CA3214708A1

CA3214708A1 - Apparatus and methodologies for detection, diagnosis, and prognosis of brain injury

Info

Publication number: CA3214708A1
Application number: CA3214708A
Authority: CA
Inventors: Chantel DEBERT; Gerlinde METZ; Tony MONTINA
Original assignee: UTI LP
Current assignee: UTI LP
Priority date: 2021-04-09
Filing date: 2022-04-07
Publication date: 2022-10-13
Also published as: WO2022213201A1

Abstract

Apparatus and methodologies are provided for determining whether an individual is likely to have an injury, and the likelihood of recovery following the injury, involving determining a threshold reference value for at least one target biomarker, or a combination/pattern of biomarkers, obtaining at least one biological sample from the individual and measuring the concentration of the at least one of the target biomarkers in the sample, and comparing the measured concentrations of the at least one target biomarkers to the threshold reference value to determine whether a change in concentration of the at least one biomarker, or the combination/pattern of biomarkers, has occurred, wherein the change is indicative of the injury, the type of injury, and/or the likelihood of recovery from the injury.

Description

APPARATUS AND METHODOLOGIES FOR DETECTION, DIAGNOSIS, AND
PROGNOSIS OF BRAIN INJURY
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of priority to U.S.
Provisional Patent Application No. 63/172,876 entitled "Apparatus and Methodologies for Diagnosing Brain Injury" and filed April 9th, 2021, which is specifically incorporated by reference herein for all that it discloses or teaches.
FIELD

[0002] Embodiments herein generally relate to apparatus and methodologies for detecting changes in at least one biomarker profile in a biological sample of an individual, the changes in biomarker profile being indicative of an injury to the individual's nervous system. More specifically, the present apparatus and methodologies relate to the detection of changes in at least one biomarker in bodily fluids, such as up- and/or down-regulation of the metabolome, to diagnose at least one nervous system injury.
BACKGROUND

[0003] Central and peripheral nervous system injury, ranging from traumatic brain injury, to stroke, to neurodegenerative disease, are major causes of lifelong neurological sequelae, with a lack of early, effective diagnostic and prognostic tools.
Identification of early biomarkers for detection and diagnosis of central and peripheral nervous system injury and monitoring subsequent recovery could enable more effective management and targeted therapies to improve and perhaps even restore function.

[0004] By way of example, it is estimated that 1.6 to 3.8 million traumatic brain injuries, such as sport-related concussions (SRC), occur annually in North America, such injuries occurring in both sports and recreational activities. SRC is currently diagnosed assessing the individual's clinical signs and symptoms ranging from headache and dizziness to memory impairment and loss of consciousness. There is no single "gold standard" assessment or diagnostic tool that can objectively determine whether an individual has suffered an SRC (particularly where no intercranial lesion occurs), nor how long it will take for the individual to recover. As a result, the SRC
diagnostic process is subject to interpretation and error, establishing an urgent need for improved apparatus and methodologies for diagnosing SRC.

[0005] By way of further example, Alzheimer's disease (AD) is a destructive neurodegenerative disease, which results in progressive memory loss, cognitive dysfunction, and other behaviour changes to such a degree that it affects everyday life. In the United States, in 2020, 5.8 million people (65 years or older) were living with probable AD. Without the development of a cure or treatment to slow the disease, the prevalence of probable Alzheimer's disease will nearly triple by 2050 to 13.8 million in this demographic. Additionally, AD and dementia are global health issues.
In 2010, the global prevalence of dementia (AD is one of the leading causes) was 46 million;
however, it is expected to climb to 131.5 million by 2050. AD negatively impacts global economies with an estimated 16 million unpaid caregivers (family and others in US
alone; Alzheimer's Association, 2020) and world economic cost of approximately $818 billion (USD) yearly, which is expected to double by 2030. Overall, these changes project an increased burden on global economies, health care systems and caregivers and highlight the urgent need for both the discovery of robust AD biomarkers that allow for early intervention and the development of more effective treatments.
Currently, there is no cure for AD, and treatment is primarily constrained to treating symptoms.

[0006] AD robs a person of some of their most human qualities, including their memory, reasoning, and language. The disease begins with decreased memory and cognition, also known as mild cognitive impairment. As AD progresses, other cognitive deficits emerge, such as impaired abstract reasoning, navigation, speech, and motor function. Notably, these clinical symptoms appear years after the formation of neurofibrillary tangles (NFTs) and amyloid-beta plaques (A13). These neuropathological changes are considered the key pathological markers of AD in brain tissue in post-mortem analysis; however, they are just the tip of the neurodegenerative iceberg, and their presence cannot be confirmed until after an individual's death. Other AD pathologies include cell death, inflammation, oxidative stress, and impaired energy metabolism. Therefore, a person's cause of dementia being attributed to AD
cannot occur until after the individual's death. This timing, of course, is not ideal as an earlier definitive diagnosis would ideally lead to quicker, more specialized treatments. As a result, there also remains an urgent need for improved apparatus and methodologies for diagnosing neurodegenerative diseases like AD.

[0007] Metabolomics is an emerging science dedicated to the systematic study of chemical metabolites found in tissues and biofluids as a result of various biochemical reactions. Metabolomics techniques, such as nuclear magnetic resonance (NMR) spectroscopy have been used to measure the response of metabolites in plasma caused by SRC, resistance exercise, neurological diseases, and other disease states in both animal models and humans. Such techniques have been used to detect changes in metabolites found in cerebral spinal fluid (CSF), blood, and brain tissue, including the measurement of Ap plaques, total tau, and hyperphosphorylated tau in CSF. Metabolomic techniques have also been used to show that urine metabolites are altered in many other diseases of the abnormal brain pathology.

[0008] Research into the chemical metabolites arising from metabolic processes in the body, the collection of which is referred to as the rmetabolome', has led to a greater understanding of injury-induced alterations in metabolism. As a result, many systematic profiling techniques for measuring and analyzing chemical metabolites found in tissues and biofluids due to various biochemical reactions are being developed.

[0009] NMR spectroscopy is a versatile analytical technique used in a broad range of disciplines. 1H NMR spectroscopy, also known as proton NMR, can be used to study the metabolomic compositions of biofluids, cells, and tissues to interpret and classify complex NMR-generated metabolic data sets and to extract useful biological information. 1H NMR spectroscopy provides a valuable diagnostic tool in metabolomics because it preserves the integrity of the fluid being sampled, it is non-biased, and it can be performed on several types of samples including blood, cerebrospinal fluid, and urine. 1H NMR spectra also prove useful in metabolomics because molecular pattern recognition and other chemoinformatic tools can be used to provide a characteristic "fingerprint" or profile of an organism for a range of biologically-important endogenous metabolites, particularly where the profile is changed by a disease, disorder, toxic process, or xenobiotic (e.g., drug substance).
That is, quantifiable differences in molecular (metabolite) patterns in biofluids and tissues can give information and insight into underlying molecular mechanisms of disease and disorder. Indeed, early research suggests that metabolite signatures or 'profiles', and specifically changes to metabolite profiles, can accurately reflect central nervous system inflammation and neuronal injury. For example, studies examining metabolite changes in blood plasma following SRC have detected changes in glycerophospholipids, which are associated primarily with membrane structures in the brain and inflammation.

[0010] Despite its advantages, however, 1H NMR has not been used extensively because analysis of the complex spectrum consisting of thousands of signals often require the comprehension and interpretation of a skilled technician, or the addition of supporting clinical and medical laboratory data. Deconvolution of these signals into discrete metabolites with corresponding concentrations can require considerable skill and knowledge that is not generally known in the art. For example, it is possible that 1H NMR metabolomics analysis of urine could prove particularly useful because it is easily attainable in athletes, is the body's primary vehicle for excretion of small molecules, and it is therefore sensitive to changes in biochemical pathways due to disease or injury. However, to date, applying NMR to study metabolomic changes in human urine has not realized any useful diagnostic test due, in part, because urine provides significantly more metabolite information when compared to blood (49 metabolites in blood compared to 209 in urine).
Furthermore, known correlations between metabolite information and specific diseases or injuries are typically limited to one or a small number of metabolites, which has resulted in diagnostic tests based on non-NMR techniques for detecting and quantifying one metabolite at a time. While NMR techniques can be used more efficiently to detect and quantify two or more metabolites at a time, or even all metabolites within a sample at a time, the advantages of doing so have not been realized as databases correlating such "amalgamated" metabolomic patterns or profiles with most specific diseases or injuries are currently lacking.

[0011] There remains a need for effective, non-invasive, simple method of determining whether an individual is likely to have an injury to the central and peripheral nervous systems, including brain injuries. It would be advantageous for such tools to provide means for detecting a change in metabolomic pattern or profile, such change being indicative of, and used to, diagnose the injury.
SUMMARY

[0012] According to embodiments, apparatus and methodologies of determining whether an individual is likely to have an injury are provided. In some embodiments, the methods comprise determining a reference value for at least one target biomarker, obtaining at least one biological sample from the individual, measuring the concentration of the at least one target biomarker in the sample using 1H-NMR spectroscopy, comparing the measured concentration of the measured at least one target biomarker to the reference value to determine if the concentration of the at least one biomarker has changed relative to the reference value, wherein a change in the concentration of the at least one biomarker is indicative of the injury.

[0013] In some embodiments, the methods may further comprise measuring the concentration of at least two biomarkers in the biological sample and determining whether each of the measured at least two biomarkers have a change in concentration levels relative to the reference value.

[0014] In some embodiments, the methods may further comprise measuring the concentration of the at least two biomarkers in the biological sample and determining whether one of the at least two biomarkers has a concentration less than the reference value and whether one other of the at least two biomarkers has a concentration level that is greater than the reference value.

[0015] In some embodiments, the at least one target biomarker may be selected from the group consisting of 2-Hydroxybutyrate, 3,4-dihydroxybenzeneacetate, carnitine, 4-hydroxybenzoate, caffeine, horriocitrulline, methionine, acetylcarnitine, 3-methyl-2-oxovalerate, phosphorylcholine, choline, propylene glycol, taurine, 1-methylhistadine, 3-methylhistadine, citrate, lactose, phenylalanineõ 3-indoxylsulfate, sucrose, 3-methyladipate, isobutyrate, 3-hydroxyisovalerate, 5-am inolevulinate, anserine, tyrosine, carnosine, isoleucine, leucine, threonate, and cysteine.

[0016] In some embodiments, the at least one target biomarker may be selected from phenylalanine and citrate, and the indication that the individual may be likely to have an injury is provided in the event that the measured concentration level of phenylalanine is greater that the respective baseline value for phenylalanine and the measured concentration levels of citrate is less than the respective baseline value for citrate.

[0017] In some embodiments, the change in concentration of the at least one target biomarker may be further indicative the prognosis of the injury. In some embodiments, the at least one target biomarker may be 2-hydroxybutyrate.

[0018] In some embodiments, the change in concentration of the at least one target biomarker may be further indicative of the number of symptoms of the injury. In some embodiments, the at least one target biomarker may be lactose.

[0019] In some embodiments, the at least one biological sample may be selected from urine, plasma, whole blood serum, spinal fluid, interstitial fluid, saliva, an extract or purification therefrom, and a dilution thereof.

[0020] In some embodiments, the injury may be a central or peripheral nervous system injury, wherein the central nervous system injury may be a brain injury such as a traumatic brain injury. In some embodiments, the injury may be an acute injury or a chronic injury. In some embodiments, the methods may be used for diagnosing, prognosing, or monitoring the injury.

[0021] In some embodiments, the at least one target biomarker may be selected from the group consisting of citrate, glycyl-glycine, isoleucine, glutamate, trimethylamine N-oxide, choline, choline phosphate, glucose, leucine, phenylalanine, valine, tyrosine, glutamate, methionine, galactose, glycerol, myo-Inositol, betaine, threonine, ethanol, creatine, malonic acid/malonate, pyruvatoxine, phenylalanine, alpha-ketoisovaleric, propylene glycerol, 2-oxohexane, gamma-aminobutyric acid (GABA), 2-hydroxy-3-methylvaelrate, n-acetyl-L-aspartate (NAA), 4-am inobutanoate, threonine, 3-methyl-2-oxobutanoic acid, (R)-3-hydroxybutanoate, succinate, glycolate, and acetylcholine.

[0022] In some embodiments, the at least one target biomarker may be selected from citrate and isoleucine, and the indication that the individual may be likely to have an injury is provided in the event that the measured concentration level of citrate is greater than its respective baseline value and the measured concentration level of isoleucine is less than its respective baseline value.

[0023] In some embodiments, the at least one target biomarker may be selected from the group consisting of n-acetyl-L-aspartate (NAA), ethanol, 2-am ino-3-phosphonoprionic acid, 1,3,7-trimethyluric acid, creatine phosphate, gamma-aminobutyric acid (GABA), isoleucine, leucine, phenylalanine, serine, tyrosine, valine, phosphorylcholine.

[0024] In some embodiments, the indication that the individual is likely to have an injury may be provided in the event that the measured concentration levels of NAA
and GABA are greater than their respective baseline values and the measured concentration levels of ethanol, 2-am ino-3-phosphonoprionic acid, 1,3,7-trim ethyluric acid, creatine phosphate, isoleucine, leucine, phenylalanine, serine, tyrosine, valine, phosphorylcholinen-acetyl-L-aspartate (NAA), ethanol, 2-amino-3-phosphonoprionic acid, 1,3,7-trimethyluric acid, creatine phosphate, gamma-aminobutyric acid (GABA), isoleucine, leucine, phenylalanine, serine, tyrosine, valine, phosphorylcholineis are less than their respective baseline values.

[0025] In some embodiments, the at least one target biomarker may be selected from the group consisting of acetylcholine, betaine, dimethyl sulfone, glycolate/glycolic acid, and histamine, and the indication that the individual is likely to have an injury is provided in the event that the measured concentration levels of dimethyl sulfone and histamine are greater than their respective baseline values and the measured concentration levels of acetylcholine, betaine, and glycolate/glycolic acid are less than their respective baseline values.

[0026] In some embodiments, the at least one target biomarker may be selected from the group consisting of creatine, glycerol, threonine, malonate, oxypurinol, and the indication that the individual is likely to have an injury may be provided in the event that the measured concentration levels of creatine, malonate, and oxypurinol are greater than their respective baseline values and the measured concentration levels of glycerol, threonine are less than their respective baseline values.

[0027] In some embodiments, the at least one target biomarker may be selected from the group consisting of arginine, glucose, and glycerophosphocholine, and the indication that the individual is likely to have an injury may be provided in the event that the measured concentration levels of arginine, glucose, and glycerophosphocholine are less than their respective baseline values.

[0028] In some embodiments, the at least one target biomarker may be selected from the group consisting of glutamate, citric acid, cis-aconitate, malate, pyruvate, and the indication that the individual is likely to have an injury may be provided in the event that the measured concentration levels of glutamate, citric acid, cis-aconitate, malate, pyruvate are greater than their respective baseline values.

[0029] In some embodiments, the injury is a central or peripheral nervous system injury, and the central nervous system injury may be a neurodegenerative brain disease or disorder, such Alzheimer's Disease or Parkinson's Disease. In some embodiments, the injury may be an acute injury or a chronic injury. In some embodiments, the methods may be used for diagnosing, prognosing, or monitoring the injury. In some embodiments, the methods may be used in the treatment of the injury.
BRIEF DESCRIPTION OF THE FIGURES

[0030] Figure 1 provides the results of an unsupervised principal component analysis (PCA) model, the plot revealing only a slight group difference between pre-and post-brain injury, according to embodiments;

[0031] Figure 2 provides the results of a supervised partial least squares discriminant analysis (PLS-DA) clustering analysis, the plot revealing a distinct separation between pre- and post-brain injury, according to embodiments;

[0032] Figure 3 provides the results from a further PLS-DA
analysis described in FIG. 2, the plot focusing only on 18 features identified using VIAVC and revealing an even greater separation between pre- and post-brain injury, according to embodiments;

[0033] Figure 4 provides a VIP scores plot illustrating the top five features and at least one target biomarker (metabolite) corresponding to each of the five features, according to embodiments;

[0034] Figure 5 provides an ROC constructed to determine whether the 18 features identified as the best subset can be used to accurately predict whether a biological sample belongs to the pre-injury or post-injury group, according to embodiments;

[0035] Figure 6 provides the results of a pathway topology analysis of the at least one target biomarkers (metabolites) identified according to the present apparatus and methodologies;

[0036] Figure 7 shows ROCs constructed from the features identified as the best subset for the pontine base (PB; FIG. 7A); for the dentate nucleus (DN;
FIG. 7B);
and for part of the anterior cortex (BA 24; FIG. 7C), according to embodiments;

[0037] Figure 8 provides the results of a pathway topology analysis of the at least one target biomarkers (metabolites) identified in the pontine base (PB;
FIG. 8A);
in the dentate nucleus (DN; FIG.8B) and in part of the anterior cortex (BA 24;
FIG. 8C) according to embodiments;

[0038] Figure 9 provides the results of orthogonal projections to latent structures discriminant analysis (OPLS-DA) modeling, the score plots revealing a separation between the injury (AD) and control (CN) groups in regions of interest BA
22 (FIG.9A), BA 40 (FIG. 9B), BA 17 (FIG. 9C), and BA 40 (VIAVC only; FIG.
9D), according to embodiments; and

[0039] Figure 10 shows ROCs constructed from the bins determined to be significant by VIAVC testing for regions of interest BA 22 (FIG.10A), BA 40 (FIG. 10B), and BA 17 (FIG. 10C), according to embodiments.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0040] According to embodiments, apparatus and methodologies of use for determining whether an individual is likely to have an injury to the central and/or peripheral nervous systems, and for monitoring the individual's recovery from the injury, are provided. More specifically, apparatus and methodologies for improved detection, diagnosis, and prognosis of acute and chronic central and peripheral nervous system injury, are provided.

[0041] Broadly, the present apparatus and methods of use for determining whether an individual is likely to have an injury may comprise determining a threshold reference value for at least one target biomarker, or a combination of biomarkers, obtaining at least one biological sample from the individual and measuring the concentration of the at least one of the target biomarkers in the sample, and comparing the measured concentrations of the at least one target biomarkers to the threshold reference value (i.e., the measured concentration of each at least one target biomarker is compared to its respective threshold reference values) to determine whether a change in concentration has occurred. For example, where the concentration of at least one of the target biomarkers is less than its threshold reference value, and the concentration of at least one other target biomarker is greater than its reference value, the detected change in biomarker concentration levels may be indicative of an injury. As such, the present apparatus and methods of use may provide an effective, non-invasive, objective method of determining whether an individual is likely to have an injury to the central and peripheral nervous systems by detecting a change in at least one target biomarker (or a combination of target biomarkers, i.e., a change in pattern or profile of target biomarkers), such change being indicative of, and used for, detecting, diagnosing, and/or prognosing the injury.
It is contemplated that changes in patterns or profiles or combinations of target biomarkers that are indicative of such injury may be compiled into a database (i.e., an electronic database) for simplifying and/or automating the detection, diagnosis, and/or prognosis of such injury. In such embodiments, the database may be operably connected to a processor (i.e., computer) that permits rapid or real-time detection, diagnosis, and/or prognosis of an injury by comparing the detected change in at least one target biomarker with the changes recorded in the database.

[0042] In some embodiments, the present apparatus and methods of use may comprise the identification of changes in the metabolomic signature in a biological sample from an individual, the signature changes providing a clinical biomarker indicative with one or more nervous system injuries. For example, in some embodiments, the presence of and/or changes in one or more biomarker profile (e.g., up-regulation, down-regulation, and/or no change in one or more target metabolites) may be used to detect and diagnose the nervous system injury and to identify the likely course of the injury.

[0043] Certain terminology is used in the present description and is intended to be interpreted according to the definitions provided below. Unless otherwise defined, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which the technology belongs.

[0044] Herein, the term "biological sample" means any sample of tissue, cell, fluid (i.e., bodily fluids, body fluids, biological fluids, biofluids, etc.) or other material derived from a subject including, without limitation, urine, plasma, whole blood or serum, cerebral spinal fluid, interstitial fluid, saliva, a tissue sample, or an extract or purification therefrom, or dilution thereof. For example, it is contemplated that at least one biological sample may be collected from a subject before a suspected injury, as well as within at least one hour, one day, and/or one week after the suspected brain injury. In some cases, the at least one biological sample may be collected from a subject before, during, or after a sporting event or a sporting season. It is also contemplated that at least one biological sample may be collected from a subject suspected of suffering a neurodegenerative disease. In some cases, the at least one biological sample may be collected from an individual before, during, or after the suspected onset of Alzheimer's Disease (AD) or Parkinson's Disease (PD).

[0045] Many of the metabolomic changes that are observed in blood and cerebrospinal fluid should also be detectable in urine using the present apparatus and methodologies. Urine is comprised of the biological by-products produced throughout the body, including the brain, making urine samples an ideal biofluid to examine metabolic changes linked to brain injury when obtaining other biological samples or biofluids might be difficult. Results of urine metabolomics may be influenced by kidney function, however, many water-soluble metabolites that are present and identifiable by NMR in serum and CSF are also present and identifiable by NMR in urine.
Indeed, 91.8% (45/49) of the metabolites that are identifiable by NMR in serum and 81.0%
(43/53) of the metabolites that are identifiable by NMR in CSF are present and identifiable by NMR in urine. Although a biological sample comprising a urine sample is described herein, any appropriate biological sample comprised of biological by-products produced throughout the body, including brain tissue, are contemplated.

[0046] Herein, the term "biomarker", "target biomarker, or "clinical biomarker"
means a measurable characteristic that can be objectively determined and evaluated as an indicator of biological processes within an individual. In some embodiments, "biomarkers" generally refer to at least one biochemical, e.g., at least one substance formed in or necessary for metabolism, such as a metabolite, that is differentially present in a biological sample taken from the individual. Differentially present refers to differences in the quantity and/or frequency of the at least one biomarker in a biological sample taken from a subject having a nervous system injury.

[0047] For example, according to embodiments herein, "biomarker" can mean a measurable change in biochemical signature or pattern of one or more metabolites (e.g., an increase, decrease, or no change) arising from metabolomic processes, the biochemical signature or pattern presenting differently in the individual over time (e.g., pre- and post-injury) and being indicative of an injury (e.g., brain injury, neurodegenerative disorder and disease, etc.). For example, the present methods may be incorporated into one or more clinical tests (e.g., a urinalysis device, or the like) to provide the user, whether or not trained in metabolomic or raw data analysis, with an output indicative of an injury in an individual. The output may then be used to detect the type of injury, to determine the severity of the injury and/or its likely outcome, to determine an appropriate course of treatment for the individual, or to optimize same.

[0048] Metabolites are those chemicals generally less than 1,000 Da) involved in cellular reactions for energy production, growth, development, signaling and reproduction and can be taken up or released from cells according to their cellular needs. Metabolites can include sugars, amino acids, organic acids, as well as xenobiotic compounds, the presence of which might change in a cell or system due to internal or external stress such as an injury, an injury, or disease state.
Metabolic changes can result from changes in the chemical reactions that use these metabolites (i.e., metabolic pathways), or the transporter that take up or release the metabolites.
For example, without being limited by theory, an individual impacted by an injury to the nervous system may exhibit major changes both at the cellular and systemic levels including inflammation and impaired energy metabolism. Although biomarkers comprising brain and urinary metabolites are described herein, any appropriate metabolomic markers indicative of injury, and specifically brain injury, are contemplated.

[0049] In some embodiments, examples of metabolites that may be measured as target biomarkers include, without limitation, one or more of the metabolites selected from the group consisting of 2-Hydroxybutyrate, 3,4-dihydroxybenzeneacetate, carnitine, 4-hydroxybenzoate, caffeine, horriocitrulline, methionine, acetylcarnitine, 3-methyl-2-oxovalerate, phosphorylcholine, choline, propylene glycol, taurine, 1-methylhistadine, 3-methylhistadine, citrate, lactose, phenylalanineõ 3-indoxylsulfate, sucrose, 3-methyladipate, isobutyrate, 3-hydroxyisovalerate, 5-am inolevulinate, anserine, tyrosine, carnosine, isoleucine, leucine, threonate, and cysteine, and any other metabolites that may have been characterized but have not yet been identified with certainty to date.

[0050] In some embodiments, examples of metabolites that may be measured as target biomarkers include, without limitation, one or more of the metabolites selected from the group consisting of citrate, glycyl-glycine, isoleucine, glutamate, trimethylamine N-oxide, choline, choline phosphate, glucose, leucine, phenylalanine, valine, tyrosine, glutamate, methionine, galactose, glycerol, myo-Inositol, betaine, threonine, ethanol, creatine, malonic acid/malonate, pyruvatoxine, alpha-ketoisovaleric, propylene glycerol, 2-oxohexane, gamma-aminobutyric acid (GABA), 2-hydroxy-3-methylvaelrate, N-acetyl-L-aspartate (NAA), 4-am inobutanoate, threonine, 3-methyl-2-oxobutanoic acid, (R)-3-hydroxybutanoate, succinate, glycolate/glycolic acid, acetylcholine, 2-amino-3-phosphonoprionic acid, 1,3,7-trimethyluric acid, serine, phosphorylcholine, dimethyl sulfone, glycolate/glycolic acid, histamine, oxypurinol, arginine, glycerophosphocholine, glutamate, citric acid, cis-aconitate, malate, pyruvate and any other metabolites that may have been characterized but have not yet been identified with certainty to date.

[0051]
Herein, the terms "diagnose" and "diagnosis" means the detection, identification, confirmation, and/or characterization of a nervous system injury, such as a central nervous system injury. According to embodiments, the present methods of detecting and diagnosing are useful to confirm the existence of a brain injury, for assessment of clinical screening, prognosis, choice of therapy, evaluation of therapeutic benefit, e.g., in the case of a sports related concussions, the timing for return to play.

[0052]
Herein "individual" or "subject" means a human subject. In some embodiments, the individual may suffer from an injury, such as an injury to the central and/or peripheral nervous systems. Without being limited by theory, the individual may suffer from an injury causing an alteration(s) to biochemical pathways (e.g., sum changes in genes, protein synthesis, and/or environmental factors, etc.) resulting in discernible metabolomic changes that, when detected, can be indicative of the injury.

[0053]
Herein "injury" means an injury to the central and/or peripheral nervous system including, without limitation, brain injuries, traumatic brain injuries (e.g., traumatic brain injury broadly covers mild TBI, moderate TBI, sever TBI, concussion, and other head impacts), neurodegenerative disorders and disease, etc. In some embodiments, brain injury may include any disruption in the normal function of the brain that can be caused by a blow, bump, or jolt to the head or spine, the head or spine suddenly and violently hitting an object. Without being limited by theory, injury may be any change in central and/or peripheral nervous system pathology or function causing an alteration(s) to biochemical pathways resulting in discernible metabolomic changes that, when detected, can be indicative of the change in pathology or function.
Without being limited by theory, and by way of example only, injury may include at least one neurological disorder (i.e., structural, biochemical, or electrical abnormality) that affects the brain, spinal cord, and/ or nerves found throughout the human body and spinal cord, and that can result in a range of symptoms. In some embodiments, injury might be an acute injury, while in other cases the injury might be chronic.
Although certain specific injuries, such as traumatic brain injury (e.g., sports-related concussion, SRC) and neurodegenerative injury (e.g., Alzheimer's Disease, AZD) are described herein, such injury are provided for explanatory purposes only and are in no way intended to limit the intended scope of the present apparatus and methodologies.

[0054] Herein, threshold "reference value", "reference profile/pattern" or "reference combination" means a baseline, standard, and/or pre-injury biomarker concentration signature derived from an individual. In some embodiments, the threshold reference value may be obtained from the individual being monitored for injury (e.g., during a sporting event, or sport season). In other embodiments, the threshold reference value may be fully or partially obtained from at least one privately or publicly-available database of samples (e.g., pooled samples) from healthy subjects, such as might be found in the Human Metabolome Database, or specifically characterized biofluid metabolomes including, without limitation, the urine metabolome, the serum metabolome, the saliva metabolome, or the cerebrospinal fluid metabolome (e.g., the samples being gender- or age-matched). Although threshold reference values are described herein as being derived directly from the individual, any appropriate samples sufficient to detect changes in an individual's biomarker signature or pattern are contemplated.

[0055] Although 1H NMR spectroscopy is described herein, any suitable spectroscopic technique can be used to generate the presently described metabolomic signatures including NMR spectroscopy and mass spectrometry. In contrast to other techniques, 1H NMR spectroscopy of biological samples, including fluid samples, allows for a high throughput biological sample analysis with a broad, untargeted approach to biomarker discovery. In addition, when examining the metabolome of urine, NMR spectroscopy provides quantitative information on more metabolites (209 total, 108 unique) when compared to the other mass spectrometry-based methods. Moreover, many of the metabolomic changes observed in blood and cerebrospinal fluid may be detected by NMR in urine, whereas urine advantageously also contains additional metabolomic information not in the other biofluids (e.g., including at least 18 unique urinary metabolites as described herein). In some embodiments, use of 1H NMR spectroscopy in the present apparatus and methodologies provides a clinically accessible, painless, non-invasive medium for injury detection, diagnosis, and prognosis.

[0056] As might be appreciated, 1H NMR spectroscopy information and data may be received, stored, analyzed, and modified using at least one computer processor (e.g., digital processor) in communication with the NMR, where computer processor means a device that is programmed to run at least one algorithm for performing a set of steps according to a program. Computer processors may include Central Processing Units (CPUs), electronic devices, or systems for receiving, transmitting, storing and/or manipulating data under programmed control.

[0057] Each term used and defined herein is for explanatory purposes only and in no way is intended to limit the scope of the technology.

[0058] In some embodiments, the present apparatus and methodologies may comprise determining a threshold or baseline reference value for at least one target biomarker, the threshold reference value for the at least one target biomarker being used to detect a change in the concentration of the biomarker, the change being indicative of an injury. For example, a threshold reference value of the at least one target biomarker may comprise a baseline, pre-injury, or normal concentration of the at least one target biomarker. In some embodiments, a threshold reference value for at least one target biomarker may be obtained from a biological sample from the individual, from a cohort of pooled individuals, or the like, and then used as a comparison value or baseline value to determine if a change in the concentration levels of the at least one target biomarker has changed in the individual (e.g., when compared to one or more biological samples collected from the individual following a suspected injury to the nervous system, as will be described).

[0059] In some embodiments, the threshold reference value may be determined for at least two target biomarkers, the combined reference values of the at least two target biomarkers comprising a threshold baseline biomarker 'profile' or 'pattern', wherein a change to the biomarker pattern is indicative of an injury. For example, when compared to a threshold baseline biomarker profile, the presence of and/or changes in the biochemical signature of at least two target biomarkers (e.g., up-regulation, down-regulation, and/or no change in concentration) may be used to detect and diagnose an injury and to identify the likely course of the injury.

Advantageously, recognition and detection of a specific change in concentration of at least two target biomarkers, i.e., the determination of a signature pattern or profile changes in metabolites, provides an improved method of detection, diagnosis, and prognosis (including monitoring recovery) in both acute and chronic injury.

[0060] For example, the present methods and use thereof may provide a more accurate and/or sensitive clinical test for detecting, diagnosing, and treating an injury in an individual than known methods that merely detect the presence of one or more metabolites of interest, where such known methods merely detect the presence of a metabolite of interest to determine a specific type of injury (e.g., a "stroke specific metabolite"). Reliance upon the presence of one metabolite may lead to both false positive and false negative diagnoses. Such methods are also not able to prognose the injury, but instead can simply detect the presence of a metabolite that 'might' be linked to the specific injury. The present methods and use thereof may also provide a more accurate and/or sensitive clinical test for detecting, diagnosing, and treating an injury than known methods because the present methods also account for down-regulation of metabolites, that is ¨ the absence or down-regulation of a metabolite within the change in signature or profile can be indicative of the injury.
Instead, known methods may merely detect a target metabolite and, as a result, conclude no injury has occurred, when in fact the opposite might be true.

[0061] Furthermore, the present methods and use thereof may also provide a more accurate and/or sensitive clinical test for detecting, diagnosing, and treating an injury than known methods because they are operative to measure and detect a clear change in two or more metabolites, i.e., creating a signature, pattern, or profile change, that is indicative of an injury. That is, advantageously, by examining a specifical change in two or more metabolites, i.e., i.e., a change in the signature, pattern, or profile, the present methods and use thereof provide a more sensitive clinical tool (e.g., where the error from smaller changes can be minimized by multiplexing the changes observed in two or more metabolites, increasing the sensitivity of the diagnostic).

[0062] In some embodiments, the present apparatus and methodologies may comprise obtaining at least one biological sample from an individual or a cohort of pooled individuals who have been categorized as being healthy and analyzing the sample(s) to determine whether the individual is likely to have an injury (i.e., to determine if changes in at least one target biomarker in the sample is indicative of an injury). In some embodiments, the at least one biological sample(s) may be analyzed to determine the threshold reference value of the at least one biomarker (e.g., pre-injury, baseline sample). In other embodiments, the at least one biological sample(s) may be analyzed to determine whether a change in the concentration of the at least one biomarker is indicative of an injury (e.g., post-injury, diagnosis/prognosis sample).
Advantageously, the present apparatus and methods contemplate obtaining as few as one pre-injury reference sample and one post-injury test biological sample, however it should be appreciated that any number of biological samples may be obtained and analyzed in order to determine whether the individual is likely to have an injury.

[0063] For example, one or more first biological samples may be obtained from an individual, such first sample used to determine the threshold reference value, or a baseline target biomarker profile. One or more second, third, etc. biological samples may be subsequently obtained, the one or more second samples used to determine if the concentration of the at least one biomarker has changed (i.e., to detect and determine changes in the individual's target biomarker profiles), and to discern if the changes are indicative of brain injury.

[0064] It should be appreciated that not all detected changes to the biomarker profile will be indicative of injury. Instead, as will be described, certain predetermined or signature changes to the biomarker profile may be indicative of disease, referred to as "diagnostic levels". More specifically, in the event that the measured concentration level of at least one of the target biomarkers is less than its respective threshold reference value (i.e., the target metabolite(s) are down-regulated) and the measured concentration level of at least one of the other target biomarkers is greater than its respective threshold reference value (i.e., the target metabolites(s) are up-regulated), there is an indication that the individual is likely to have an injury.
Moreover, the detected changes may also be indicative of the severity of the injury, and a prognosis of the likely course of the injury. More specifically, for example, the profile or pattern of detected changes indicative of any particular injury may differ according to the severity of the injury.

[0065] Herein, greater than and/or less than may refer to a statistically significant difference between a diagnostic amount of the at least one target biomarker measured in a post-injury biological sample when compared to a control or standard amount of the at least one target biomarker obtained from the threshold reference value, referred to as a "baseline amount". All amounts referred to herein can be either an absolute amount (e.g., pg/mL) or a relative amount (e.g., relative intensity of signals). Herein, statistically significant can be at least a difference of at least p<0.05.
By way of example, a change in the concentration levels of the at least one biomarker indicative of an injury may be approximately 0.1% _ 1%7 2% _ 10%7 11% _ 20%7 21 _ 30%, 31 ¨ 40%, 41 ¨ 50%, 51 ¨ 60%, 61 ¨ 70%, 71 ¨ 80%, 81 ¨ 90%, 91 ¨ 100%, or > 100% when compared to a baseline reference concentration value.

[0066] In some embodiments, the present apparatus and methods involve measuring the concentration of the at least one target biomarker using nuclear magnetic resonance (NMR), including 1H NMR, combined with multivariate statistical analyses to develop pre-injury threshold reference values (i.e., metabolite profile patterns) and to detect post-injury changes in at least one target biomarker compared to its respective threshold value, such change being indicative of nervous system injury. As would be appreciated, individual metabolomics exhibit unique spectral signatures that are consistent and reproducible for a given set of overall sample conditions, whereby concentrations of a single metabolite in a given sample can be accurately determined by reference to an internal standard. As will be demonstrated, 1H NMR provides an automated pattern recognition technique for simple, effective analysis of an individual's injury state, shortening analysis time and eliminating subjective data interpretation. That is, herein, using appropriate preparation of at least one pre-injury biological sample (containing an estimated 1400 endogenous urinary metabolites) and intelligent software tools that model the spectrum using the threshold value, an accurate listing of at least one target biomarker and its corresponding concentration indicative of nervous system injury was obtained.

[0067] For example, without being limited by theory, potential target biomarkers may comprise at least one metabolite, the concentration of which might be impacted by neuropathological changes caused by brain injury, disorder, or disease.

[0068] The present apparatus and methodologies will now be illustrated in more detail by way of the following Examples.

[0069] EXAMPLES:

[0070] EXAMPLE 1: According to embodiments, the present example demonstrates the use of the present apparatus and methodologies for determining whether an individual is likely to have an injury, such as a brain injury.
More specifically, the present example demonstrates the use of the present apparatus and methodologies for the detection and diagnosis of at least one brain injury, such as a sports-related concussion, and further for predicting recovery from the at least one brain injury.

[0071] According to embodiments, the present example involved individuals or subjects comprised of Canadian national team athletes, national development athletes, and amateur ice hockey players, ages 14 - 40 years, who were participating in the WinSport Concussion Clinic at WinSport (Calgary, Alberta, Canada) from August 2015-2016. Exclusion criteria include age greater than 40 years and less than 12 years, female, a previous history of chronic medical conditions (e.g., metabolic or nephritic disorders), neurological conditions such as stroke, seizure, moderate to severe traumatic brain injury and/or congenital intracranial abnormalities.

[0072] According to embodiments, a preclinical assessment of each individual was performed. In some embodiments, the preclinical assessment comprised obtaining and analyzing at least one first biological sample (e.g., a urine sample), the preclinical sample serving as a threshold reference value or pre-injury metabolic profile for the at least one target biomarker. For example, in some embodiments, at least one bodily fluid sample comprising a urine sample was obtained from each individual. The bodily fluid samples were collected in the morning hours between 7-9 am and before the first meal to provide a non-concussed assessment. Athletes were asked to only drink water prior to sample collection.

[0073] Following the pre-injury assessment, each individual was monitored for at least a year-long sports season (e.g., throughout the sport season between 2015 ¨
2016). At one or more time periods during the sports season (e.g., where it was believed that an individual suffered a brain injury), a detailed physical assessment of each individual was performed, including a clinical diagnosis of the SRC based on current International Consensus on Sport Concussion recommendations (see Table 1). There were no other injuries at the time of the SRC.

[0074] Having regard to Table 1, twenty six (26) individuals were found to have suffered an SRC during the sports season. Individual characteristics and past medical history of the injured individuals are provided in Table 1 and include age, sex, medical history, medication (prescribed over the counter), length of post-traumatic amnesia, and pre- and post- number of symptoms.

[0075] TABLE 1: SRC patient characteristics 1 15 - 1 Ice Hockey 2 16 asthma Ventolin, Protein powder 0 Ice Hockey 3 14 Vitamin D, Coenzyme 0 Ice Hockey Q10, Vitamin C
4 16 asthma Ventolin, Protein Powder 0 Ice Hockey 16 acne Biosteel Protein, 0 Ice Hockey Minocycline 6 16 - 2 Ice Hockey 7 16 depression Protein Powder, Creatine 1 Ice Hockey 8 16 - Mesavant 0 Ice Hockey 9 15 Migraine 1 Ice Hockey Headaches 14 Migraine - 3 Ice Hockey Headaches 11 15 Generalized Ventolin 1 Ice Hockey anxiety Disorder, asthma 12 13 0 Ice Hockey 13 13 - 0 Ice Hockey 14 15 - Naturopathic Growth 0 Ice Hockey Hormone 13 0 Ice Hockey 16 13 Vegan Protein 0 Ice Hockey 17 16 Acne, acutane 0 Ice Hockey previous whiplash injury of the neck Luge 19 15 - - 1 Ice Hockey 20 14 - - 1 Ice Hockey 21 12 Migraine - 0 Taekwondo Headaches 22 14 - Vitamin D, Vitamin B, 0 Ice Hockey Protein Powder 23 15 Protein Powder 1 Ice Hockey 24 16 Acne, Minocycline 0 Ice Hockey Migraine Headaches 25 15 - Protein Powder, Vitamin 0 Ice Hockey C, Vitamin D, Creatine 26 13 - Cod Liver oil, Vitamin D 1 Ice Hockey

[0076] In addition to foregoing individual characteristics, a sports concussion assessment Tool 3 (SCAT3) was used to assess SRC in the 26 individuals within hours of their injury. The SCAT3 assessment and a history of previous head injuries are provided in Table 2 and include a description of recovery from previous head injuries, neurological conditions, medications, sport-participation and biographical information.

[0077] TABLE 2: Symptom Score, Length of Return to Sport, Length of Loss of Consciousness, Length of Post-traumatic Amnesia.

[0078] According to embodiments, the post-injury assessment comprised obtaining at least one second bodily fluid sample, said sample operative as a test or post-injury metabolic profile. For example, in some embodiment, at least one second bodily fluid sample comprising a urine sample was obtained from each individual, wherein the second bodily fluid sample was a 12-hour fasting urine sample between 7-9 am within a time window of 24 hours and 72 hours post-injury.

[0079] Each of the second biological (urine) samples obtained were immediately stored at -80 C, batched, and prepared for analysis. In preparation for 1H
NMR spectroscopy, 400 pL aliquots of urine were added to 200 pL aliquots of phosphate urine buffer in 2 mL centrifuge tubes. The phosphate buffer was prepared as a 4:1 ratio of KH2PO4 in a 4:1 H20:D20 solution to a final concentration of 0.5M.
The D20 included 0.05% (by weight) trim ethylsilyl propanoic acid (TSP) as a chemical shift and concentration reference. To protect the metabolite profile integrity, 0.02%
(weight/volume) of sodium azide (NaN3) was added to the solution as an antimicrobial agent. The buffer solution was then titrated to pH 7.4 using HCI or NaOH, depending on the initial pH. The tubes containing urine and buffer were centrifuged at 12,000 rpm for 5 minutes at 4 C. After centrifugation, 550 pL of the supernatant was transferred to a 5 mm NMR tube for NMR analysis. Samples were immediately analyzed using the NMR spectrometer.

[0080] NMR spectra were collected on a 700 MHz Bruker Avance III HD
spectrometer (Bruker, ON, Canada). The Bruker 1-D NOESY gradient water suppression pulse sequence was used. Each sample was run for 128 scans and the total acquisition size was 128k. The spectra were zero filled to 256k, automatically phased, baseline corrected, and line-broadened to 0.3Hz. The processed spectra were exported as ascii files to Matlab (The MathWorks, MA, USA) for statistical analysis. Spectra were first binned using dynamic adaptive binning, and then manually adjusted to optimize number of variables and ensure accuracy. All spectra had the regions corresponding to water and urea removed before normalization to the total area of all spectral bins. The peaks corresponding to acetaminophen derivatives were also removed to account for the fact that the individuals tended to take pain medication following an SRC. The binned spectra were then pareto scaled to increase the influence of weak peaks and deemphasize the influence of larger peaks. All the peaks in each spectrum were referenced to TSP (0.00o).

[0081] All univariate and multivariate testing was carried out using MATLAB
(The MathWorks, MA, USA) and MetaboanalystR (v 2.0), respectively. As would be appreciated, multivariate statistical analysis can be applied to the collected data or complex spectral data to aid in the characterizations of changes related to a biological perturbation or injury.

[0082] According to embodiments, data visualization to determine sample structure and the presence of distinct groups was performed using Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Ten-fold double cross-validation and 2,000 permutation tests were performed to validate the results of the supervised PLS-DA testing, as recommended in the art for validating PLS-DA models. Variable Importance to the Projection (VIP) scores can indicate which spectral bins (hereafter referred to as "features") contribute the most to the separation observed in the PLS-DA scores plot. This score represents a weighted sum of squares of the PLS-DA loadings in each dimension. All tests were performed using the online metabolomics statistics software platform Metaboanalyst.

[0083] To focus on potential biomarkers, Variable Importance Analysis based on random Variable Combination (VIAVC) was used as a feature selection tool.
The VIAVC algorithm combines random permutations of variable inclusion with a ten-fold cross validation of model. This reveals a best-subset of target biomarkers (metabolites) that have the greatest effect on group differences. VIAVC p-values were calculated using a t-test and distribution of how many times a biomarker (metabolite) was removed to improve the model during various permutations. Each of the at least one target biomarkers (metabolites) in the best-subset generated by the algorithm was therefore strongly informative in separating the samples into groups, and synergistic effects between the biomarkers were revealed. All VIAVC tests were carried out using MATLAB. To further test for significant features between pre-injury and post-SRC
paired t-tests were completed.

[0084] The VIAVC method, VIP scores, and paired t-tests each provided a set of features that were considered significantly altered across the comparison groups.
The at least one target biomarkers (metabolites) corresponding to these features were identified using the profiler tool in the Chenomx 8.2 NMR Suite (Chenomx Inc., Edmonton, Alberta, Canada), and receiver operator characteristic (ROC) curves were used to graphically represent the true positive rate versus 1-specificity. The accuracy of a classifier was visualized by the area under the ROC curve.

[0085] Biological significance of target biomarkers (important metabolites) was investigated using the free pathway topology analysis tool available through Metaboanalyst. Data were collated as a list of metabolites, and the human pathway library was chosen. The library was built using the detailed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway diagrams. A list of the most relevant biological pathways involved in conditions of the study was then generated to help draw connections between potential biomarker metabolites and relevant biological processes.

[0086] Spearman's correlations compared the normalized concentration of significantly altered metabolites post-SRC to the total number of symptoms reported, the symptom severity score, and the length of return to play. A p-value of <
0.05 was considered significant and metabolite concentrations were normalized with respect to the entire urinary metabolome.

[0087] Initially, exploratory analysis using both supervised and unsupervised models of all data was completed, with the unsupervised principal component analysis (PCA) being used to cluster the raw data and highlight any separation between groups. Having regard to FIG. 1, the PCA scores plot revealed only a slight group difference (with + indicating pre-injury baseline, or controls and L
indicating an injury).
Having regard to FIG. 2, supervised partial least squares discriminant analysis (PLS-DA) clustering analysis was performed, revealing distinct separation between the pre-injury baseline, or controls (+) and post-injury or concussion groups (a).

[0088] Variable importance analysis based on random variable combination (VIAVC) was then used to remove unimportant or interfering features from the data.
VIAVC resulted in the discovery of a subset of 18 features that have a strong effect on the differences between control (e.g., pre-injury) and injured groups (e.g., post-SRC). The VIAVC p-value represents a measurement of whether the inclusion of a feature in 1000 randomly chosen subsets of features improved or reduced the overall class separation of the model. Thus, VIAVC can determine synergistic effects across features.

[0089] PLS-DA analysis was completed again to focus only on the 18 features identified by VIAVC. FIG. 3 provides a partial least squares discriminant analysis (PLS-DA) 2D scores plot showing separation between baseline (pre-injury) and post-injury urine samples (based on the VIAVC best subset of features corresponding to the metabolites provided in Table 3). A clear separation was found between groups with Component 1 and 2 accounting for 21.9% and 7.9% of the variance in the data, respectively. The percentages shown along the axis indicate the amount of variance in the data set given by each component and the shaded ellipses designate the 95%
confidence interval of each group. The model passed permutation testing using permutations (p = 0.0005) confirming that the separation is real and not due to chance.
This model also passed ten-fold cross validation testing.

[0090] Having regard to FIG. 4, provides variable importance in the projection (VIP) scores for the top five target biomarkers (metabolites) used in the PLS-DA
model, where the higher the VIP score, the more the biomarker(s) contributed to the separation observed between groups in the PLSD-DA model shown in FIG.3. For example, the VIP scores plot shown in FIG. 4 illustrates the top five features and the biomarkers corresponding to each feature, with the coloured boxes on the right side indicating whether the biomarker (metabolite) was up- or downregulated in the post-injury sample compared with the respective baseline threshold value in the pre-injury/control sample. The heat map (right) indicates the directionality of the changes, i.e., up- or downregulation from the injury (e.g., SRC).

[0091] In some embodiments, as shown in FIG.4, a change in at least one target biomarker selected from the group consisting of phenylalanine and 3-indoxysulfate may be indicative of brain injury, particularly where the concentration of such at least one target biomarkers in the biological sample is greater than its respective baseline threshold baseline value (i.e., the biomarkers are upregulated following brain injury (e.g., SRC)). In other embodiments, as also shown in FIG.4, a change in at least one target biomarker selected from the group consisting of Citrate and propylene glycol may be indicative of brain injury, particularly where the concentration of such at least one target biomarkers in the biological sample is less than its respective baseline threshold baseline value (i.e., the biomarkers are downregulated following brain injury (e.g., SRC)).

[0092] Having regard to FIG. 5, a receiver operator curve (ROC) was constructed for the VIAVC best subset of features corresponding to the at least one target biomarker (e.g., metabolites as provided in Table 3) in order to test if the 18 features identified as the VIAVC best subset may be used to accurately predict whether a sample belongs in a baseline/control (pre-injury) or post-injury group. The model graphs the true positive rate on the y-axis versus the false positive rate on the x-axis. As shown, the ROC model had a corresponding area under the curve (AUC) of 0.887 with a 95% confidence range of 0.731-0.997 and a predictive accuracy of 81.6%.

[0093] A paired t-test was also completed to compare baseline/control (pre-injury) and post-injury samples of each individual. This test identified 19 features as significant (p < 0.05), with 7 of these features being common to the VIAVC
results.
Table 3 provides a list of the 31 metabolites identified as significant by VIAVC or a paired t-test or both tests.

[0094] Table 3: Target Biomarkers/Metabolites Metabolite VIAVC P- PLS-DA T-Test Regulation value VIP Score P-value 2-Hydroxybutyrate 5.42E-45 0.41803 Not Sig. Down 3,4- 4.86E-19 0.14659 Not Sig. Up Dihydroxybenzeneacetate.1/Carnitine.

4-Hydroxybenzoate 3.98E-85 0.58827 Not Sig. Up Caffeine 3.29E-52 0.35628 Not Sig. Down Carnitine.2 1.56E-38 0.54019 Not Sig. Up Homocitrulline 2.15E-77 0.44871 Not Sig. Down Methionine/Acetylcarnitine.1 2.59E-50 0.4312 Not Sig. Up 3-Methyl-2-0xovalerate 8.18E-26 0.41031 Not Sig. Up Phosphorylcholine/Choline/Acetylcarnit 1.52E-128 0.55238 Not Sig. Up ine.2 Propylene Glycol 4.50E-77 0.94917 Not Sig. Down Taurine/3,4- 1.40E-41 0.37166 Not Sig. Up Dihydroxybenzeneacetate.2/Carnitine.

*1-Methylhistadine/3-Methylhistadine 3.69E-50 0.91339 0.03871 Down *Citrate 9.08E-100 0.95022 0.032117 Down *Lactose 8.24E-43 0.78734 0.043613 Up *Phenylalanine.1 1.60E-43 1.1556 0.00719 Up *Phenylalanine.2 1.71E-42 2.4739 0.000322 Up *Phenylalanine.3/3-Indoxylsulfate.1 2.30E-72 2.26 0.002267 Up *Sucrose 2.22E-42 0.62691 0.037745 Down 3-Methyladipate/lsobutyrate Not Sig. N/A
0.04133 Down 3-Hydroxyisovalerate Not Sig. N/A 0.020594 Down 3-Indoxylsulfate.2 Not Sig. N/A 0.006534 Up 5-Aminolevulinate Not Sig. N/A 0.019592 Up Anserine.1/Tyrosine Not Sig. N/A 0.026885 Up Anserine.2 Not Sig. N/A 0.037285 Up Carnosine/Anserine.3 Not Sig. N/A 0.024957 Up Isoleucine/Leucine Not Sig. N/A 0.02161 Up Phenylalanine.4 Not Sig. N/A 0.002615 Up Phenylalanine.5 Not Sig. N/A 0.005616 Up Phenylalanine.6 Not Sig. N/A 0.033934 Up Threonate/Cysteine Not Sig. N/A 0.011865 Down

[0095] Having regard to FIG. 6, a complete list of target biomarkers (metabolites) identified by both methods (as listed in Table 3) was used to carry out Pathway Topology analysis, to determine which metabolic processes may be most affected following a brain injury, such as an SRC. The Pathway Topology analysis was completed by entering a list of at least one significant metabolite(s) found by both VIAVC and paired t-test into known web-based tool MetPAm. Each circle shown in FIG. 6 indicates a specific metabolic pathway or biological function as labeled to the left, with the x-axis being the pathway impact scores which represents the magnitude of impact by significant biomarkers, as shown by the size of each circle, and the y-axis being the p-values, given as -In(p), with red circles indicating a lower p-value and yellow a higher p-value. Only pathways with p 0.06 are shown.

[0096] According to embodiments, as shown in FIG. 6, aminoacyl-tRNA
biosynthesis (#1; p<0.001) and beta-alanine metabolism (#2; p<0.01) were the two pathways with most significant change. Taurine and hypotaurine metabolism (#12), pantothenate and CoA biosynthesis (#11), and phenylalanine metabolism (#10;
p<0.005 and pathway impact of 0.11906) show the highest pathway impacts.
Moreover, there was a significant positive clinical correlation found between the post-injury normalized concentration of 2-hydroxybutyrate and the length of return to play (R = 0.482, p = 0.02), and a significant positive clinical correlation between the total number of symptoms and the post-injury normalized concentration of lactose (R
=
0.422, p = 0.036).

[0097] In some embodiments, the present example demonstrates the detection of changes in concentration of at least one target biomarker (metabolites) in a biological sample from an individual for determining whether the individual is likely to have suffered a brain injury, and specifically to diagnosing a sports-related concussion. The present example demonstrates that the detection of changes in concentration of at least one target biomarker (metabolite), or combination of biomarkers), is indicative of brain injury, such changes comprising an up-regulation, a down-regulation, and/or no change of the biomarker or combination of biomarkers relative to a threshold reference value. Without being limited to theory, the present example demonstrates that the changes in concentration of at least one target biomarker (metabolites), or combination of biomarkers may arise from alterations of key biological pathways involved in primary and secondary brain injury and recovery processes.

[0098] In some embodiments, the present example also demonstrates the detection of changes in concentration of at least one target biomarker (metabolites) in a biological sample from an individual for determining whether the individual is likely to have suffered a brain injury and for diagnosing the type of injury (a sports-related concussion), as well as for detecting increased symptom burden and prognosing recovery (e.g., return to play). In such embodiments, the present example demonstrates that the detection of changes in concentration of at least one target biomarker (metabolite), or combination of biomarkers, may further be indicative of the prognosis of the brain injury and number of symptoms resulting the brain injury, such changes comprising an up-regulation, a down-regulation, and/or no change of the biomarker or combination of biomarkers relative to a threshold reference value.

[0099] As may be appreciated, the present apparatus and methodologies of detecting whether an individual is likely to have an injury may be used to assess the risk that the injury might lead to long-term dysfunction, enabling a determination of therapeutic intervention at an early stage. For example, in some embodiments, the present apparatus and methodologies might be used to detect/diagnose a brain injury, such as an SRC, and then to further assess when it might be suitable for the individual (e.g., a professional athlete, amateur athlete, or recreational player) to return to play the sport, to return to work, or to other daily activities following the injury.

[0100] According to embodiments, it is contemplated that an effective diagnostic tool for brain injury, including SRC, may be based upon a group of several biomarkers in order to generate a biomarker signature. For example, the ROC
analysis shown in FIG. 5 illustrates that the combination of at least 18 target biomarkers (metabolites) identified in Table 3 serves as a classifier of SRC
in urine.
Without being limited to theory, the potentially relevant metabolic pathways, and their role in brain injury (e.g., SRC), are discussed below.

[0101] Phenylalanine levels are known to be upregulated in brain tissue in a mouse concussion model, but are also known to decrease in the serum of individuals who suffered a traumatic brain injury (TB I) with cognitive impairments or to not change in serum after moderate-severe TBI and SRC in humans. Phenylalanine is an essential amino acid that is metabolized to tyrosine by the biopterin-dependent aromatic amino acid hydroxylase (AAAH) tyrosine hydroxylase. Tyrosine is further metabolized to L-DOPA which in turn is metabolized to the neurotransmitter dopamine (DA) by DOPA decarboxylase DA is converted by dopamine-13-hydroxylase into noradrenaline (NA). DA and NA may be responsible for decreased mood among concussion patients, as these molecules have a significant role in mood and depression.

[0102] According to embodiments, phenylalanine was found to be upregulated following brain injury (e.g., SRC), and phenylalanine biosynthesis and metabolism topology analysis was significantly affected. It is possible that an increased concentration of phenylalanine in brain injured individuals may have a downstream effect on DA and NA levels. In turn, these changes may influence symptoms following injury such as fatigue, decrease mood and anxiety.

[0103] Citrate is a tricarboxylic acid (TCA) cycle intermediate involved in converting phosphoenolpyruvate to malate and/or pyruvate, eventually producing ATP for every molecule of pyruvate. Citrate chelation is involved in peroxidation rates and thus oxidative stress, which has been associated with many of the pathophysiological changes after concussion. Because concentration changes in various metabolites are implicated in oxidative stress, the present apparatus and methodologies may provide a means for better understanding the pathophysiological mechanisms leading to brain injury-induced neurological symptoms.

[0104] According to embodiments, citrate levels were found to be downregulated following brain injury (e.g., SRC) in both VIAVC and paired t-test, and citrate was the fourth most important feature in the VIP scores plot. Citrate levels are known to be reduced in moderate-severe traumatic brain injured patients, resulting in citrate and six other metabolites (pyroglutamic acid, serine, phenylalanine, galactose, palmitic acid, 2,3,4-trihydroxybutyrate, linoleic acid, and arachidonic acid) potentially serving as a potential biomarker panel for diagnosing moderate-severe traumatic brain injury in patients with cognitive impairments.

[0105] According to embodiments, both phenylalanine and citrate, along with the other metabolites presented in Table 3 that have a VIAVC p-value of <0.05, may provide an accurate, non-invasive, objective method of determining whether an individual is likely to have a brain injury (e.g., SRC), as indicated by the ROC curve and its corresponding AUC (FIG. 5).

[0106] According to embodiments, 3-Indoxysulfate (3-IS) was found to be upregulated following brain injury (e.g., SRC), with 3-IS being the feature found to have the second highest VIP score. 3-IS is a metabolic product of indole, which is itself a product of the amino acid tryptophan. Elevated levels of 3-IS are known to increase the levels of reactive oxygen species and induce oxidative stress, which may be associated with many of the pathophysiological changes following brain injury (e.g., SRC).

[0107] According to embodiments, the present apparatus and methods also detected that several additional target biomarkers (metabolites) were altered following brain injury, suggesting such biomarkers may be involved in nervous system impairment. For example, propylene glycol was identified by VIAVC analysis and had the fifth highest score in the VIP plot. In some embodiments, propylene glycol levels were found to be downregulated following brain injury (e.g., SRC).

[0108] According to embodiments, target biomarkers anserine and carnosine were both found to be increased following brain injury by the paired t-test.
Carnitine and acetylcarnitine were also found to be increased by VIAVC. Carnosine is highly concentrated in both muscle and brain tissues and has been implicated in suppressing biological processes in Alzheimer's disease, cardiovascular ischemic damage, and inflammatory diseases. Carnosine may also be a neuroprotective agent for ischemic stroke, which could also relate to similar effects following brain injury (e.g., SRC).
Acetylcarnitine has been shown to possess a neuroprotective ability for cerebral ischemia and has been studied for therapeutic purposes in patients with Alzheimer's disease, as well as for chronic fatigue syndrome. It is contemplated that such target biomarkers (metabolites) could be increased following brain injury as a potential protective mechanism following injury.

[0109] According to embodiments, target biomarkers 1-methylhistidine and 3-methylhistidine were found to be decreased following brain injury (e.g., SRC).

methylhistidine is primarily a measure of protein breakdown in muscle, with elevated levels being related to increased fatigue in individuals (e.g., athletes).
Although 1-methylhistdine is primarily derived from dietary anserine, high levels tend to inhibit the enzyme carnosinase, which increases anserine levels and affects the metabolism of carnosine. Reduced levels of serum carnosinase have been found in patients with multiple sclerosis, stroke, and Parkinson's disease. Decreased levels of 1-methylhistidine in biological samples could be related to carnosinase activity following brain injury, which may also explain why carnosine and anserine were found to be significantly altered.

[0110] According to embodiments, target biomarkers phosphorylcholine and 2-hydroxybutyrate were also altered post-brain injury, with phosphorylcholine levels being found to be upregulated and 2-hydroxybutyrate being downregulated.
Phosphorylcholine levels in brain tissue are known to change in a fluid percussion rat model of head injury, where choline-containing metabolites that are associated with the plasma membrane are reduced in cortex and hippocampus brain tissue (e.g., urinary). 2-hydroxybutyrate is also significantly decreased in human serum when compared to healthy controls and was positively correlated with the severity of head injury. It is understood that urinary excretion of 2-hydroxybutyrate can reflect shifts in the rate of glutathione synthesis, and therefore oxidative stress.

[0111] According to embodiments, urinary levels of target biomarkers tyrosine, methionine and 3,4-Dihydroxybenzeneacetic acid were found to be upregulated following brain injury. Urinary levels of tyrosine are known to be altered in patients diagnosed with major depressive disorder, a symptom that often occurs following head injury. As mentioned above, DA and NA are both involved in mood regulation and depression, and tyrosine is a precursor metabolite required to produce these two neurotransmitters. Urinary levels of methionine are known to be upregulated in mouse models of Alzheimer's disease (AD), while plasma levels are known to be decreased in patients with traumatic brain injuries. Finally, urinary levels of 3,4-dihydroxybenzeneacetic acid (DOPAC) are known to be upregulated following SRC.

DA is broken down into 3,4-dihydroxyphenylacetaldehyde (DOPAL), which is then metabolized primarily to DOPAC, this transformation being relevant as DOPAL
has neurotoxic actions and related to oxidative stress in Parkinson's disease.
Upregulated DOPAC may indicate changes to dopamine metabolism caused by SRC.

[0112] According to embodiments, target biomarkers leucine, isoleucine, and valine were found to be altered post-brain injury (e.g., SRC), each metabolite being branched chain amino acids (BCAA) which are particularly involved in stress, energy, and muscle metabolism. Plasma BCAA levels can be significantly decreased in TBI
patients. In some embodiments, leucine and isoleucine were found to be upregulated post-brain injury (e.g., SRC), with the pathway topology analysis supporting that that the degradation and biosynthesis of BCAA were affected, along with aminoacyl-tRNA
biosynthesis (FIG. 6). BCAA are transported across the blood brain barrier by the same, competitive mechanism as aromatic amino acids (AAA) such as phenylalanine and tyrosine. As noted above, these AAA were also significantly altered following brain injury, and are important to the synthesis and release of the catecholamine neurotransmitters DA and NA.

[0113] According to embodiments, the present apparatus and methodologies may also be used to determine whether an individual is likely to have a brain injury, and then to further determine the individual's symptom burden and length of recovery (e.g., return to play following SRC). For example, in some embodiments, the present methodologies were used to detect a positive correlation between length of return to sport and an increased concentration of target biomarker 2-hydroxyisobutyrate.

Without limitation, previous studies in individuals with severe traumatic brain injury have also shown a correlation between severity of injury and 2-hydroxybutyrate, suggesting 2-hydroxybutyrate may be a target metabolite that is altered following traumatic brain injury of all severities and may reflect more severe injury and prolong recovery.

[0114] According to embodiments, the present apparatus and methodologies may also be used to determine whether an individual is likely to have a brain injury, and then to further determine the total number of symptoms post-injury. For example, in some embodiments, the present methodologies were used to detect a positive correlation between total number of symptoms and an increased concentration of lactose. Without limitation, urine lactulose levels are known to be elevated in patients with head injury compared to healthy controls and patients with extra-cerebral injuries, where the more severe the head injury the higher the urine lactulose, and the increase in lactulose potentially reflecting increased catabolism of brain gangliosides following injury.

[0115] EXAMPLE 2: According to embodiments, the present example demonstrates the use of the present apparatus and methodologies for determining whether an individual is likely to have an injury, such as a brain injury.
More specifically, the present example demonstrates the use of the present apparatus and methodologies for the detection and diagnosis of at least one brain injury, such as a neurodegenerative brain disorder (e.g., Alzheimer's Disease, "AD"), and further for prognosing the at least one brain injury.

[0116] According to embodiments, the present example involved post-mortem brain tissue samples collected from individuals or subjects comprised of donors to the Calgary Brain Bank (Calgary, Alberta, Canada), with metabolomic changes being assessed across three regions of interest (ROI) at the end stages of AD
including the pontine base (PB), dentate nucleus (DN), and associated Brodmann Area BA 24 (part of the anterior cingulate cortex). Without being limited by theory, although various elements of AD pathology have been observed in each of these regions, the progression of AD in the regions of interest (ROI) are less well studied than in regions like the hippocampus and entorhinal cortex, which are known to be drastically devastated by AD.

[0117] Having regard to Table 4, 11 subjects were categorized as belonging to the Alzheimer's Disease group (AD) and 13 were categorized as control group (CN) based on three neuropathological scores: amyloid plaque distribution (indicated with an A), Break tau stage (indicated with a B), and CERAD score (indicated with a C;
collectively referred to as an 'ABC' score), with each category being individually ranked from one to three, and where the sum of all three categories was greater than five, the subject was classified as belonging to the diseased (AD) group. In some embodiments, biological samples obtained from control (CN) individuals served as a threshold baseline reference value or pre-disease metabolic profile for the at least one target biomarker.

[0118] Table 4: AZD patient characteristics Diagnosis Age Sex Description AD 89 M AD-A3B2C2; CAA-T3L3C1 AD 71 M AD-A3B3C3; LBD-M3B6 AD 85 M AD-Al B2C2;

AD 84 F LBD-M3B6; AD-A2B202 AD 71 M AD-A3B3C3, LBD-M3B5 AD 90 F AD-A3B3C3; CAA-T2L200 CN 74 M A0B100; PART-B

ON 60 M Normal ON 78 F Normal

[0119] More specifically, description relates to the amyloid distribution score (A), Braak tau stage score (B), and CERAD score (C), where each category was ranked from 1-3. If the sum of the ABC > 5, the individual was categorized as having AD. The diagnosis 'normal' indicates that the participant had no history of cognitive difficulties. Cerebral amyloid angiopathy (CAA) was ranked via the following codes:
Thal CAA stage (T) (0-3); Love CAA score (L) (0-3); CAA capillary vasculopathy (C) (0-1), Vasculopathy (V) (0-2). Lew Body Disease Neuropathological Changes (LBD) were scored via the following codes: McKeith stage (M) (0 - none; 1 -brainstem; 2-limbic; 3 - neocortical; 4 - amygdala); Braak stage (B) (0 none; 1 ¨ medulla;
2 ¨ pons;
3¨ nnidbrain; 4¨ transentorhinal and annygdala; 5¨ association neocortex; 6¨
primary neocortex); Unified staging system for Lewy body disease (U) (0 - none; 1 -olfactory bulb only; 2a - brainstem predominant; 2b - limbic predominant; 3 - brainstem and limbic; 4 - neocortical). Primary Age-Related Tauopathy (PART) was scored via Braak and Braak neurofibrillary tangles stage (B) (0-6). Exclusion criteria included ABC

scores equal to or greater than five and the individual exhibited dementia-related neuropathology, resulting the number of subjects being narrowed to 22 (n=11 AD, n=11 CN), where DN had 21 samples (n=10 AD, n=11 CN), PB had 19 samples (n =
8 AD, n = 11 CN), and BA 24 had 18 samples (n= 7 AD, n=11 CN).

[0120] Each of the biological samples (e.g., brain tissues) obtained from the subjects were prepared via ultrafiltration to extract water-soluble metabolites. Briefly, samples were removed from -80 C storage and allowed to thaw at room temperature while on ice until samples were ready to be weighed. Approximately 150 mg of each sample, 375 uL of metabolomics buffer, and 150 mg zirconium oxide beads were added to a centrifuge tube. The metabolomics buffer was a solution of 4:1 K2HPO4 to KH2PO4 in dH20 at a 0.625M concentration resulting in a final pH of 7.41. The buffer also contained 3.75 mM NaN3 to act as an antimicrobial agent. Samples were then homogenized using a Bullet Blender (Next Advance, NY, USA) for 1 minute at intensity setting 8 and the homogenate was then centrifuged for 5 minutes at 14,000 g.

[0121] Following this, 365 uL of homogenate and 135 uL of metabolomics buffer were transferred to an Am icon Ultra 0.5 ml 3K centrifuge filter and centrifuged for 30 min at 14,000 g. Centrifuge filters were washed 10 times with Millipore water before use to ensure complete glycerol removal from the membrane. Following filtration, 360 uL of the filtrate, an additional 120 uL of metabolomics buffer, and 120 uL of D20 with w/v 0.03% trimethylsilyl propanoic acid (TSP) were transferred to a new centrifuge tube. The final dH20:D20 ratio was 4:1, resulting in a final concentration of 0.5M for the buffer salts. The samples were then centrifuged for 5 minutes at 12 000 rpm and 550 uL of the supernatant was transferred to a 5mm NMR
tube.

[0122] NMR spectra were collected on a 700 MHz Bruker Avance III HD
spectrometer conducting 1024 scans per sample (Bruker, ON, Canada). Each NMR
spectrum was phased using TopSpin (v. 4Ø6.) using the TSP peak (0.00 ppm) and water peaks (4.95 ppm) were used as a reference for chemical shift. The spectra were exported to MATLAB (The MathWorks, MA, USA) to undergo further processing and statistical analysis. Spectral binning was done using the dynamic adaptive binning algorithm followed by manual inspection and correction for any errors. The binned spectra were then pareto scaled, normalized to the total unit area of all bins (excluding the water peak), and log transformed prior to carrying out statistical analysis.

[0123] All univariate and multivariate testing was carried out using MATLAB
(The MathWorks, MA, USA) and MetaboanalystR (v 2.0), respectively, and the decision tree algorithm was applied to determine which univariate statistical tests was appropriate for the data in each comparison. In all cases, a Mann-Whitney U
Test (MW) was used and bins with a p-value < 0.05 were considered significant.
Bonferroni-Holm correction was also applied to account for multiple comparisons.
Supervised multivariate data analyses were carried out, including, variable importance analysis based on random variable combination (VIAVC), orthogonal projections to latent structures discriminant analysis (OPLS-DA), receiver operator characteristic (ROC) curves, and predictive accuracy. Multivariate modelling was initially carried out on all bins and subsequently carried out for the bins determined to be significant by MW or VIAVC statistical testing. In the case of the ROC curve and predictive accuracy, only the bins determined to be significant by VIAVC testing were used.

[0124] Identification of at least one biomarker or metabolite was carried out for the bins determined to be significant by either the MW or VIAVC tests using Chenomx 8.2 NMR Suite (Chenomx Inc., Edmonton, Alberta, Canada). Target biomarkers were further verified using the Human Metabolite Database and only metabolites previously observed in brain tissue, cerebrospinal fluid (CSF), or blood were used.
Pathway topology analysis were carried out in Metaboanalyst (v 4.0) using the Homo sapien KEGG pathway library (v Oct.2019). The hypergeometric test was selected for over-representation analysis and relative-betweenness centrality was selected for pathway topology analysis.

[0125] The OPLS-DA statistical models produced from MW and VIAVC Best Subset bins were used to visualize class separation between bins for DN and BA
24.
Due to the small number of VIAC Best Subset bins (2 bins) for PB, an OPLS-DA
was constructed from MW and VIAVC F-Ranked bins. OPLS-DA models were used to remain consistent across a larger study.

[0126] The OPLS-DA models were used to visualize the supervised separation of data based on statistical differences between bins. The OPLS-DA p-values from further permutation (Q2 and R2Y) testing indicated that separation was not biased due to overfitting (p<0.05), with the exception of the results from BA 24. The Q2 value was insignificant for that region and failed permutation testing, indicating poor model quality. Though these multivariate analyses failed for this region, there were still significant and real differences between bins based on MW and VIAVC Best Subset tests.

[0127] Having regard to FIGS. 7A ¨ 7C, the VIAVC Best Subset was used for each region to construct ROC curves for pontine base (PB; FIG. 7A); for the dentate nucleus (DN; FIG. 7B); and for part of the anterior cortex (BA 24; FIG. 7C).
Table 5 provides the predictive accuracy, the area under the curve (AUC), number of bins used in the model, and metabolites corresponding to those bins for the ROC
models can be seen. For each region, the significant (p<0.05) MW and VIAVC Best Subset bins were used for metabolite identification.

[0128] Table 5: Summary of the number of VIAVC Best Subset bins used to build the ROC for each brain region and the predicative accuracy, 95%
confidence interval, AUC, and metabolites for each ROC.
Region Number Of Predictive 95% AUC Corresponding metabolites Bins Accuracy Confidence Interval PB 2 92.7% 0.901-1 0.993 Citrate, L-Isoleucine DN 7 81% 0.75-1 0.915 Serine/Glycyl-glycine, Trimethylamine N-oxide, Methylguanidine, Succinic acid, (R)-3-Hydroxybutyric acid, Unidentified singlet at 1.15 ppm, Unidentified doublet at 2.5275 PPm BA 24 3 94.9% 0.957-1 0.997 2-Hydroxy-3-methylvalerate, gamma-Aminobutyric acid, unidentified multiplot at 3.1425 PPm

[0129] Overall, 26 metabolites were identified from 53 bins for the PB
region, 27 metabolites were identified from 60 bins for the DN region, and 24 metabolites were identified from 61 bins for the BA 24 region, as identified from the MW and VIAVC
Best Subset bins.

[0130] Table 6: Summary of MW and VIAVC Best Subset significant bins for each brain region with the corresponding number of at least one target biomarker metabolite. Q2 and R2Y (and respective p- values) from the OPLS-DA built from the significant bins are also reported. For PB these values are from the OPLS-DA
constructed from the MW and VIAVC F-Ranked bins.
Reg ion Number Of Corresponding Q2 Value R2Y
Value Significant Bins Number Of Out Of Total Bins Identified Metabolites PB 53/299 26 0.624 (p<5e-04) 0.789 (p<5e-04) DN 63/355 27 0.512 (p=5e-04) 0.749 (p=0.004) BA 24 61/365 24 0.00601 0.727 (p=0.238) (p=0.0375)

[0131] Having regard to Table 7, at least one target biomarker change shared across all three regions include citrate (citric acid) (upregulated in all three regions), glycyl-glycine (downregulated in all three regions), and L-isoleucine (downregulated in all three regions. A positive sign indicates upregulation of the target biomarker in the AD group compared to CN, while a negative sign indicates a downregulation of the target biomarker between the AD and CN groups.

[0132] Table 7: Target Biomarkers/Metabolites Shared Across Regions Regulation per region Regions PB DN BA

All regions Citrate (-F) Citrate (+) Citrate (-F) Glycyl-g lycine (-) Glycyl-g lycine (-) Glycyl-g lycine (-) L-isoleucine (-) L-isoleucine (-) L-isoleucine (-) PB & DN Glutamate (+) Glutamate (-) Trimethylamine N-oxide (-) Trimethylamine N-oxide (-0 PB & BA 24 Choline (-F) Choline (-F) Glucose (-) Glucose (-) L-Ieucine (-) L-Ieucine (-) L-phenylalanine (-) L-phenylalanine (-) L-valine (-) L-valine (-) DN & BA 24 Beta ine (-) Betaine (-) Ethanol (+) Ethanol (+) Malonic acid/malonate Malonic (-0 acid/malonate (+)

[0133] For PB, the metabolites identified in the three most significant bins by MW p-value were L-leucine (p=1.06x10-4), pyruvatoxine (p=3.18x10-4) and L-phenylalanine (p=1.19x10-3).

[0134] For DN, two bins sharing alpha-ketoisovaleric acid and propylene glycerol were identified in the first and third most significant bins (p=1.013x10-3 and p=1.29x10-3). The second most significant bin contained 2-oxohexane (p=1.29x10-3).

[0135] For BA 24, the metabolites identified in the three most significant bins according to MW-p-value were: gamma-aminobutyric acid (GABA; p=1.19x10-3) 2-hydroxy-3-methylvaelrate (p=1 .77x10-3), and N-acetyl-L-aspartate (p=2.54x10-3).
Table 5 provides a list of metabolites from the VIAVC Best Subset that correspond to bins used for the construction of ROC curves.

[0136] Pathway topology analysis was carried out for each brain region with a p-value less than 0.05, as shown in FIGS. 8A ¨ 8C (with corresponding data shown in Tables 8 ¨ 10). Note regulation of each metabolites is shown in brackets beside its name, down regulation in the AD group compared to controls is indicated by negative sign and positive sign indicates upregulation.

[0137] Table 8: biochemical pathways identified from Pathway Topology Analysis via Metaboanalyst from MW and VIAVC Best Subset bins for PB, with corresponding metabolites, p-value, and impact score.
Metabolites Raw p Impact 1. Aminoacyl-tRNA L-Phenylalanine (-), L-Valine (-), L- 7.28E-05 0 biosynthesis Isoleucine (-), L-Leucine (-), L-Tyrosine (-), L-Glutamate (+) 2. Valine, leucine and L-Leucine (-), L-Isoleucine (-), L-Valine (-) 0.00019708 0 isoleucine biosynthesis 3.
Phenylalanine, tyrosine L-Phenylalanine (-), L-Tyrosine (-) 0.0014699 and tryptophan biosynthesis 4. Galactose metabolism D-Galactose (-), Glycerol (-F), myo-Inositol 0.0084019 0.05288 (+) 5. Phenylalanine L-Phenylalanine (-), L-Tyrosine (-) 0.010387 0.35714 metabolism 6. Valine, leucine and L-Valine (-), L-Isoleucine (-), L-Leucine (-) 0.024713 0 isoleucine degradation

[0138] Table 9: Biochemical pathways identified from Pathway Topology Analysis via Metaboanalyst from MW and VIAVC Best Subset bins for DN, with corresponding metabolites, p-value, and impact score.
Metabolites Raw p Impact 1. Valine, leucine and L-Threonine (-), 3-Methyl-2- 0.00017385 0 isoleucine oxobutanoic acid (+), L-Isoleucine (-) biosynthesis 2. 2. Butanoate (R)-3-Hydroxybutanoate (+), L- 0.001315 0 metabolism Glutamate (-), Succinate (+) 3. Alanine, aspartate and L-Glutamate (-), Citrate (+), 0.0082928 0.19712 glutamate metabolism Succinate (-F) 4.
Glyoxylate and Glycolate (-), Citrate (+), L-Glutamate 0.012055 0.11112 dicarboxylate (-) metabolism 5. Glycine, serine and Betaine (-), Threonine (-), Creatine (-) 0.013126 0.05034 threonine metabolism 6. Aminoacyl-tRNA L-Isoleucine (-), L-Threonine (-), L- 0.035735 0 biosynthesis Glutamate (-) 7. Citrate cycle (TCA Succinate (+), Citrate (+) 0.036843 0.12311 cycle)

[0139]
Table 10: Biochemical pathways identified from Pathway Topology Analysis via Metaboanalyst from MW and VIAVC Best Subset bins for BA 24, with corresponding metabolites, p-value, and impact score.
Metabolites Raw p Impact 1. Valine, leucine and L-Leucine (-), L- 0.00017385 0 isoleucine Isoleucine (-), L-biosynthesis Valine (-) 2. Aminoacyl-tRNA L-Phenylalanine 0.00062947 0 biosynthesis (-), L-Methionine (-), L-Valine (-), L-Isoleucine (-), L-Leucine (-) 3. Alanine, aspartate and N-Acetyl-L- 0.0082928 0.17308 glutamate metabolism aspartate (+), 4-Aminobutanoate (-F), Citrate (+) 4. Glycerophospholipid Choline 0.016662 0.03519 metabolism phosphate (-), Choline (+), Acetylcholine (-) 5. Valine, leucine and L-Valine (-), L- 0.022138 0 isoleucine degradation Isoleucine (-), L-Leucine (-)

[0140]
Significant (p<0.05) biochemical pathways common to all three regions were valine, leucine and isoleucine biosynthesis and am inoacyl-tRNA
biosynthesis.
BA 24 and DN shared the pathway alanine, aspartate and glutamate metabolism.
BA
24 and PB shared valine, leucine and isoleucine degradation. DN and PB did not share any similar pathways. The three most significant pathways for DN by p-value were valine, leucine and isoleucine biosynthesis, butanoate metabolism, and alanine, aspartate and glutamate metabolism. The three most significant pathways for PB
were aminoacyl-tRNA biosynthesis, valine leucine and isoleucine biosynthesis, and phenylalanine, tyrosine and tryptophan biosynthesis. The three most significant pathways for BA 24 were valine, leucine and isoleucine biosynthesis, aminoacyl-tRNA
biosynthesis, and alanine aspartate and glutamate metabolism.

[0141] The present example demonstrates alterations in at least one clinical biomarkers found in at least one biological sample of an individual that suffered an central or peripheral nervous system injury, such as a neurodegenerative disease or disorder. Several of the at least one target biomarkers indicative of injury processes showed characteristic alterations following injury.

[0142] In some embodiments, the present apparatus and methodologies provide for the detection of target AD-related metabolomic alterations in brain regions of interest including, without limitation, the pontine base (PB), the dentate nucleus (DN), and areas of the anterior cingulate cortex (BA 24).

[0143] In some embodiments, a change in at least one target biomarker concentration level was detected across all regions of interest, such target biomarkers including, without limitation, citrate and L-isoleucine. For example, an increase in the concentration level of citrate was detected, suggesting an up-regulation of the biomarker compared to a threshold baseline value, and a decrease in the concentration level of L-isoleucine was detected, suggesting a down-regulation of the biomarker compared to its threshold baseline value.

[0144] In some embodiments, a change in at least one target biomarker concentration level was detected, such at least one target biomarkers including, without limitation, L-leucine, L-valine, and choline. For example, a change in each of the foregoing at least one target biomarker levels were detected in both the BA 24 and PB regions of interest.

[0145] In some embodiments, a change in at least one target biomarker concentration level was detected, such at least one target biomarkers including, without limitation, betaine. For example, a change in the foregoing at least one target biomarker level was detected in both the BA 24 and DN regions of interest.

[0146] In some embodiments, a change in at least one target biomarker concentration level was detected, such at least one target biomarkers including, without limitation, glutamate. For example, a change in the foregoing at least one target biomarker level was detected in both the DN and PB regions of interest.

[0147] According to embodiments, without being limited to theory, the present example demonstrates that various altered target biomarkers may be indicative or changes in important biochemical pathways. For example, changes in valine, leucine and isoleucine biosynthesis, and am inoacyl-tRNA biosynthesis were significant in all the regions of interest. In BA 24 and DN, alanine, aspartate and glutamate metabolism was identified. Lastly, in BA 24 and PB, valine, leucine and isoleucine degradation was a significant pathway. Together these altered target biomarkers and pathways can indicate impairment in protein synthesis, neurotransmission, inflammation, and energy metabolism, thereby indicating the presence (diagnosis) and severity (prognosis) of an injury.

[0148] In all three ROI, am inoacyl-tRNA biosynthesis was identified as a significant pathway (p<0.05; Table 5-7). Altered am inoacyl-tRNA biosynthesis in the cytoplasm has been identified in AD patients plasma, CSF, and blood. Aminoacyl-tRNA biosynthesis involves esterifying an amino acid with its matching tRNA
molecule (corresponding with the correct anticodon triplet of the matching amino acid) via aminoacyl-tRNA synthetases. This process is essential for the synthesis of proteins, as the now 'activated' tRNA molecule (has the amino acid attached) brings the amino acid to the ribosome during protein synthesis.

[0149] Without being limited to theory, it is unclear from the results if aminoacyl-tRNA biosynthesis is indirectly affected by alteration of amino acid levels or if AD
pathology directly affects this biochemical pathway. However, it could be that the downregulation of the amino acids in these brain regions would affect their availability for protein synthesis. This change could impair protein synthesis in these regions.
Conversely, AD may directly affect elements of protein synthesis. For example, ribosome dysfunction has been observed in the inferior parietal lobe (BA 40), superior middle temporal gyri (BA22) but not in the cerebellum in the post-mortem brain tissue of AD compared to controls. Therefore, it appears that an impairment in protein synthesis was only indicated in the former two regions and not the latter. If indeed there are no alterations in ribosome function in the cerebellum, then in DN, at least the alterations in aminoacyl-tRNA biosynthesis could be due to upstream alterations in amino acid availability.

[0150] Alterations in brain chain amino acids (BCAAs; valine, leucine and isoleucine) were observed in the three ROI. For BA 24 and PB, all three BCAAs were downregulated. For DN, only L-isoleucine was down-regulated, while 3-methy1-2-oxobutanoic acid (alpha-ketoisovaleric acid/a-KIVA) was upregulated. A
reduction of BCAA has been seen in AD patients' brain tissue, CSF, and blood. Indeed, the identification of reduced BCAA in this study's brain regions supports it as a potentially stereotypical future of AD pathology. For DN, alterations of a-KIVA in the AD
group compared to controls indicate incomplete valine breakdown; specifically, it is produced via the second step of valine degradation via branch-chain amino transaminase ¨1.
Interestingly, BCAA degradation was not identified as a significant or impacted pathway for this brain region by MetaboAnalyst.

[0151] Additionally, valine, leucine and isoleucine biosynthesis was identified in all three brain regions. This identification is a physiological impossibility as the human body cannot synthesize the BCAAs. Regarding BA 24 and PB, though BCAA
biosynthesis does not occur, other processes could be affected. Indeed, for both these regions, valine, leucine and isoleucine degradation was a significant pathway, with the same metabolites altered in valine, leucine and isoleucine biosynthesis.
Therefore, the error could be on the part of our software, MetaboAnalyst, attributing changes in BCAA degradation to BCAA biosynthesis even though this cannot occur in humans.

[0152] The degradation of branch chain amino acids is the first step in producing glutamate in astrocytes. In PB, there was an increase in glutamate in the AD group compared to controls. Potentially, this indicates that the reduction of BCAA
in this region is due to an increase in glutamate synthesis. However, further study is needed to verify if the downregulation of BCAA in PB leads to the upregulation of glutamate via glutamate production (including analysis of enzyme regulation and expression). Unlike PB, glutamate was reduced in DN. This different change in glutamate in DN could suggest that in this regions, a mechanism other than glutamate production leads to the downregulation of BCAAs in those brain regions.

[0153] Regarding glutamate metabolism, the biochemical pathway alanine, aspartate and glutamate metabolism was identified as significant for DN and BA
24.

However, the identified metabolites involved in these pathways differed between the two regions. This difference indicates that potentially different elements of this biochemical pathway are impacted by AD. For DN, these identified metabolites were citrate and succinate (both upregulated) and glutamate (downregulated). The alterations in the former two metabolites and glutamate could suggest alterations in energy metabolism, particularly the citrate cycle, and highlights glutamate's role in energy metabolism rather than neurotransmission to be affected by AD in this region.

[0154] In the cortical and hippocampal regions, damage to glutamatergic neurons has been observed as part of AD pathology. Part of this damage in the HPC
is related to increased glutamate synthesis resulting in excitotoxicity.
Therefore, it is surprising that in DN, there is a reduction of glutamate in the AD group compared to controls. However, a similar reduction of glutamate concentration has been found in the posterior cingulate cortex of AD patients. As well, lower concentrations of glutamate and GABA have been associated with ageing in other brain regions.
Therefore, the reduction of glutamate could be a region-specific AD-related occurrence in DN (as well as for the posterior cingulate cortex), or the regulation reflects normal ageing processes and therefore may not be altered by AD.

[0155] For BA 24, N-acetyl-aspartate (NAA), GABA, and citrate were identified and all upregulated. Though all three of these metabolites are found within alanine, aspartate and glutamate metabolism, they are not directly connected.
Therefore, it is unlikely that the upregulation of one metabolite is connected to the upregulation of another metabolite. The upregulation of NAA found in BA 24 is unexpected as a reduction in NAA is a typical biomarker for AD. Regarding the upregulation of GABA

in BA 24, the same result was found in BA 22, BA 17, and BA 40 of the AD
cohort in this study. This change in regulation could be part of a broader trend for cerebral tissues; however, more research is needed.

[0156] Metabolism of another neurotransmitter, acetylcholine, may have been altered in BA 24. Glycerophospholipid metabolism was identified as a significant pathway, and the metabolites identified within this pathway (as well as their regulations) indicate that acetylcholine synthesis is impaired. It is important to note that BA 24 is densely innervated by the cholinergic system. Choline is formed from choline phosphate within acetylcholine synthesis via choline kinase alpha.
From choline, acetylcholine is produced via the enzyme choline 0-acetyltransferase (ChAT). Within BA 24, choline phosphate was down-regulated, choline was upregulated, and acetylcholine was downregulated in the AD group compared to controls. In the AD group, this could indicate an increased synthesis of choline synthesis from choline phosphate. However, within this group, less acetylcholine is being formed from choline, indicating impairment of the enzyme ChAT. ChAT and acetylcholinesterase (degrade acetylcholine into choline) have been observed to be reduced in post-mortem brain tissue of end-stage AD patients. The regulation of choline and acetylcholine in BA 24 indicates impairment of ChAT, not acetylcholinesterase (i.e., if acetylcholine was upregulated and choline downregulated, then acetylcholinesterase would be the enzyme of concern).
However, enzyme analysis would clarify which enzymes could be affected leading to these alterations. Lastly, it is unclear what is causing the initial downregulation of choline phosphate in this area. Analysis with other spectroscopy may reveal if other metabolites may be affected within this pathway, as lipid-based metabolites were removed from samples in preparation.

[0157] Betaine can be synthesized from choline or derived from diet (. In DN
and PB, betaine was both downregulated. The primary role of betaine is as an osmoprotectant. However, it also has a role in potentially preventing inflammation.
Potentially, the reduction of betaine in these regions may perpetuate inflammatory AD
pathology. Additionally, supplementation of betaine in the diet has been proposed to mitigate inflammatory processes and suppress the production of amyloid-beta plaques and phosphorylation of tau. Both inflammation and impaired energy metabolism are mechanisms of AD pathology.

[0158] Alterations in citrate in all three regions indicate the possibility of impaired energy metabolism, specifically the citric acid (TCA cycle).
Additionally, citrate was a biomarker in the region PB as it was one of the VIAVC Best Subset bins (see Table 3). However, this pathway was only identified as significant for DN. Both citrate and succinate were upregulated in the AD group compared to controls for this region. It is unclear how this pathway could be affected as no metabolites between citrate and succinate within the cycle were identified as altered. Both age and AD-related degeneration of mitochondria via oxidative stress affect oxidative phosphorylation. However, it is unclear if this contributed to the potential dysregulation observed presently.

[0159] Within BA 24, there was a down-regulation of glucose in the AD group compared to controls. This finding supports previous evidence of reduced glucose metabolism in the cingulate gyrus, hippocampus, and superior and medial temporal gyri. Dysregulation of glucose is a feature of AD pathology. Glucose hypometabolism has been found in the brain tissue of AD patients in the posterior cingulate but not the cerebellum. Hypometabolism of glucose (as measured with neuroimaging) is a biomarker of AD. However, it is hypothesized that A13 resistant cells in the AD brain survive due to increased glucose metabolism via increased use of the anaerobic glycolysis and pentose shunt, which is related to the upregulation of antioxidant mechanisms in these surviving cells. These mechanisms could lead to a reduction of glucose. Though the brain tissue of the AD patients could have been at various stages of decline, it should be noted that the various cells had survived up to the individual's death. Therefore, one can assume that at least some neurons were resistant to oxidative stress induced by A13. Additionally, areas less resistant (higher cortical regions) to AD pathology have been shown to have higher concentrations of glucose compared to controls. Therefore, more resistant areas may have lower concentrations of glucose. However, impaired cerebral glucose is a feature of AD pathology.
Therefore, it is vital to keep in mind that the reduction of glucose in this region could be due to less uptake of glucose within this region and not due to a survival mechanism. Enzymatic analysis and transporter analysis may elucidate the mechanism behind this result.

[0160] EXAMPLE 3: According to embodiments, the present example further demonstrates the use of the present apparatus and methodologies for determining whether an individual is likely to have an injury, such as a brain injury.
More specifically, the present example demonstrates the use of the present apparatus and methodologies for the detection and diagnosis of at least one brain injury, such as a neurodegenerative brain disorder (e.g., Alzheimer's Disease, "AD"), and further for prognosing the at least one brain injury.

[0161] According to embodiments, the present example involved post-mortem brain samples collected from individuals or subjects comprised of donors to the Calgary Brain Bank (Calgary, AB, Canada), with metabolic changes being assessed across three regions of interest (ROI) including in associative Brodmann Area (BA) 22 (superior temporal gyrus) and BA 40 (supramarginal gyrus) and are less common in the primary sensory area BA 17 (primary visual cortex).

[0162] Having regard to Table 11, 11 subjects were categorized as belonging to the Alzheimer's Disease group (AD) and 11 were categorized as control (CN) with the number of samples per region being 22 from BA 22 (n= 11 AD, n= 11 controls), 21 samples from BA 17 (n=10 AD, n=11 controls), and 16 samples from BA 40 (n=

AD, n=9 controls). Subjects were categorized based on three neuropathological scores: amyloid plaque distribution (indicated with an A), Braak tau stage (indicated with a B), and CERAD score (indicated with a C; collectively referred to as an `ABC' score), with each category being individually ranked on a scale from zero to three, with three being most deviated from typical non-diseased characteristics. If the summation of each of these rankings was greater than five, the tissue was classified as having AD. Samples were collected and scored by an experienced neuropathologist and stored at -80 C until sample preparation.

[0163] Table 11: AZD patient characteristics DIAGNOSIS AGE SEX ABC SCORE

ON 74 ivi A0B100 CN 60 M Normal ON 78 ivi A1B200 ON 78 F Normal

[0164] Each of the biological samples (e.g., brain tissues) obtained from the subjects were prepared via ultrafiltration to extract water-soluble metabolites. Briefly, tissue samples were removed from -80 C storage and allowed to thaw at room temperature while on ice until samples were ready to be weighed. Approximately mg of each sample, 375 uL of metabolomics buffer, and 150 mg of zirconium oxide beads were added to a centrifuge tube. The metabolomics buffer was a solution of 4:1 K2HPO4 to KH2PO4 in dH20 at a 0.625M concentration resulting in a final pH of 7.41.
The buffer also contained 3.75 mM NaN3 to act as an antimicrobial agent.
Samples were then homogenized using a Bullet Blender (Next Advance, NY, USA) for 1 minute at intensity setting 8 and the homogenate was then centrifuged for 5 minutes at 14,000 g.

[0165] Following this, 365 uL of homogenate and 135 uL of metabolomics buffer were transferred to an Am icon Ultra 0.5 ml 3K centrifuge filter and centrifuged for 30 min at 14,000 g. Centrifuge filters were washed 10 times with Millipore water before use to ensure complete glycerol removal from the membrane. Following filtration, 360 uL of the filtrate, an additional 120 uL of metabolomics buffer, and 120 uL of D20 with w/v 0.03% trimethylsilyl propanoic acid (TSP) were transferred to a new centrifuge tube. The final dH20:D20 ratio was 4:1, resulting in a final concentration of 0.5M for the buffer salts. The samples were then centrifuged for 5 minutes at 12 000 rpm and 550 uL of the supernatant was transferred to a 5mm NMR
tube.

[0166] NMR spectra were collected on a 700 MHz Bruker Avance III HD
spectrometer (Bruker, ON, Canada) equipped with a TBO-Z probe was used for NMR

data acquisition. Data was collected using the noesygppr1 d pulse sequence and the following acquisition parameters: number of scans (NS) = 1024, mixing time =
10 ms, spectral window = 20.5 ppm, total number of points (td) = 128k, total acquisition time = 4.56 s, transmitter offset (o1p) = 4.7ppm, and recycle delay (d1) = 1 second. NMR
spectra were manually phased using TopSpin (v 4Ø6) and the TSP peak was used as a reference for chemical shift. The spectra were exported to MATLAB (The MathWorks, MA, USA) for statistical analysis. Spectra were first binned using dynamic adaptive binning, and then manually adjusted to optimize number of variables and ensure accuracy. The binned spectra were then pareto scaled, normalized to the total unit area of all bins (excluding the water peak), and log transformed prior to carrying out statistical analysis.

[0167] All univariate and multivariate testing was carried out using MATLAB
(The MathWorks, MA, USA) and MetaboanalystR (v 2.0), respectively, and the decision tree algorithm was applied to determine which univariate statistical tests was appropriate for the data in each comparison. In all cases, a Mann-Whitney U
Test (MW) was used and bins with a p-value < 0.05 were considered significant.
Bonferroni-Holm correction was also applied to account for multiple comparisons.
Supervised multivariate data analyses were carried out, including, variable importance analysis based on random variable combination (VIAVC), orthogonal projections to latent structures discriminant analysis (OPLS-DA), receiver operator characteristic (ROC) curves, and predictive accuracy. Multivariate modelling was initially carried out on all bins and subsequently carried out for the bins determined to be significant by MW or VIAVC statistical testing. In the case of the ROC curve and predictive accuracy, only the bins determined to be significant by VIAVC testing were used.

[0168] Identification of at least one target biomarker (metabolite) was carried out for the bins determined to be significant by either the MW or VIAVC tests using Chenomx 8.2 NMR Suite (Chenomx Inc., Edmonton, Alberta, Canada). Target biomarkers were further verified using the Human Metabolite Database and only metabolites previously observed in brain tissue, cerebrospinal fluid (CSF), or blood were used. Pathway topology analysis were carried out in Metaboanalyst (v 4.0) using the Homo sapien KEGG pathway library (v Oct.2019). The hypergeometric test was selected for over-representation analysis and relative-betweenness centrality was selected for pathway topology analysis. The binning process resulted in 380, 378, and 364 bins for the BA 22, BA 40, and BA 17 regions, respectively. Out of these bins, 157, 84, and 110 were identified as significantly altered by either univariate MW or multivariate VIAVC testing for the BA 22, BA 40, and BA 17, respectively.

[0169] Having regard to FIGS. 9A ¨ 9C, the OPLS-DA modeling carried out using the significantly altered bins provide score plots that showed separation between the AD and CN tissues for regions of interest BA 22 (FIG.9A), BA 40 (FIG.
9B), and BA 17 (FIG. 9C). Cross validation of the BA 22 and BA 17 regions resulted in a good model quality (BA 22- Q2 = 0.738, p <0.001; BA 17¨ Q2 = 0.736, p <0.001) and total variance explained by the model (BA 22 - R2 = 0.867, p < 0.001; BA
17 ¨ R2 = 0.967, p < 0.01), indicating that the separation observed is not a result of model overfitting. However, cross validation for the BA 40 region had a poor quality of the model (Q2 = 0.173) and failed permutation testing (p = 0.107), indicating this result could be due to model over fitting.

[0170] Having regard to FIG. 9D, OPLS-DA modeling of the BA 40 tissues was carried out using only the bins determined to be significant by VIAVC testing resulting in separation of the two groups and passed cross validation and permutation testing with a model fit and total variance explained by the model (Q2 = 0.684, p =
0.0015; R2 = 0.834, p = 0.0015) that is similar to the other two regions.

[0171] Having regard to FIG. 10, the bins determined to be significant by VIAVC
testing were used to construct ROC curves for BA 11 (FIG. 10A), BA 40 (FIG.
10B), and BA 17 (FIG. 100). These ROC curves show that the target biomarker metabolites corresponding to the VIAVC bins for each region of interest lead to almost perfect class separation for all three brain regions as indicated by the high predictive accuracies, area under the curve (AUC) values close to or equal to one, and small confidence intervals.

[0172] Table 12: Summary of at least one target biomarker metabolite identified as being significant by either MW or VIAVC in BA 22.
Metabolite Identification VIAVC p- MW p- PPM PPM End Regulation value value Start FAPy-adenine 1.81E-02 8.372 8.361 43.42236 Niacinamide, Hydrochlorothiazide 1.51E-02 8.27 8.259 38.6102 Unidentified singlet at 8.226 or doublet at 8.221 1.03E-03 8.231 8.221 35.41009 Oxypurinol 2.52E-03 8.221 8.212 24.34414 N-Acetyl-L-aspartate 2.36E-04 7.996 7.969 56.34925 FAPy-adenine 7.10E-03 7.915 7.907 29.50864 Phenylalanine 4.88E-02 7.451 7.44 -24.02654 Phenylalanine 7.10E-03 7.44 7.43 -25.05343 Phenylalanine 3.86E-03 7.43 7.422 -25.61539 Phenylalanine 7.10E-03 7.399 7.39 -33.89216 Phenylalanine 2.52E-03 7.39 7.38 -38.70936 Phenylalanine 2.81E-04 3.04E-04 7.347 7.336 -41.3444 Phenylalanine 3.04E-04 7.336 7.327 -42.57734 Tyrosine 1.26E-02 7.208 7.198 -23.364 Tyrosine 1.04E-02 7.198 7.188 -25.04566 Tyrosine 3.02E-02 6.926 6.919 -21.75922 Tyrosine 8.62E-03 6.911 6.907 -25.00862 Unidentified doublet at 4.529 ppm 2.15E-02 4.533 4.529 24.41176 Unidentified doublet at 4.529 ppm 5.82E-03 4.529 4.525 25.24415 N-Acetyl-L-aspartate 6.39E-04 4.408 4.403 46.89239 N-Acetyl-L-aspartate 1.40E-04 4.403 4.397 51.24052 N-Acetyl-L-aspartate 1.40E-04 4.397 4.388 50.81335 N-Acetyl-L-aspartate 1.40E-04 4.388 4.383 47.14871 N-Acetyl-L-aspartate 1.40E-04 4.383 4.377 48.13157 N-Acetyl-L-aspartate 1.03E-03 4.377 4.372 40.70919 Malate 1.81E-02 4.313 4.306 17.23831 Malate 4.18E-02 4.302 4.299 26.54689 Malate 7.10E-03 4.299 4.294 25.14747 L-Threonine 3.02E-02 4.271 4.265 14.33152 Unidentified broad peak at 4.198ppm 3.13E-03 4.202 4.194 26.04036 Unidentified multiplet at4.172ppm 4.88E-02 4.191 4.187 16.5787 Unidentified multiplet 4.172ppm 4.88E-02 4.187 4.182 10.66606 L-Serine, Phenylalanine 4.88E-02 4.013 4.008 -17.00443 L-Serine, Phenylalanine 1.29E-03 4.008 4 -16.75821 L-Serine 4.18E-02 3.986 3.977 12.86407 L-Serine, Tyrosine, Glycolic acid 3.56E-02 3.97 3.964 -8.733203 Tyrosine, Creatine phosphate 2.56E-02 3.959 3.952 -13.2977 Tyrosine, Creatine phosphate 1.26E-02 3.952 3.945 -12.68411 Creatine 1.26E-02 3.945 3.932 11.32984 Mannose 1.03E-03 3.932 3.921 -45.62407 Mannose 3.86E-03 3.921 3.918 -43.93848 7-Methylxanthine 2.36E-04 3.918 3.909 -53.70398 Unidentified singlet at 3.906 1.62E-03 3.909 3.903 -26.42733 Unidentified singlet at 3.9005 5.01E-04 3.903 3.898 -31.7006 Unidentified singlet at 3.8945 8.15E-05 3.898 3.891 -50.31197 Betaine 4.75E-03 3.891 3.884 -51.3641 Mannose 1.26E-02 3.884 3.877 -24.90246 Mannose, L-Methionine 3.13E-03 3.877 3.868 -37.83963 Mannose, L-Methionine 3.56E-02 3.868 3.864 -36.55115 Mannose, L-Serine 1.29E-03 3.854 3.85 -31.31593 Mannose, L-Serine 7.10E-03 3.85 3.846 -18.30865 Glycerol, Guanidoacetate, L-Alanine 1.51E-02 3.789 3.785 -16.52483 L-Alanine, Glutamate 1.62E-03 3.782 3.772 -19.00188 Unidentifieded part of a doublet at 3.77 ppm 4.88E-02 3.772 3.768 -8.109975 Acetylcholine 2.15E-02 3.755 3.751 -49.29167 Acetylcholine, L-Leucine 7.10E-03 3.751 3.745 -38.01584 Mannose, L-Leucine 1.51E-02 3.741 3.737 -65.60145 Mannose, L-Leucine 5.82E-03 3.737 3.734 -64.78962 Mannose, L-Leucine 1.51E-02 3.731 3.724 -62.33471 Mannose 1.81E-02 3.696 3.69 -33.81872 Mannose 1.81E-02 3.69 3.685 -26.92146 Mannose 2.52E-03 3.685 3.68 -32.13628 Ethanol, Mannose 1.81E-02 3.68 3.674 -12.95804 Unidentified singlet peak at 3.635 ppm 1.03E-03 3.638 3.632 -24.32827 Unidentified singlet peak at 1.41E-05 8.15E-05 3.623 3.618 -19.48902 3.6205 ppm Phosphocholine 6.39E-04 3.61 3.605 -29.11 L-Valine, L-Threonine 1.81E-02 3.605 3.601 -17.74673 L-Valine, L-Threonine 7.10E-03 3.598 3.594 -19.16303 Mannose, Phosphocholine 1.51E-02 3.594 3.59 -17.26303 Unidentified singlet at 3.506ppm 2.15E-02 3.508 3.504 -153.5797 Unidentified singlet at 3.285 ppm 3.56E-02 3.33 3.324 15.08362 Unidentified singlet at 4.276ppm 3.13E-03 3.277 3.275 -17.78468 Unidentified singlet at 3.262 1.26E-02 3.266 3.258 -91.98722 Betaine 2.15E-02 3.258 3.251 -30.43985 Unidentified singlet at (singlet shoulder) at 1.04E-02 3.251 3.246 -68.90698 3.2485ppm 1,3,7-Trimethyluric acid 8.62E-03 3.246 3.24 -17.71505 Acetylcholine 3.13E-03 3.24 3.229 -48.11245 Unidentified peak at 3.2165 ppm 1.81E-02 3.219 3.214 19.785 1,9-Dimethyluric acid 1.81E-02 3.214 3.203 56.79816 Dimethyl sulfone 3.13E-03 3.153 3.148 46.02481 Unidentified broad singlet with shoulder at 3.13E-03 3.148 3.138 38.66216 3.143ppm Malonate 2.03E-03 3.138 3.132 30.06713 Cis-Aconitate 4.88E-02 3.124 3.116 18.53842 Unidentified singlet at 3.0875ppm 4.88E-02 3.092 3.083 12.51542 Unidentified singlet at 3.072 1.81E-02 3.074 3.07 36.6566 Creatine 2.03E-03 3.047 3.033 17.45481 Histamine 2.03E-03 3.02 3.013 27.98211 Histamine 2.36E-04 3.013 3.001 44.90641 Unidentified singlet peak at 2.9965 1.82E-04 3.001 2.992 48.78256 Unidentified singlet peak at 2.765 1.51E-02 2.759 2.753 31.87011 Unidentified singlet peak at 2.7495 3.86E-03 2.753 2.746 34.10191 Unidentified singlet peak at 2.743 3.02E-02 2.746 2.74 25.91736 N-Acetyl-L-aspartate 3.91E-04 2.709 2.702 30.68549 N-Acetyl-L-aspartate 1.07E-04 2.702 2.697 45.04516 Unidentified singlet at 2.694 ppm of a quartet 4.75E-03 2.697 2.691 21.80152 Citric acid 1.81E-02 2.691 2.686 19.28203 N-Acetyl-L-aspartate 1.40E-04 2.686 2.68 45.70028 N-Acetyl-L-aspartate 1.82E-04 2.68 2.672 52.68386 L-Methionine 3.02E-02 2.651 2.64 -19.84409 Unidentified broad singlet 2.567 ppm 1.04E-02 2.57 2.564 33.21796 Unidentified broad singlet at 2.562 ppm 2.56E-02 2.564 2.56 33.67739 Unidentified broad singlet at 2.557 ppm 2.56E-02 2.56 2.554 35.91216 Unidentified broad singlet at 2.552 ppm 2.56E-02 2.554 2.55 37.06089 Citric acid 7.10E-03 2.543 2.538 34.53084 Citric acid 8.62E-03 2.538 2.534 39.89098 N-Acetyl-L-aspartate 2.07E-04 1.07E-04 2.534 2.521 52.04648 N-Acetyl-L-aspartate 1.82E-04 1.07E-04 2.521 2.507 50.80757 N-Acetyl-L-aspartate 6.39E-04 2.507 2.497 37.53952 3-Hydroxymethylglutaric acid, pyruvic acid 1.51E-02 2.483 2.474 -41.41376 3-Hydroxymethylglutaric acid, 4-pyridoxate 1.51E-02 2.474 2.462 -49.00834 Unidentified singlet at 2.456ppm 1.51E-02 2.462 2.45 -47.00065 3-Hydroxymethylglutaric acid 1.51E-02 2.45 2.437 -40.33066 3-Hydroxymethylglutaric acid 1.81E-02 2.437 2.429 -35.39668 Glyoxylic acid, Glutamate 1.51E-02 2.387 2.381 16.87814 Glutamate 2.56E-02 2.379 2.373 17.1487 Glutamate, Pyruvate 7.10E-03 2.373 2.37 26.71487 Glutamate 3.56E-02 2.317 2.313 19.98687 Unidentified peak of a doublet at 2.308 ppm 3.91E-04 2.313 2.308 45.44882 Glutamate 3.86E-03 2.308 2.304 26.65472 Unidentified singlet at 2.2985 1.07E-04 2.302 2.295 51.39959 Unidentified singlet at 2.2885ppm 3.04E-04 2.292 2.285 40.50099 Unidentified peaks at 2.1665ppm 4.88E-02 2.17 2.163 -14.39843 L-Methionine 1.81E-02 2.163 2.157 -27.29842 Unidentified broad peak at 2.155ppm 1.51E-02 2.157 2.153 -26.80173 2-Amino-3-phosphonoprionic acid 4.88E-02 2.153 2.149 -10.37848 Acetylcholine 3.13E-03 2.149 2.137 -20.78833 2-Amino-3-phosphonoprionic acid 4.18E-02 2.137 2.133 -9.960317 Glutamate 1.81E-02 2.064 2.054 18.50034 Glutamate 6.39E-04 2.041 2.034 27.23617 N-Acetyl-L-aspartate 1.82E-04 2.034 2.021 57.502 Gamma-Aminobutyric acid 8.11E-04 1.914 1.901 29.61986 Gamma-Aminobutyric acid 1.29E-03 1.901 1.892 30.92844 L-Leucine 2.56E-02 1.752 1.746 -18.6471 L-Leucine 3.86E-03 1.746 1.738 -23.78011 L-Leucine, 2-Amino-3-phosphonopropionic acid 1.04E-02 1.738 1.729 -23.98428 L-Leucine, 2-Amino-3-phosphonopropionic acid 2.52E-03 1.729 1.722 -25.46223 L-Leucine, 2-Amino-3-phosphonopropionic acid 1.26E-02 1.714 1.707 -21.40546 L-Leucine 7.10E-03 1.707 1.698 -24.27505 L-Leucine 3.02E-02 1.69 1.684 -17.77244 L-Leucine 4.18E-02 1.68 1.674 -11.82181 Unidentified doublet at 1.148 ppm 3.02E-02 1.153 1.147 63.03259 3.02E-02 1.144 1.138 64.86548 L-Valine 2.03E-03 1.055 1.045 -28.71924 L-Valine 1.62E-03 1.045 1.035 -29.60371 L-Isoleucine 1.62E-03 1.022 1.013 -30.71506 L-Isoleucine 3.13E-03 1.013 1.004 -29.07401 L-Valine 5.82E-03 1.004 0.9945 -24.92976 L-Valine 1.26E-02 0.9945 0.9849 -24.65163 L-Leucine 6.39E-04 0.969 0.9592 -33.43016 L-Leucine 6.39E-04 0.9592 0.9477 -33.52599 L-Isoleucine 3.86E-03 0.9477 0.9369 -32.72504 L-Isoleucine 2.15E-02 0.9369 0.9271 -20.87773 Unidentified singlet at 0.08278 ppm 1.81E-02 0.08497 0.08059 38.49853

[0173]
Table 13: Summary of at least one target biomarker metabolite identified as being significant by either MW or VIAVC in BA 40.
Metabolite ID VIAVC p- MW p-value PPM
PPM Regulation value Start End Phenylalanine 3.11E-02 7.44 7.43 -44.964149 Phenylalanine 1.15E-02 7.43 7.418 -46.465674 Phenylalanine 3.11E-02 7.389 7.378 -42.799754 Phenylalanine 1.15E-02 7.346 7.336 -49.087759 Phenylalanine 1.15E-02 7.336 7.326 -48.925159 Tyrosine 1.15E-02 7.211 7.197 -38.450455 Tyrosine 1.64E-02 7.197 7.185 -35.346518 Histamium/Histamine 4.18E-02 7.149 7.132 -28.600318 Tyrosine 7.87E-03 6.928 6.915 -38.568822 Tyrosine 3.32E-03 6.915 6.903 -39.196092 Glucose 3.11E-02 4.676 4.663 -105.9544 N-Acetyl-L-aspartate 1.64E-02 4.397 4.388 35.1619327 N-Acetyl-L-aspartate 4.18E-02 4.388 4.383 36.08903659 Glycerophosphocholine (sn-Glycero-3- 3.11E-02 4.349 4.318 -46.90306 phosphocholine) Phosphorylcholine 1.96E-06 4.192 4.187 -6.0339389 2-Hydroxyvalerate 3.11E-02 4.047 4.039 -35.375208 L-Serine, Phenylalanine 2.29E-02 4.022 4.018 -40.694512 L-Serine, Phenylalanine 2.10E-03 4.018 4.008 -45.933932 L-Serine 2.29E-02 4.008 4 -25.566239 Tyrosine, L-Serine, Caffeine 2.29E-02 3.97 3.962 -27.296063 Tyrosine, Glycerophosphocholine, Glycolate 1.64E-02 3.962 3.954 -28.543817 Creatine phosphate, Glycerophosphocholine 2.29E-02 3.954 3.945 -25.59149 Glycerophosphocholine 1.64E-02 3.93 3.918 -57.39949 Aspartate, Syringate, Glucose 2.29E-02 3.918 3.909 -73.926098 Aspartate, Syringate 3.11E-02 3.903 3.898 -39.064346 Aspartate, Betaine, Glucose, 7.87E-03 3.898 3.89 -75.17905 Glycerophosphocholine Glycerophosphocholine, Homovanillic acicd 1.15E-02 3.89 3.884 -64.091916 Glycerophosphocholine 1.15E-02 3.884 3.877 -39.026185 Glycerophosphocholine, 0-Acetylcarnitine 1.64E-02 3.877 3.868 -55.661784 Glycerophosphocholine, 0-Acetylcarnitine 1.15E-02 3.868 3.859 -45.026752 L-serine, Glucose 2.29E-02 3.859 3.854 -44.895707 L-serine, Glucose, 0-Acetylcarnitine 7.87E-03 3.854 3.846 -45.097877 N-Acetylglycine, Acetylcholine, Glucose 5.24E-03 3.751 3.744 -56.027065 Methylacetoacetic acid, Glucose, Leucine 1.64E-02 3.741 3.734 -57.658134 Methylacetoacetic acid, Glucose, Leucine 1.15E-02 3.731 3.725 -82.7898 Ethanol 7.87E-03 3.674 3.67 23.9305118 0-Acetylcarnitine, Glycerophosphocholine 5.24E-03 3.637 3.633 -44.927639 Glycerophosphocholine, 1.32E-07 3.32E-03 3.622 3.618 -32.790992 Sarcosine Phosphorylcholine 1.64E-02 3.608 3.605 -34.910139 Valine, Glycerophosphocholine 1.64E-02 3.605 3.601 -26.953009 Phosphorylcholine 4.18E-02 3.601 3.597 -25.449666 Valine, Phosphocholine 3.11E-02 3.597 3.594 -25.537262 Phosphorylcholine 3.11E-02 3.594 3.589 -26.714297 Glucose, Proline 1.64E-02 3.405 3.398 -81.903175 1,3,7-Trimethyluric acid, Caffeine 3.11E-02 3.357 3.351 -23.067843 Glucose 5.24E-03 3.267 3.258 -119.85795 Arginine 1.64E-02 3.258 3.251 -45.913487 Arginine 4.18E-02 3.248 3.241 -42.213845 Arginine 2.29E-02 3.241 3.229 -53.523421 Tyrosine 1.64E-02 3.198 3.189 -26.884251 N-Nitrosodimethylamine, Phenylalanine 1.15E-02 3.169 3.162 -31.347884 Methylmalonate, Phenylalanine 4.18E-02 3.162 3.153 -24.221963 Dimethylsulfone 1.53E-08 3.153 3.148 29.5872388 Unidentified singlet at 3.0615 2.53E-11 3.065 3.058 17.3150557 PPm Histamine, Histamium 2.29E-02 3.03 3.021 -26.007227 N-Acetyl-L-aspartate 1.64E-02 2.703 2.697 34.8847998 N-Acetyl-L-aspartate 3.11E-02 2.686 2.68 38.5052793 0-Acetylcarnitine 2.10E-03 2.652 2.642 -36.353133 0-Acetylcarnitine 3.11E-02 2.642 2.632 -33.309771 N-Acetylaspartate 4.18E-02 2.534 2.521 33.3321864 Gamma-Aminobutyric acid 6.41E-08 2.303 2.293 31.6719877 L-Valine 4.18E-02 2.264 2.255 -29.149154 L-Valine, Citramalic acid 1.64E-02 2.255 2.244 -31.304509 D-Ribose, Acetone, Levulinate 2.29E-02 2.244 2.233 -40.567464 N-Acetylaspartate, Acetamide 1.15E-02 2.034 2.02 45.614812 Proline, Canthaxanthin 2.29E-02 2.012 2.003 -26.762604 Proline, L-Isoleucine 2.29E-02 2.003 1.994 -25.720411 Proline, L-Isoleucine 1.15E-02 1.994 1.985 -25.143854 L-isoleucine, Proline 2.29E-02 1.985 1.976 -25.178743 L-isoleucine, Proline 4.18E-02 1.976 1.967 -24.598484 Argine, 2-Amino-3-phosphonopropionic acid, 6.99E-04 1.757 1.707 -45.089628 Leucine, 2-Hydroxyvalerate 2-Amino-3-phosophonopropionic acid, 2.10E-03 1.707 1.696 -40.086156 Leucine, 2-Hydroxyvalerate, Arginine Leucine, 2-Hydroxyvalerate 3.50E-04 1.696 1.681 -44.639008 2-Amino-3-phosphonopropionic 2.25E-07 7.87E-03 1.681 1.671 -38.385183 acid, Arginine, 2-Hydroxyvalerate, Leucine, L-Isoleucine 1.15E-02 1.457 1.444 -33.141345 L-Valine 6.99E-04 1.056 1.045 -47.300641 L-Valine 6.99E-04 1.045 1.033 -45.885765 L-Isoleucine 1.75E-04 1.025 1.012 -52.073343 L-Isoleucine 7.87E-03 1.012 1.004 -40.494063 L-Valine 7.87E-03 1.004 0.9948 -35.618616 L-Valine 2.10E-03 0.9948 0.984 -35.230846 Leucine 6.99E-04 0.9791 0.9484 -49.238603 L-Isoleucine 6.99E-04 0.9484 0.938 -52.720803 L-Isoleucine 2.10E-03 0.938 0.9279 -42.579416

[0174] Table 14: Summary of at least one target biomarker metabolite identified as being significant by either MW or VIAVC in BA 17.
Metabolite ID VIAVC MW p-value PPM Start PPM End Regulation p-value Oxypurinol 4.48E-02 8.221 8.209 21.9998268 N-Acetyl-L-aspartate 3.78E-02 8.005 7.96 27.0531674 Phenylalanine 3.78E-02 7.441 7.43 -38.299915 Phenylalanine 4.48E-02 7.43 7.421 -34.996608 Phenylalanine 1.51E-02 7.353 7.337 -42.727464 Phenylalanine 3.78E-02 7.337 7.326 -38.66171 Myo-Inositol 3.78E-02 4.091 4.075 27.3063164 Ascorbate 2.65E-02 4.029 4.022 -25.885135 L-Serine 1.24E-02 4.022 4.017 -31.552766 L-Serine, Phenylalanine 2.21E-02 4.017 4.008 -37.582737 L-Serine, Phenylalanine 3.17E-02 4.008 3.999 -24.734428 L-Serine 9.22E- 1.02E-02 3.992 3.985 -21.423341 L-Serine 4.48E-02 3.985 3.979 -15.566777 Creatine phosphate, 3.78E-02 3.97 3.961 -11.914741 Galactarate Tyrosine, 1.83E-02 3.959 3.953 -17.454935 Glycerophosphocholine Tyrosine, 3.78E-02 3.953 3.945 -22.591442 Glycerophosphocholine Creatine 2.65E-02 3.945 3.932 22.9048956 Glycerophosphocholine, 2.65E-02 3.932 3.917 -39.870954 Uridine Unidentified singlet 3.9125 1.83E-02 3.917 3.908 -50.018275 PPm Unidentified singlet at 3.9055 1.83E-02 3.908 3.903 -13.871078 PPm Unidentified singlet 3.9 ppm 1.51E-02 3.903 3.897 -23.106893 Glycerophosphocholine 5.41E-03 3.897 3.89 -49.025992 Glycerophosphocholine 1.06E-03 3.89 3.885 -42.600087 Glycerophosphocholine 1.06E-03 3.885 3.877 -32.312468 Glycerophosphocholine 4.35E-03 3.877 3.867 -41.922764 Glycerophosphocholine 1.02E-02 3.867 3.859 -28.039859 L-Serine, Glycyl-glycine 4.48E-02 3.859 3.846 -34.930337 L-Serine 8.27E-03 3.846 3.842 -27.758811 Ascorbate 5.41E-03 3.754 3.75 -41.62794 Ascorbate 2.19E-03 3.75 3.745 -43.39917 Methylacetoacetic acid 2.76E-03 3.745 3.74 -48.857062 Ascorbate, Leucine 4.48E-02 3.74 3.737 -24.557959 Leucine, Ascorbate 5.41E-03 3.737 3.734 -53.681668 Ascorbate 4.48E-02 3.734 3.731 -38.351622 Methylacetoacetic acid, N,N- 2.19E-03 3.731 3.725 -69.614732 Dimethylglycine Glycerophosphocholine 2.76E-03 3.71 3.702 -49.425408 Glycerophosphocholine, L- 6.71E-03 3.696 3.689 -28.025211 Isoleucine Glycerophosphocholine, L- 3.47E-03 3.685 3.679 -24.875937 Isoleucine Glycerol 1.73E- 1.24E-04 3.674 3.67 22.5957407 Glycerol, Ethanol 4.35E-03 3.67 3.662 26.3036986 Glycerol, Ethanol 2.76E-03 3.662 3.652 27.7924566 Glycerol, Ethanol 6.37E-04 3.652 3.645 23.9757005 Myo-Inositol 2.21E-02 3.645 3.638 18.6745661 Glycerophosphocholine 2.21E-02 3.638 3.632 -14.119599 Myo-Inositol 3.17E-02 3.632 3.623 22.4048476 L-Valine 1.51E-02 3.623 3.618 -8.2030568 L-Threonine, 0- 8.27E-03 3.598 3.59 -14.875811 Phosphocholine Glycerol 1.36E-03 3.587 3.576 26.086604 Glycine 1.02E-02 3.574 3.565 19.4605806 Glycerol 1.58E- 2.18E-04 3.565 3.561 21.543914 Myo-Inositol 1.24E-02 3.561 3.557 24.0179321 Myo-Inositol, Glycerol 1.02E-02 3.557 3.549 22.2202148 Myo-Inositol 1.83E-02 3.543 3.537 18.8493879 Glucose 6.37E-04 3.511 3.502 -122.13447 Glucose 5.41E-03 3.502 3.489 -109.56485 Glucose 4.35E-03 3.489 3.481 -79.423072 Glucose 1.02E-02 3.481 3.467 -108.91944 Glucose 3.47E-03 3.467 3.457 -76.570989 Glucose 1.83E-02 3.449 3.437 -55.101671 Glucose 2.21E-02 3.437 3.422 -81.777884 1,3,7-Trimethyluric acid 2.65E-02 3.422 3.414 -49.959174 Glucose 2.21E-02 3.414 3.411 -55.728939 Glucose 2.21E-02 3.405 3.398 -41.929977 Methanol 3.17E-02 3.37 3.357 38.9460235 Myo-Inositol, 1-Methyluric 2.21E-02 3.303 3.294 24.9961196 acid Trimethylamine N-oxide 1.06E-03 3.267 3.258 -93.572982 Arginine, Glucose 8.23E-04 3.258 3.25 -40.824445 1,3,7-Trimethyluric acid, 8.23E-04 3.25 3.24 -50.480258 Arginine, 0-Phosphocholine, 8.27E-03 3.24 3.229 -33.051289 Glycerophosphocholine Citicoline 1.02E-02 3.219 3.213 19.4316238 Unidentifed singlet 2.87E-04 3.213 3.202 78.2600306 3.2075ppm Unidentified peak (multiple!) 2.76E-03 3.148 3.14 36.2074301 at 3.144ppm Malonate 5.41E-03 3.14 3.129 29.2301434 Creatine 2.76E-03 3.046 3.03 26.7269151 Gamma-Aminobutyric acid 1.73E-03 3.011 2.999 28.7004455 Gamma-Aminobutyric acid 2.21E-02 2.999 2.988 21.1472718 N-Acetyl-L-aspartate 1.73E-03 2.709 2.702 28.3004901 N-Acetyl-L-aspartate 5.41E-03 2.702 2.697 36.1768345 N-Acetyl-L-aspartate 6.71E-03 2.686 2.68 38.6220897 N-Acetyl-L-aspartate 1.73E-03 2.68 2.671 36.1922092 Selenomethionine 3.78E-02 2.652 2.642 -24.978054 N-Acetyl-L-aspartate 2.19E-03 2.532 2.52 38.7212969 N-Acetyl-L-aspartate 1.06E-03 2.52 2.507 40.7265782 N-Acetyl-L-aspartate 2.76E-03 2.507 2.498 31.9251003 N-Acetyl-L-aspartate 3.47E-03 2.495 2.483 25.5878949 Gamma-Aminobutyric acid 1.73E-03 2.304 2.293 36.4934594 Gamma-Aminobutyric acid 4.48E-02 2.293 2.283 22.4911007 N-Acetyl-L-aspartate 8.27E-03 2.064 2.054 18.824413 N-Acetyl-L-aspartate, 1.73E-03 2.034 2.02 54.6645004 Acetamide Gamma-Aminobutyric acid 3.17E-02 1.913 1.9 15.2595026 Gamma-Aminobutyric acid 6.71E-03 1.9 1.892 21.4555295 L-Arginine, L-Leucine 4.35E-03 1.756 1.746 -33.423955 2-Amino-3- 1.51E-02 1.746 1.713 -39.490472 phosphonopropionic acid, L-Arginine, L-Leucine, 2-Amino-3- 4.48E-02 1.713 1.708 -26.594356 phosphonopropionic acid, L-Leucine, L-Arginine L-Leucine 1.36E-03 1.708 1.696 -36.686028 2-Amino-3- 1.73E-03 1.696 1 683 -38.446794 phosphonopropionic acid, Leucine Ethanol 4.48E-02 1.193 1.183 36.7667617 Ethanol 4.48E-02 1.183 1.172 32.3208462 L-Valine 8.23E-04 1.056 1.045 -37.065 L-Valine 1.36E-03 1.045 1.032 -38.057687 L-Isoleucine 1.06E-03 1.024 1.012 -41.399388 L-Isoleucine 2.19E-03 1.012 1.004 -32.499884 L-Valine 6.71E-03 1.004 0.9939 -23.121975 L-Valine 4.35E-03 0.9939 0.9831 -23.343468 L-Leucine 1.83E-02 0.979 0.9481 -42.473997 L-Isoleucine 6.37E-04 0.9481 0.9367 -45.857017 L-Isoleucine 3.47E-03 0.9367 0.9266 -39.198252

[0175]
According to embodiments, at least one target biomarker change shared across all regions of interest include 1,3,7-trimethyluric acid, 2-am ino-3-phosphonopropionic acid, ethanol, gamma-aminobutyric acid (GABA), isoleucine, leucine, N-Acetyl-L-aspartate (NAA), phenylalanine, serine, tyrosine, and valine (Table 15).

[0176]
Table 15: common and noteworthy metabolites Identified from MW and VIAVC Bet Subset bins. A positive sign indicates upregulation of the target biomarker in the AD group compared to CN, while a negative sign indicates a downregulation of the target biomarker between the AD and CN groups.
Regulation per region Regions BA22 BA 40 BA17 All regions NAA (+) NAA (+) NAA (+) Ethanol (-) Ethanol (+) Ethanol (+) 2-Amino-3-Phosphonoprionic 2-Amino-3- 2-Amino-3-acid (-) Phosphonoprionic acid (-Phosphonoprionic acid (-) 1,3,7-Trimethyluric acid (-) ) 1,3,7-Trimethyluric acid (-Creatine Phosphate (-) 1,3,7-Trimethyluric acid ) GABA (+) (-) Creatine Phosphate (-) L-Leucine (-) Creatine Phosphate (-) GABA
(+) L-Isoleucine (-) GABA (+) L-Leucine (-) L-Phenylalanine (-) L-Leucine (-) L-Isoleucine (-) L-Serine (-) L-Isoleucine (-) L-Phenylalanine (-) L-Tyrosine (-) L-Phenylalanine (-) L-Serine (-) L-Valine (-) L-Serine (-) L-Tyrosine (-) Phosphorylcholine (-) L-Tyrosine (-) L-Valine (-) L-Valine (-) Phosphorylcholine (-) Phosphorylcholine (-) BA 22 & BA Acetylcholine (-) Acetylcholine (-) 40 Betaine (-) Betaine (-) Dimethyl sulfone (+) Dimethyl sulfone (+) Glycolate/Glycolic acid (-) Glycolate/Glycolic acid (-Histamine (+) ) Histamine (-) BA 22 & BA Creatine (+) Creatine (+) 17 Glycerol(-) Glycerol (+) L-Threonine (-) L-Threonine (-) Malonate (+) Malonate acid (+) Oxypurinol (+) Oxypurinol (+
BA 40 & BA Arginine (-) Arginine (-) 17 Glucose (-) Glucose (-) Glycerophosphocholine (-) Glycerophosphocholine (-Notable Glutamate (+) Myo-Inositol (+) metabolites Citric acid (+) Glycerol (+) unique to Cis-Aconitate (-F) each region Malate (+) Pyruvate (+)

[0177] Metabolites common to pairs included acetylcholine for BA22 and BA40, as well as glucose for BA40 and BA17. Unique metabolites for BA22 were glutamate and citric acid, while myoinositol and glycerol were unique metabolite for BA17. The three most altered metabolites for each region, as determined by MW p-value, corresponding to the following: BA 22 - NAA, 7-methylxanthine, and histamine;

- L-isoleucine, leucine and 2-hydroxyvalerate; BA 17 ¨ glycerol, ethanol, and glucose.

[0178] Pathway topology analysis was carried out for each brain region, as shown in Tables 16 - 18.

[0179] Table 16: biochemical pathways identified from pathway topology analysis using metabolites identified from either univariate MW or multivariate VIAVC
best subset tests for BA 22, with corresponding p-value and impact score.
Pathway Metabolites Raw p-value Impact 1. Aminoacyl-tRNA biosynthesis L-Alanine (-), L-Glutamate (+), L-1.00E-07 0.16667 Isoleucine (-), L-Leucine (-), L-Methionine (-), L-Phenylalanine (-), L-Threonine (-F), L-Tyrosine (-), L-Serine (+), L-Valine (-) 2. Glyoxylate and dicarboxylate Cis-Aconitate (+), Citric acid (+), Glycolic 7.77E-06 0.37302 metabolism acid (-), Glyoxylic acid (+), L-Glutamate (+), L-Serine (+), Pyruvate (+) 3. Glycine, serine and threonine Creatine (+), Betaine (-), Glyoxylic acid 9.69E-06 0.29355 metabolism (-F), Guanidoacetate (-), L-Serine (-F), L-Threonine (+), Pyruvate (+) 4. Valine, leucine and isoleucine L-Leucine (-), L-Isoleucine (-), L- 2.23E-05 0 biosynthesis Threonine (+), L-Valine (-) 5. Arginine and proline Creatine (+), Creatine-phosphate (-), 2.61E-05 0.14543 metabolism GABA (+), Glyoxylic acid (+), Guanidoacetate (-), L-Glutamate (+), Pyruvate (+) 6. Alanine, aspartate and GABA (+), Citric acid (+), L-Alanine (-), 4.29E-05 0.3702 glutamate metabolism L-Glutamate (+), N-Acetyl-L-aspartate (+), Pyruvate (+) 7. Citrate cycle (TCA cycle) Citric acid (+), Cis-Aconitate (+), Malate 1.24E-03 0.23087 (+), Pyruvate (+) 8. Phenylalanine, tyrosine and L-Phenylalanine (-), L-Tyrosine (-) 3.59E-03 1 tryptophan biosynthesis 9. Phenylalanine metabolism L-Phenylalanine (-), L-Tyrosine (-) 2.44E-02 0.35714 10. Cysteine and methionine L-Methionine (-), L-Serine (-F), Pyruvate 4.77E-02 0.1263 metabolism (+)

[0180]
Table 17: biochemical pathways identified from pathway topology analysis using metabolites identified from either univariate MW or multivariate VIAVC
best subset tests for BA 40, with corresponding p-value and impact score.
Pathway Metabolites Raw p Impact 1. Aminoacyl-tRNA biosynthesis L-Arginine (-), L-Aspartate (-), L- 3.55E-07 0.16667 Isoleucine (-), L-Phenylalanine (-), L-Proline (-), L-Serine (-), L-Tyrosine (-), L-Valine (-) 2. Valine, leucine and isoleucine L-Isoleucine (-), L-Leucine (-), L-Valine 0.00050157 0 biosynthesis (-) 3. Phenylalanine, tyrosine and L-Phenylalanine (-), L-Tyrosine (-) 0.0027272 1 tryptophan biosynthesis 4. Arginine and proline metabolism L-Arginine (-), L-Proline (-), Creatine 0.008406 0.15951 phosphate (-), GABA (+) 5. Valine, leucine and isoleucine L-Isoleucine (-), L-Leucine (-), L-Valine 0.010089 0.02264 degradation (-), Methylmalonate (-) 6. Phenylalanine metabolism L-Phenylalanine (-), L-Tyrosine (-) 0.018829 0.35714 7. Alanine, aspartate and N-Acetyl-L-aspartate (+), L-Aspartate 0.021727 0.39664 glutamate metabolism (-), GABA (+) 8. Arginine biosynthesis L-Arginine (-), L-Aspartate (-) 0.036045 0.07614 9. Glycerophospholipid metabolism Acetylcholine (-), Phosphorylcholine (-0.042036 0.05751 ), Glycerophosphocholine (-) 10. Histidine metabolism Histamine (-), L-Aspartate (-) 0.046252 0.18852

[0181]
Table 18: biochemical pathways identified from pathway topology analysis using metabolites identified from either univariate MW or multivariate VIAVC
best subset tests for BA 17, with corresponding p-value and impact score.
Pathway Metabolites Raw p Impact Glycine (+), L-Arginine (-), L-Isoleucine (-), L-1. Aminoacyl-tRNA Leucine (-), L-Phenylalanine (-), L-Serine (-), L-Threonine (-), L-Tyrosine (-), L-Valine (-) 2.67E-07 biosynthesis 0.16667 2. Valine, leucine and L-Isoleucine (-), L-Leucine (-), L-Threonine (-isoleucine biosynthesis ), L-Valine (-) 1.13E-05 3. Glycine, serine and Creatine (+), Glycine (+), L-Serine (-), L-threonine metabolism Threonine (-), N,N-Dimethylglycine (-) 0.00049632 0.53544 4. Phenylalanine, tyrosine L-Phenylalanine (-), L-Tyrosine (-) and tryptophan biosynthesis 0.002569 5. Phenylalanine L-Phenylalanine (-), L-Tyrosine (-) metabolism 0.017783 0.35714 6. Galactose metabolism Glucose (-), Glycerol (+), Myo-inositol (+) 0.018163 0.03499 7. Glycerophospholipid Phosphorylcholine (-), citicoline (+), metabolism glycerophosphocholine (-) 0.038938 0.07676 8. Arginine and proline Creatine (-F), GABA (+), L-Arginine (-) metabolism 0.044701 0.09383

[0182] Am inoacyl-tRNA biosynthesis was the most significant biochemical pathway for all three brain regions, with the second most significant pathways common to both the BA 40 and BA 17 regions were valine, leucine, and isoleucine biosynthesis. The second most significant pathways for BA22 were glyoxylate and dicarboxylate metabolism for BA 22. Similarly, BA 22 and BA 17 shared the third most significant pathway, glycine, serine, and threonine metabolism, while this was phenylalanine, tyrosine and tryptophan biosynthesis for BA 40.

[0183] Having regard to Table 19, eight biochemical pathways common to multiple regions were found, with five of the pathways being common to all three regions of interest. In contrast, there were six pathways that were found to be unique to one region including the citric acid cycle for BA 22 and galactose metabolism for BA 17.

[0184] Table 19: common biochemical pathways identified from pathway topology analysis for each brain region. Metabolites used for the analysis were identified as significant for each region by either univariate MW or multivariate VIAVC
testing. Pathways are listed alphabetically.
Pathways BA 22 BA 40 BA 17 Alanine, aspartate and glutamate metabolism Aminoacyl-tRNA biosynthesis Arginine and proline metabolism Glycerophospholipid metabolism Glycine, serine and threonine metabolism Phenylalanine, tyrosine and tryptophan biosynthesis Phenylalanine metabolism Valine, leucine and isoleucine biosynthesis

[0185] The present example demonstrates alterations in at least one clinical biomarkers found in at least one biological sample of an individual that suffered an central or peripheral nervous system injury, such as a neurodegenerative disease or disorder. Several of the at least one target biomarkers indicative of injury processes showed characteristic alterations following injury.

[0186] In some embodiments, the present apparatus and methodologies provide for the detection of target AD-related metabolomic alterations in at least the branch chain amino acids valine, leucine and isoleucine, GABA, N-acetyl-L-aspartic acid (NAA), phenylalanine, and tyrosine.

[0187] In some embodiments, a change in at least one target biomarker concentration level was detected, such target biomarkers including, without limitation, acetylcholine, myoinositol, citric acid, and glutamate. For example, a decrease in the concentration level of acetylcholine was detected (e.g., in BA22 and BA40) suggesting a down-regulation of the biomarker compared to a threshold baseline value, and an increase in myoinositol (e.g., in BA17), citric acid, and glutamate (e.g., in BA22), suggesting an up-regulation of the biomarker compared with a threshold baseline value.

[0188] Without being limited to theory, in some embodiments, the variation in the at least one target biomarker metabolites and pathways may result from different underlying pathologies contributing to region shared and unique biochemical processes including protein synthesis upstream to translation, and alterations in excitotoxicity, neurotransmission and energy metabolism. For example, the aminoacyl-tRNA biosynthesis pathway necessary for the translation of proteins as it activates the joining of an amino acid with the correct non-activated t-RNA
molecule by the appropriate aminoacyl-tRNA synthetase was the most significantly affected pathway across all ROls (Tables 16¨ 18). Moreover, in some embodiments, changes in at least one target biomarker in this pathway in primary visual cortex tissue (BA17) of individuals with AD, may demonstrate a potential upstream alteration to translation that occurs as a result of AD. Specifically, dysregulation to upstream amino acid metabolism could alter amino acids' availability to form activated tRNA
molecules.
Moreover, aminoacyl-tRNA biosynthesis contains nine different amino acid pathways that are essential to produce activated tRNA molecules, with five of these pathways being significant for BA 22 and four were significant for each of BA 40 and BA

(Table 14), suggesting that amino acid metabolism may be altered in AD
patients.

[0189] As above, according to embodiments, the branch chain amino acids (BCAA) valine, leucine and isoleucine may play a key role as gatekeepers feeding into upstream and downstream molecular pathways, including those involved in the pathophysiology of AD and another disease. All three BCAA were downregulated in the AD group compared to controls across all three ROls. Without limitation, reduced valine in the cerebral spinal fluid (CSF) and serum of living AD patients has been shown, and specifically in BA 22, the reduction of BCAA could result in increased synthesis of glutamate and gamma-aminobutyric acid (GABA) due to increased expression of the enzyme branch chain amino transferase (BCAT). An increase in BCAT expression would result in an increase conversion of the BCAA to glutamate.
Therefore, altered BCAAs could impact glutamate synthesis which is part of alanine, aspartate and glutamate metabolism, which was significantly altered for BA22 and BA40, and consequently, influences production of glutamate and GABA. Further, an upregulation of BCAT is observed regionally in the AD brain, could support the regional variation in glutamate and GABA regulation.

[0190] According to embodiments, glutamate upregulation in BA22 and GABA
upregulation in BA22, BA 40 and BA 17 has been shown in the superior frontal and medial cortex and superior temporal cortex (BA22) in AD brain tissues.
Increased synaptic glutamate may lead to excitotoxicity by activating N-methyl-D-aspartic acid and glutamate receptors, resulting ultimately in AD pathology, including cell death due to oxidative stress, cytoskeletal and membrane degeneration. Without limitation, the presently demonstrated GABA alterations in BA22 and BA17 in AD patients could indicate that these changes may be brain region-specific. In addition to BCAAs, other precursor molecules essential for neurotransmitters synthesis are altered in AD.

[0191] According to embodiments, phenylalanine and tyrosine are catecholamine neurotransmitter precursors. Both phenylalanine and tyrosine were downregulated across all ROls of the AD group when compared to controls.
Additionally, in BA 22, phenylalanine was part of the VIAVC Best Subset (Table 12), thus highlighting the potential role of phenylalanine as a biomarker for AD
within this region. Moreover, identified pathways specific to these metabolites were phenylalanine, tyrosine and tryptophan biosynthesis, and phenylalanine metabolism.
As humans cannot synthesize phenylalanine and tryptophan, altered concentration levels of these amino acids are unrelated to phenylalanine, tyrosine and tryptophan biosynthesis. Instead, these changes may be related to phenylalanine metabolism which was altered across all ROls. Within this pathway, phenylalanine is converted to tyrosine via phenylalanine hydroxylase, and a decrease in tyrosine could be attributed to the decrease in the availability of phenylalanine.

[0192] According to embodiments, phenylalanine metabolism may be an upstream component of catecholamine synthesis. Phenylalanine and tyrosine are essential precursors to the catecholamine neurotransmitters, including dopamine (DA), norepinephrine (NE), and epinephrine. Similarly, phenylalanine and tyrosine levels are known to be reduced in the serum, CSF, and brain tissue of AD
patients when compared to controls. DA and NE are known to decrease in the plasma, CSF, urine and brain tissue of AD samples. The decrease of these neurotransmitters would imply alterations in the availability of their amino acid precursors, phenylalanine and tyrosine. In the present study, the catecholamine neurotransmitters were not altered between the AD and control groups.

[0193] According to embodiments, n-acetylaspartate (NAA) may play a role in neuronal health and one of the highest concentrations of this amino acid are found in the brain and was upregulated in all three ROls in the AD group compared to controls.
Analysis of post-mortem AD tissues by 1H NMR has shown an NAA reduction in the grey matters of the calcarine sulcus (BA 17), and superior temporal gyrus (BA
22) among other regions. NAA reduction is a proposed marker of neuronal loss in AD, as NAA is a biomarker for neuron health due to its typical high concentration in neuronal tissue. Contrary to this evidence, this study found an upregulation of NAA in all regions indicating a potential compensatory increase in NAA synthesis to combat neuronal loss.

[0194] According to embodiments, acetylcholine is a major neurotransmitter and neuromodulator that has a role in arousal, attention, memory, and motivation.
Acetylcholine was downregulated in BA 22 and BA 40, and this dysregulation coincide with the clinical symptomology of AD. Moreover, alterations in this metabolite may be related to changes to the branch of glycerophospholipid metabolism responsible for acetylcholine synthesis. Interestingly, glycerophospholipid metabolism was significant for BA 40 and BA 17 (Tables 17 and 18). Within this pathway, choline can be converted to acetylcholine via choline acetyltransferase (ChAT). Likewise, acetylcholine can be converted back to choline via acetylcholine transferase.
Dysregulation of acetylcholine within BA 40 may be due to alterations in acetylcholine or choline formation. Indeed, degradation of acetylcholine synthesis and decreased ChAT activity are well-known parts of AD pathology. Within BA 40, choline was not identified as significantly altered, which has previously been shown in an AD
group compared to MCI and age-sex matched controls. It is unclear why acetylcholine, but not choline, is altered within BA40, as choline is one of the two necessary precursors for acetylcholine. Additionally, the significant down-regulation of phosphorylcholine (a precursor to choline) in BA 40 and BA 22 could contribute to the impairment in acetylcholine synthesis. Notably, since acetylcholine is not altered in BA17, it may indicate differences in AD pathology or resiliency to it.

[0195] According to embodiments, glucose was downregulated in BA 40 and BA 17 supporting evidence that reduced glucose metabolism is a part of AD
pathology. For example, reduced glucose metabolism has been observed in the superior temporal gyrus/middle temporal gyrus of AD patients as measured by PET
or cerebral metabolic rate of glucose consumption, however, no changes within the superior temporal gyrus (BA 22) were observed. Since reduced glucose was found in BA 40 and BA 17, this change may potentially be specific to higher associative and primary sensory areas. Glucose is the essential precursor for aerobic respiration which includes the tricarboxylic acid (TCA) cycle.

[0196] According to embodiments, the TCA cycle (citrate/citric acid cycle), a step within aerobic respiration, was uniquely altered in BA 22 only. Within this pathway and region, pyruvate citric acid, cis-aconitate, and malate were upregulated in the AD
group compared to controls, as previously noted in CSF, serum, plasma, and brain tissues of AD and MCI patients. Potential impairments in the TCA cycle may indicate a decrease in energy production in AD due to impairment in oxidative phosphorylation via oxidative stress, however, based solely on the regulation of metabolites within the TCA cycle it is unclear whether there is an increase or decrease in energy availability.

Moreover, it is unclear how the upregulation of glutamate in BA 22 may affect the TCA
cycle. The conversion of BCAA to glutamate via BCAT involves the transfer of an amino group from the BCAA to a-ketoglutarate to form glutamate. If the over-expression of BCAT (as described earlier) is responsible for the downregulation of BCAA and upregulation of glutamate, then a down-regulation of a-ketoglutarate is expected. This is not observed. However, a-ketoglutarate is an intermediate of the TCA cycle and potentially alterations already occurring in the TCA cycle mask alterations in a-ketoglutarate.

[0197] According to embodiments, myo-Inositol (ml), a previous biomarker candidate for AD, was upregulated in BA17 only. Previously, high levels of ml were observed in CSF and post-mortem AD tissues. Without limitation, change in ml levels may be indicative of Ap pathology in at-risk and asymptomatic individuals. A
previous study comparing individuals with Down syndrome, a population considered to at risk of AD, found higher levels of ml in BA 17 compared to the association parietal cortex in older individuals. Though upregulated ml was observed in BA 17 in the present examples, this region may be partially resistant to Ap pathology as vision problems do not present until very late in the disease indicating that ml may be a good new candidate for further biomarker discovery for AD.

[0198] According to embodiments, the present apparatus and methodologies provides: (1) the identification of many metabolites altered between AD and ON

individuals in all three ROls (including BA 17, a region otherwise more resistant to stereotypical pathological changes), (2) discovery of potential biomarkers for AD
within each ROI, and (3) evidence of diverse pathological mechanisms involved in AD.

[0199] Although a few embodiments have been shown and described, it will be appreciated by those skilled in the art that various changes and modifications can be made to these embodiments without changing or departing from their scope, intent or functionality. The terms and expressions used in the preceding specification have been used herein as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding equivalents of the features shown and the described portions thereof.

Claims

WE CLAIM:

1. A method of determining whether an individual is likely to have an injury, the method comprising:
determining a reference value for at least one target biomarker, obtaining at least one biological sample from the individual, measuring the concentration of the at least one target biomarker in the sample using 1H-NMR spectroscopy, comparing the measured concentration of the measured at least one target biomarker to the reference value to determine if the concentration of the at least one biomarker has changed relative to the reference value, wherein a change in the concentration of the at least one biomarker is indicative of the injury.

2. The method of claim 1, wherein the method further comprises measuring the concentration of at least two biomarkers in the biological sample and determining whether each of the measured at least two biomarkers have a change in concentration levels relative to the reference value.

3. The method of claim 2, wherein the method further comprises:
measuring the concentration of the at least two biomarkers in the biological sample and determining whether one of the at least two biomarkers has a concentration less than the reference value and whether one other of the at least two biomarkers has a concentration level that is greater than the reference value.

4. The method of claim 1, wherein the at least one target biomarker is selected from the group consisting of 2-Hydroxybutyrate, 3,4-dihydroxybenzeneacetate, carnitine, 4-hydroxybenzoate, caffeine, homocitrulline, methionine, acetylcarnitine, 3-methy1-2-oxovalerate, phosphorylcholine, choline, propylene glycol, taurine, 1-methylhistadine, 3-methylhistadine, citrate, lactose, phenylalanineõ 3-indoxylsulfate, sucrose, 3-methyladipate, isobutyrate, 3-hydroxyisovalerate, 5-aminolevulinate, anserine, tyrosine, carnosine, isoleucine, leucine, threonate, and cysteine.

5. The method of claim 4, wherein the at least one target biomarker is selected from phenylalanine and citrate.

6. The method of claim 5, wherein the indication that the individual is likely to have an injury is provided in the event that the measured concentration level of phenylalanine is greater that the respective baseline value for phenylalanine and the measured concentration levels of citrate is less than the respective baseline value for citrate.

7. The method of claim 4, wherein the change in concentration of the at least one target biomarker is further indicative the prognosis of the injury.

8. The method of claim 7, wherein the at least one target biomarker is 2-hydroxybutyrate.

9. The method of claim 4, wherein the change in concentration of the at least one target biomarker is further indicative of the number of symptoms of the injury.

10. The method of claim 9, wherein the at least one target biomarker is lactose.

11. The method of claim 1, wherein the at least one biological sample is selected from urine, plasma, whole blood serum, spinal fluid, interstitial fluid, saliva, an extract or purification therefrom, and a dilution thereof.

12.The method of claim 1, wherein the injury is a central or peripheral nervous system injury.

13.The method of claim 12, wherein the central nervous system injury is a brain injury.

14.The method of claim 13, wherein the brain injury is a traumatic brain injury.

15.The method of any one of claims 12 ¨ 14, wherein the injury is an acute injury or a chronic injury.

16.The method of any one of claims 12 ¨ 14, for diagnosing, prognosing, or monitoring the injury.

17.The method of claim 1, wherein the at least one target biomarker is selected from the group consisting of citrate, glycyl-glycine, isoleucine, glutamate, trimethylamine N-oxide, choline, choline phosphate, glucose, leucine, phenylalanine, valine, tyrosine, glutamate, methionine, galactose, glycerol, myo-Inositol, betaine, threonine, ethanol, creatine, malonic acid/malonate, pyruvatoxine, phenylalanine, alpha-ketoisovaleric, propylene glycerol, 2-oxohexane, gamma-am inobutyric acid (GABA), 2-hydroxy-3-methylvaelrate, n-acetyl-L-aspartate (NAA), 4-aminobutanoate, threonine, 3-methy1-2-oxobutanoic acid, (R)-3-hydroxybutanoate, succinate, glycolate, and acetylcholine.

18.The method of claim 17, wherein the at least one target biomarker is selected from citrate and isoleucine.

19.The method of claim 18, wherein the indication that the individual is likely to have an injury is provided in the event that the measured concentration level of citrate is greater than its respective baseline value and the measured concentration level of isoleucine is less than its respective baseline value.

20. The method of claim 1, wherein the at least one target biomarker is selected from the group consisting of n-acetyl-L-aspartate (NAA), ethanol, 2-amino-3-phosphonoprionic acid, 1,3,7-trimethyluric acid, creatine phosphate, gamma-aminobutyric acid (GABA), isoleucine, leucine, phenylalanine, serine, tyrosine, valine, phosphorylcholine.

21. The method of claim 20, wherein the indication that the individual is likely to have an injury is provided in the event that the measured concentration levels of NAA
and GABA are greater than their respective baseline values and the measured concentration levels of ethanol, 2-amino-3-phosphonoprionic acid, 1,3,7-trimethyluric acid, creatine phosphate, isoleucine, leucine, phenylalanine, serine, tyrosine, valine, phosphorylcholinen-acetyl-L-aspartate (NAA), ethanol, 2-am ino-3-phosphonoprionic acid, 1,3,7-trimethyluric acid, creatine phosphate, gamma-aminobutyric acid (GABA), isoleucine, leucine, phenylalanine, serine, tyrosine, valine, phosphorylcholineis are less than their respective baseline values.

22. The method of claim 1, wherein the at least one target biomarker is selected from the group consisting of acetylcholine, betaine, dimethyl sulfone, glycolate/glycolic acid, and histamine.

23. The method of claim 22, wherein the indication that the individual is likely to have an injury is provided in the event that the measured concentration levels of dimethyl sulfone and histamine are greater than their respective baseline values and the measured concentration levels of acetylcholine, betaine, and glycolate/glycolic acid are less than their respective baseline values.

24. The method of claim 1, wherein the at least one target biomarker is selected from the group consisting of creatine, glycerol, threonine, malonate, oxypurinol.

25. The method of claim 24, wherein the indication that the individual is likely to have an injury is provided in the event that the measured concentration levels of creatine, malonate, and oxypurinol are greater than their respective baseline values and the measured concentration levels of glycerol, threonine are less than their respective baseline values.

26. The method of claim 1, wherein the at least one target biomarker is selected from the group consisting of arginine, glucose, and glycerophosphocholine.

27. The method of claim 26, wherein the indication that the individual is likely to have an injury is provided in the event that the measured concentration levels of arginine, glucose, and glycerophosphocholine are less than their respective baseline values.

28. The method of claim 1, wherein the at least one target biomarker is selected from the group consisting of glutamate, citric acid, cis-aconitate, malate, pyruvate.

29. The method of claim 28, wherein the indication that the individual is likely to have an injury is provided in the event that the measured concentration levels of glutamate, citric acid, cis-aconitate, malate, pyruvate are greater than their respective baseline values.

30. The method of claim 17, wherein the injury is a central or peripheral nervous system injury.

31. The method of claim 30, wherein the central nervous system injury is a neurodegenerative brain disease or disorder.

32.The method of claim 31, wherein the neurodegenerative disease is Alzheimer's Disease.

33.The method of any one of claims 30 ¨ 32, wherein the injury is an acute injury or a chronic injury.

34.The method of any one of claims 30 ¨ 33, for diagnosing, prognosing, or monitoring the injury.