2


delta-tocopherol
33418
−28% 
1.67E−01
4.72E−01
Study 2


chiro-inositol
37112
83%
1.69E−01
4.74E−01
Study 2


palmitoylcarnitine
22189
14%
1.70E−01
4.74E−01
Study 2


dimethylglycine
5086
23%
1.71E−01
1.63E−01
Study 1


glycolate (hydroxyacetate)
15737
−5%
1.74E−01
4.82E−01
Study 2


3-methylhistidine
15677
−41% 
1.83E−01
1.69E−01
Study 1


andro steroid monosulfate 2
32792
31%
1.83E−01
4.88E−01
Study 2


1-docosahexaenoylglycerophospho
33822
16%
1.87E−01
1.70E−01
Study 1


choline


phenylalanine
12756
−7%
1.90E−01
5.33E−01
Study 4


1-myristoylglycerophosphocholine
35626
27%
1.91E−01
1.72E−01
Study 1


1-oleoylglycerophosphoethanolamine
35628
−9%
1.91E−01
4.96E−01
Study 2


cis-4-decenoyl carnitine
38178
−19% 
1.92E−01
4.96E−01
Study 2


pelargonate (9:0)
12035
−8%
1.93E−01
4.96E−01
Study 2


3-indoxyl sulfate
5809
−10% 
1.98E−01
1.22E−01
Study 3


3-(3-hydroxyphenyl)propionate
35635
−10% 
1.99E−01
5.04E−01
Study 2


glucuronate
15443
 7%
1.99E−01
1.76E−01
Study 1


2-hydroxyhippurate (salicylurate)
18281
25%
2.04E−01
5.10E−01
Study 2


1-
33228
 6%
2.05E−01
5.10E−01
Study 2


arachidonoylglycerophosphocholine


2-
36593
−9%
2.06E−01
5.10E−01
Study 2


linoleoylglycerophosphoethanolamine


5alpha-androstan-3alpha,17beta-diol
37184
 5%
2.07E−01
5.10E−01
Study 2


disulfate


gamma-CEHC
37462
−10% 
2.10E−01
5.16E−01
Study 2


sebacate (decanedioate)
32398
 8%
2.11E−01
5.16E−01
Study 2


cinnamoylglycine
38637
−10% 
2.13E−01
5.19E−01
Study 2


gamma-glutamyltyrosine
2734
−6%
2.24E−01
5.38E−01
Study 2


2-
32815
12%
2.26E−01
1.90E−01
Study 1


arachidonoylglycerophosphoethanol


amine


lysine
16107
15%
2.32E−01
5.80E−01
Study 4


3-(4-hydroxyphenyl)lactate
32197
−15% 
2.39E−01
1.99E−01
Study 1


nonadecanoate (19:0)
1356
10%
2.39E−01
5.59E−01
Study 2


erythrose
8677
 7%
2.40E−01
1.42E−01
Study 3


urobilinogen
32426
13%
2.41E−01
5.59E−01
Study 2


1-
33821
 8%
2.43E−01
2.00E−01
Study 1


eicosatrienoylglycerophosphocholine


3-indolepropionate
8300
−37% 
2.46E−01
5.89E−01
Study 4


carnitine-1
6401
 8%
2.48E−01
1.46E−01
Study 3


deoxycholate
1114
35%
2.57E−01
5.84E−01
Study 2


octanoate(caprylate (8:0))
12609
28%
2.61E−01
5.89E−01
Study 4


stachydrine
34384
48%
2.61E−01
5.90E−01
Study 2


4-acetamidobutanoate
1558
 5%
2.63E−01
2.11E−01
Study 1


taurocholate
18497
−75% 
2.67E−01
2.11E−01
Study 1


arginine
12659
−8%
2.67E−01
5.94E−01
Study 4


hydroxyisovaleroyl carnitine
35433
−11% 
2.69E−01
5.97E−01
Study 2


cholate
22842
88%
2.69E−01
5.97E−01
Study 2


gamma-glutamylmethionine
37539
−10% 
2.73E−01
2.13E−01
Study 1


cysteine-glutathione disulfide
35159
 8%
2.74E−01
2.13E−01
Study 1


1,6-anhydroglucose
21049
27%
2.81E−01
6.16E−01
Study 2


hydroxyproline form of bradykinin
10143
−35% 
2.82E−01
6.06E−01
Study 4


decanoylcarnitine
33941
−35% 
2.85E−01
6.21E−01
Study 2


saccharin
10644
63%
2.87E−01
6.06E−01
Study 4


gamma-tocopherol
16518
−18% 
2.91E−01
6.06E−01
Study 4


1,5-anhydroglucitol (1,5-AG)
20675
 8%
2.93E−01
6.28E−01
Study 2


glycocholate
8091
39%
2.95E−01
6.11E−01
Study 4


ribitol
15772
−10% 
2.96E−01
6.28E−01
Study 2


N-acetylserine
37076
10%
2.96E−01
6.28E−01
Study 2


taurochenodeoxycholate
18494
21%
2.96E−01
6.28E−01
Study 2


1-methylurate
34395
−5%
3.00E−01
6.33E−01
Study 2


3-hydroxyoctanoate
22001
−14% 
3.08E−01
6.47E−01
Study 2


asparagine
16665
−9%
3.08E−01
6.22E−01
Study 4


cotinine
553
81%
3.13E−01
6.55E−01
Study 2


gamma-glu-leu
10438
 6%
3.27E−01
6.31E−01
Study 4


pregnen-diol disulfate
32562
13%
3.30E−01
6.76E−01
Study 2


glycochenodeoxycholate
32346
−12% 
3.40E−01
2.46E−01
Study 1


myo-inositol
19934
−6%
3.42E−01
2.46E−01
Study 1


caprate (10:0)
1642
 9%
3.58E−01
2.52E−01
Study 1


dehydroisoandrosterone sulfate
32425
−9%
3.61E−01
7.18E−01
Study 2


(DHEA-S)


4-androsten-3beta,17beta-diol
37202
 7%
3.67E−01
7.24E−01
Study 2


disulfate 1


2-hydroxyglutarate
37253
 7%
3.69E−01
7.24E−01
Study 2


indoleacetate
27513
−6%
3.69E−01
7.24E−01
Study 2


2-oleoylglycerophosphoethanolamine
35687
−6%
3.70E−01
7.24E−01
Study 2


4-ethylphenylsulfate
36099
−15% 
3.72E−01
7.24E−01
Study 2


2-hydroxyisobutyrate
22030
13%
3.74E−01
7.24E−01
Study 2


sorbitol plus a 204 ion
12753
−27% 
3.74E−01
6.47E−01
Study 4


octanoylcarnitine
33936
−33% 
3.74E−01
7.24E−01
Study 2


N-acetylthreonine
33939
 7%
3.82E−01
2.68E−01
Study 1


gamma-glutamylvaline
32393
26%
3.95E−01
7.45E−01
Study 2


methylglutaroylcarnitine
37060
 6%
3.98E−01
7.46E−01
Study 2


xylonate
35638
52%
4.02E−01
7.49E−01
Study 2


erythro-sphingosine-1-phosphate
34445
10%
4.10E−01
7.58E−01
Study 2


2-linoleoylglycerophosphocholine
35257
−9%
4.31E−01
7.74E−01
Study 2


iminodiacetate
16653
−22% 
4.33E−01
6.71E−01
Study 4


trans-2,3,4-trimethoxycinnamic acid
7957
12%
4.34E−01
2.20E−01
Study 3


androsterone sulfate
5647
−11% 
4.35E−01
2.20E−01
Study 3


2-amino butyrate
12645
−8%
4.39E−01
6.71E−01
Study 4


hippuric acid
6513
−8%
4.44E−01
2.23E−01
Study 3


lysine-3TMS
16092
−16% 
4.49E−01
6.71E−01
Study 4


4-androsten-3beta,17beta-diol
37203
 5%
4.50E−01
7.81E−01
Study 2


disulfate 2


epiandrosterone sulfate
33973
−9%
4.54E−01
7.83E−01
Study 2


taurolithocholate 3-sulfate
38782
17%
4.65E−01
7.94E−01
Study 2


5alpha-androstan-3beta,17alpha-diol
37187
 9%
4.66E−01
2.96E−01
Study 1


disulfate


glycodeoxycholate
18477
−32% 
4.70E−01
2.96E−01
Study 1


1,2-propanediol
38002
38%
4.70E−01
7.98E−01
Study 2


pregnenolone sulfate
38170
−10% 
4.71E−01
2.96E−01
Study 1


p-hydroxybenzaldehyde
7446
12%
4.74E−01
6.78E−01
Study 4


taurodeoxycholate
12261
−32% 
4.78E−01
8.06E−01
Study 2


sucrose
15336
30%
4.79E−01
2.98E−01
Study 1


azelate (nonanedioate)
18362
 6%
4.85E−01
8.06E−01
Study 2


beta-hydroxyisovalerate
12129
 5%
4.88E−01
8.06E−01
Study 2


taurocholenate sulfate
32807
 7%
4.99E−01
8.09E−01
Study 2


N6-acetyllysine
36752
−6%
5.00E−01
8.09E−01
Study 2


(s)-2-hydroxybutyrate
5711
15%
5.04E−01
6.90E−01
Study 4


cysteine
16071
 6%
5.08E−01
6.90E−01
Study 4


ADSGEGDFXAEGGGVR (SEQ ID
33084
79%
5.09E−01
8.12E−01
Study 2


NO: 5)


glucose
16655
10%
5.10E−01
6.90E−01
Study 4


p-cresol sulfate
6362
16%
5.14E−01
6.91E−01
Study 4


gamma-glutamylisoleucine
34456
19%
5.15E−01
8.15E−01
Study 2


oxalacetate
16650
−14% 
5.20E−01
6.93E−01
Study 4


leucine
12656
−5%
5.52E−01
7.06E−01
Study 4


methyl palmitate (15 or 2)
38768
 6%
5.60E−01
8.45E−01
Study 2


3-carboxy-4-Methyl-5-propyl-2
14837
20%
5.60E−01
7.06E−01
Study 4


furanpropanoate


pregn steroid monosulfate
32619
 6%
5.62E−01
3.31E−01
Study 1


glycocholenate sulfate
32599
 7%
5.63E−01
8.45E−01
Study 2


1,5-anhydro-D-glucitol
12739
10%
5.76E−01
7.14E−01
Study 4


bilirubin (E,E)
32586
18%
5.76E−01
8.58E−01
Study 2


isobutyrylcarnitine
33441
−8%
5.90E−01
3.39E−01
Study 1


xylose
15835
−11% 
6.19E−01
8.88E−01
Study 2


iminodiacetate (IDA)
35837
12%
6.21E−01
8.88E−01
Study 2


pro-leu
13018
11%
6.28E−01
7.31E−01
Study 4


salicyluric acid
6493
35%
6.30E−01
2.86E−01
Study 3


campesterol
39511
−5%
6.67E−01
9.15E−01
Study 2


docosahexaenoate (DHA; 22:6n3)
19323
−5%
6.90E−01
9.20E−01
Study 2


5alpha-androstan-3beta,17beta-diol
37190
16%
7.48E−01
9.58E−01
Study 2


disulfate


3-hydroxydecanoate
22053
−10% 
7.65E−01
9.67E−01
Study 2


beta-tocopherol
35702
−6%
7.87E−01
9.84E−01
Study 2


bilirubin (Z,Z)
27716
10%
7.90E−01
9.84E−01
Study 2


pentadecanoate (15:0)
1361
 6%
8.35E−01
1.00E+00
Study 2


riluzole glucuronide
10872
13%
8.62E−01
3.47E−01
Study 3


serotonin (5HT)
2342
 5%
9.55E−01
1.00E+00
Study 2


caprylate (8:0)
32492
22%
9.77E−01
1.00E+00
Study 2


N-methyl proline
37431
32%
9.98E−01
1.00E+00
Study 2


[H]HWESASLLR[OH] (SEQ ID
33964
151% 
1.31E−01
4.42E−01
Study 2


NO: 6)


XHWESASXXR (SEQ ID NO: 7)
31538
127% 
2.95E−01
6.28E−01
Study 2









The biomarkers were evaluated using Random Forest analysis to classify subjects into ALS or Healthy control groups. Plasma samples from 172 ALS subjects and 50 healthy control subjects not diagnosed with ALS were used in this analysis.


Random Forest results show that the samples can be classified with 77% prediction accuracy. The Confusion Matrix presented in Table 2 shows the number of samples predicted for each classification and the actual in each group (ALS or Healthy). The “Out-of-Bag” (OOB) Error rate gives an estimate of how accurately new observations can be predicted using the Random Forest model (e.g., whether a sample is from an ALS subject or a healthy control subject). The OOB error from this Random Forest was approximately 23%, and the model estimated that, when used on a new set of subjects, the identity of healthy control subjects could be predicted correctly 78% of the time and ALS subjects could be predicted 76% of the time. The results are summarized in Table 3.









TABLE 2







Results of Random Forest, Plasma: ALS vs. Healthy control












Predicted Group












ALS
Healthy
class. error

















Actual
ALS
131
41
0.238372



Group
Healthy
11
39
0.22

















TABLE 3







Results of Metabolomic Predictions









Random Forest Analysis














Overall
77%



Healthy Control
78%



ALS
76%










Based on the OOB Error rate of 23%, the Random Forest model that was created predicted whether a sample was from an individual with ALS with about 77% accuracy from analysis of the levels of the biomarkers in samples from the subject. Exemplary biomarkers for distinguishing the groups are creatine, pro-hydroxy-pro, tryptophan betaine, theophylline, cortisone, paraxanthine, n1-methyladenosine, 1-palmtoleoylglycerophosphocholine, indolepropionate, caffeine, quinate, levulinate-4-oxovalerate, 1-heptadecanoylglycerophosphocholine, 1,3-7-trimethlurate, cortisol, Theobromine, catechol sulfate, pseudouridine, biliverdin, creatine, bradykinin, 4-vinylphenol sulfate, 2-hydroxybutyrate, 10-undecenoante (11:1n1), citrate, HWESASXX (SEQ ID NO:1), alpha-ketobutyrate, C-glycosyltryptophan, histidine and oleoylcarnitine. The biomarkers were ranked based on their importance for the predictions and are shown in the Importance Plot in FIG. 1.


The Random Forest results demonstrated that by using the biomarkers, ALS subjects were distinguished from healthy subjects with 76% sensitivity, 78% specificity, 92% Positive Predictive Value (PPV), and 49% Negative Predictive Value (NPV). These results are summarized in Table 4. The area under the curve (AUC) for the receiver operating characteristic (ROC) curve was 0.85 and the plot is graphically presented in FIG. 2.









TABLE 4







Diagnostic parameters for ALS vs. Healthy Control classification













Sensitivity
Specificity
PPV
NPV
AUC
















ALS vs. Healthy
76%
78%
92%
49%
0.85









Example 2
Biomarkers for Differentiating ALS from Symptom Mimic Diseases in Plasma

Metabolomic analysis was carried out on blood plasma samples to identify biomarkers that were useful to distinguish ALS patients from patients with symptom mimic diseases, that is, neurological diseases that cause symptoms that appear clinically similar to ALS (e.g., multi-focal motor neuropathy, spinal muscular atrophy, Kennedy's disease, multiple sclerosis). The plasma samples used for the analysis were from 172 ALS subjects, and 73 symptom mimic disease subjects (subjects with diseases that cause symptoms that appear clinically similar to ALS). After the levels of metabolites were determined, the data were analyzed using T-tests to identify biomarkers that differed between the ALS patients and the symptom mimic disease patients. The biomarkers are listed in Table 5.


Table 5 includes, for each biomarker, an indication of the percentage difference in the ALS mean as compared to the symptom mimic disease mean (positive values represent an increase in ALS, and negative values represent a decrease in ALS), and the p-value and the q-value, determined in the statistical analysis of the data concerning the biomarkers. The heading “Comp ID” refers to the identifier for that biomarker in the internal chemical library database.









TABLE 5







ALS Biomarkers from blood plasma samples that


distinguish ALS from symptom mimic Diseases













%





Comp
Change


Biochemical Name
ID
in ALS
p-value
q-value














4-vinylphenol sulfate
36098
−29% 
0.006
0.117


iminodiacetate (IDA)
35837
16%
0.002
0.093


delta-tocopherol
33418
−98% 
<0.001
0.021


palmitoyl sphingomyelin
37506
11%
0.003
0.103


phosphate
11438
11%
<0.001
<0.001


cortisone
1769
14%
0.001
0.057


3-methylxanthine
32445
−58% 
<0.001
0.02


creatine
27718
22%
0.012
0.141


5,6-dihydrouracil
1559
27%
0.029
0.249


theobromine
18392
−43% 
0.001
0.061


10-undecenoate (11:1n1)
32497
16%
0.003
0.103


octadecanedioate
36754
25%
0.025
0.237


7-methylxanthine
34390
−45% 
0.001
0.061


3-dehydrocarnitine
32654
−22% 
0.003
0.103


urate
1604
−7.41%   
0.02767
0.2442


1,2-propanediol
38002
47%
0.255
0.618


serine
32315
11%
0.039
0.281


cysteine
31453
−10% 
0.042
0.293


proline
1898
−10.00%    
0.032397
0.274025


hexadecanedioate
35678
27%
0.004
0.103


2-hydroxybutyrate (AHB)
21044
19%
0.007
0.117


alpha-ketobutyrate
4968
20%
0.005
0.103


1-methylurate
34395
−15% 
0.011
0.141


pyroglutamine
32672
−25% 
0.019
0.19


dodecanedioate
32388
 8%
0.069
0.355


cholesterol
63
 5%
0.086
0.398


paraxanthine
18254
−17.36%    
0.014531
0.166972


pro-hydroxy-pro
35127
11%
0.118
0.431


creatinine
513
−9.09%   
0.019427
0.190417


1-stearoylglycerophosphoinositol
19324
14%
0.009
0.132


arachidonate (20:4n6)
1110
18%
0.002
0.078


glutamine
53
−2%
0.4971
0.8261


2-arachidonoylglycerophosphoethanolamine
32815
18%
0.003
0.103


erythronate
33477
−17% 
0.005
0.103


glycocholenate sulfate
32599
20%
0.005
0.103


pregnen-diol disulfate
32562
27%
0.006
0.117


1-arachidonoylglycerophosphoinositol
34214
12%
0.007
0.124


eicosenoate (20:1n9 or 11)
33587
28%
0.008
0.125


theophylline
18394
−30% 
0.009
0.132


sarcosine (N-Methylglycine)
1516
30%
0.009
0.132


caprylate (8:0)
32492
47%
0.009
0.132


2-hydroxystearate
17945
 9%
0.01
0.132


caprate (10:0)
1642
25%
0.01
0.133


10-nonadecenoate (19:1n9)
33972
19%
0.011
0.137


dihomo-linolenate (20:3n3 or n6)
35718
14%
0.012
0.141


adrenate (22:4n6)
32980
15%
0.015
0.17


13-HODE + 9-HODE
37752
15%
0.016
0.171


oleate (18:1n9)
1359
22%
0.018
0.19


2-hydroxypalmitate
35675
 6%
0.019
0.19


3-methoxytyrosine
12017
35%
0.019
0.19


1,7-dimethylurate
34400
−20% 
0.02
0.19


cis-vaccenate (18:1n7)
33970
15%
0.025
0.234


indolelactate
18349
−16% 
0.026
0.241


hippurate
15753
−33% 
0.027
0.244


deoxycarnitine
36747
−10% 
0.027
0.244


catechol sulfate
35320
−30% 
0.032
0.271


isobutyrylcarnitine
33441
−14% 
0.033
0.275


carnitine
15500
 4%
0.034
0.275


dihomo-linoleate (20:2n6)
17805
20%
0.034
0.275


threitol
35854
−18% 
0.035
0.277


butyrylcarnitine
32412
−19% 
0.036
0.277


1-stearoylglycerophosphocholine
33961
18%
0.037
0.277


mannitol
15335
−136% 
0.039
0.281


fumarate
1643
 6%
0.039
0.281


1-arachidonoylglycerophosphoethanolamine
35186
10%
0.039
0.281


nonadecanoate (19:0)
1356
11%
0.043
0.294


methylphosphate
37070
 6%
0.046
0.309


docosadienoate (22:2n6)
32415
15%
0.047
0.309


tetradecanedioate
35669
15%
0.047
0.309


cortisol
1712
 8%
0.053
0.33


[H]HWESASLLR[OH] (SEQ ID NO: 6)
33964
233% 
0.053
0.33


linolenate [alpha or gamma; (18:3n3 or 6)]
34035
14%
0.056
0.335


4-androsten-3beta,17beta-diol disulfate 2
37203
15%
0.056
0.335


xylitol
4966
−14% 
0.057
0.335


1,3,7-trimethylurate
34404
−20% 
0.058
0.335


stearoyl sphingomyelin
19503
10%
0.058
0.335


taurocholenate sulfate
32807
15%
0.058
0.335


dimethylglycine
5086
10%
0.06
0.335


docosapentaenoate (n3 DPA; 22:5n3)
32504
19%
0.062
0.338


linoleate (18:2n6)
1105
10%
0.066
0.355


saccharin
21151
−75% 
0.067
0.355


3-carboxy-4-methyl-5-propyl-2-furanpropanoate
31787
−95% 
0.069
0.355


(CMPF)


propionylcarnitine
32452
−6%
0.07
0.355


asparagine
34283
−8%
0.072
0.363


margarate (17:0)
1121
11%
0.073
0.363


3-(3-hydroxyphenyl)propionate
35635
−30% 
0.078
0.382


2-oleoylglycerophosphocholine
35254
 9%
0.079
0.384


palmitate (16:0)
1336
 8%
0.081
0.386


10-heptadecenoate (17:1n7)
33971
10%
0.082
0.388


glycerol
15122
 9%
0.083
0.393


bilirubin (E,Z or Z,E)
34106
15%
0.089
0.402


3-hydroxybutyrate (BHBA)
542
27%
0.089
0.402


2-hydroxyhippurate (salicylurate)
18281
−62% 
0.093
0.404


indolepropionate
32405
−15% 
0.093
0.404


mannose
584
10%
0.093
0.404


1-arachidonoylglycerophosphocholine
33228
 8%
0.095
0.404


phenylacetylglutamine
35126
−22% 
0.097
0.404


3-methylhistidine
15677
−30% 
0.098
0.404


17-methylstearate
38296
10%
0.099
0.405


caproate (6:0)
32489
 5%
0.103
0.417


arabinose
575
16%
0.106
0.423


isovalerylcarnitine
34407
−8%
0.11
0.423


2-palmitoylglycerophosphocholine
35253
 9%
0.111
0.423


trans-4-hydroxyproline
32319
−14% 
0.115
0.43


hydroxyisovaleroyl carnitine
35433
−12% 
0.116
0.43


1-oleoylglycerophosphocholine
33960
 6%
0.124
0.44


XHWESASXXR (SEQ ID NO: 7)
31538
138% 
0.128
0.444


erythritol
20699
−207% 
0.135
0.456


pregn steroid monosulfate
32619
10%
0.135
0.456


stearate (18:0)
1358
 6%
0.138
0.456


glycoursodeoxycholate
39379
 8%
0.141
0.456


hexanoylcarnitine
32328
 8%
0.144
0.462


1-eicosadienoylglycerophosphocholine
33871
10%
0.149
0.471


cotinine
553
92%
0.155
0.48


2-stearoylglycerophosphocholine
35255
14%
0.16
0.494


biliverdin
2137
 9%
0.163
0.497


tryptophan betaine
37097
−18% 
0.173
0.513


xylonate
35638
14%
0.173
0.513


2-hydroxyoctanoate
22036
−7%
0.175
0.514


gamma-glutamylalanine
37063
−6%
0.185
0.53


taurolithocholate 3-sulfate
38782
12%
0.19
0.535


phenyllactate (PLA)
22130
−15% 
0.192
0.535


N6-acetyllysine
36752
−8%
0.193
0.535


glutaroyl carnitine
35439
−8%
0.194
0.535


2-aminobutyrate
32348
 6%
0.195
0.535


myristate (14:0)
1365
 6%
0.198
0.537


1-oleoylglycerophosphoethanolamine
35628
11%
0.199
0.537


arabitol
15964
−7%
0.211
0.553


1-palmitoylplasmenylethanolamine
39270
11%
0.215
0.563


bilirubin (E,E)
32586
14%
0.218
0.564


gamma-glutamylvaline
32393
28%
0.219
0.564


pentadecanoate (15:0)
1361
12%
0.235
0.592


isovalerate
34732
 6%
0.242
0.598


glycerol 3-phosphate (G3P)
15365
 6%
0.243
0.598


succinylcarnitine
37058
−8%
0.245
0.598


sebacate (decanedioate)
32398
 8%
0.257
0.621


andro steroid monosulfate 2
32792
11%
0.263
0.621


HWESASXX (SEQ ID NO: 1)
32836
22%
0.263
0.621


2-methylbutyroylcarnitine
35431
−7%
0.267
0.621


1-heptadecanoylglycerophosphocholine
33957
12%
0.267
0.621


gamma-CEHC
37462
−10% 
0.27
0.624


1-methylxanthine
34389
−16% 
0.276
0.63


1,3-dihydroxyacetone
35981
 5%
0.281
0.632


3-hydroxyisobutyrate
1549
−7%
0.283
0.632


glycolithocholate sulfate
32620
11%
0.283
0.632


3-indoxyl sulfate
27672
−14% 
0.287
0.634


N-(2-furoyl)glycine
31536
−22% 
0.293
0.643


stearidonate (18:4n3)
33969
10%
0.293
0.643


tiglyl carnitine
35428
−7%
0.306
0.66


xylose
15835
−17% 
0.311
0.668


lactate
527
−5%
0.317
0.674


1-palmitoleoylglycerophosphocholine
33230
 5%
0.319
0.675


fructose
31266
−31% 
0.322
0.675


21-hydroxypregnenolone disulfate
37173
 5%
0.324
0.675


4-hydroxyphenylacetate
541
−13% 
0.338
0.693


docosapentaenoate (n6 DPA; 22:5n6)
37478
11%
0.344
0.697


3-(cystein-S-yl)acetaminophen
34365
−37% 
0.351
0.699


urobilinogen
32426
−25% 
0.359
0.71


4-ethylphenylsulfate
36099
32%
0.366
0.72


2-oleoylglycerophosphoethanolamine
35687
10%
0.371
0.72


N-acetylglycine
27710
10%
0.372
0.72


bradykinin, des-arg(9)
34420
150% 
0.389
0.743


taurodeoxycholate
12261
−25% 
0.394
0.747


DSGEGDFXAEGGGVR (SEQ ID NO: 4)
31548
33%
0.398
0.75


oleoylcarnitine
35160
 8%
0.405
0.755


erythrulose
37427
10%
0.405
0.755


bradykinin, hydroxy-pro(3)
33962
117% 
0.406
0.755


lathosterol
33488
14%
0.434
0.775


2-hydroxyglutarate
37253
10%
0.454
0.793


N-methyl proline
37431
−8%
0.456
0.794


indoleacetate
27513
−16% 
0.472
0.805


alpha-tocopherol
1561
 5%
0.498
0.826


gamma-tocopherol
33420
−17% 
0.499
0.826


3-hydroxyoctanoate
22001
−5%
0.514
0.84


1-pentadecanoylglycerophosphocholine
37418
 5%
0.522
0.84


heme
32593
−5%
0.531
0.848


erythro-sphingosine-1-phosphate
34445
−6%
0.541
0.854


bradykinin
22154
194% 
0.541
0.854


campesterol
39511
11%
0.547
0.856


5alpha-pregnan-3beta,20alpha-diol disulfate
37198
−26% 
0.562
0.866


pipecolate
1444
−13% 
0.577
0.874


bilirubin (Z,Z)
27716
−10% 
0.585
0.874


quinate
18335
−22% 
0.596
0.882


2-hydroxyisobutyrate
22030
 8%
0.602
0.885


4-hydroxyhippurate
35527
15%
0.604
0.886


thymol sulfate
36095
−17% 
0.611
0.89


glucuronate
15443
 8%
0.642
0.917


laurylcarnitine
34534
−6%
0.646
0.917


gamma-glutamylleucine
18369
14%
0.646
0.917


1,5-anhydroglucitol (1,5-AG)
20675
 6%
0.661
0.93


N-acetylornithine
15630
−11% 
0.663
0.93


p-cresol sulfate
36103
−26% 
0.671
0.933


glycine
32338
 6%
0.68
0.935


pyridoxate
31555
−62% 
0.684
0.938


acetoacetate
33963
 9%
0.693
0.94


cholate
22842
14%
0.708
0.946


methyl palmitate (15 or 2)
38768
 5%
0.711
0.948


gamma-glutamylmethionine
37539
 5%
0.741
0.959


cyclo(leu-pro)
37104
 8%
0.741
0.959


3-(4-hydroxyphenyl)-1-(2,4,6-trihydroxyphenyl)-
38153
456% 
0.756
0.959


1-propanone


decanoylcarnitine
33941
−8%
0.761
0.959


deoxycholate
1114
 8%
0.821
0.986


1,6-anhydroglucose
21049
 9%
0.834
0.996


alpha-ketoglutarate
33453
 6%
0.841
0.998


octanoylcarnitine
33936
−9%
0.855
1


methylglutaroylcarnitine
37060
−6%
0.862
1


phenol sulfate
32553
 9%
0.889
1


gamma-glutamylisoleucine
34456
19%
0.9
1


tartarate
15336
25%
0.909
1


laurate (12:0)
1645
−8%
0.949
1


glycodeoxycholate
18477
−22% 
0.965
1


oxalate (ethanedioate)
20694
−6%
0.969
1


glycocholate
18476
−30% 
0.974
1









In further statistical analysis, Random Forest analysis was used to classify samples into ALS or symptom mimic Disease groups. The Random Forest results show that the samples were classified with 63% prediction accuracy. The confusion matrix presented in Table 6 shows the number of samples predicted for each classification and the actual in each group (ALS or symptom mimic Diseases). The “Out-of-Bag” (OOB) Error rate gives an estimate of how accurately new observations can be predicted using the Random Forest model (e.g., whether a sample is from an ALS patient or a symptom mimic disease patient). The OOB error was approximately 37%, and the model estimated that, when used on a new set of subjects, the identity of symptom mimic disease subjects could be predicted correctly 66% of the time and ALS subjects could be predicted 62% of the time as presented in Table 7.









TABLE 6







Results of Random Forest, Plasma:


ALS vs. symptom mimic Diseases












Predicted Group












ALS
Mimic
class. error

















Actual
ALS
107
65
0.377907



Group
Mimic
25
48
0.342466

















TABLE 7







Results of metabolomic predictions, Plasma:


ALS vs. symptom mimic Diseases









Random Forest Analysis














Overall
63%



Symptom mimic diseases
66%



ALS
62%










Based on the OOB Error rate of 37%, the Random Forest model that was created predicted whether a sample was from an individual with ALS with about 63% accuracy by measuring the levels of the biomarkers in samples from the subject. Examplary biomarkers for distinguishing the groups are phosphate, cortisone, 3-mthylxanthine, delta-tocopherol, creatine, 5,6-dihydrouracil, theobromine, iminodiacetate (IDA), palmitoyl-sphingomyelin, 10-undecenoate (11:1n1), octadecanedioate,7-methylxanthine, 3-dehydrocarnitine, urate, 1-2-propanediol, 4-vinylphenol sulfate, serine, cysteine, proline, hexadecanedioate, 2-hydroxybutyrate, alpha-ketobutyrate, 1-methylurate, pyroglutamine, dodecanedioate, cholesterol, paraxanthine, pro-hydroxy-pro, creatinine, 1-stearoylglycerophosphoinositol. These biomarkers were ranked based on their importance for the predictions and are shown in Table 9 below and in the Importance Plot in FIG. 3.


The Random Forest results demonstrated that by using the biomarkers, ALS subjects were distinguished from symptom mimic disease subjects with 62% sensitivity, 66% specificity, 81% PPV, 42% NPV. The results are summarized in Table 8. The area under the curve (AUC) for the receiver operating characteristic (ROC) curve was 0.68 and is graphically illustrated in FIG. 4.









TABLE 8







Random Forest diagnostic parameters for


ALS vs. symptom mimic Diseases (plasma)













Sensitivity
Specificity
PPV
NPV
AUC
















ALS vs. Symp.
62%
66%
81%
42%
0.68


Mimic Diseases









Wilcoxon analysis was used as another statistical method to identify biomarkers that distinguish ALS subjects from symptom mimic disease subjects. Biomarkers with a false discovery rate (FDR) of less than 0.15 by Wilcoxon were identified and are shown in Table 9 below. Also in Table 9 are 30 exemplary biomarkers for distinguishing ALS subjects from symptom mimic disease subjects by Random Forest. Table 9 includes, for each biomarker, the direction of change in ALS patients relative to symptom mimic disease patients and the test used to identify the biomarker. The heading “Comp ID” refers to the identifier for that biomarker in the internal chemical library database.









TABLE 9







Biomarkers that distinguish ALS subjects


from symptom mimic disease subjects.











Direction




Biochemical
of change


Name
in ALS
Test
CompID













iminodiacetate (IDA)
Higher
RF, Wilcoxon
35837


10-undecenoate (11:1n1)
Higher
RF, Wilcoxon
32497


3-dehydrocarnitine
Lower
RF, Wilcoxon
32654


4-vinylphenol sulfate
Lower
RF, Wilcoxon
36098


phosphate
Higher
RF, Wilcoxon
11438


cortisone
Higher
RF, Wilcoxon
1769


creatine
Higher
RF, Wilcoxon
27718


theobromine
Lower
RF, Wilcoxon
18392


palmitoyl sphingomyelin
Higher
RF, Wilcoxon
37506


serine
Higher
RF, Wilcoxon
32315


hexadecanedioate (C16)
Higher
RF, Wilcoxon
35678


2-hydroxybutyrate (AHB)
Higher
RF, Wilcoxon
21044


pyroglutamine
Lower
RF, Wilcoxon
32672


3-methylxanthine
Lower
RF
32445


delta-tocopherol
Lower
RF
33418


5,6-dihydrouracil
Higher
RF
1559


octadecanedioate (C18)
Higher
RF
36754


7-methylxanthine
Lower
RF
34390


urate
Lower
RF
1604


1,2-propanediol
Higher
RF
38002


cysteine
Lower
RF
16071


proline
Lower
RF
12650


alpha-ketobutyrate
Higher
RF
4968


1-methylurate
Lower
RF
34395


dodecanedioate (C12)
Higher
RF
32388


cholesterol
Higher
RF
63


paraxanthine
Lower
RF
18254


prolylhydroxyproline
Higher
RF
35127


creatinine
Lower
RF
513


1-stearoyl-GPI (18:0)
Higher
RF
19324


arachidonate (20:4n6)
Higher
Wilcoxon
1110


glutamine
Lower
Wilcoxon
53









In further statistical analysis, a LASSO prediction model was used to classify samples into ALS or symptom mimic Disease groups. The LASSO prediction model predicted whether a sample was from an individual with ALS with a sensitivity of 65% and a specificity of 81% from analysis of the levels of the biomarkers in samples from the subject. The results are summarized in Table 10. The area under the curve (AUC) for the receiver operating characteristic (ROC) curve was 0.76 and is graphically illustrated in FIG. 5.









TABLE 10







LASSO diagnostic parameters for ALS


vs. symptom mimic Diseases (plasma)











AUC
Sensitivity
Specificity
















ALS vs.
0.76
0.65
0.81



Symptom mimic



Diseases










Example 3
Predictive Performance of a Panel of Biomarkers to Distinguish ALS Subjects from Symptom Mimic Disease Subjects

In one example, the biomarkers identified in Table 9 that distinguish ALS patients from symptom mimic disease subjects were statistically analyzed using a LASSO prediction model to estimate their predictive performance to classify a subject as having ALS or having a disease with symptoms that mimic ALS. The resulting model estimated that, if used on a new set of subjects, the biomarkers identified in Table 9 could predict the identity of ALS subjects with a specificity of 90% and a sensitivity of 58%. The area under the curve (AUC) for the receiver operating characteristic (ROC) curve was 0.81 and is graphically illustrated in FIG. 6.


To further demonstrate the effectiveness of the biomarkers in Table 9 to distinguish ALS patients from symptom mimic disease subjects, we constructed a scenario using the null hypothesis (i.e., random permutations). We compared the predictive performance of the random permutations to that using the biomarkers in Table 9. A random permutation was performed 1000 times to construct the null hypothesis. For each random permutation, the top 32 metabolites that distinguished ALS from symptom mimic disease subjects were selected, and the LOO permuted AUC was computed. Less than 0.1% of the 1000 AUCs from the null hypothesis were as good as the AUC of 0.81 obtained using the 32 biomarkers identified in Table 9 for separating ALS from symptom mimic disease subjects.


Example 4
ALS Biomarkers that Distinguish ALS from Non-ALS Motor Neuron Disease (Non-ALS MND) in Plasma

In another example, biomarkers were discovered by (1) analyzing plasma samples from different groups of human subjects to determine the levels of metabolites in the samples and then (2) statistically analyzing the results to determine those metabolites that are differentially present in the two groups.


Metabolomic analysis was carried out on blood plasma samples to identify biomarkers that were useful to distinguish ALS patients from patients with non-ALS motor neuron disease (non-ALS MND) (i.e., patients diagnosed with either pure upper motor neuron disease (UMD) or pure lower motor neuron disease (LMD)). The plasma samples used for the analysis were from 172 patients with ALS and 28 patients with non-ALS MND. After the levels of metabolites were determined, the data were analyzed using univariate T-tests (i.e., Welch's T-test) as described in the General Methods section (Table 11).


Biomarkers

As listed below in Table 11, biomarkers were discovered that were differentially present between samples from ALS patients and non-ALS MND patients.


Table 11 includes, for each biomarker, an indication of the percentage difference in the ALS mean as compared to the non-ALS MND mean (positive values represent an increase in ALS, and negative values represent a decrease in ALS) and the p-value and the q-value, determined in the statistical analysis of the data concerning the biomarkers. CompID refers to the identifier for that biomarker in the internal chemical library database.









TABLE 11







ALS Biomarkers from plasma samples that distinguish ALS from non-ALS MND.









ALS/MND













%






Change


BIOCHEMICAL NAME
CompID
in ALS
p-value
q-value














tryptophan betaine
37097
−40%
0.035
0.6655


bilirubin (Z,Z)
27716
−80%
0.0018
0.2133


2-aminobutyrate
32348
−23%
0.0036
0.3161


3-carboxy-4-methyl-5-propyl-2-furanpropanoate
31787
−53%
0.0046
0.3498


(CMPF)


3-hydroxyisobutyrate
1549
−29%
0.0103
0.5144


cysteine
31453
−18%
0.0108
0.5144


bradykinin
22154
285%
0.0153
0.5172


isovalerylcarnitine
34407
−30%
0.0176
0.5627


cystine
31454
−16%
0.02
0.5627


methylglutaroylcarnitine
37060
 54%
0.0208
0.5627


alpha-hydroxyisovalerate
33937
−149% 
0.0209
0.5627


glutaroyl carnitine
35439
−18%
0.0305
0.6642


urate
1604
−11%
0.0346
0.6655


caproate (6:0)
32489
 11%
0.0416
0.6778


glutamine
53
 −7%
0.0462
0.6778


histidine
59
 −7%
0.0488
0.6778


3-(4-hydroxyphenyl)lactate
32197
−19%
0.0573
0.6778


pyroglutamine
32672
−16%
0.076
0.7231


asparagine
34283
−14%
0.0892
0.7442


acetoacetate
33963
−16%
0.1125
0.7794


2-palmitoylglycerophosphocholine
35253
 16%
0.166
0.7815


gamma-glutamylphenylalanine
33422
 −7%
0.1789
0.7815


glycerate
1572
 16%
0.1837
0.7815


5alpha-androstan-3alpha,17beta-diol disulfate
37184
 5%
0.1898
0.7919


arachidonate (20:4n6)
1110
 5%
0.2598
0.8782


gamma-glutamylvaline
32393
 30%
0.3009
0.8924


gamma-glutamylalanine
37063
 11%
0.3523
0.9008


gamma-glutamylisoleucine
34456
 22%
0.4139
0.9285


erythronate
33477
 6%
0.6173
0.9894


gamma-glutamyltyrosine
2734
 −3%
0.8022
1


gamma-glutamylleucine
18369
 4%
0.8336
1


C-glycosyltryptophan
32675
 1%
0.8932
1


13-HODE + 9-HODE
37752
 1%
0.9357
1


gamma-glutamylmethionine
37539
 2%
0.9877
1


glutamate
32322
 18%
0.4223
0.9285


pipecolate
1444
−97%
0.1122
0.7794


N-methyl proline
37431
−65%
0.5104
0.9431


chiro-inositol
37112
−64%
0.1455
0.7794


xylitol
4966
−54%
0.9431
1


2-hydroxyisobutyrate
22030
−51%
0.2421
0.8489


mannitol
15335
−37%
0.224
0.8213


5alpha-androstan-3beta,17beta-diol disulfate
37190
−35%
0.0478
0.6778


octanoylcarnitine
33936
−35%
0.2178
0.8118


7-alpha-hydroxy-3-oxo-4-cholestenoate (7-
36776
−29%
0.2136
0.8118


Hoca)


decanoylcarnitine
33941
−29%
0.2672
0.8782


stearidonate (18:4n3)
33969
−27%
0.0626
0.6778


2-hydroxybutyrate (AHB)
21044
−27%
0.107
0.7794


4-androsten-3beta,17beta-diol disulfate 1
37202
−27%
0.1398
0.7794


saccharin
21151
−27%
0.7113
1


cholate
22842
−26%
0.0602
0.6778


campesterol
39511
−26%
0.0886
0.7442


alpha-ketobutyrate
4968
−26%
0.1511
0.7815


glycocholenate sulfate
32599
−25%
0.2038
0.8058


ribitol
15772
−25%
0.3369
0.9008


3-methylhistidine
15677
−25%
0.8734
1


gamma-CEHC
37462
−23%
0.1489
0.7815


androsterone sulfate
31591
−23%
0.3022
0.8924


tartarate
15336
−23%
0.8689
1


beta-hydroxyisovalerate
12129
−21%
0.0331
0.6655


docosadienoate (22:2n6)
32415
−21%
0.0799
0.7357


hydroxyisovaleroyl carnitine
35433
−21%
0.3077
0.8924


epiandrosterone sulfate
33973
−20%
0.1789
0.7815


pregnen-diol disulfate
32562
−20%
0.2397
0.8489


stachydrine
34384
−20%
0.3708
0.9008


N-acetylornithine
15630
−19%
0.2474
0.856


N-acetylserine
37076
−18%
0.1328
0.7794


4-vinylphenol sulfate
36098
−18%
0.1616
0.7815


4-androsten-3beta,17beta-diol disulfate 2
37203
−18%
0.379
0.9016


cis-4-decenoyl carnitine
38178
−18%
0.3979
0.9136


4-hydroxyphenylacetate
541
−18%
0.8583
1


1-stearoylglycerophosphoinositol
19324
−17%
0.1459
0.7794


2-methylbutyroylcarnitine
35431
−17%
0.3177
0.8972


indoleacetate
27513
−17%
0.4963
0.9431


5alpha-pregnan-3beta,20alpha-diol disulfate
37198
−16%
0.1152
0.7794


3-hydroxybutyrate (BHBA)
542
−16%
0.3122
0.8972


pregn steroid monosulfate
32619
−15%
0.1254
0.7794


10-undecenoate (11:1n1)
32497
−15%
0.1781
0.7815


threonine
1284
−15%
0.1931
0.793


palmitoleate (16:1n7)
33447
−15%
0.2915
0.8924


taurocholenate sulfate
32807
−15%
0.3703
0.9008


tiglyl carnitine
35428
−15%
0.3813
0.9016


2-oleoylglycerophosphoethanolamine
35687
−15%
0.3849
0.9016


andro steroid monosulfate 1
32827
−15%
0.574
0.9711


trans-4-hydroxyproline
32319
−14%
0.2498
0.8595


3-dehydrocarnitine
32654
−14%
0.3401
0.9008


myristoleate (14:1n5)
32418
−14%
0.4522
0.9369


methionine
1302
−13%
0.0229
0.5627


2-octenoyl carnitine
35440
−13%
0.07
0.6991


linolenate [alpha or gamma; (18:3n3 or 6)]
34035
−13%
0.1458
0.7794


cis-vaccenate (18:1n7)
33970
−13%
0.5413
0.9496


hexanoylcarnitine
32328
−13%
0.5853
0.9763


pregnenolone sulfate
38170
−13%
0.6586
1


3-methyl-2-oxovalerate
15676
−12%
0.0654
0.6784


4-methyl-2-oxopentanoate
22116
−12%
0.0852
0.7369


stearate (18:0)
1358
−12%
0.0972
0.7794


uridine
606
−12%
0.1688
0.7815


17-methylstearate
38296
−12%
0.2346
0.8454


docosahexaenoate (DHA; 22:6n3)
19323
−12%
0.2377
0.8489


10-heptadecenoate (17:1n7)
33971
−12%
0.3617
0.9008


dihomo-linoleate (20:2n6)
17805
−12%
0.3749
0.9016


isovalerate
34732
−12%
0.6937
1


5alpha-androstan-3beta,17alpha-diol disulfate
37187
−11%
0.0125
0.5144


palmitate (16:0)
1336
−11%
0.172
0.7815


linoleate (18:2n6)
1105
−11%
0.1973
0.7959


21-hydroxypregnenolone disulfate
37173
−11%
0.2711
0.8782


5-dodecenoate (12:1n7)
33968
−11%
0.5452
0.9496


glycoursodeoxycholate
39379
−11%
0.6343
0.9985


andro steroid monosulfate 2
32792
−11%
0.702
1


delta-tocopherol
33418
−11%
0.8354
1


creatinine
513
−10%
0.0634
0.6778


pentadecanoate (15:0)
1361
−10%
0.2026
0.8058


ornithine
35832
−10%
0.2252
0.8213


eicosenoate (20:1n9 or 11)
33587
−10%
0.3534
0.9008


phenyllactate (PLA)
22130
−10%
0.4063
0.9198


leucine
60
 −9%
0.06
0.6778


3-methyl-2-oxobutyrate
21047
 −9%
0.0785
0.7357


glycolate (hydroxyacetate)
15737
 −9%
0.1412
0.7794


isoleucine
1125
 −9%
0.1533
0.7815


acetylcarnitine
32198
 −9%
0.163
0.7815


N6-acetyllysine
36752
 −9%
0.2067
0.8118


beta-alanine
35838
 −9%
0.2656
0.8782


1-oleoylglycerophosphoethanolamine
35628
 −9%
0.5113
0.9431


valine
1649
 −8%
0.0551
0.6778


betaine
3141
 −8%
0.1215
0.7794


myo-inositol
19934
 −8%
0.321
0.8972


myristate (14:0)
1365
 −8%
0.3571
0.9008


dimethylglycine
5086
 −8%
0.414
0.9285


10-nonadecenoate (19:1n9)
33972
 −8%
0.4518
0.9369


1-methylurate
34395
 −8%
0.496
0.9431


ethanolamine
34285
 −8%
0.5046
0.9431


docosapentaenoate (n3 DPA; 22:5n3)
32504
 −8%
0.5811
0.9763


glycocholate
18476
 −8%
0.7538
1


serotonin (5HT)
2342
 −8%
0.7664
1


laurylcarnitine
34534
 −8%
0.8019
1


threonate
27738
 −7%
0.1752
0.7815


kynurenine
15140
 −7%
0.184
0.7815


nonadecanoate (19:0)
1356
 −7%
0.2948
0.8924


arabitol
15964
 −7%
0.4439
0.9369


eicosapentaenoate (EPA; 20:5n3)
18467
 −7%
0.6103
0.9888


adrenate (22:4n6)
32980
 −7%
0.8405
1


deoxycarnitine
36747
 −6%
0.1941
0.793


tyrosine
1299
 −6%
0.2684
0.8782


proline
1898
 −6%
0.2895
0.8924


1,5-anhydroglucitol (1,5-AG)
20675
 −6%
0.3012
0.8924


margarate (17:0)
1121
 −6%
0.4688
0.9431


1-arachidonoylglycerophosphoinositol
34214
 −6%
0.5345
0.9496


theophylline
18394
 −6%
0.8331
1


2-hydroxypalmitate
35675
 −5%
0.2553
0.8707


octadecanedioate
36754
 −5%
0.2797
0.8924


choline
15506
 −5%
0.3647
0.9008


3-hydroxydecanoate
22053
 −5%
0.422
0.9285


urea
1670
 −5%
0.4284
0.932


alanine
32339
 −5%
0.5072
0.9431


palmitoylcarnitine
22189
 −5%
0.5295
0.9496


biliverdin
2137
 −5%
0.9552
1


docosapentaenoate (n6 DPA; 22:5n6)
37478
 −5%
0.9993
1


phosphate
11438
 5%
0.0518
0.6778


succinylcarnitine
37058
 5%
0.3002
0.8924


acetylphosphate
15488
 5%
0.3165
0.8972


1-linoleoylglycerophosphocholine
34419
 5%
0.4524
0.9369


1-stearoylglycerophosphoethanolamine
34416
 5%
0.5013
0.9431


glucose
20489
 5%
0.5453
0.9496


2-palmitoylglycerophosphoethanolamine
35688
 5%
0.6309
0.9985


bilirubin (E,E)
32586
 5%
0.8112
1


bilirubin (E,Z or Z,E)
34106
 5%
0.8463
1


cholesterol
63
 6%
0.1677
0.7815


palmitoyl sphingomyelin
37506
 6%
0.2832
0.8924


1-eicosatrienoylglycerophosphocholine
33821
 6%
0.4427
0.9369


sebacate (decanedioate)
32398
 6%
0.4639
0.9417


1-docosahexaenoylglycerophosphocholine
33822
 6%
0.4755
0.9431


3-hydroxy-2-ethylpropionate
32397
 6%
0.7022
1


oxalate (ethanedioate)
20694
 6%
0.8394
1


glycerol
15122
 8%
0.1418
0.7794


taurolithocholate 3-sulfate
38782
 8%
0.3532
0.9008


1-arachidonoylglycerophosphocholine
33228
 8%
0.3592
0.9008


arabinose
575
 8%
0.4178
0.9285


1,3-dihydroxyacetone
35981
 8%
0.5241
0.9496


pyruvate
599
 8%
0.5564
0.9552


heme
32593
 8%
0.7335
1


pyrophosphate (PPi)
2078
 8%
0.8887
1


glycerol 3-phosphate (G3P)
15365
 9%
0.0453
0.6778


phenylacetate
15958
 9%
0.2635
0.8782


1,3,7-trimethylurate
34404
 9%
0.3991
0.9136


1-eicosadienoylglycerophosphocholine
33871
 9%
0.4339
0.9369


hexadecanedioate
35678
 9%
0.6301
0.9985


1,6-anhydroglucose
21049
 9%
0.8726
1


glucuronate
15443
 9%
0.9613
1


1-palmitoleoylglycerophosphocholine
33230
 10%
0.1167
0.7794


hippurate
15753
 10%
0.3077
0.8924


glycine
32338
 10%
0.3321
0.9008


succinate
1437
 11%
0.3408
0.9008


pro-hydroxy-pro
35127
 11%
0.5126
0.9431


caffeine
569
 11%
0.6036
0.9882


threitol
35854
 11%
0.6084
0.9888


theobromine
18392
 11%
0.7367
1


1-palmitoylglycerophosphocholine
33955
 12%
0.0231
0.5627


pelargonate (9:0)
12035
 12%
0.1017
0.7794


heptanoate (7:0)
1644
 12%
0.1097
0.7794


butyrylcarnitine
32412
 12%
0.6137
0.9888


catechol sulfate
35320
 12%
0.8471
1


taurochenodeoxycholate
18494
 12%
0.9789
1


1-oleoylglycerophosphocholine
33960
 14%
0.1109
0.7794


stearoyl sphingomyelin
19503
 14%
0.1272
0.7794


sarcosine (N-Methylglycine)
1516
 14%
0.2141
0.8118


fructose
31266
 14%
0.2199
0.8118


deoxycholate
1114
 14%
0.2559
0.8707


7-methylxanthine
34390
 14%
0.4737
0.9431


homostachydrine
33009
 14%
0.6468
1


1-docosapentaenoylglycerophosphocholine
37231
 15%
0.1518
0.7815


adenoslne 5′-monophosphate (AMP)
32342
 15%
0.2818
0.8924


glycolithocholate sulfate
32620
 15%
0.316
0.8972


pantothenate
1508
 15%
0.452
0.9369


1-pentadecanoylglycerophosphocholine
37418
 16%
0.0545
0.6778


2-oleoylglycerophosphocholine
35254
 16%
0.1829
0.7815


1-methylxanthine
34389
 16%
0.3795
0.9016


3-phenylpropionate (hydrocinnamate)
15749
 16%
0.4432
0.9369


lathosterol
33488
 16%
0.5457
0.9496


thymol sulfate
36095
 16%
0.5544
0.9552


3-methylxanthine
32445
 16%
0.6354
0.9985


creatine
27718
 19%
0.0657
0.6784


phenylacetylglutamine
35126
 19%
0.161
0.7815


quinate
18335
 19%
0.9575
1


1-stearoylglycerophosphocholine
33961
 20%
0.055
0.6778


tetradecanedioate
35669
 20%
0.1632
0.7815


3-indoxyl sulfate
27672
 22%
0.105
0.7794


erythro-sphingosine-1-phosphate
34445
 22%
0.1848
0.7815


erythrulose
37427
 22%
0.5855
0.9763


3-methoxytyrosine
12017
 22%
0.6477
1


p-cresol sulfate
36103
 23%
0.2662
0.8782


4-hydroxyhippurate
35527
 23%
0.3586
0.9008


2-hydroxyglutarate
37253
 23%
0.4984
0.9431


12,13-hydroxyoctadec-9(Z)-enoate
38395
 25%
0.1953
0.793


caprate (10:0)
1642
 27%
0.1318
0.7794


pyridoxate
31555
 27%
0.4984
0.9431


2-stearoylglycerophosphocholine
35255
 28%
0.0611
0.6778


phenol sulfate
32553
 30%
0.2952
0.8924


taurodeoxycholate
12261
 30%
0.3202
0.8972


1-heptadecanoylglycerophosphocholine
33957
 32%
0.0251
0.5871


glycodeoxycholate
18477
 45%
0.1395
0.7794


N-(2-furoyl)glycine
31536
 52%
0.3385
0.9008


caprylate (8:0)
32492
 54%
0.0746
0.7215


bradykinin, des-arg(9)
34420
 56%
0.3675
0.9008


erythritol
20699
 56%
0.8436
1


taurocholate
18497
 59%
0.4999
0.9431


xylonate
35638
 75%
0.2343
0.8454


cotinine
553
 79%
0.4252
0.9314


1,2-propanediol
38002
 82%
0.5069
0.9431


xylose
15835
 96%
0.0493
0.6778


bradykinin, hydroxy-pro(3)
33962
 96%
0.2415
0.8489


DSGEGDFXAEGGGVR (SEQ ID NO: 4)
31548
−342% 
0.6378
0.9985


ADSGEGDFXAEGGGVR (SEQ ID NO: 5)
33084
−204% 
0.7426
1


2-hydroxyhippurate (salicylurate)
18281
170%
0.8503
1


HWESASXX (SEQ ID NO: 1)
32836
257%
<0.001
0.0278


[H]HWESASLLR[OH] (SEQ ID NO: 6)
33964
809%
<0.001
0.0127


XHWESASXXR (SEQ ID NO: 7)
31538
178%
0.0599
0.6778


4-ethylphenylsulfate
36099
186%
0.3713
0.9008


3-(4-hydroxyphenyl)-1-(2,4,6-
38153
1567% 
0.2697
0.8782


trihydroxyphenyl)-1-propanone









In further statistical analysis, a Bayesian factor analysis approach was used to classify samples into ALS or non-ALS MND groups. The area under the curve (AUC) for the receiver operating characteristic (ROC) curve was 0.79 and is graphically illustrated in FIG. 7. Exemplary biomarkers for distinguishing the groups are listed in Table 12.









TABLE 12







Biomarkers that distinguish ALS from non-ALS MND










Biochemical Name
CompID














2-palmitoylglycerophosphocholine
35253



13-HODE + 9-HODE
37752



2-aminobutyrate
32348



3-(4-hydroxyphenyl)lactate
32197



3-carboxy-4-methyl-5-propyl-2-furanpropanoate
31787



(CMPF)



3-hydroxyisobutyrate
1549



5alpha-androstan-3alpha,17beta-diol disulfate
37184



acetoacetate
33963



alpha-hydroxyisovalerate
33937



arachidonate (20:4n6)
1110



asparagine
34283



bilirubin (Z,Z)
27716



bradykinin
22154



C-glycosyltryptophan
32675



caproate (6:0)
32489



cysteine
31453



cystine
31454



erythronate
33477



gamma-glutamylalanine
37063



gamma-glutamylisoleucine
34456



gamma-glutamylleucine
18369



gamma-glutamylmethionine
37539



gamma-glutamylphenylalanine
33422



gamma-glutamyltyrosine
2734



gamma-glutamylvaline
32393



glutamate
32322



glutamine
53



glutaroyl carnitine
35439



glycerate
1572



histidine
59



HWESASXX (SEQ ID NO: 1)
32836



isovalerylcarnitine
34407



methylglutaroylcarnitine
37060



pyroglutamine
32672



tryptophan betaine
37097



urate
1604



[H]HWESASLLR[OH] (SEQ ID NO: 6)
33964










Example 5
Biomarkers that Distinguish MND from Symptom Mimic Disease Subjects

Metabolomic analysis was carried out on blood plasma samples to identify biomarkers that were useful to distinguish MND patients from patients with symptom mimic diseases that are not MND, that is, neurodegenerative diseases that cause symptoms that appear clinically similar to MND (e.g., multi-focal motor neuropathy, spinal muscular atrophy, Kennedy's disease, multiple sclerosis). The plasma samples used for the analysis were from 200 MND subjects, and 73 symptom mimic disease patients. After the levels of metabolites were determined, the data were analyzed using Wilcoxon test to identify biomarkers that differed between the MND patients and the symptom mimic disease patients. The biomarkers with a FDR of less than 0.15 are listed in Table 13.









TABLE 13







Biomarkers that distinguish MND from non-MND symptom mimic


disease subjects












Direction of




Biochemical Name
change in MND
CompID















10-undecenoate (11:1n1)
Higher
32497



2-hydroxybutyrate (AHB)
Higher
21044



3-dehydrocarnitine
Lower
32654



3-methylxanthine
Lower
32445



cortisone
Higher
1769



creatine
Higher
27718



hexadecanedioate
Higher
35678



iminodiacetate (IDA)
Higher
35837



octadecanedioate
Higher
36754



palmitoyl sphingomyelin
Higher
37506



phosphate
Higher
11438



theobromine
Lower
18392










In further statistical analysis, a SVM prediction model was used to classify samples into MND or symptom mimic Disease groups. The SVM prediction model predicted whether a sample was from an individual with MND with a specificity of 90% and a sensitivity of 51% for analysis of the levels of the biomarkers in samples from the subject. Alternatively, if the model was adjusted to be 100% specific for predicting MND, then the sensitivity was 23%. The area under the curve (AUC) for the receiver operating characteristic (ROC) curve was 0.78 and is graphically illustrated in FIG. 8.


Example 6
Predictive Performance of a Panel of Biomarkers to Distinguish MND Subjects from Symptom Mimic Disease Subjects

In one example, the biomarkers identified in Table 13 to distinguish MND from symptom mimic disease subjects were statistically analyzed to estimate their predictive performance. A LASSO prediction model was used. The resulting model estimated that, if used on a new set of subjects, the biomarkers identified in Table 13 could predict the identity of MND subjects with a specificity of 88% and a sensitivity of 60%. The area under the curve (AUC) for the receiver operating characteristic (ROC) curve was 0.79 and is graphically illustrated in FIG. 9.


To further demonstrate the effectiveness of the biomarkers in Table 13 to distinguish MND from symptom mimic disease patients, we constructed a scenario using the null hypothesis (i.e., random permutations). We compared the predictive performance of the random permutations to that using the biomarkers in Table 13. A random permutation was performed 1000 times to construct the null hypothesis. For each permutation, the top 12 metabolites that distinguished MND from symptom mimic disease subjects were selected and the LOO permuted AUC was computed. Less than 0.1% of the 1000 AUCs from the null hypothesis were as good as the AUC of 0.79 obtained using the 12 biomarkers identified in Table 13 for separating MND from symptom mimic disease subjects.


Example 7
Plasma Biomarkers for Disease Progression

Currently, one measure of ALS disease severity is the ALS Functional Rating Scale (ALSFRS). This subjective rating scale is used clinically for monitoring the progression of disability in patients with ALS. The scale has been revised (ALSFRS-R) and correlates with patient quality of life. A high score indicates less severe disability and a lower score indicates more severe disability.


To identify biomarkers of disease progression, plasma samples collected from 172 ALS subjects with ALSFRS-R scores ranging from 8 (most severe) to 48 (least severe) were analyzed metabolomically. After the levels of metabolites were determined, biomarkers of disease progression were identified using correlation analysis. The correlation analysis was performed between ALSFRS-R score, which had values ranging from 8 to 48, and the log transformed value of the metabolite intensity. Since higher ALSFRS-R scores indicate less severe disease and lower ALSFRS-R scores indicate increased disease severity, a positive correlation indicates higher biomarker levels were associated with higher scores and less severe disease while a negative correlation indicates higher biomarker levels were associated with lower scores and more severe disease. That is, as disease severity increases (i.e., disease progresses), the levels of biomarkers that are positively correlated will decrease and the levels of biomarkers that are negatively correlated will increase. The biomarkers identified are listed in Table 14.


Table 14 includes, for each listed biomarker and non-biomarker compound, the correlation value, the p-value and the q-value determined in the statistical analysis of the data concerning the biomarkers. In the table, the column “CompID” refers to the identifier for that biomarker in the internal chemical library database.









TABLE 14







Biomarkers in plasma of ALS progression: correlation with ALSFRS-


R scores.













Correlation
Correlation
Correlation


Biochemical Name
CompID
Value
P-value
Q-value














tryptophan betaine
37097
0.251
0.001
0.0213


indolepropionate
32405
0.176
0.0224
0.0986


4-vinylphenol sulfate
36098
0.152
0.0497
0.1497


prolylhydroxyproline
35127
−0.382
<0.0001
0.0001


creatinine
513
0.355
<0.0001
0.0004


gamma-glutamylvaline
32393
−0.298
0.0001
0.0063


glutamate
32322
−0.281
0.0002
0.0105


catechol sulfate
35320
0.255
0.0008
0.0192


erythronate
33477
−0.255
0.0009
0.0192


deoxycarnitine
36747
0.242
0.0016
0.0289


theophylline
18394
0.241
0.0016
0.0289


cortisol
1712
−0.24
0.0017
0.0289


creatine
27718
−0.24
0.0018
0.0289


C-glycosyltryptophan
32675
−0.237
0.002
0.0292


hexanoylcarnitine (C6)
32328
−0.237
0.002
0.0292


gamma-glutamylisoleucine
34456
−0.231
0.0026
0.033


caffeine
569
0.231
0.0026
0.033


methyl palmitate (15 or 2)
38768
0.219
0.0043
0.0469


1-methylxanthine
34389
0.217
0.0047
0.0501


paraxanthine
18254
0.215
0.0052
0.0521


3-methoxytyrosine
12017
−0.21
0.0064
0.0557


mannose
584
−0.203
0.0084
0.0672


histidine
59
0.201
0.009
0.0699


2-palmitoyl-GPE (16:0)
35688
0.192
0.0126
0.0899


glycerol
15122
−0.189
0.014
0.0899


tiglyl carnitine (C5)
35428
0.189
0.0141
0.0899


1,6-anhydroglucose
21049
−0.187
0.0154
0.0932


2-octenoylcarnitine (C8)
35440
0.186
0.0158
0.0932


1,3,7-trimethylurate
34404
0.186
0.0159
0.0932


carnitine
15500
−0.184
0.0171
0.0939


alpha-hydroxyisovalerate
33937
0.184
0.0172
0.0939


4-ethylphenyl sulfate
36099
−0.184
0.0172
0.0939


phosphate
11438
−0.182
0.018
0.0939


ethanolamine
34285
−0.181
0.019
0.0963


quinate
18335
0.179
0.0202
0.0986


lactate
527
−0.178
0.021
0.0986


2-hydroxyisobutyrate
22030
−0.177
0.0217
0.0986


gamma-tocopherol
33420
0.176
0.0226
0.0986


(Hyp-3)-Bradykinin
33962
−0.176
0.0227
0.0986


gamma-glutamylmethionine
37539
−0.176
0.0228
0.0986


1,7-dimethylurate
34400
0.175
0.0233
0.0998


glutamine
53
0.174
0.0243
0.1025


phenyllactate (PLA)
22130
0.173
0.0246
0.1027


hippurate
15753
0.172
0.026
0.1051


gamma-glutamylleucine
18369
−0.171
0.0263
0.1051


theobromine
18392
0.171
0.0263
0.1051


levulinate (4-oxovalerate)
22177
0.171
0.0265
0.1051


1-palmitoyl-GPE (16:0)
35631
0.169
0.0289
0.1118


pyroglutamine
32672
0.168
0.0292
0.1118


2-ethylhexanoic acid
1554
0.166
0.0317
0.1198


sphingomyelin
19503
−0.165
0.0326
0.1218


1-linoleoyl-GPC (18:2)
34419
0.165
0.0329
0.1218


cinnamoylglycine
38637
0.164
0.0337
0.1234


glutaroylcarnitine (C5)
35439
0.163
0.0345
0.1248


N-(2-furoyl)glycine
31536
0.162
0.0358
0.1276


3-carboxy-4-methyl-5-propyl-2-
31787
0.161
0.0368
0.1276


furanpropanoate (CMPF)


pseudouridine
33442
−0.161
0.0368
0.1276


benzoate
15778
0.161
0.0368
0.1276


pantothenate (Vitamin B5)
1508
−0.161
0.0374
0.1281


epiandrosterone sulfate
33973
0.156
0.0432
0.141


1,2-propanediol
38002
−0.156
0.0432
0.141


2-hydroxybutyrate (AHB)
21044
−0.152
0.0486
0.1497


4-methyl-2-oxopentanoate
22116
0.152
0.0488
0.1497


1-stearoyl-GPE (18:0)
34416
0.152
0.0494
0.1497


glycerate
1572
−0.152
0.0496
0.1497


cortisone
1769
−0.151
0.0514
0.151


lathosterol
33488
−0.15
0.0523
0.1524


andro steroid monosulfate 2
32792
−0.15
0.0531
0.1535


10-undecenoate (11:1n1)
32497
0.148
0.0551
0.1565


propionylcarnitine (C3)
32452
−0.147
0.0576
0.1607


3-phenylpropionate (hydrocinnamate)
15749
0.146
0.0588
0.1616


3-(4-hydroxyphenyl)lactate (HPLA)
32197
0.146
0.0589
0.1616


1-stearoyl-GPI (18:0)
19324
−0.145
0.0606
0.165


1-linoleoyl-GPE (18:2)
32635
0.145
0.0616
0.166


1-eicosatrienoyl-GPC (20:3)
33821
0.141
0.0692
0.1775


alpha-tocopherol
1561
−0.14
0.0701
0.1775


beta-hydroxyisovalerate
12129
0.138
0.0747
0.1854


2-linoleoyl-GPE (18:2)
36593
0.138
0.0749
0.1854


1-arachidonoyl-GPE (20:4)
35186
0.137
0.0762
0.1862


sarcosine (N-Methylglycine)
1516
−0.137
0.0764
0.1862


hypoxanthine
3127
−0.135
0.0802
0.1899


thymol sulfate
36095
−0.135
0.0813
0.1911


2-arachidonoyl-GPE (20:4)
32815
0.133
0.0865
0.1999


1-methyladenosine
15650
−0.132
0.0886
0.2018


2-oleoyl-GPE (18:1)
35687
0.131
0.0895
0.2019


tyrosine
1299
−0.131
0.0916
0.2048


dihydroxyacetone
35981
−0.13
0.0921
0.2048


choline
15506
−0.13
0.0937
0.2071


oleoylcarnitine (C18)
35160
−0.129
0.0946
0.2071


1-eicosadienoyl-GPC (20:2)
33871
0.129
0.095
0.2071


cyclo(leu-pro)
37104
0.128
0.0972
0.2072


N2,N2-dimethylguanosine
35137
−0.128
0.0976
0.2072


butyrylcarnitine (C4)
32412
−0.127
0.101
0.2095


arginine
1638
0.127
0.1012
0.2095


stachydrine
34384
0.126
0.1025
0.2109


palmitoyl sphingomyelin
37506
−0.126
0.1051
0.2149


1-palmitoylplasmenylethanolamine
39270
0.122
0.1157
0.2298


hydroxyproline
32319
−0.122
0.1162
0.2298


ornithine
35832
−0.121
0.1174
0.2298


succinate
1437
−0.121
0.1194
0.2298


acetylphosphate
15488
−0.12
0.12
0.2298


gamma-glutamyltyrosine
2734
−0.12
0.1208
0.2298


fumarate
1643
−0.119
0.1251
0.2298


acetylcarnitine (C2)
32198
−0.119
0.126
0.2298


3-dehydrocarnitine
32654
0.118
0.1277
0.2298


oleate (18:1n9)
1359
−0.118
0.1277
0.2298


glucose
20489
−0.118
0.1279
0.2298


proline
1898
−0.118
0.128
0.2298


erythro-sphingosine-1-phosphate
34445
−0.117
0.1313
0.2324


phenylacetylglutamine
35126
−0.116
0.1347
0.2347


piperine
33935
0.116
0.1353
0.2347


campesterol
39511
0.114
0.1409
0.2391


asparagine
34283
0.114
0.1428
0.2411


1-myristoyl-GPC (14:0)
35626
0.111
0.1504
0.2497


chiro-inositol
37112
0.111
0.1518
0.2499


pentadecanoate (15:0)
1361
−0.11
0.1577
0.2571


taurocholate
18497
−0.109
0.1612
0.2615


1-docosapentaenoyl-GPC (22:5)
37231
0.107
0.1658
0.265


bilirubin (E,Z or Z,E)
34106
−0.106
0.1706
0.2687


docosapentaenoate (n6 DPA; 22:5n6)
37478
0.106
0.1715
0.2687


pyruvate
599
−0.106
0.1724
0.2687


pelargonate (9:0)
12035
0.105
0.1738
0.2687


aspartylphenylalanine
22175
0.105
0.1747
0.2687


bradykinin
22154
−0.104
0.1798
0.2731


1-stearoyl-GPC (18:0)
33961
0.104
0.18
0.2731


1,5-anhydroglucitol (1,5-AG)
20675
0.102
0.1871
0.28


1-arachidonoyl-GPC (20:4)
33228
0.102
0.1901
0.2819


3-hydroxyoctanoate
22001
0.101
0.1914
0.2826


acetoacetate
33963
0.099
0.2005
0.2934


1-docosahexaenoyl-GPC (22:6)
33822
0.098
0.2047
0.2981


N-acetylalanine
1585
0.097
0.2089
0.3002


3-methylxanthine
32445
0.097
0.2097
0.3002


caprylate (8:0)
32492
−0.096
0.2136
0.3013


bradykinin, des-arg(9)
34420
−0.096
0.2139
0.3013


3-indoxyl sulfate
27672
−0.096
0.2158
0.3023


arabitol
15964
0.095
0.2224
0.3058


pregnen-diol disulfate
32562
−0.094
0.2274
0.3068


heptanoate (7:0)
1644
0.093
0.2333
0.3109


biliverdin
2137
0.092
0.2356
0.3128


glucuronate
15443
−0.092
0.2368
0.3131


xylitol
4966
−0.09
0.2451
0.3218


1-methylurate
34395
0.088
0.255
0.3332


eicosenoate (20:1n9 or 1n11)
33587
−0.088
0.2562
0.3334


uridine
606
0.087
0.2647
0.3417


docosadienoate (22:2n6)
32415
−0.086
0.2682
0.3422


urate
1604
0.085
0.2719
0.3443


threonine
1284
0.085
0.2759
0.3467


taurocholenate sulfate
32807
−0.084
0.278
0.3478


2-linoleoyl-GPC (18:2)
35257
0.084
0.2789
0.3478


5alpha-pregnan-3alpha,20beta-diol
37201
0.084
0.2807
0.3478


disulfate 1


linoleate (18:2n6)
1105
−0.083
0.2858
0.3524


cystine
31454
0.082
0.2884
0.3543


cysteine
31453
0.082
0.2927
0.3583


1-oleoyl-GPE (18:1)
35628
0.081
0.2975
0.36


alpha-ketoglutarate
33453
−0.081
0.2976
0.36


taurolithocholate 3-sulfate
38782
−0.081
0.2992
0.36


7-methylxanthine
34390
0.08
0.3034
0.3632


androsterone sulfate
31591
0.079
0.308
0.366


phenylalanine
64
−0.078
0.3152
0.3725


alanine
32339
0.078
0.3174
0.3725


4-hydroxyhippurate
35527
0.077
0.3193
0.3725


5alpha-androstan-3beta,17beta-diol
37190
0.077
0.3208
0.3725


disulfate


isobutyrylcarnitine (C4)
33441
−0.077
0.3213
0.3725


gamma-glutamylglutamate
36738
0.077
0.3245
0.3732


isovalerate (C5)
34732
−0.076
0.3255
0.3732


linolenate (18:3n3 or 3n6)
34035
−0.076
0.3275
0.3732


dihomolinoleate (20:2n6)
17805
−0.074
0.339
0.381


12,13-hydroxyoctadec-9(Z)-enoate
38395
0.073
0.3443
0.3842


beta-hydroxypyruvate
15686
−0.073
0.3482
0.3844


caprate (10:0)
1642
−0.073
0.3496
0.3844


threonate
27738
−0.072
0.3514
0.3844


3-hydroxydecanoate
22053
0.072
0.3514
0.3844


dehydroisoandrosterone sulfate (DHEA-
32425
0.072
0.3562
0.385


S)


indolelactate
18349
0.071
0.3591
0.3864


3-hydroxybutyrate (BHBA)
542
−0.07
0.3646
0.3886


tetradecanedioate (C14)
35669
0.07
0.3651
0.3886


glycine
32338
0.07
0.3703
0.389


cholesterol
63
−0.068
0.3804
0.3961


gamma-glutamylalanine
37063
−0.068
0.3828
0.3961


3-(3-hydroxyphenyl)propionate
35635
0.067
0.3899
0.3967


valine
1649
−0.067
0.3899
0.3967


2-hydroxyglutarate
37253
0.065
0.4005
0.4026


2-aminobutyrate
32348
−0.065
0.4011
0.4026


arabinose
575
−0.065
0.4061
0.4064


fructose
31266
−0.064
0.4075
0.4065


vaccenate (18:1n7)
33970
−0.064
0.4101
0.408


adrenate (22:4n6)
32980
−0.063
0.4183
0.4124


threitol
35854
0.063
0.4198
0.4126


glycocholate
18476
−0.062
0.4218
0.4133


2-methylbutyroylcarnitine (C5)
35431
−0.062
0.4263
0.4139


glycoursodeoxycholate
39379
−0.061
0.4298
0.4139


13-HODE + 9-HODE
37752
−0.061
0.4331
0.4139


betaine
3141
−0.061
0.4342
0.4139


2-hydroxyhippurate (salicylurate)
18281
−0.061
0.4358
0.4139


pyrophosphate (PPi)
2078
−0.06
0.4371
0.4139


bilirubin
27716
0.06
0.4387
0.4139


phenylacetate
15958
−0.06
0.44
0.4139


docosahexaenoate (DHA; 22:6n3)
19323
0.06
0.442
0.4146


homostachydrine
33009
0.059
0.4453
0.4164


palmitoylcarnitine (C16)
22189
−0.059
0.4465
0.4164


pyridoxate
31555
−0.059
0.45
0.4173


glycochenodeoxycholate
32346
−0.057
0.467
0.4263


1-oleoyl-GPC (18:1)
33960
0.056
0.469
0.4265


10-nonadecenoate (19:1n9)
33972
−0.055
0.4787
0.4325


citrate
1564
−0.055
0.4812
0.4328


10-heptadecenoate (17:1n7)
33971
−0.054
0.4857
0.4345


nonadecanoate (19:0)
1356
0.054
0.4873
0.4348


pipecolate
1444
−0.053
0.4936
0.4376


N-acetylornithine
15630
0.053
0.4944
0.4376


sebacate (decanedioate)
32398
−0.053
0.4978
0.4394


palmitoleate (16:1n7)
33447
−0.052
0.4997
0.4399


5,6-dihydrouracil
1559
−0.052
0.5049
0.4433


4-hydroxyphenylpyruvate
1669
0.051
0.5119
0.4459


gamma-CEHC
37462
0.051
0.5139
0.4459


N-methyl proline
37431
0.051
0.5145
0.4459


2-hydroxyoctanoate
22036
0.051
0.5159
0.4459


3-methyl-2-oxobutyrate
21047
−0.05
0.5162
0.4459


AMP
32342
−0.05
0.5202
0.4459


beta-alanine
35838
0.05
0.5223
0.4459


iminodiacetate (IDA)
35837
−0.05
0.5228
0.4459


17-methylstearate
38296
0.049
0.5251
0.4467


glycolate (hydroxyacetate)
15737
−0.049
0.5269
0.4471


gamma-glutamylphenylalanine
33422
−0.049
0.529
0.4478


succinylcarnitine (C4)
37058
−0.048
0.5347
0.4503


delta-tocopherol
33418
0.048
0.5362
0.4504


urea
1670
0.047
0.5486
0.4559


isovalerylcarnitine (C5)
34407
−0.046
0.5577
0.4614


undecanoate (11:0)
12067
0.045
0.5593
0.4615


N6-acetyllysine
36752
0.045
0.561
0.4618


4-androsten-3beta,17beta-diol disulfate 1
37202
0.045
0.5661
0.4631


laurylcarnitine (C12)
34534
−0.045
0.5669
0.4631


methylphosphate
37070
−0.044
0.5726
0.4667


kynurenine
15140
0.041
0.6012
0.4799


myo-inositol
19934
0.041
0.6016
0.4799


stearidonate (18:4n3)
33969
−0.04
0.6053
0.4799


EDTA
32511
−0.04
0.6074
0.4799


4-hydroxyphenylacetate
541
−0.04
0.6075
0.4799


1-arachidonoyl-GPI (20:4)
34214
−0.04
0.608
0.4799


5-oxoproline
1494
0.04
0.6087
0.4799


erythritol
20699
0.04
0.6098
0.4799


malate
1303
−0.04
0.6104
0.4799


decanoylcarnitine (C10)
33941
0.04
0.6108
0.4799


3-(4-hydroxyphenyl)-1-(2,4,6-
38153
0.039
0.619
0.4844


trihydroxyphenyl)-1-propanone


1-pentadecanoylglycerophosphocholine
37418
0.039
0.6194
0.4844


lysine
1301
−0.038
0.6222
0.4854


3-methylglutaroylcarnitine (C6)
37060
0.038
0.6271
0.4878


p-cresol sulfate
36103
−0.038
0.6282
0.4878


cis-4-decenoyl carnitine
38178
−0.037
0.6312
0.4879


cholate
22842
−0.037
0.6384
0.4911


3-hydroxyisobutyrate
1549
−0.036
0.6441
0.492


glycodeoxycholate
18477
−0.035
0.65
0.4954


3-methylhistidine
15677
0.035
0.6529
0.4955


oxalate (ethanedioate)
20694
−0.035
0.6533
0.4955


7-HOCA
36776
−0.035
0.6547
0.4955


serine
32315
−0.034
0.6654
0.4965


glycerol 3-phosphate (G3P)
15365
0.033
0.669
0.4965


octanoylcarnitine (C8)
33936
−0.032
0.6775
0.4977


tartarate
15336
0.032
0.6786
0.4977


cinnamate
12115
0.032
0.6788
0.4977


allantoin
1107
−0.031
0.6898
0.5035


docosapentaenoate (DPA; 22:5n3)
32504
−0.03
0.6965
0.5073


laurate (12:0)
1645
0.03
0.6983
0.5075


myristoleate (14:1n5)
32418
−0.029
0.7048
0.51


dimethylglycine
5086
−0.029
0.7085
0.5104


2-stearoyl-GPC (18:0)
35255
0.029
0.7118
0.5117


xylonate
35638
0.028
0.7164
0.5128


5-dodecenoate (12:1n7)
33968
0.027
0.7315
0.5186


heme
32593
−0.027
0.7325
0.5186


dihomolinolenate (20:3n3 or 3n6)
35718
−0.026
0.7403
0.5225


21-hydroxypregnenolone disulfate
37173
−0.026
0.7411
0.5225


3-methyl-2-oxovalerate
15676
0.025
0.7448
0.5236


azelate (nonanedioate; C9)
18362
0.025
0.7459
0.5236


4-androsten-3beta,17beta-diol disulfate 2
37203
−0.025
0.749
0.5241


stearate (18:0)
1358
0.025
0.7498
0.5241


bilirubin (E,E)
32586
0.023
0.7693
0.532


2-hydroxypalmitate
35675
−0.023
0.77
0.532


octadecanedioate (C18)
36754
−0.023
0.7703
0.532


hydroxyisovaleroylcarnitine (C5)
35433
−0.023
0.7707
0.532


1-heptadecanoyl-GPC (17:0)
33957
0.022
0.7761
0.5338


5alpha-androstan-3alpha,17beta-diol
37184
−0.022
0.7811
0.5338


disulfate


methionine
1302
−0.021
0.7847
0.5338


glycocholenate sulfate
32599
0.021
0.7847
0.5338


isoleucine
1125
−0.021
0.7887
0.5338


sucralose
36649
−0.021
0.7902
0.5338


1-palmitoyl-GPC (16:0)
33955
0.021
0.7924
0.5338


indoleacetate
27513
−0.02
0.7929
0.5338


eicosapentaenoate (EPA; 20:5n3)
18467
−0.02
0.7956
0.5345


margarate (17:0)
1121
−0.02
0.8012
0.5372


taurodeoxycholate
12261
0.019
0.805
0.5378


alpha-ketobutyrate
4968
−0.019
0.8053
0.5378


urobilinogen
32426
−0.019
0.8082
0.5387


tryptophan
54
−0.018
0.8154
0.5406


pregnenolone sulfate
38170
−0.018
0.8208
0.5415


leucine
60
0.017
0.827
0.5445


saccharin
21151
0.016
0.8387
0.5478


beta-tocopherol
35702
0.016
0.8421
0.5479


caproate (6:0)
32489
0.015
0.8447
0.5485


pregn steroid monosulfate
32619
−0.015
0.8468
0.5488


arachidonate (20:4n6)
1110
−0.015
0.8509
0.5499


phenol sulfate
32553
0.015
0.8519
0.5499


5alpha-androstan-3beta,17alpha-diol
37187
0.013
0.8706
0.5555


disulfate


erythrulose
37427
−0.012
0.8761
0.5559


serotonin (5HT)
2342
0.012
0.8773
0.5559


dodecanedioate (C12)
32388
−0.012
0.8808
0.5559


5alpha-pregnan-3beta,20alpha-diol
37198
0.012
0.8829
0.5559


disulfate


hexadecanedioate (C16)
35678
0.011
0.8855
0.5559


deoxycholate
1114
−0.011
0.8893
0.5559


3-hydroxy-2-ethylpropionate
32397
−0.011
0.8898
0.5559


taurochenodeoxycholate
18494
0.01
0.8933
0.5566


citrulline
2132
−0.01
0.8944
0.5566


myristate (14:0)
1365
−0.01
0.8993
0.5576


cotinine
553
0.009
0.9084
0.5611


andro steroid monosulfate 1
32827
−0.008
0.9216
0.564


1-palmitoleoyl-GPC (16:1)
33230
−0.008
0.9216
0.564


N-acetylserine
37076
−0.007
0.9262
0.565


palmitate (16:0)
1336
−0.007
0.9309
0.5665


2-hydroxystearate
17945
0.006
0.9414
0.5691


ribitol
15772
−0.005
0.9467
0.5708


glycolithocholate sulfate
32620
−0.005
0.9514
0.5726


N-acetylglycine
27710
0.003
0.9721
0.5808


xylose
15835
−0.002
0.9834
0.5854


mannitol
15335
−0.001
0.9853
0.5855


2-oleoyl-GPC (18:1)
35254
−0.001
0.9935
0.5861


[H]HWESASLLR[OH] (SEQ ID NO: 6)
33964
−0.279
0.0003
0.0105


HWESASXX (SEQ ID NO: 1)
32836
−0.192
0.0125
0.0899


XHWESASXXR (SEQ ID NO: 7)
31538
−0.19
0.0138
0.0899


ADSGEGDFXAEGGGVR (SEQ ID
33084
−0.117
0.1313
0.2324


NO: 5)


DSGEGDFXAEGGGVR (SEQ ID NO: 4)
31548
−0.14
0.07
0.1775


2-palmitoyl-GPC (16:0)
35253
0
0.9978
0.5861









In another experiment, biomarkers of disease progression were discovered by (1) analyzing plasma samples collected from human subjects with ALS at various times after symptom onset to determine the levels of metabolites in the samples and then (2) statistically analyzing the results to determine those metabolites that were differentially present at the various time points. The plasma samples used for the analysis were collected from 40 ALS subjects at several times during the course of the disease. Samples were collected early (screening/month 0 and/or month 1), at 6 months after screening, and at 12 months after screening. After the levels of metabolites were determined, the data from the subjects was analyzed using T-tests.


Biomarkers were identified by comparing the levels of the compounds in the samples collected at month 1 to the levels of the compounds in the samples at month 12 using a T-test analysis. All patients with both the 1 month and 12 month time points were included in the analysis (37 patients total). The standard matched-pairs T-test was used to perform this analysis. As listed below in Table 15, biomarkers were discovered that were differentially present among samples from ALS subjects over the course of the disease that indicate the progression of the disease. The Table includes, for each listed biomarker and non-biomarker compound, an indication of the percentage difference in the mean level at 1 month after screening as compared to the mean level at 12 months after screening, where a positive percentage change indicates that there was an increase in the metabolite level as the disease progressed and a negative percentage change indicates that there was a decrease in the metabolite level as the disease progressed.


Table 15 includes, for each listed biomarker and non-biomarker compound, the p-value and the q-value determined in the statistical analysis of the data concerning the biomarkers. Throughout the tables, the column heading “LIB_ID” indicates the analytical platform used to measure the level of the compound. The number “61” indicates that the levels of those compounds were measured using LC-MS, and the number “50” indicates that the levels of those compounds were measured using GC-MS. “COMP_ID” refers to the identifier for that biomarker in the internal chemical library database.









TABLE 15







Biomarkers in plasma of ALS disease progression over time.

















% Change







with disease


Biochemical Name
CompID
LIB_ID
p-value
q-value
progression















3-indolepropionate
8300
61
0.0286
0.1619
−21%


gamma-glutamylphenylalanine
13214
61
<0.0001
0.0022
−22%


alpha-hydroxyisovalerate
20950
50
0.0438
0.1945
−13%


erythronic acid
17028
50
0.0026
0.0435
−15%


glutamyl-valine
11053
61
0.0028
0.0435
−17%


cysteine
16071
50
0.0136
0.0987
−14%


pro-leu
13018
61
0.022
0.1335
−20%


stearamide
19377
50
0.0477
0.1953
−15%


hippuric acid
16848
50
0.0517
0.1981
−12%


3-carboxy-4-Methyl-5-propyl-2
14837
61
0.0712
0.2397
43%


furanpropanoic acid


erythrose
18384
50
0.1652
0.3699
−13%


propionylcarnitine
9130
61
0.2034
0.4255
12%


3-indoxyl sulfate
5809
61
0.2685
0.5024
−11%


glutamate
12751
50
<0.0001
1.00E−04
−19%


proline
12650
50
4.00E−04
0.0172
−14%


erythrose
18384
50
0.2914
0.5218
−21%


L-asparagine
16665
50
0.3352
0.5511
8%


pseudouridine
16865
50
0.4516
0.61
4%


palmitoylsphingomylein
21011
50
0.5303
0.6438
−3%


erythrose
8677
61
0.6983
0.7038
3%


stearoylsphingomyelin
21012
50
0.7108
0.7076
−3%


trans-hydroxyproline
12673
50
0.7211
0.7104
1%


choline
5702
61
0.749
0.7182
7%


octanoic acid (caprylate (8:0))
12609
50
0.7649
0.7211
2%


acetyl phosphate
12604
50
0.7884
0.7229
−1%


2-amino butyrate
12645
50
0.7901
0.7229
−2%


p-cresol sulfate
6362
61
0.8124
0.7266
2%


isovaleryl-, valeryl- and/or 2-
9491

0.9112
0.7635
1%


methylbutytl-carnitine


pseudouridine
20194
61
0.9393
0.766
−1%


octamethyltetrasiloxane
12559
50
0.9725
0.7739
2%


HWESASXXR (SEQ ID NO: 8)
6144
61
0.4933
0.6359
9%


DSGEGDFLAEGGGVR (SEQ
6208
61
0.7149
0.7088
−15%


ID NO: 3)









Example 8
Biomarkers in CSF that Distinguish ALS from Healthy Subjects

In another example, biomarkers were discovered by (1) analyzing CSF samples from different groups of human subjects to determine the levels of metabolites in the samples and then (2) statistically analyzing the results to determine those metabolites that are differentially present in the two groups.


The CSF samples used for the analysis were from 99 ALS subjects (14 FALS subjects and 85 SALS subjects) and 36 control subjects not diagnosed with ALS. After the levels of metabolites were determined, the data was analyzed using univariate T-tests (i.e., Welch's T-test). As listed below in Table 16, biomarkers were discovered that were differentially present between samples from ALS subjects and Control subjects not diagnosed with ALS.


Table 16 includes, for each listed biomarker and non-biomarker compound, an indication of the percentage difference in the ALS mean as compared to the control mean (a “+” value indicating a higher mean in ALS samples as compared to the control samples and a “−” value indicating a lower mean in ALS samples as compared to the control samples), and the p-value and the q-value determined in the statistical analysis of the data concerning the biomarkers. CompID refers to the identifier for that biomarker in the internal chemical library database.









TABLE 16







ALS Biomarkers from CSF samples - T-test Analysis of ALS vs.


Healthy Controls












% Change in





Compound
ALS
p-value
q-value
CompID














acetylcarnitine
63%
4.55E−06
0.0003651
5697


isovaleryl-, valeryl- and/or 2-
43%
7.00E−05
0.0022041
9491


methylbutytl-carnitine


pro-leu
88%
0.0001245
0.0024996
13018


(s)-2-hydroxybutyric acid
29%
0.0012909
0.0103692
5711


glutamyl-valine
56%
0.0029525
0.0165572
11053


propionylcarnitine
47%
0.0326778
0.073937
9130


erythrose
14%
0.1452021
0.1704341
8677


alpha-4-dihydroxybenzenepropanoic
−21%
0.2464745
0.2230964
6415


acid


1-methylguanosine
11%
0.2470386
0.2230964
9458


choline
8%
0.3178316
0.2554132
5702


gamma-glutamylphenylalanine
5%
0.4944613
0.3225891
13214


L-alpha-glycerophosphorylcholine
11%
0.5534239
0.3374168
5563


4-Guanidinobutanoic acid
11%
0.6500879
0.3614211
7670


n-acetyl-L-aspartic acid
−9%
0.7090126
0.3734411
7359


4-methyl-2-oxopentanoate
−2%
0.8983403
0.4392748
5808









Example 9
Biomarkers in CSF for Differentiating ALS from Other Neurological Diseases

Metabolomic analysis was carried out on CSF samples to identify biomarkers that were useful to distinguish ALS patients from patients with symptom mimic diseases, that is, neurological diseases that cause symptoms that appear clinically similar to ALS (e.g., multi-focal motor neuropathy, spinal muscular atrophy, Kennedy's disease, multiple sclerosis). The CSF samples used for the analysis were from 63 ALS subjects, and 30 symptom mimic disease patients (subjects with diseases that cause symptoms that appear clinically similar to ALS). After the levels of metabolites were determined, the data were analyzed using T-tests to identify biomarkers that differed between the ALS patients and the symptom mimic disease patients. The biomarkers are listed in Table 17.


Table 17 includes, for each biomarker, an indication of the percentage difference in the ALS mean as compared to the symptom mimic disease mean (positive values represent an increase in ALS, and negative values represent a decrease in ALS), and the p-value and the q-value, determined in the statistical analysis of the data concerning the biomarkers. The heading Comp ID refers to the identifier for that biomarker in the internal chemical library database.









TABLE 17







ALS Biomarkers from CSF samples that distinguish ALS patients


from Symptom Mimic Disease patients.









ALS/



Symptom Mimic Disease













%





COMP
Change


BIOCHEMICAL NAME
ID
in ALS
p-value
q-value














5-oxoproline
1494
13%
0.0033
0.761


N-acetyl-beta-alanine
37432
−19%
0.0117
0.761


1,5-anhydroglucitol (1,5-AG)
20675
24%
0.0124
0.761


threitol
35854
−25%
0.0135
0.761


hypoxanthine
3127
−23%
0.0181
0.761


xylose
15835
−45%
0.0231
0.761


5-hydroxyindoleacetate
437
26%
0.0387
0.799


kynurenine
15140
24%
0.0394
0.799


homocarnosine
1633
−43%
0.0395
0.799


histidine
59
9%
0.0429
0.799


gluconate
587
15%
0.0469
0.799


cortisol
1712
15%
0.0479
0.799


lactate
527
6%
0.0516
0.799


beta-hydroxyisovalerate
12129
−15%
0.0875
0.9694


ornithine
35832
10%
0.0965
0.9694


N-formylmethionine
2829
−15%
0.0966
0.9694


citrate
1564
9%
0.1014
0.9694


cotinine
553
−28%
0.1195
0.9694


glycine
32338
15%
0.1222
0.9694


alpha-hydroxyisovalerate
33937
−19%
0.1377
0.9694


phenylalanine
64
7%
0.1381
0.9694


theophylline
18394
−22%
0.1391
0.9694


5-methylthioadenosine (MTA)
1419
−9%
0.1407
0.9694


theobromine
18392
−19%
0.1408
0.9694


3-hydroxybutyrate (BHBA)
542
14%
0.1459
0.9694


fucose
15821
21%
0.1496
0.9694


1,3-dihydroxyacetone
35981
−30%
0.1608
0.9694


choline
15506
7%
0.1629
0.9694


betaine
3141
6%
0.1646
0.9694


pro-hydroxy-pro
35127
40%
0.1715
0.9694


serine
32315
7%
0.1729
0.9694


7-alpha-hydroxy-3-oxo-4-
36776
6%
0.1804
0.9694


cholestenoate (7-Hoca)


fructose
577
−47%
0.1823
0.9694


dimethylarginine (SDMA +
36808
11%
0.1824
0.9694


ADMA)


alpha-tocopherol
1561
−12%
0.1833
0.9694


acetylcarnitine
32198
12%
0.1893
0.9694


alanine
32339
6%
0.1938
0.9694


3-hydroxy-2-ethylpropionate
32397
−12%
0.2095
0.9694


N-acetylhistidine
33946
−9%
0.2119
0.9694


3-methyl-2-oxovalerate
15676
−11%
0.2175
0.9694


4-acetamidobutanoate
1558
−6%
0.2175
0.9694


pseudouridine
33442
6%
0.221
0.9694


ribulose
35855
−16%
0.2276
0.9694


glutamine
53
5%
0.2278
0.9694


pantothenate
1508
12%
0.2284
0.9694


catechol sulfate
35320
−28%
0.2312
0.9694


deoxycarnitine
36747
14%
0.2336
0.9694


3-ureidopropionate
3155
36%
0.2339
0.9694


5,6-dihydrouracil
1559
10%
0.2537
0.9694


3-methyl-2-oxobutyrate
21047
−6%
0.2564
0.9694


4-methyl-2-oxopentanoate
22116
−9%
0.2686
0.9694


stachydrine
34384
58%
0.2737
0.9694


N-acetylmethionine
1589
−9%
0.275
0.9694


carnitine
15500
6%
0.2851
0.9694


isovalerate
34732
−8%
0.3179
0.9694


cortisone
1769
9%
0.3205
0.9694


tryptophan betaine
37097
−19%
0.33
0.9694


adenosine
555
−5%
0.3343
0.9694


caprylate (8:0)
32492
−5%
0.3347
0.9694


butyrylcarnitine
32412
6%
0.3426
0.9694


phenylacetylglutamine
35126
−18%
0.3443
0.9694


N-acetylglycine
27710
10%
0.3562
0.9694


3-methylhistidine
15677
−43%
0.367
0.9694


urate
1604
−9%
0.3889
0.9694


N4-acetylcytidine
35130
6%
0.3895
0.9694


isobutyrylcarnitine
33441
9%
0.4003
0.9694


alpha-ketobutyrate
4968
−12%
0.4097
0.9694


sorbitol
15053
−16%
0.4167
0.9694


5-methyluridine (ribothymidine)
35136
5%
0.418
0.9694


N-acetylaspartate (NAA)
22185
−8%
0.426
0.9694


gamma-glutamylisoleucine
34456
6%
0.4313
0.9694


hippurate
15753
−19%
0.4367
0.9694


erythritol
20699
−43%
0.4382
0.9694


3-indoxyl sulfate
27672
7%
0.4434
0.9694


gamma-glutamylvaline
32393
7%
0.4488
0.9694


succinate
1437
12%
0.4582
0.9694


3-carboxy-4-methyl-5-propyl-2-
31787
−59%
0.459
0.9694


furanpropanoate (CMPF)


malate
1303
−900%
0.468
0.9694


isovalerylcarnitine
34407
5%
0.4786
0.9694


gamma-glutamyltyrosine
2734
−11%
0.4788
0.9694


dipropylene glycol
40176
−11%
0.4807
0.9694


N-acetylvaline
1591
−6%
0.4826
0.9694


mannitol
15335
−22%
0.489
0.9694


2-aminobutyrate
32309
5%
0.4918
0.9694


galactitol (dulcitol)
1117
−12%
0.5136
0.9694


1-stearoylglycerophosphocholine
33961
−5%
0.5232
0.9694


N-acetyl-aspartyl-glutamate
35665
−6%
0.5417
0.9708


(NAAG)


arabonate
37516
6%
0.5436
0.9708


pyruvate
599
5%
0.5826
0.9797


gamma-glutamylphenylalanine
33422
5%
0.5836
0.9797


bilirubin (E,E)
32586
−41%
0.5845
0.9797


DSGEGDFXAEGGGVR
31548
−96%
0.5923
0.9797


(SEQ ID NO: 4)


arachidonate (20:4n6)
1110
−43%
0.5991
0.9847


phenol sulfate
32553
−23%
0.6035
0.9858


undecanoate (11:0)
12067
−6%
0.6104
0.991


homostachydrine
33009
24%
0.6146
0.9911


hydroxyisovaleroyl carnitine
35433
−6%
0.6262
0.9921


paraxanthine
18254
−6%
0.6509
1


1,2-propanediol
38002
−9%
0.6576
1


1,6-anhydroglucose
21049
−25%
0.7338
1


1-palmitoylglycerophosphocholine
33955
−10%
0.7495
1


4-androsten-3beta,17beta-diol
37202
−18%
0.7574
1


disulfate 1


lidocaine
35661
−426%
0.7581
1


pipecolate
1444
−6%
0.7647
1


hydroxycotinine
38661
9%
0.7706
1


dehydroisoandrosterone sulfate
32425
−100%
0.8459
1


(DHEA-S)


quinate
18335
−12%
0.8629
1


heme
32593
37%
0.8801
1


glycerate
1572
7%
0.9194
1


N-acetylornithine
15630
−16%
0.9195
1


proline
1898
−12%
0.9575
1









Example 10
ALS Biomarkers that Distinguish ALS from Non-ALS MND in CSF

In another example, biomarkers were discovered by (1) analyzing CSF samples from different groups of human subjects to determine the levels of metabolites in the samples and then (2) statistically analyzing the results to determine those metabolites that were differentially present in the two groups.


Metabolomic analysis was carried out on CSF samples to identify biomarkers that were useful to distinguish ALS patients from patients with non-ALS motor neuron disease (non-ALS MND) (i.e., patients diagnosed with either upper motor neuron disease (UMD) or lower motor neuron disease (LMD)). The CSF samples used for the analysis were from 63 patients with ALS and 13 patients with non-ALS MND. After the levels of metabolites were determined, the data were analyzed using univariate T-tests (i.e., Welch's T-test) as described in the General Methods section. As listed below in Table 18, biomarkers were discovered that were differentially present between samples from ALS patients and non-ALS MND patients.


Table 18 includes, for each biomarker, an indication of the percentage difference in the ALS mean as compared to the non-ALS MND mean (positive values represent an increase in ALS, and negative values represent a decrease in ALS) and the p-value determined in the statistical analysis of the data concerning the biomarkers. CompID refers to the identifier for that biomarker in the internal chemical library database.









TABLE 18







ALS Biomarkers from CSF samples that distinguish ALS from non-


ALS MND.









ALS/non-ALS



MND












%





Change


Biochemical Name
CompID
in ALS
p-value













tryptophan betaine
37097
−69%
0.1434


quinate
18335
154%
0.0234


2-methylbutyroylcarnitine
35431
15%
0.0496


tryptophan
54
13%
0.0513


mannose
584
12%
0.0861


caproate (6:0)
32489
−14%
0.1013


threonine
32292
15%
0.1025


caprylate (8:0)
32492
−15%
0.103


N1-methyladenosine
15650
12%
0.118


N-acetyl-aspartyl-glutamate (NAAG)
35665
−27%
0.1198


hippurate
15753
36%
0.1283


p-cresol sulfate
36103
38%
0.134


ribulose
35855
−23%
0.1363


histidine
59
12%
0.1366


phenylacetylglutamine
35126
16%
0.1439


cyclo(leu-pro)
37104
49%
0.148


malate
1303
−285%
0.1548


catechol sulfate
35320
30%
0.1628


threonate
27738
−19%
0.1634


butyrylcarnitine
32412
26%
0.1648


propionylcarnitine
32452
11%
0.1675


bilirubin (E,E)
32586
30%
0.1715


phenol sulfate
32553
50%
0.1751


2-methylcitrate
37494
−15%
0.1796


acetylcarnitine
32198
13%
0.1894


1,6-anhydroglucose
21049
−108%
0.1965


gamma-glutamylvaline
32393
−16%
0.2143


ribitol
15772
−11%
0.2262


N6-carbamoylthreonyladenosine
35157
−9%
0.2272


arabitol
15964
−12%
0.2309


ornithine
35832
6%
0.2376


creatine
27718
5%
0.2607


1-palmitoylglycerophosphocholine
33955
136%
0.2718


serine
32315
7%
0.2764


homostachydrine
33009
36%
0.2832


pro-hydroxy-pro
35127
32%
0.2843


erythronate
33477
−9%
0.2851


3-indoxyl sulfate
27672
21%
0.2915


1,2-propanediol
38002
−43%
0.3112


1,3-dihydroxyacetone
35981
−18%
0.3196


N-acetylvaline
1591
−10%
0.3202


7-alpha-hydroxy-3-oxo-4-
36776
15%
0.321


cholestenoate (7-Hoca)


galactitol (dulcitol)
1117
−18%
0.3347


heme
32593
128%
0.3423


1,5-anhydroglucitol (1,5-AG)
20675
−11%
0.3478


xanthine
3147
8%
0.348


N-acetylneuraminate
1592
−14%
0.3493


threitol
35854
−8%
0.3494


succinylcarnitine
37058
−10%
0.3606


erythritol
20699
−11%
0.3684


proline
1898
9%
0.3732


arabinose
575
−6%
0.3759


succinate
1437
8%
0.3768


5-methylthioadenosine (MTA)
1419
−5%
0.3841


4-androsten-3beta,17beta-diol
37202
16%
0.3908


disulfate 1


gamma-glutamylisoleucine
34456
−9%
0.3909


C-glycosyltryptophan
32675
−5%
0.4003


carnitine
15500
6%
0.4016


5-oxoproline
1494
5%
0.406


citrate
1564
−6%
0.4061


choline
15506
6%
0.4223


valine
1649
5%
0.43


heptanoate (7:0)
1644
−12%
0.434


methylglutaroylcarnitine
37060
11%
0.4359


N1-methylguanosine
31609
6%
0.4391


adenosine
555
30%
0.4404


paraxanthine
18254
44%
0.4463


hydroxycotinine
38661
14%
0.4516


alanine
32339
6%
0.4608


pyruvate
599
11%
0.4633


N-acetylglucosamine
15095
−15%
0.4682


dehydroisoandrosterone sulfate
32425
32%
0.4903


(DHEA-S)


kynurenine
15140
−9%
0.4918


xylonate
35638
−9%
0.5009


fructose
577
−19%
0.501


3-(4-hydroxyphenyl)lactate
32197
6%
0.5024


theophylline
18394
17%
0.5069


betaine
3141
5%
0.5204


glutaroyl carnitine
35439
7%
0.5219


ascorbate (Vitamin C)
1640
−6%
0.5391


homocarnosine
1633
15%
0.5548


gamma-glutamylleucine
18369
7%
0.556


caffeine
569
44%
0.5628


5,6-dihydrouracil
1559
−5%
0.5654


3-methyl-2-oxovalerate
15676
5%
0.581


dipropylene glycol
40176
−23%
0.5838


N-acetylhistidine
33946
−6%
0.59


arachidonate (20:4n6)
1110
13%
0.6132


3-methylhistidine
15677
−16%
0.6192


2-hydroxybutyrate (AHB)
21044
5%
0.6226


cortisol
1712
7%
0.6244


alpha-ketobutyrate
4968
−11%
0.6336


isobutyrylcarnitine
33441
5%
0.6393


cotinine
553
93%
0.6476


lidocaine
35661
165%
0.6572


deoxycarnitine
36747
6%
0.6734


sorbitol
15053
−12%
0.6757


3-hydroxybutyrate (BHBA)
542
10%
0.6869


N-acetylornithine
15630
−14%
0.6875


2-aminobutyrate
32309
5%
0.7011


DSGEGDFXAEGGGVR (SEQ ID
31548
61%
0.7201


NO: 4)


gluconate
587
8%
0.7291


3-carboxy-4-methyl-5-propyl-2-
31787
−5%
0.7314


furanpropanoate (CMPF)


N-acetylmethionine
1589
5%
0.7719


pipecolate
1444
−10%
0.778


mannitol
15335
−6%
0.8726


1-stearoylglycerophosphocholine
33961
45%
0.8728


isovalerate
34732
16%
0.904


5-hydroxyindoleacetate
437
6%
0.9349


glycine
32338
7%
0.9472


3-hydroxy-2-ethylpropionate
32397
6%
0.9495









Example 11
Biomarkers for Disease Progression, CSF

To identify biomarkers of disease progression, CSF samples collected from 63 ALS subjects with ALSFRS-R scores ranging from 20 (most severe) to 47 (least severe) were analyzed metabolomically. After the levels of metabolites were determined, biomarkers of disease progression were identified using correlation analysis. The correlation analysis was performed between ALSFRS-R score, which had values ranging from 20 to 47, and the log transformed value of the metabolite intensity. Since higher ALSFRS-R scores indicate less severe disease and lower ALSFRS-R scores indicate increased disease severity, a positive correlation indicates higher biomarker levels were associated with higher scores and less severe disease while a negative correlation indicates higher biomarker levels were associated with lower scores and more severe disease. That is, as disease severity increases (i.e., disease progresses), the levels of biomarkers that are positively correlated will decrease and the levels of biomarkers that are negatively correlated will increase.


As listed below in Table 19, biomarkers were identified that were differentially present among samples from ALS subjects over the course of the disease that indicate the progression of the disease. Table 19 includes, for each listed biomarker and non-biomarker compound, the correlation value, the p-value and the q-value determined in the statistical analysis of the data concerning the biomarkers. In Table 19, the column “CompID” refers to the identifier for that biomarker in the internal chemical library database.









TABLE 19







CSF Biomarkers of ALS Disease Progression.














Corelation
Correlation


Biochemical Name
CompID
Correlation
P-value
Q-value














beta-hydroxyisovalerate
12129
0.2786
0.0311
0.6463


methionine
32320
−0.4128
0.001
0.1302


pseudouridine
33442
−0.3783
0.0029
0.2271


fucose
15821
−0.3697
0.0036
0.2271


pyruvate
599
0.3374
0.0084
0.3288


succinate
1437
−0.3349
0.0089
0.3288


mannose
584
−0.3321
0.0095
0.3288


gamma-glutamylleucine
18369
0.2955
0.0219
0.5523


citrate
1564
−0.2592
0.0455
0.7356


threonate
27738
−0.2586
0.0461
0.7356


fructose
577
−0.2418
0.0627
0.7356


pro-hydroxy-pro
35127
−0.2403
0.0644
0.7356


sorbitol
15053
−0.2385
0.0664
0.7356


glutaroyl carnitine
35439
0.2337
0.0723
0.7356


tiglyl carnitine
35428
0.2269
0.0812
0.7356


glucose
20488
−0.2263
0.0821
0.7356


N-acetylglycine
27710
0.224
0.0853
0.7356


xylose
15835
−0.2188
0.093
0.7463


glycerol
15122
−0.2186
0.0934
0.7463


mannitol
15335
−0.2113
0.1051
0.7463


phenylalanine
64
−0.2107
0.1061
0.7463


serine
32315
0.2089
0.1091
0.7463


caffeine
569
0.2009
0.1238
0.7707


adenosine
555
−0.1973
0.1309
0.7949


theophylline
18394
0.1956
0.1343
0.7962


glycerate
1572
−0.1896
0.1469
0.8143


4-methyl-2-oxopentanoate
22116
0.1894
0.1472
0.8143


threitol
35854
−0.1801
0.1684
0.8414


4-androsten-3beta,17beta-diol disulfate 1
37202
0.1788
0.1718
0.8414


tyrosine
1299
−0.1783
0.1729
0.8414


N1-methylguanosine
31609
−0.1766
0.177
0.8414


1,5-anhydroglucitol (1,5-AG)
20675
−0.1734
0.1852
0.8542


isoleucine
1125
−0.1709
0.1917
0.868


3-(4-hydroxyphenyl)lactate
32197
−0.1619
0.2164
0.878


heme
32593
−0.1611
0.2188
0.878


valine
1649
−0.1594
0.2237
0.878


betaine
3141
−0.1589
0.2253
0.878


isovalerylcarnitine
34407
0.1553
0.2362
0.878


urate
1604
−0.1551
0.2368
0.878


cyclo(leu-pro)
37104
0.1526
0.2444
0.878


N-acetylornithine
15630
−0.1526
0.2445
0.878


2-methylcitrate
37494
−0.1519
0.2466
0.878


N4-acetylcytidine
35130
−0.1492
0.2552
0.878


phenol sulfate
32553
−0.1489
0.2561
0.878


glycerol 3-phosphate (G3P)
15365
0.1437
0.2732
0.878


creatine
27718
−0.1426
0.277
0.878


N-acetyl-aspartyl-glutamate (NAAG)
35665
−0.1415
0.2809
0.878


3-methylhistidine
15677
−0.1403
0.2851
0.878


succinylcarnitine
37058
−0.1373
0.2954
0.878


N1-methyladenosine
15650
0.1369
0.2969
0.878


5-oxoproline
1494
−0.1362
0.2994
0.878


acetylcarnitine
32198
−0.1355
0.3019
0.878


alanine
32339
−0.1346
0.3053
0.878


gluconate
587
−0.1338
0.3081
0.878


5,6-dihydrouracil
1559
−0.1333
0.31
0.878


dipropylene glycol
40176
−0.1319
0.3153
0.878


paraxanthine
18254
0.1313
0.3174
0.878


carnitine
15500
−0.128
0.3298
0.9023


1,2-propanediol
38002
−0.1269
0.3339
0.9038


stachydrine
34384
−0.1253
0.3402
0.9092


3-carboxy-4-methyl-5-propyl-2-
31787
0.1245
0.3432
0.9092


furanpropanoate (CMPF)


1,6-anhydroglucose
21049
−0.123
0.3491
0.9149


1-palmitoylglycerophosphocholine
33955
−0.1203
0.3598
0.9241


ascorbate (Vitamin C)
1640
0.118
0.3694
0.9241


3-hydroxybutyrate (BHBA)
542
−0.1178
0.37
0.9241


phenylacetylglutamine
35126
−0.1163
0.3761
0.9241


2-methylbutyroylcarnitine
35431
−0.1163
0.3764
0.9241


arachidonate (20:4n6)
1110
−0.1151
0.3814
0.9241


tryptophan betaine
37097
0.1103
0.4013
0.9356


2-aminobutyrate
32309
−0.1102
0.4021
0.9356


xanthine
3147
0.1089
0.4077
0.94


N-acetylserine
37076
0.1079
0.4119
0.9409


3-dehydrocarnitine
32654
−0.1055
0.4224
0.9562


3-methyl-2-oxovalerate
15676
−0.103
0.4335
0.9724


pipecolate
1444
−0.0983
0.4549
0.9832


cortisone
1769
−0.0981
0.4559
0.9832


cortisol
1712
−0.098
0.4563
0.9832


urea
1670
0.0968
0.4619
0.9832


ornithine
35832
0.0966
0.4628
0.9832


N-acetylthreonine
33939
0.0956
0.4676
0.9832


leucine
60
−0.0919
0.4851
0.9832


ribulose
35855
0.0909
0.4899
0.9832


cotinine
553
0.0907
0.4906
0.9832


hydroxycotinine
38661
0.0875
0.506
0.9832


N-acetylaspartate (NAA)
22185
0.0871
0.5083
0.9832


myo-inositol
19934
−0.087
0.5086
0.9832


N-acetylneuraminate
1592
0.0868
0.5095
0.9832


gamma-glutamyltyrosine
2734
−0.0852
0.5173
0.9832


glycine
32338
−0.0833
0.5267
0.9861


cytidine
514
0.0797
0.5451
0.9962


hydroxyisovaleroyl carnitine
35433
0.0791
0.5481
0.9962


scyllo-inositol
32379
0.0784
0.5513
0.9962


glutamine
53
−0.0784
0.5516
0.9962


malate
1303
0.0773
0.5571
0.9962


asparagine
34283
−0.0773
0.5572
0.9962


glycolate (hydroxyacetate)
15737
−0.0758
0.565
0.9962


butyrylcarnitine
32412
0.0756
0.5661
0.9962


dimethylarginine (SDMA + ADMA)
36808
−0.0749
0.5694
0.9962


caprylate (8:0)
32492
−0.0741
0.5736
0.9962


ribitol
15772
−0.0725
0.5818
0.9962


N-acetylvaline
1591
−0.0722
0.5838
0.9962


gamma-glutamylphenylalanine
33422
0.0714
0.5876
0.9962


lactate
527
0.0685
0.6032
0.9982


3-hydroxyisobutyrate
1549
0.0666
0.6131
0.9982


quinate
18335
0.0654
0.6196
0.9982


proline
1898
−0.0643
0.6254
0.9982


N-acetylglucosamine
15095
−0.0642
0.626
0.9982


1-stearoylglycerophosphocholine
33961
0.0635
0.63
0.9982


methylglutaroylcarnitine
37060
−0.0627
0.6341
0.9982


hippurate
15753
−0.0603
0.6474
0.9982


isobutyrylcarnitine
33441
0.0596
0.6512
0.9982


galactitol (dulcitol)
1117
0.0576
0.6619
0.9982


N-acetylmethionine
1589
0.0571
0.6645
0.9982


p-cresol sulfate
36103
0.0554
0.6742
0.9982


5-hydroxyindoleacetate
437
−0.0551
0.6757
0.9982


arginine
37016
0.0545
0.679
0.9982


7-alpha-hydroxy-3-oxo-4-cholestenoate
36776
−0.0513
0.6971
0.9982


(7-Hoca)


propionylcarnitine
32452
−0.0477
0.7171
0.9982


uridine
606
0.0472
0.7204
0.9982


5-methyluridine (ribothymidine)
35136
−0.0471
0.7208
0.9982


5-methylthioadenosine (MTA)
1419
−0.0467
0.7229
0.9982


caproate (6:0)
32489
−0.0467
0.7232
0.9982


choline
15506
0.0461
0.7262
0.9982


xylonate
35638
−0.0454
0.7305
0.9982


isovalerate
34732
−0.045
0.7327
0.9982


kynurenine
15140
−0.0441
0.7381
0.9982


alpha-tocopherol
1561
0.0429
0.7449
0.9982


creatinine
513
0.0412
0.7548
0.9982


gamma-glutamylisoleucine
34456
−0.0402
0.7603
0.9982


homocarnosine
1633
0.0391
0.7665
0.9982


histidine
59
0.0389
0.7681
0.9982


1,3-dihydroxyacetone
35981
0.0377
0.775
0.9982


theobromine
18392
0.0343
0.7945
0.9982


2-hydroxybutyrate (AHB)
21044
−0.0339
0.797
0.9982


DSGEGDFXAEGGGVR (SEQ ID NO: 4)
31548
0.0332
0.8012
0.9982


heptanoate (7:0)
1644
0.032
0.8081
0.9982


arabonate
37516
−0.0314
0.812
0.9982


inosine
1123
0.0309
0.8145
0.9982


3-ureidopropionate
3155
0.0309
0.8148
0.9982


N-formylmethionine
2829
−0.03
0.82
0.9982


3-methyl-2-oxobutyrate
21047
0.0294
0.8237
0.9982


hypoxanthine
3127
0.0291
0.8255
0.9982


N-acetylalanine
1585
0.0272
0.8366
0.9982


alpha-ketobutyrate
4968
0.0269
0.8386
0.9982


undecanoate (11:0)
12067
−0.0247
0.8516
0.9982


erythronate
33477
0.0239
0.8559
0.9982


lysine
35836
0.0222
0.8662
0.9982


alpha-hydroxyisovalerate
33937
0.0175
0.8943
0.9982


arabinose
575
−0.0161
0.9025
0.9982


N-acetylhistidine
33946
0.015
0.9092
0.9982


bilirubin (E,E)
32586
0.0132
0.9205
0.9982


tryptophan
54
−0.0128
0.9224
0.9982


deoxycarnitine
36747
0.012
0.9273
0.9982


dehydroisoandrosterone sulfate (DHEA-S)
32425
−0.0115
0.9307
0.9982


erythritol
20699
−0.0101
0.939
0.9982


C-glycosyltryptophan
32675
−0.0094
0.9433
0.9982


N6-carbamoylthreonyladenosine
35157
−0.0087
0.9475
0.9982


3-hydroxy-2-ethylpropionate
32397
−0.0068
0.9587
0.9982


pantothenate
1508
0.0048
0.9709
0.9982


4-acetamidobutanoate
1558
−0.0043
0.9738
0.9982


3-indoxyl sulfate
27672
−0.0042
0.9744
0.9982


catechol sulfate
35320
−0.0041
0.9753
0.9982


threonine
32292
−0.0037
0.9779
0.9982


homostachydrine
33009
0.0033
0.9801
0.9982


N-acetyl-beta-alanine
37432
−0.0018
0.9893
0.9982


gamma-glutamylvaline
32393
0.0016
0.9905
0.9982


arabitol
15964
−0.0005
0.9968
0.9982









Example 12
Identification of Drug Targets and Drug Screens Using Said Targets

To identify drug targets for ALS, plasma samples from 172 ALS subjects and 50 healthy control subjects not diagnosed with ALS were analyzed to determine the levels of metabolites in the samples, then the results were statistically analyzed using univariate T-tests (i.e., Welch's T-test) to determine those metabolites that were differentially present in the two groups, and then the metabolic pathways of the differentially present metabolites were analyzed in a biological context to identify associated metabolites, enzymes and/or proteins. The metabolites, enzymes and/or proteins associated with the differentially present metabolites represent drug targets for ALS. The levels of metabolites that are aberrant (higher or lower) in ALS subjects relative to healthy control subjects can be modulated to bring them into the normal range, which can be therapeutic. Such metabolites or enzymes involved in the associated metabolic pathways and proteins involved in their transport within and between cells can provide targets for therapeutic agents.


For example, plasma levels of tryptophan-betaine were found to be lower in ALS subjects, indicating that the circulating levels of this biomarker were lower in the ALS patients. Additonally, higher levels of tryptophan-betaine were correlated with higher ALSFRS-R scores (i.e. higher function in the patient), which indicated that as tryptophan-betaine levels decrease, ALS progresses (gets worse). Thus, modulation of tryptophan-betaine levels in plasma provides a target for a therapeutic agent (drug). For example, said agent may modulate tryptophan-betaine plasma levels by increasing the biosynthesis of tryptophan-betaine.


Without being bound by theory, it is believed that tryptophan-betaine may compete with carnitine or other quaternary amines for transporter uptake, indicating that such transporters (for example, OCTN2, a polyspecific quaternary amine transporter) are drug targets. An agent may modulate plasma tryptophan-betaine levels by affecting the uptake of the metabolite by such quaternary amine transporters.


It is desirable to identify metabolites, enzymes and/or proteins that modify the levels of tryptophan-betaine in isolated motor neurons. As tryptophan-betaine is a quaternary amine and binds to, for example, OCTN2, a quaternary amine transporter, any of the methods commonly used in the art may potentially be used to identify other quaternary amine transporters. Modification of these quaternary amine transporters, for example by genetic mutation, antibody binding, or other methods of modification commonly used in art may be used to regulate the amount of tryptophan-betaine uptake into the cell. Thus, these quaternary amine transporters represent drug targets of ALS.


The identification of biomarkers for ALS can be useful for screening therapeutic compounds. For example, tryptophan-betaine, indolepropionate or any biomarker(s) aberrant in ALS as identified in Tables 1, 5, 9, 11, 12, 16, 17, and 18 can be used in a variety of drug screening techniques.


One exemplary method of drug screening utilizes eukaryotic or prokaryotic host cells such as primary motor neurons. In this prophetic example, cells are plated in 96-well plates. Test wells are incubated in the presence of test compounds from the NIH Clinical Collection Library (available from BioFocus DPI) at a final concentration of 50 μM. Negative control wells receive no addition or are incubated with a vehicle compound (e.g., DMSO) at a concentration equivalent to that present in some of the test compound solutions. Positive control wells are incubated in the presence of tryptophan-betaine. After incubation for 24 hours, test compound solutions are removed and metabolites are extracted from cells, and tryptophan betaine levels are measured as described in the General Methods section. Agents that lower the level of tryptophan-betaine in the cell are considered therapeutic.


Additionally, plasma levels of the biomarker indolepropionate were found to be lower in ALS subjects. Indolepropionate serves as an agonist for sphingosine-1-phosphate (S1P) and peroxisome proliferator-activated receptors (PPAR). Modification of these receptors, for example by genetic mutation, antibody binding, or other methods of modification commonly used in art may be used simulate the effect of indolepropionate by activating downstream signaling pathways. Alternatively, a drug with the property to modulate indolepropionate levels by targeting the enzymes involved in the aberrant biosynthesis, catabolism or uptake of indolepropionate may lead to therapeutic effects.


Example 13
Method of Treating ALS

Studies were carried out to identify metabolites for treating ALS using plasma samples from 172 ALS subjects and 50 healthy control subjects not diagnosed with ALS. After the levels of metabolites were determined, the data were analyzed using univariate T-tests (i.e., Welch's T-test) as described in the General Methods section. Metabolites aberrant (higher or lower) in ALS relative to healthy control subjects can be modulated to bring them into the normal range, which can be a treatment for ALS.


For example, the levels of the antioxidants indolepropionate and homocarnosine were found to be reduced in the plasma of ALS patients relative to healthy control subjects. Oxidative damage is implicated as a factor in the pathology of ALS (e.g., SOD1 mutation in FALS, increased ROS in animal models of ALS, glutamate excitotoxicity and ROS production, etc.). The role of antioxidants is to combat cellular damage by inhibiting oxidation reactions, thus keeping oxidants at a manageable level. The levels of indolepropionate and homocarnosine, which are neuroprotective antioxidants, were reduced in the plasma and/or CSF of ALS patients relative to healthy control subjects. When antioxidants, such as indolepropionate and/or homocarnosine, are decreased, the potential for oxidative damage and cell death is increased. Thus, increasing the plasma levels of indolepropionate and homocarnosine by administering the metabolite(s) as a drug or pro-drug represents one possible method of treating ALS.


Additionally, the biomarker metabolite tryptophan betaine was found to be decreased in plasma of ALS subjects. Thus, administering tryptophan-betaine as a drug or pro-drug represents a possible method of treating ALS.


While the invention has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the invention.

Claims
  • 1-43. (canceled)
  • 44. A method of determining or aiding in determining whether a subject has amyotrophic lateral sclerosis (ALS), comprising: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the one or more biomarkers are selected from Tables 1, 5, 9, 11, 12, 16, 17, 18 and combinations thereof wherein the analysis method is mass spectrometry; andcomparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to determine whether the subject has amyotrophic lateral sclerosis.
  • 45. The method of claim 44, wherein the ALS-negative reference levels of the one or more biomarkers comprise levels of the one or more biomarkers in one or more samples from one or more subjects not having ALS and the ALS-positive reference levels of the one or more biomarkers comprise levels of the one or more biomarkers in one or more samples from one or more subjects who have been determined to have ALS.
  • 46. The method of claim 45, wherein differential levels of the one or more biomarkers between the sample and the ALS-negative reference levels are indicative of a determination of ALS in the subject.
  • 47. The method of claim 45, wherein differential levels of the one or more biomarkers between the sample and the ALS-positive reference levels are indicative of a determination of no ALS in the subject.
  • 48. The method of claim 45, wherein levels of the one or more biomarkers in the sample corresponding to the ALS-positive reference levels are indicative of a determination of ALS in the subject.
  • 49. The method of claim 45, wherein levels of the one or more biomarkers in the sample corresponding to the ALS-negative reference levels are indicative of a determination of no ALS in the subject.
  • 50. The method of claim 44, wherein the one or more biomarkers comprise tryptophan betaine.
  • 51. The method of claim 44, wherein the one or more biomarkers comprise indolepropionate.
  • 52. The method of claim 44, wherein the one or more biomarkers comprise indolepropionate and/or tryptophan-betaine.
  • 53. The method of claim 44, wherein the biological sample is cerebral spinal fluid and the one or more biomarkers are selected from Tables 16, 17, 18 and combinations thereof.
  • 54. The method of claim 44, wherein the biological sample is blood plasma and the one or more biomarkers are selected from Tables 1, 5, 9, 11, 12 and combinations thereof.
  • 55. The method of claim 44, wherein an ALS Probability Score is determined using the determined level(s) of the one or more biomarkers for amyotrophic lateral sclerosis in the sample and is used to determine whether the subject has amyotrophic lateral sclerosis.
  • 56. The method of claim 44, wherein the determined level(s) of the one or more biomarkers for amyotrophic lateral sclerosis are used in a mathematical model in order to determine whether the subject has amyotrophic lateral sclerosis.
  • 57. The method of claim 44, wherein determining whether a subject has ALS comprises distinguishing whether the subject has ALS or has a symptom mimic disease, and wherein the one or more biomarkers are selected from Tables 5, 9, 17 and combinations thereof.
  • 58. A method of identifying subjects for clinical trials and/or treatment based on a diagnosis of ALS in the subjects, comprising; analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers selected from Tables 1, 5, 9, 11, 12, 16, 17, 18 and combinations thereof;determining the level of the one or more biomarkers; andcomparing the level(s) of the one or more biomarkers in the sample to ALS-positive and/or ALS-negative reference levels of the one or more biomarkers in order to identify subjects for clinical trials and/or treatment based on assessment of ALS diagnosis.
  • 59. A method of monitoring progression/regression of amyotrophic lateral sclerosis (ALS) in a subject comprising: analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers for amyotrophic lateral sclerosis in the sample, wherein the first sample is obtained from the subject at a first time point and the one or more biomarkers are selected from Tables 14, 15, 19 and combinations thereof;analyzing a second biological sample from a subject to determine the level(s) of the one or more biomarkers, wherein the second sample is obtained from the subject at a second time point; andcomparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to monitor the progression/regression of ALS in the subject.
  • 60. The method of claim 59, wherein the method further comprises comparing the level(s) of one or more biomarkers in the first sample, the level(s) of one or more biomarkers in the second sample, and/or the results of the comparison of the level(s) of the one or more biomarkers in the first and second samples to ALS-positive reference levels, ALS-negative reference levels, ALS-progression-positive reference levels, and/or ALS-regression-positive reference levels of the one or more biomarkers.
  • 61. The method of claim 59, wherein an ALS Status Score is determined using the determined level(s) of the one or more biomarkers for amyotrophic lateral sclerosis in the first sample and the second sample and is used to monitor the progression/regression of ALS in the subject.
  • 62. The method of claim 59, wherein the determined level(s) of the one or more biomarkers for amyotrophic lateral sclerosis in the first sample and the second sample are used in a mathematical model in order to monitor the progression/regression of ALS in the subject.
  • 63. The method of claim 59, wherein said first biological sample is obtained from the subject prior to a therapeutic intervention and said second biological sample is obtained from said subject after therapeutic intervention.
  • 64. The method of claim 63, wherein the therapeutic intervention is the administration of a composition.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 61/548,318, filed Oct. 18, 2011, the entire contents of which are hereby incorporated herein by reference.

PCT Information
Filing Document Filing Date Country Kind 371c Date
PCT/US12/60492 10/17/2012 WO 00 4/15/2014
Provisional Applications (1)
Number Date Country
61548318 Oct 2011 US