Biomarkers for early detection and monitoring of cognitive impairment

Abstract

Provided are non-imaging biomarkers for early detection of cognitive impairment (CI) in subjects having neurodegenerative disease such as multiple sclerosis (MS). The biomarkers include miRNA biomarkers and / or protein biomarkers. Also provides are methods of determining the risk or presence of CI, methods of treating CI, and related kits and compositions.

Description

BIOMARKERS FOR EARLY DETECTION AND MONITORING OF COGNITIVE IMPAIRMENT CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of United States Provisional Patent Application Nos. 63 / 586,988 filed September 29, 2023, and 63 / 635,699 filed April 18, 2024, the disclosures of which are incorporated herein by reference in their entireties. SEQUENCE LISTING

[0002] The instant application contains a Sequence Listing XML which has been submitted electronically as an XML formatted sequence listing and is hereby incorporated by reference in its entirety. Said XML formatted sequence listing, created on September 27, 2024, is named 202318505WO_SL.xml and is 14 kilobytes in size. TECHNICAL FIELD

[0003] The present disclosure is generally related to the field of molecular biology and neurological health e.g., peripheral blood biomarkers for identifying persons who have, or are at risk for, cognitive impairment (CI) due to neurological or neurodegenerative disease such as multiple sclerosis (MS). BACKGROUND

[0004] Multiple sclerosis (MS) is a chronic inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. Apart from physical disability, at least 34%, and according to some reports, as high as 65% of patients with early- and late-stage MS experience cognitive impairment (CI). Often when diagnosed, patients are in the most productive period of their lives, both personally and professionally. It is a debilitating disease, in which patients progressively lose motor autonomy and other neurological functions. Cognitive impairment occurs across multiple phenotypes: in an estimated 20–25% of patients with clinically isolated and radiologically isolated syndromes, 30–45% of patients with relapsing remitting multiple sclerosis, and 50–75% of patients with secondary progressive multiple sclerosis.Cognitive function lower than in controls was associated with Primary Progressive MS as early as two decades before diagnosis of MS. CI severely disrupts the ability of these patients to manage their daily lives. The most common affected cognitive domains are information processing, visual learning and memory, and executive functions. For example, subjects may demonstrate difficulties with memory function, information processing, attention and concentration, executive function (high-level processes of planning, prioritizing, and problem-solving), visual perceptual skills (simple perception or recognition of objects), and verbal fluency. Cognitive decline varies across individuals and while interventions have been shown to be effective, it is difficult for physicians to know who will benefit from early and aggressive intervention.

[0005] Prior art of interest includes U.S. Patent No. 9,556,487 entitled Methods of using miRNA from bodily fluids for early detection and monitoring of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) to Umansky et al. (herein entirely incorporated by reference).

[0006] Current evidence for molecular biomarkers of CI in MS is inconclusive. Early intervention guided by evidence ahead of clinical manifestation could create a new paradigm that would allow physicians to preserve their patients’ quality of life.

[0007] There remains a need for objective methods for early detection of CI that are sensitive, accurate, robust and reliable. Further there is a need for a noninvasive or minimally invasive test for early detection and monitoring of cognitive impairment, MS, and preceding asymptomatic as well as other neurodegenerative diseases. Moreover, there remains a need for methods of predicting the likelihood of progression from pre-MCI to MCI in subjects or amongst various stages of progression (such as via MSEE comparative scoring) in one or more subjects, e.g., prior to the development or presentation of neurological or neurodegenerative disease such as MS, or across stages of disease development.SUMMARY

[0008] The present disclosure provides highly sensitive and noninvasive or minimally invasive diagnostic and monitoring methods, kits, and compositions based on quantification of cognitive impairment biomarkers in bodily fluids or patient samples. The methods, kits, and compositions of the present disclosure allow diagnosis and monitoring of pre-MCI, MCI, and CI throughout stages of neurological or neurodegenerative disease presentation. An example of a neurodegenerative disease is MS. The methods, kits, and compositions of the present disclosure allow diagnosis and monitoring of pre-MCI, MCI, and CI throughout stages of asymptomatic MS and MS. In embodiments, the methods, kits, and compositions of the present disclosure provide treatment, diagnosis and / or monitoring of pre-MCI, MCI, and CI throughout the course of clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS), and / or relapsing MS (RMS). In embodiments, the methods, kits, and compositions of the present disclosure as suitable for providing treatment, diagnosis and / or monitoring of pre-MCI, MCI, and CI in progressive MS such as PPMS and SPMS.

[0009] In embodiments, methods, kits, and compositions of the present disclosure are suitable for predicting risk of, or detecting, a cognitive impairment in a subject. The methods include applying an expression characteristic of one or more biomarkers in a sample obtained from the subject to a prediction equation. The one or more biomarkers include miRNA biomarkers (or characteristic fragments, functional variants, or functional fragments thereof) or protein biomarkers (or characteristic fragments, functional variants, or functional fragments thereof), either alone or in combination. Based on the expression characteristic of the one or more biomarkers, the prediction equation predicts the risk of, or detects, the cognitive impairment at a specificity and / or sensitivity of at least 0.5.

[0010] In embodiments, methods of the present disclosure include applying an expression characteristic of one or more miRNA biomarkers and one or more protein biomarkers to the prediction equation. The one or more miRNA biomarkers can include any one or any combination of miRNA listed in Tables 1 and 3. The one or more miRNA biomarkers can include any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, characteristic fragments thereof, functional variants thereof, and functional fragments thereof. In embodiments, the one or more miRNA biomarkers can include a combination of hsa-miR486-3p and hsa-miR-629-3p.

[0011] In embodiments, one or more protein biomarkers include any one or any combination of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), chitinase-3 like-protein-1 (CHI3L1), chitinase-3 like-protein-2 7(CHI3L2), vitamin D, amyloid beta (Abeta) 40, Abeta 42, the ratio of Abeta42 / Abeta40, apolipoprotein E (ApoE),characteristic fragments thereof, functional variants thereof, and functional fragments thereof.

[0012] In embodiments, the one or more protein biomarkers can include any one or any combination of the biomarkers described below: Protein Full name UniProtKB NfL Neurofilament light chain P07196

[0013] In embodiments, the one or more molecular biomarkers can include any one or more miRNA and any one or more protein biomarker combination, where the miRNA biomarker is selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR- 320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa- miR-629-5p, characteristic fragments thereof, functional variants thereof, and functional fragments thereof, and the protein biomarker is selected from GFAP, NfL, Abeta 40, Abeta 42, Abeta 40 and Abeta 42 (a ratio of Abeta42 / Abeta40), characteristic fragments thereof, functional variants thereof, and functional fragments thereof. In embodiments, the one or more biomarkers can include a combination of hsa-miR-629-3p and NfL, characteristic fragments thereof, functional variants thereof, or functional fragments thereof.

[0014] The one or more biomarkers can include a combination of hsa-miR- 629-3p and NfL or characteristic fragments thereof. In embodiments, the one or more biomarkers can include a combination of hsa-miR-320c and NfL or functional variants or functional fragments or characteristic fragments thereof. The one or more biomarkers can include a combination of hsa-miR-374a-3p and NfL or functional variants or functional fragments thereof. The one or more biomarkers can include a combination of hsa-miR-486-3p and NfL or functional variants or functional fragments thereof. The one or more biomarkers can include a combination of hsa-miR-629-3p and Abeta40 or functional variants or functional fragments thereof. The one or more biomarkers can include a combination of hsa-miR-320c and Abeta40 or functional variants or functional fragments thereof. The one or more biomarkers can include a combination of hsa-miR-374a-3p and Abeta40 or characteristic fragments, functional variants, or functional fragments thereof. The one or more biomarkers can include a combination of hsa-miR-486-3p and Abeta40 or functional variants or functional fragments thereof.

[0015] The one or more biomarkers can include a combination of hsa-miR- 486-3p and hsa-miR-629-3p or characteristic fragments, functional variants, or functional fragments thereof. The one or more biomarkers can include a combination of hsa-miR-320c and hsa-miR-629-3p or characteristic fragments, functional variants, or functional fragments thereof. The one or more biomarkers can include a combination of hsa-miR-374a-3p and hsa-miR-629-3p or characteristic fragments, functional variants, or functional fragments thereof. The one or more biomarkers can include a combination of hsa-miR-642a and hsa-miR-629-3p or characteristic fragments, functional variants, or functional fragments thereof.

[0016] According to some embodiments, the prediction equation can be one or more prediction equations including a parametric ranking, non-parametric ranking, predictor screening, correlation screening, receiver operating characteristic (ROC) analysis, nominal logistic fits, nominal logistic analysis, support vector machine analysis, decision tree-based algorithms, and other machine learning or deep learning approaches. In embodiments, the prediction equation can be based on a predetermined threshold value.

[0017] In embodiments, the expression characteristic can include a concentration or a fold change in expression of the one or more miRNA biomarkers in the sample. The sample can include cerebrospinal fluid or plasma obtained from the subject.

[0018] In some embodiments, the subject is a patient with neurological or neurodegenerative disease. In some embodiments, the subject is a patient with multiple sclerosis. In some embodiments, the subject is a patient with multiple sclerosis without detected or diagnosed cognitive impairment. In some embodiments, the subject is free of symptoms of cognitive impairment and has an unknown risk of developing cognitive impairment. In some embodiments, the subject is a patient without detected or diagnosed multiple sclerosis and without detected or diagnosed cognitive impairment.

[0019] Also described are methods of treating a subject with neurological or neurodegenerative disease, or a subject with multiple sclerosis, with a cognition preserving intervention. The methods of treating can include administering a cognition preserving intervention to the subject when the expression characteristic of the one or more biomarkers selected from miRNA biomarkers or miRNA and protein biomarkers (or characteristic fragments, functional variants, or functional fragments thereof) in a sample from the subject is elevated relative to the expression characteristic of the same one or more biomarkers (or characteristic fragments, functional variants, or functional fragments thereof) in a control population. The elevated expression characteristic of the one or more biomarkers (or characteristic fragments, functional variants, or functional fragments thereof) in the subject with neurological or neurodegenerative disease, or in the subject with multiple sclerosis, can include an expression characteristic that is at least 1.5 times higher than the expression characteristic of the one or more biomarkers (or characteristic fragments, functional variants, or functional fragments thereof) in the control population. In some embodiments the subject is characterized as having clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS), and / or relapsing MS (RMS). In some embodiments the subject is characterized as having MS characterized or diagnosed as secondary progressive MS (SPMS), or primary progressive MS (PPMS).

[0020] In embodiments, the elevated expression characteristic of the one or more biomarkers (or characteristic fragments, functional variants, or functional fragments thereof) in the subject with neurological or neurodegenerative disease, or in the subject with multiple sclerosis can include an expression characteristic that is greater than a threshold value, such as a predetermined threshold value.

[0021] In embodiments, the expression characteristic can include a concentration or a fold change in expression of the one or more biomarkers in the sample.

[0022] In embodiments, the subject with neurological or neurodegenerative disease, or the subject with multiple sclerosis can include a subject who does not demonstrate cognitive impairment when tested with a mini mental state examination (MMSE) Mini-Mental State Examination (MMSE) (Folstein et al., J Psychiatry. Res 1975;12:1289-1298, or a subject with multiple sclerosis and MMSE score between about 24 and about 30. The subject with neurological or neurodegenerative disease, or the subject with multiple sclerosis, may have an MMSE score of between about 20 and about 24, which is indicative of CI characterized as mild CI (MCI). The subject with neurological or neurodegenerative disease, or the subject with multiple sclerosis, may have an MMSE score of between about 13 and about 20, which is indicative of CI characterized as moderate CI. The subject with neurological or neurodegenerative disease, or the subject with multiple sclerosis, may have a CI with an MMSE score of less than 13, which is indicative of CI characterized as severe CI. In embodiments, methods are provided for monitoring patients in need and characterizing such patients by MMSE score. In embodiments, the monitoring may occur at several points in time over a duration, such as 1X daily for 1 week, or 3X daily for 1 month.

[0023] The control population can include a population of healthy subjects, a population of subjects without neurological or neurodegenerative disease, a population of subjects without multiple sclerosis, a population of subjects with neurological or neurodegenerative disease having an MMSE score between about 24 and about 30, or a population of subjects with multiple sclerosis having an MMSE score between about 24 and about 30.

[0024] The methods can also include preventing, such as by preventative intervention, a subject from cognitive impairment when high risk of developingcognitive impairment is detected in the subject. The methods can include providing cognition preserving intervention to the subject with neurological or neurodegenerative disease without detected or diagnosed cognitive impairment but with detected high risk of developing cognitive impairment. The methods can include providing cognition preserving intervention to the subject with multiple sclerosis without detected or diagnosed cognitive impairment but with detected high risk of developing cognitive impairment.

[0025] In embodiments, the methods of treating a subject with neurological or neurodegenerative disease with a cognition preserving intervention can include: a) applying an expression characteristic of one or more biomarkers selected from miRNA biomarkers or miRNA and protein biomarkers in a sample from the subject to a prediction equation, b) detecting a high risk of cognitive impairment in the subject at a specificity and / or sensitivity of at least 0.5 from the prediction equation, and c) treating the subject with the cognition preserving intervention, wherein the subject is free of symptoms of cognitive impairment at the time of treating.

[0026] The methods of treating a subject with multiple sclerosis with a cognition preserving intervention can include: d) applying an expression characteristic of one or more biomarkers selected from miRNA biomarkers or miRNA and protein biomarkers in a sample from the subject to a prediction equation, e) detecting a high risk of cognitive impairment in the subject at a specificity and / or sensitivity of at least 0.5 from the prediction equation, and f) treating the subject with the cognition preserving intervention, wherein the subject is free of symptoms of cognitive impairment at the time of treating.

[0027] Other and further embodiments of the present disclosure are described below.BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Embodiments of the present disclosure, briefly summarized above and discussed in greater detail below, can be understood by reference to the illustrative embodiments of the disclosure depicted in the appended drawings. However, the appended drawings illustrate only typical embodiments of the disclosure and are therefore not to be considered limiting of scope, for the disclosure may admit to other equally effective embodiments. The drawings illustrate generally, by way of example, but not by way of limitation, various aspects discussed in the present document. In the drawings:

[0029] FIG.1A, FIG.1B, FIG.1C and FIG.1D are graphs depicting differential expressions of microRNA (miRNA)s from a study described in the Examples. The data are presented in volcano plots for each subgroup of patients: FIG. 1A – relapsing- remitting MS (RRMS)-No CI vs. healthy controls (HC) (all miRNA expression fold change comparing RRMS without cognitive impairment to healthy normal), FIG.1B – RRMS-Mild CI vs. HC (all miRNA expression fold change comparing RRMS with mild cognitive impairment vs healthy normal), and FIG. C – RRMS-Mild CI vs. RRMS-No CI (all miRNA expression fold change comparing RRMS without and with mild cognitive impairment). The vertical dotted lines correspond to a cutoff of 2-fold up and down regulated miRNA expression respectively, and the horizontal dotted lines represent a significance cutoff at a P-value of 0.05 for each miRNA expression comparison. The points from the top left rectangle and top right rectangle from the volcano plots are transposed to the Venn diagram (FIG.1D) as counts of statistically over or under expressed miRNAs uniquely found within each group-by-group comparison and those in common between the subgroup analyses. A single miRNA out of approximately 500 detected was found to be uniquely associated with the CI subgroup (FIG. 1D). Legend: Circles - Healthy Controls, Square - RRMS without cognitive impairment, triangle - RRMS with mild cognitive impairment.

[0030] FIG. 2A and FIG. 2B are graphs depicting the data from the study described in the Example 1. Plasma glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) concentrations were compared between the three subgroups. In FIG. 2A, plasma GFAP levels for the RRMS-Mild CI group were significantly increased 75.9% and 69.8% compared to healthy controls and RRMS-NoCI respectively (*P<0.05). Average GFAP concentrations were found to be 99.1pg / mL in healthy controls, 102.6pg / mL in the RRMS-No CI group, and 174.3pg / mL in the RRMS-Mild CI group. In FIG. 2B, plasma NfL levels in RRMS-Mild CI group were significantly increased 230.5% and 156.1% compared with healthy controls and RRMS-No CI respectively (*P<0.05). NfL concentrations were found to be 9.00pg / mL in healthy controls, 11.6pg / mL in the RRMS-No CI group and 29.6pg / mL in the RRMS- Mild CI group.

[0031] FIG. 3A, FIG. 3B and FIG. 3C are tables depicting data from protein biomarker (predictor) screening for Mini-Mental State Examination (MMSE) Score (FIG. 3A), MMSE Level (FIG. 3B), and CI (FIG. 3C). The contribution of NfL was highest, but amyloid beta 40 (AB40, Abeta40) was second highest for the first four biomarkers tested.

[0032] FIG.4A, FIG.4B, FIG.4C and FIG.4D are graphs depicting bivariate fit of MMSE Score by protein biomarker concentration (pg / ml) Abeta40 (FIG. 4A), Abeta42 (FIG.4B), GFAP (FIG.4C) and NfL (FIG.4D).

[0033] FIGs. 5A-5F are graphs depicting levels of protein biomarkers in the patients with CI (Yes) or without CI (No). FIG.5A depicts the ratio of Abeta42 / 40, FIG 5B depicts levels of Abeta40 (pg / ml), FIG.5C depicts levels of Abeta42 (pg / ml), FIG. 5D depicts levels of GFAP (pg / ml), and FIG.5E depicts levels of NfL (pg / ml). FIG.5F is a table depicting correlation between the protein biomarker and the indicated variables.

[0034] FIG.6A and FIG.6B are graphs depicting pairwise comparison of the three patient groups using one way analysis of NfL (FIG.6A) and Abeta40 (FIG.6B) with CI (Yes) or without CI (No). Means comparisons statistics are also provided. Legend: Circles - Healthy Controls, Square - RRMS without cognitive impairment, triangle - RRMS with mild cognitive impairment.

[0035] FIGs.7A-7D are tables depicting the predictor screening for biomarker proteins for Expanded Disability Status Scale (EDSS) Score (FIG.7A), MMSE Score (FIG.7B), MS (FIG.7C), and CI (FIG.7D). AB40 and NfL are the best predictors of CI.

[0036] FIG.8A is an equation depicting a prediction expression and Predicted Rate and Predicted Count obtained from the prediction expression on training andvalidation data sets. FIGs. 8B and 8C depict a Receiver Operating Characteristic (ROC) curve for CI with training data (FIG.8B) and validation data (FIG.8C).

[0037] FIGs. 9A-9C are data depicting outcomes from training data with different decision thresholds.

[0038] FIGs.10A-10C are graphs and data depicting model screening using 25% hold-back validation set. The ROC on training data is presented in FIG.10A. The ROC on validation data is presented in FIG. 10B. The probability threshold on the training and validation data are shown in FIG.10C.

[0039] FIGs.11A-11D are graphs depicting significant association of protein biomarkers and two miRNAs with CI. These were protein biomarkers Abeta40 (FIG. 11A) and NfL (FIG.11B) and miRNAs hsa-miR-486-3p (FIG.11C) and hsa-miR-629- 3p (FIG.11D).

[0040] FIG.12 is a table depicting data from pairwise correlation analysis of the indicated variables.

[0041] FIGs.13A-13C are graphs and data depicting Nominal logistics model fits on the training and validation data using NfL and miR-629-3p. The Receiver Operating Characteristic (ROC) on training data is presented in FIG.13A. The ROC on validation data is presented in FIG.13B. The probability threshold on the training and validation data are shown in FIG.13C.

[0042] FIG. 14 is a graph depicting probability that predicted cognitive impairment aligns with actual RRMS MMSE scores (CI prediction probability (Prob[Yes]) versus MMSE score). Legend: Circles - Healthy Controls, Square - RRMS without cognitive impairment, triangle - RRMS with mild cognitive impairment.

[0043] FIG. 15 is a graph depicting prediction of cognitive impairment using miRNA629-3p and NfL as biomarkers (CI versus miR-629-3p and NfL) fit by nominal logistics model. Legend: Circles - Healthy Controls, Square - RRMS without cognitive impairment, triangle - RRMS with mild cognitive impairment. Top axis- fold change of has-miR-629-3p, right axis- NfL level in pg / mL.

[0044] FIGs. 16A-16D are graphs depicting the Contribution of the listed Predictor for CI (FIGs.16A and 16B) and for MS (FIGs.16C and 16D). The Predictors are ordered by the level of contribution (descending).

[0045] FIG, 17 depicts an exemplary block diagram of a computer system 1100.

[0046] FIG. 18 depicts an exemplary flow chart of an embodiment of the present disclosure.

[0047] FIG. 19 depicts an exemplary flow chart of an embodiment of the present disclosure.

[0048] FIG.20 is a block diagram of an example of a network environment for use in the methods and systems described herein.

[0049] FIG, 21 depicts is a block diagram of an example of a computing device and an example mobile computing device.

[0050] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. The figures are not drawn to scale and may be simplified for clarity. Elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation. DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0051] Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative of the disclosure that may be embodied in various forms. In addition, each of the examples given in connection with the various embodiments of the disclosure is intended to be illustrative, and not restrictive. Definitions

[0052] All terms used herein are intended to have their ordinary meaning in the art unless otherwise provided. All concentrations are in terms of percentage by weight of the specified component relative to the entire weight of the topical composition, unless otherwise defined.

[0053] As used herein, “a” or “an” shall mean one or more. As used herein when used in conjunction with the word “comprising,” the words “a” or “an” mean one or more than one.

[0054] As used herein “another” means at least a second or more.

[0055] As used herein, all ranges of numeric values include the endpoints and all possible values disclosed between the disclosed values. The exact values of all half integral numeric values are also contemplated as specifically disclosed and as limits for all subsets of the disclosed range. For example, a range of from 0.1% to 3% specifically discloses a percentage of 0.1%, 1%, 1.5%, 2.0%, 2.5%, and 3%. Additionally, a range of 0.1 to 3% includes subsets of the original range including from 0.5% to 2.5%, from 1% to 3%, from 0.1% to 2.5%, etc. It will be understood that the sum of all weight % of individual components will not exceed 100%.

[0056] About. As used herein when referring to a value such as an amount, concentration, expression value, and the like, the term “about” is meant to encompass variations of ± 5%, ± 1%, or ± 0.1% from the specified value, as such variations are appropriate to perform the disclosed methods.

[0057] The term “comprising” as used herein is synonymous with “including,” “containing,” or “characterized by,” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. The phrase “consisting of” as used herein excludes any element, step, or ingredient not specified in the claim. The phrase “consisting essentially of” limits the scope of a claim to the specified materials or steps “and those that do not materially affect the basic and novel characteristic(s)” of the claimed invention. Embodiments described in terms of the phrase “comprising” (or its equivalents) also provide as embodiments those independently described in terms of “consisting of” and “consisting essentially of.”

[0058] Biomarker: The term “biomarker” or “biological marker” is used herein, consistent with its use in the art, to refer to an entity whose presence, level, or form, correlates with a particular biological event or state of interest, so that it is considered to be a “marker” of that event or state. To give but a few examples, in some embodiments, a biomarker may be or include a marker for a particular disease state, or for likelihood that a particular disease, disorder or condition may develop, occur, or reoccur. In some embodiments, a biomarker may be or include a marker for a particular disease or therapeutic outcome, or likelihood thereof. Thus, in some embodiments, a biomarker is predictive, in some embodiments, a biomarker is prognostic, in some embodiments, a biomarker is diagnostic, of the relevant biological event or state of interest. In some embodiments, a biomarker is a possible biomarkerof the relevant biological event or state of interest. A biomarker may be an entity of any chemical class. For example, in some embodiments, a biomarker may be or include a nucleic acid, a polypeptide, a small molecule, or a combination thereof. In some embodiments, a biomarker is a cell surface marker. In some embodiments, a biomarker is an intracellular protein. In some embodiments, a biomarker is found in a particular tissue. In some embodiments, a biomarker is found outside of cells (e.g., is secreted or is otherwise generated or present outside of cells, e.g., in a body fluid such as blood, plasma, urine, tears, saliva, cerebrospinal fluid, etc.

[0059] Characteristic fragment: The term “characteristic fragment” refers to a fragment of a biomarker that is sufficient to identify the biomarker from which the fragment was derived. For example, in some embodiments of protein biomarkers, a “characteristic fragment” of a biomarker is one that contains an amino acid sequence, or a collection of amino acid sequences, that together allow for the biomarker from which the fragment was derived to be distinguished from other possible biomarkers, proteins, or polypeptides. In some embodiments, a characteristic fragment of protein biomarkers includes at least 10, at least 20, at least 30, at least 40, or at least 50 consecutive amino acids of the amino acid sequence of the reference protein biomarkers. In some embodiments of miRNA biomarkers, a “characteristic fragment” of a biomarker is one that contains a nucleic acid sequence, or a collection of nucleotides, that together allow for the biomarker from which the fragment was derived to be distinguished from other possible biomarkers, nucleic acid molecules, miRNA. In some embodiments, a characteristic fragment of miRNA biomarkers includes at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, or at least 24 consecutive nucleotides of the nucleic acid sequences of miRNA biomarkers.

[0060] As used herein, “functional fragment” refers to a nucleic acid molecule or a protein having sufficient sequence length of the reference nucleic acid molecule or protein so that the functional fragment and its reference nucleic acid molecule or protein do not substantially differ in function. The functional fragment may have about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, or about 99% of the entire length of its reference sequence.

[0061] Gene product or expression product: As used herein, the term “gene product” generally refers to an RNA transcribed from the gene (pre-and / or post- processing) or a polypeptide (pre- and / or post-modification) encoded by an RNA transcribed from the gene.

[0062] Detection agent: The term “detection agent” as used herein refers to any element, molecule, functional group, compound, fragment or moiety that is detectable. In some embodiments, a detection agent is provided or utilized alone. In some embodiments, a detection agent is provided and / or utilized in association with (e.g., joined to) another agent. Examples of detection agents include, but are not limited to: various ligands, radionuclides (e.g.,3H,14C,18F,19F,32P,35S,135I,125I,123I,64Cu,187Re,111In,90Y,99mTc,177Lu,89Zr etc.), fluorescent dyes, chemiluminescent agents (such as, for example, acridinum esters, stabilized dioxetanes, and the like), bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals (i.e., quantum dots), metal nanoparticles (e.g., gold, silver, copper, platinum, etc.) nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels (such as, for example, dyes, colloidal gold, and the like), biotin, dioxigenin, haptens, and proteins for which antisera or monoclonal antibodies are available.

[0063] As used herein, the term “miRNA” or “miR” or “microRNA” means a non-coding RNA between 17 and 25 nucleobases in length which hybridizes to and regulates the expression of a coding RNA. A 17-25 nucleotide miRNA molecule can be obtained from a miR precursor through natural processing routes (e.g., using intact cells or cell lysates) or by synthetic processing routes (e.g., using isolated processing enzymes, such as isolated Dicer, Argonaut, or RNAase III). It is understood that the 17-25 nucleotide RNA molecule can also be produced directly by biological or chemical syntheses, without having been processed from a miR precursor. As used herein, “hsa-miRNA” refers to Homo sapiens miRNA. The hsa-miRNA of the disclosure may be between 17 and 25 nucleotides long, such as about 17 nucleotides, about 18 nucleotides, about 19 nucleotides, about 20 nucleotides, about 21 nucleotides, about 22 nucleotides, about 23 nucleotides, about 24 nucleotides, or about 25 nucleotides long. The “hsa-miRNA” may be any one of the miRNA described in Tables 1-3 and 12. The “hsa-miRNA” may be the characteristic fragments of the miRNA described in Tables 1-3 and 12. The characteristic fragments of the miRNAsdescribed in Tables 1-3 and 12 may have of an average length of at least about 10 nucleotides, at least about 11 nucleotides, at least about 12 nucleotides, at least about 13 nucleotides, at least about 14 nucleotides, at least about 15 nucleotides, at least about 16 nucleotides, at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, at least about 21 nucleotides, at least about 22 nucleotides, at least about 23 nucleotides, or at least about 24 nucleotides.

[0064] As used herein the term “miRNA molecule” refers to any nucleic acid molecule representing a miRNA. Included within this definition is natural miRNA molecules, pre-miRNA, pri-miRNA, miRNA molecules identical in nucleic acid sequence to the natural forms as well as nucleic acid sequences, wherein one or more nucleic acids has been replaced or is represented by one or more DNA nucleotide and / or nucleic acid analogue. miRNA molecules is in the present specification occasionally referred to as a nucleic acid molecule encoding a miRNA or simply nucleic acid molecule.

[0065] As used herein, the term “miR precursor,” “pre-miRNA” or “pre- miR” means a non-coding RNA having a hairpin structure, which contains a miRNA. In certain embodiments, a pre-miRNA is the product of cleavage of a primary mi-RNA transcript, or “pri-miR” by the double-stranded RNA-specific ribonuclease known as Drosha, but a pre-miRNAs can also be produced directly by biological or chemical synthesis without having been processed from a pri-miR.

[0066] Diagnostic test: As used herein, “diagnostic test” is a step or series of steps that is or has been performed to attain information that is useful in determining whether a patient has a disease, disorder or condition and / or in classifying a disease, disorder or condition into a phenotypic category or any category having significance with regard to prognosis of a disease, disorder or condition, or likely response to treatment (either treatment in general or any particular treatment) of a disease, disorder or condition. Similarly, “diagnosis” refers to providing any type of diagnostic information, including, but not limited to, whether a subject is likely to have or develop a disease, disorder or condition, state, staging or characteristic of a disease, disorder or condition as manifested in the subject, information related to prognosis and / or information useful in selecting an appropriate treatment or additional diagnostictesting. Selection of additional diagnostic testing may include more specific testing for a given disease, disorder, or condition.

[0067] By "isolated" is meant material that is substantially or essentially free from components that normally accompany it in its native state.

[0068] The term "polynucleotide" or "nucleic acid" as used herein Designates mRNA, miRNA, RNA, cRNA, cDNA or DNA. The term typically refers to oligonucleotides greater than 30 nucleotides in length. “Polypeptide", "peptide" and "protein" are used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same. Thus, these terms apply to amino acid polymers in which one or more amino acid residues is a synthetic non-naturally occurring amino acid, such as a chemical analogue of a corresponding naturally occurring amino acid, as well as to naturally-occurring amino acid polymers.

[0069] "Sample: As used herein, the term “sample” refers to a biological sample obtained or derived from a human subject, as described herein. In some embodiments, a biological sample includes biological tissue or fluid. In some embodiments, a biological sample may include blood; blood components, such as plasma or blood cells; tissue or fine needle biopsy samples; cell-containing body fluids; free floating nucleic acids; cerebrospinal fluid; lymph; tissue biopsy specimens; surgical specimens; other body fluids, secretions, and / or excretions; and / or cells therefrom. In some embodiments, a biological sample includes cells obtained from an individual, e.g., from a human or animal subject. In some embodiments, obtained cells are or include cells from an individual from whom the sample is obtained. In some embodiments, a sample is a “primary sample” obtained directly from a source of interest by any appropriate means. For example, in some embodiments, a primary biological sample is obtained by methods selected from the group consisting of biopsy (e.g., fine needle aspiration or tissue biopsy), surgery, collection of body fluid (e.g., blood or cerebrospinal fluid). In some embodiments, as will be clear from context, the term “sample” refers to a preparation that is obtained by processing (e.g., by removing one or more components of and / or by adding one or more agents to) a primary sample. For example, filtering using a semi-permeable membrane. As another example of sample processing, the sample may be a plasma sample that is treated with an anticoagulant selected from the group consisting of EDTA, heparin, and citrate. Asanother example of sample processing, the sample may be processed to isolate one or more proteins (e.g., by capturing proteins with one or more antibodies). A “processed sample” may include, for example, nucleic acids or polypeptides extracted from a sample or obtained by subjecting a primary sample to techniques such as amplification or reverse transcription of mRNA, isolation and / or purification of certain components.

[0070] The term "sequence identity" as used herein refers to the extent that sequences are identical on a nucleotide-by-nucleotide basis or an amino acid-by- amino acid basis over a window of comparison. Thus, a "percentage of sequence identity" is calculated by comparing two optimally aligned sequences over the window of comparison, determining the number of positions at which the identical nucleic acid base (e.g., A, T, C, G, I) or the identical amino acid residue (e.g., Ala, Pro, Ser, Thr, Gly, Val, Leu, Ile, Phe, Tyr, Trp, Lys, Arg, His, Asp, Glu, Asn, Gln, Cys and Met) occurs in both sequences to yield the number of matched positions, dividing the number of matched positions by the total number of positions in the window of comparison (i.e., the window size), and multiplying the result by 100 to yield the percentage of sequence identity. For the purposes of the present disclosure, "sequence identity" will be understood to mean the "match percentage" calculated by the DNASIS computer program (Version 2.5 or later for Windows; available from Hitachi Software engineering Co., Ltd., South San Francisco, California, USA) using standard defaults as used in the reference manual accompanying the software. Other suitable software for sequence identity include Basic Local Alignment Search Tool (BLAST®) and Clustal Omega (by EMBL's European Bioinformatics Institute).

[0071] As used herein, “functional variant” refers to a nucleic acid molecule or a protein having sufficient sequence identity to the reference nucleic acid molecule or protein so that the functional variant and its reference nucleic acid molecule or protein do not substantially differ in function. The functional variant may have at least about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, or about 99% sequence identity to its reference sequence.

[0072] "Similarity" refers to the percentage number of amino acids that are identical or constitute conservative substitutions. Similarity may be determined usingsequence comparison programs such as GAP (Deveraux et al.1984, Nucleic Acids Research 12, 387-395). In this way, sequences of a similar or substantially different length to those cited herein might be compared by insertion of gaps into the alignment, such gaps being determined, for example, by the comparison algorithm used by GAP. Terms used to describe sequence relationships between two or more polynucleotides or polypeptides include "reference sequence", "comparison window", "sequence identity", "percentage of sequence identity" and "substantial identity". A "reference sequence" is at least 12 but frequently 15 to 18 and often at least 25 monomer units, inclusive of nucleotides and amino acid residues; in length. Because two polynucleotides may each include (1) a sequence (i.e., only a portion of the complete polynucleotide sequence) that is similar between the two polynucleotides, and (2) a sequence that is divergent between the two polynucleotides, sequence comparisons between two (or more) polynucleotides are typically performed by comparing sequences of the two polynucleotides over a "comparison window" to identify and compare local regions of sequence similarity. A "comparison window" refers to a conceptual segment of at least 6 contiguous positions, usually about 50 to about 100, more usually about 100 to about 150 in which a sequence is compared to a reference sequence of the same number of contiguous positions after the two sequences are optimally aligned. The comparison window may include additions or deletions (i.e., gaps) of about 20% or less as compared to the reference sequence (which does not include additions or deletions) for optimal alignment of the two sequences. Optimal alignment of sequences for aligning a comparison window may be conducted by computerized implementations of algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package Release 7.0, Genetics Computer Group, 575 Science Drive Madison, WI, USA) or by inspection and the best alignment (i,e., resulting in the highest percentage homology over the comparison window) generated by any of the various methods selected. Reference also may be made to the BLAST family of programs as for example disclosed by Altschul et αl., 1997, Nucl. Acids Res.25:3389. The NCBI Basic Local Alignment Search Tool (BLAST™) (Altschul et al.1990 , J. Mol. Biol.215:403-410) is readily available from several sources, including the National Center for Biotechnology Information (NCBI, Bethesda, Md.) and on the Internet, for use in connection with the sequence analysis programs blastp, blastn,blastx, tblastn and tblastx. A detailed discussion of sequence analysis can be found in Unit 19.3 of Ausubel et αl., "Current Protocols in Molecular Biology", John Wiley & Sons Inc, 1994-1998, Chapter 15.

[0073] Subject: As used herein, the term “subject” refers to an organism, for example, a mammal (e.g., a human). In some embodiments a human subject is an adult, adolescent, or pediatric subject. In some embodiments, a subject is at least 30, at least 40, is at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, or at least 80 years of age. In some embodiments, a subject is suffering from a disease, disorder or condition, e.g., a disease, disorder or condition. In some embodiments, a subject is susceptible to a disease, disorder, or condition; in some embodiments, a susceptible subject is predisposed to and / or shows an increased risk (as compared to the average risk observed in a reference subject or population) of developing the disease, disorder or condition. In some embodiments, a subject displays one or more symptoms of a disease, disorder or condition. In some embodiments, a subject does not display a particular symptom (e.g., clinical manifestation of disease) or characteristic of a disease, disorder, or condition. In some embodiments, a subject does not display any symptom or characteristic of a disease, disorder, or condition. In some embodiments, a subject is a patient. In some embodiments, a subject is an individual to whom diagnosis and / or therapy is and / or has been administered.

[0074] Cognitive abilities, or ``cognition,'' include attention and concentration, learning complex tasks and concepts, memory (the acquisition, retention, and recall of new information in the short and / or long term), and information processing (the ability to gather information through the five senses), visual-spatial functions (visual perception, depth perception, use of mental imagery, copying drawings, constructing objects or shapes), language production and comprehension, speech fluency (vocabulary), and mental processes that include resolution, decision making, and executive functions (planning and prioritization). "Cognitive decline" refers to a progressive decline in one or more of these abilities, such as memory, language, thinking, judgment, etc. "MS-related cognitive impairment" means cognitive impairment associated with the progression of MS from an asymptomaticstage to a positively diagnosed MS disease state. This progression may include the development of mild cognitive impairment (MCI).

[0075] Treatment. As used herein, the term “treatment” within the context of the present invention is meant to include therapeutic treatment as well as prophylactic, or preventative measures for the disease or disorder. As used herein, the term “treatment” and associated terms such as “treat” and “treating” means reduction of the progression, severity and / or duration of a disease or condition or at least one symptom thereof. The term “treatment” therefore refers to any regimen that can benefit a subject. The treatment may be in respect of an existing condition or may be prophylactic (preventative treatment). Treatment may include curative, alleviative or prophylactic effects. References herein to “therapeutic” and “prophylactic” treatments are to be considered in their broadest context. The term “therapeutic” does not necessarily imply that a subject is treated until total recovery. Similarly, “prophylactic” does not necessarily mean that the subject will not eventually develop a disease or condition. Thus, for example, the term treatment includes the administration of cognition preserving intervention prior to or following the onset of a disease or disorder thereby preventing or removing one or more signs of the disease or disorder. As another example, administration of the cognition preserving intervention after clinical manifestation of the disease to combat the symptoms of the disease includes “treatment” of the disease.

[0076] Effective amount or therapeutically effective amount. The term “effective amount” or “therapeutically effective amount” as used herein refers to the amount of a medication, such as a therapeutic molecule, a pharmaceutically active agent, a pharmaceutical composition, or an exercise regime involving physical and / or mental exercises, effective to achieve a particular biological or therapeutic result. A particular biological or therapeutic result includes, but is not limited to, amelioration of one or more symptoms of cognitive impairment. A therapeutically effective amount of a medication, or a therapeutically effective amount of exercise regime, may vary according to factors such as the disease state, age, sex, body surface area, and body weight of the individual, and the ability of the medication or exercise to elicit a desired response in the individual. Such results may include, but are not limited to, theprevention, stabilization, or treatment of cognitive impairment, as determined by any means suitable in the art.

[0077] Expression characteristic. As used herein, the term “expression characteristic” refers to the amount, concentration, or level of the one or more biomarkers as expressed by the subject and detected in the sample from the subject. The expression characteristic of the one more biomarkers can be measured by any method suitable for measuring the amount, concentration, or the fold change in expression of the one or more biomarkers. Suitable methods include immunoassays, (e.g., enzyme-linked immunosorbent assay (ELISA)), electrophoresis and immunoblotting assays (e,g,, gel electrophoresis followed by immunoblotting to detect the expression characteristic of the one or more biomarkers), mass spectrometry, spectrophotometry, affinity chromatography, polymerase chain reaction (PCR), nucleic acid sequencing, nanopore sequencing, and nucleic acid probe hybridization.

[0078] Sensitivity. As used herein, the term “sensitivity” refers to the proportion of subjects who actually have the target disease or condition and are tested positive.

[0079] Specificity. As used herein, the term “specificity” refers to the proportion of subjects who do not have the target disease or condition and are tested negative.

[0080] False Positive (FP). As used herein, the term “false positive” refers to the proportion of subjects who do not have the target disease or condition but are incorrectly tested as positive for the target disease or condition.

[0081] False Negative (FN). As used herein, the term “false negative” refers to the proportion of subjects who have the target disease or condition but are incorrectly tested negative for the target disease or condition.

[0082] Threshold value: As used herein, the term “threshold value” refers to a value (or values) that are used as a reference to attain information on and / or classify the results of a measurement, for example, the results of a measurement attained in an assay. A threshold value can be determined based on one or more control samples. A threshold value can be determined prior to, concurrently with, or after the measurement of interest is taken. In some embodiments, a threshold value can be arange of values. In some embodiments, a threshold value can be a value (or range of values) reported in the relevant field (e.g., a value found in a standard table).

[0083] High Risk. As used herein, the term “high risk” refers to a probability of about 0.5 (about 50%) or greater for a subject with multiple sclerosis to develop cognitive impairment over a period of time. The period of time may be about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more.

[0084] Low Risk. As used herein, the term “low risk” refers to a probability of less than 0.5 (less than 50%) for a subject with multiple sclerosis to develop cognitive impairment over a period of time. The period of time may be a week, a few weeks, a month, few months, six months, nine months, 12 months, 1.25 years, 1.5 years, 1.75 years, 2 years, or more.

[0085] Abbreviations, acronyms, and symbols CI = cognitive impairment MS = multiple sclerosis mRNA = messenger RNA PwMS = patients with MS DNA = Deoxyribonucleic acid RNA = Ribonucleic acid CSF = cerebrospinal fluid miRNA = microRNA RRMS = relapsing-remitting MS MMSE = Mini-Mental State Examination EDSS = Expanded Disability Status Scale IVD = in vitro diagnostic GFAP = glial fibrillary acidic protein NfL = neurofilament light chain CHI3L1 = chitinase-3 like-protein-1 CHI3L2 = chitinase-3 like-protein-2 ApoE = apolipoprotein E FDR = false discovery rateexceRpt = extra-cellular RNA processing toolkit CERTAIN EMBODIMENTS OF THE PRESENT DISCLOSURE

[0086] The present disclosure provides highly sensitive and noninvasive or minimally invasive diagnostic and monitoring methods based on quantification of cognitive impairment biomarkers in bodily fluids or patient samples. In embodiments, the methods, kits, compositions and treatments of the present disclosure allow diagnosis and monitoring of pre-MCI, MCI, and neurodegenerative disease throughout stages of disease presentation, e.g., asymptomatic neurodegenerative disease to neurodegenerative disease (such as MS). In embodiments, the methods, kits, and compositions of the present disclosure allow diagnosis and / or monitoring of pre-MCI, MCI, and CI throughout stages of asymptomatic MS and MS. In embodiments, the methods, kits, and compositions of the present disclosure provide treatment, diagnosis and / or monitoring of pre-MCI, MCI, and CI throughout the course of clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS), and / or relapsing MS (RMS). In embodiments, the methods, kits, and compositions of the present disclosure as suitable for providing treatment, diagnosis and / or monitoring of pre-MCI, MCI, and CI in progressive MS such as PPMS and SPMS.

[0087] Embodiments described herein provide advantages using biomarkers for cognitive impairment in e.g., individuals suffering from neurological disease such as MS. Technologies of the present disclosure are non-invasive, require minimal patient discomfort, are quick to perform, and are relatively cost-effective, and may be used to mitigate the risk of cognitive impairment development. In embodiments, methods of the present disclosure are suitable for predicting risk of developing, or detecting, a cognitive impairment in a subject. The subject may have a neurological or a neurodegenerative disease without MCI. The subject may have a neurodegenerative disease such as multiple sclerosis without MCI. The subject may have a neurological or a neurodegenerative disease with MCI. The subject may have a neurodegenerative disease such as multiple sclerosis with MCI. Mild cognitive impairment (MCI) is a stage between normal cognitive changes that may occur with age and more serious symptoms that indicate dementia. Symptoms of MCI can include problems withthinking, judgment, memory, and language, but the loss doesn't significantly interfere with the ability to handle everyday activities. Symptoms of MCI include mild memory loss; difficulty with planning or organization; trouble finding words; frequently losing or misplacing things; and forgetting names, conversations, and events. Subjects with MCI may be at greater risk of eventually developing Alzheimer's or another type of dementia. MCI may not always progress to moderate or severe CI. The subject with neurological or neurodegenerative disease, or the subject with multiple sclerosis, may have an MMSE score of between about 20 and about 23, which is indicative of CI characterized as mild CI (MCI). The subject with neurological or neurodegenerative disease, or the subject with multiple sclerosis, may have an MMSE score of between about 13 and about 20, which is indicative of CI characterized as moderate CI. The subject with neurological or neurodegenerative disease, or the subject with multiple sclerosis, may have a CI with an MMSE score of less than 12, which is indicative of CI characterized as severe CI. In embodiments, the methods include applying an expression characteristic of one or more biomarkers including miRNA biomarkers or functional variants or fragment thereof, and protein biomarkers, or functional variants or fragments thereof, in a sample from the subject to a prediction equation and predicting the risk of, or detecting, mild cognitive impairment from the prediction equation. Subsequently, a treatment regimen or strategy may be employed to mitigate the risk of developing a mild cognitive impairment e.g., due to neurodegenerative disease. Exemplary neurological and neurodegenerative diseases include stroke, aneurism, spongiform enchephalopathies, Alzheimer’s disease, frontotemporal dementia, Lewy body dementia, HIV-associated dementia, Huntington’s disease, chronic traumatic encephalopathy, Creutzfeldt-Jakob disease, Argyrophilic grain disease, Parkinson’s disease, and multiple sclerosis.

[0088] In embodiments, the present disclosure relates particularly to biomarkers for cognitive impairment in a neurological or a neurodegenerative disease, including a neurodegenerative disease such as multiple sclerosis. Such biomarkers are referred to herein as “Cognitive Impairment Biomarkers.” Cognitive Impairment Biomarkers can include, for example, miRNA biomarkers, or functional variants or fragments thereof and protein biomarkers, characteristic or functional fragments thereof, and / or functional variants thereof. In some embodiments, one or moreCognitive Impairment Biomarkers as described herein are or include one or more miRNA biomarkers including any one or any combination of miRNA listed in Tables 1- 3 and 12 or functional variants or fragments thereof. In embodiments, the one or more miRNA Cognitive Impairment Biomarkers include any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR- 629-5p, and functional variants and fragments thereof.

[0089] In embodiments, the one or more protein biomarkers include any one or any combination of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, the ratio of Abeta42 / Abeta40, and functional fragments or functional variants thereof. In embodiments, the expression characteristic includes a concentration or a fold change in expression of the one or more miRNA and / or protein Cognitive Impairment Biomarkers in the sample. In embodiments, the one or more Cognitive Impairment Biomarkers include a combination of at least one miRNA nucleic acid Cognitive Impairment Biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa- miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and functional fragments or functional variants thereof, and at least one protein Cognitive Impairment Biomarkers selected from GFAP, NfL, Abeta 40, Abeta42, the ratio of Abeta42 / Abeta40, and functional fragments or functional variants thereof. In embodiments, the one or more miRNA biomarkers include a combination of hsa-miR- 486-3p and hsa-miR-629-3p or functional fragments or functional variants thereof. In embodiments, the one or more biomarkers include a combination of miR-629-3p and NfL or functional fragments or functional variants thereof.

[0090] Cognitive Impairment Biomarkers also encompass full-length nucleic acids, as well as fragments (e.g., characteristic fragments) of a nucleic acid Cognitive Impairment Biomarker. For example, in some embodiments, a nucleic acid Cognitive Impairment Biomarkers can include a nucleic acid Cognitive Impairment Biomarker including any one or any combination of miRNA listed in Tables 1-3 and 12 or functional variants or functional fragments thereof. In embodiments, the one or more miRNA Cognitive Impairment Biomarker include any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and functional variants or functional fragments thereof. In embodiments, the expression characteristic includes a concentration or a fold change in expression of the one or more Cognitive Impairment Biomarkers in the sample. In embodiments, the one or more Cognitive Impairment Biomarkers includes a combination of at least one miRNA biomarker selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR- 374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, and hsa-miR-629-5p. In embodiments, the one or more miRNA Cognitive Impairment Biomarker includes a combination of hsa-miR-486-3p and hsa-miR-629-3p.

[0091] In some embodiments, a nucleic acid Cognitive Impairment Biomarker includes a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, at least about 21 nucleotides, at least about 22 nucleotides, about 23 nucleotides, at least about 24 nucleotides, or at least about 25 nucleotides of a corresponding parent nucleic acid sequence of miRNA such as those in Tables 1-3 and 12. For example, the Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 1. The Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 2. The Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 3. The Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 4. The Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 5. The Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 6. The Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 7. The Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 8. The Cognitive Impairment Biomarker may include a fragment having a nucleic acid sequence identical to a contiguous span of at least about 17 nucleotides, at least about 18 nucleotides, at least about 19 nucleotides, at least about 20 nucleotides, or at least about 21 nucleotides of SEQ ID NO: 9. In some embodiments, a nucleic acid Cognitive Impairment Biomarker includes a fragment having at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of a parent miRNA shown in Tables 1-3 and 12. For example, the nucleic acid Cognitive Impairment Biomarker may include a fragment having at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 1. The nucleic acid Cognitive Impairment Biomarker may include a fragment having at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 2. The nucleic acid Cognitive Impairment Biomarker may include a fragment having at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 3. The nucleic acid Cognitive Impairment Biomarker may include a fragment having at least 60%, at least 65%, at least 70%, at least 75%, at least 80%,at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 4. The nucleic acid Cognitive Impairment Biomarker may include a fragment having at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 5. The nucleic acid Cognitive Impairment Biomarker may include a fragment having at least at least 60%, at least 65%, 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 6. The nucleic acid Cognitive Impairment Biomarker may include a fragment having at least at least 60%, at least 65%, 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 7. The nucleic acid Cognitive Impairment Biomarker may include a fragment having at least at least 60%, at least 65%, 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 8. The nucleic acid Cognitive Impairment Biomarker may include a fragment having at least at least 60%, at least 65%, 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of SEQ ID NO: 9. Variant or alternative forms of the biomarker include for example functional nucleic acid variants and / or fragments of the nucleic acid Cognitive Impairment Biomarkers of Tables 1-3 and 12.

[0092] In embodiments, Biomarkers contemplated herein also include truncated forms or nucleic acid fragments of any of the nucleic acid polymers described herein such as nucleic acid Cognitive Impairment Biomarkers. Truncated forms or nucleic acid fragments of a nucleic acid Cognitive Impairment Biomarkers can include 5’-terminally deleted or truncated forms and 3’-terminally deleted or truncated forms. Truncated forms or fragments of a nucleic acid Cognitive Impairment Biomarkers can include fragments arising by any mechanism, such as, without limitation, by degradation, for example, by physical, chemical and / or enzymatic degradation. Without limitation, a nucleic acid Cognitive Impairment Biomarkers mayinclude a truncated nucleic acid molecule or a fragment of a nucleic acid molecule having at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99%, or at least 100% of the nucleic acid sequence length of an nucleic acid Cognitive Impairment Biomarker such as the miRNA as set forth in Tables 1-3and SEQ ID NOs: 1-9 as set forth in Table 12.

[0093] In embodiments, the one or more biomarkers of the present disclosure include a combination of nucleic acid Cognitive Impairment Biomarkers such as miR- 629-3p or functional variant or fragment thereof and a protein Cognitive Impairment Biomarker such as NfL and functional variant or fragment thereof. In embodiments, NfL has an amino acid sequence shown and described in UniProtKB P07196.

[0094] In embodiments, a Cognitive Impairment Biomarker includes gene products associated with the specific recited biomarker. For example, Cognitive Impairment Biomarkers can include, for example, a protein or a nucleic acid molecule (e.g., RNA, e.g., mRNA). Cognitive Impairment Biomarkers also encompass full- length proteins, as well as fragments (e.g., characteristic fragments) of a Cognitive Impairment Biomarker. For example, in some embodiments, a protein Cognitive Impairment Biomarkers can include a protein such as glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, and a combination thereof, or a predetermined ratio of Abeta42 / Abeta40. In some embodiments, a protein Cognitive Impairment Biomarker includes a fragment having an amino acid sequence identical to a contiguous span of at least 10 amino acids, at least 20 amino acids, at least 30 amino acids, at least 40 amino acids, at least 50 amino acids, at least 60 amino acids, at least 70 amino acids, at least 80 amino acids, at least 90 amino acids, or at least 100 amino acids of a corresponding parent amino acid sequence such as glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, and / or Abeta 42. (See e.g., UniProtKB, P14136-1, P07196, NCBl PubChem CID 57339250 and the like). In some embodiments, a protein Cognitive Impairment Biomarker includes a fragment having at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to an amino acid sequence of a parent glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloidbeta (Abeta) 40, and / or Abeta 42. Variant or alternative forms of the biomarker include for example polypeptides encoded by any splice-variants of transcripts encoding the disclosed biomarkers.

[0095] Biomarkers contemplated herein also include truncated forms or polypeptide fragments of any of the proteins described herein. Truncated forms or polypeptide fragments of a protein can include N-terminally deleted or truncated forms and C-terminally deleted or truncated forms. Truncated forms or fragments of a protein can include fragments arising by any mechanism, such as, without limitation, by alternative translation, exo- and / or endo-proteolysis and / or degradation, for example, by physical, chemical and / or enzymatic proteolysis. Without limitation, a protein biomarker may include a truncated protein or a fragment of a protein, polypeptide, or peptide that is at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45 %, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 8%, at least 99%, or at least 100% sequence identity to the amino acid sequence of a protein Cognitive Impairment Biomarker of the present disclosure.

[0096] In some instances, a fragment is N-terminally and / or C-terminally truncated by 1-20 amino acids, such as, for example, by 1-15 amino acids, by 1-10 amino acids, or by 1-5 amino acids, compared to the corresponding mature, full-length Cognitive Impairment Biomarker protein.

[0097] In embodiments, a protein Cognitive Impairment Biomarker of the present disclosure such as a peptide, polypeptide or protein and / or functional variants or functional fragments thereof may also encompass modified forms of the protein biomarker, peptide, polypeptide or protein and fragments. For example, the protein Cognitive Impairment Biomarker or functional variants or functional fragments thereof may be bearing post-expression modifications including but not limited to, modifications such as phosphorylation, glycosylation, lipidation, methylation, selenocystine modification, cysteinylation, sulphonation, glutathionylation, acetylation, and / or oxidation of methionine to methionine sulphoxide or methionine sulphone.

[0098] In some embodiments, a Cognitive Impairment Biomarker of the present disclosure has different isoforms. Although only one or more isoforms maybe disclosed herein, all isoforms of the Cognitive Impairment Biomarker are contemplated for use in the disclosed technologies.

[0099] The protein Cognitive Impairment Biomarkers may be detected a sample from the subject using kits and assays available in the art. Exemplary assays include single molecule array (SIMOA), enzyme-linked immunosorbent assays (ELISAs), bench-top automated ELISA platforms (Ella™ platforms), and electro- chemiluminescence-based assays (ECL assays). Kits and detailed methodology for these assays are offered by Quanterix® (Quanterix, Lexington, MA,USA), Siemens® (Kemnath, Germany), Bio-Techne® (Minneapolis, MN, USA), R&D Systems® (Minneapolis, MN, USA), ThermoFisher Scientific® (Waltham, MA, USA), and Roche® Diagnostics (Little Falls, NJ, USA). Exemplary kits include Ella arrays (for human NF- L Simple Plex assay (ProteinSimple, CA, USA)), NF-light™ Advantage Kit (Item 103,400) for the Single Molecule Array (SiMoA) immunoassay SR-X (Quanterix, Lexington, MA, USA), human GFAP ELISA kits (e.g., catalog # EEL079 by ThermoFisher Scientific®), human Abeta40 ELISA kit (e.g,, catalog # DAB140B by R&D Systems®), and human Abeta42 ELISA kit (e.g,, catalog # DAB142 by R&D Systems®).

[0100] The sample from the subject may be processed to extract the miRNA present in the sample. The sample from the subject may be blood, plasma, or serum that includes central nervous system (CNS)-derived exosomes. Methods for extracting miRNA from samples such as blood, serum, plasma, tissue blocks, cells, CNS-derived exosomes, or cerebrospinal fluid are known in the art. The methods for extracting the miRNA present in the sample typically include cell lysis, if needed, then column capture and elution, or the Trizol-silica method.

[0101] The miRNA Cognitive Impairment Biomarkers may be detected a sample from the subject using kits and assays available in the art. Exemplary assays include microarray hybridization assays (e.g, by LC Sciences®, Houston, TX, USA, or GeneChip™ miRNA 4.0 Array by Applied Biosystems® (Eugene, OR, USA), TaqMan™ Advanced miRNA Human A Card by Applied Biosystems®), and real-time polymerase chain reaction (RT-PCR) assays (e.g., Applied Biosystems®TaqMan™ Advanced miRNA Assays). Exemplary kits include RT-PCR kits for detecting hsa-miR- 629-3p (e.g., catalog # A25576, 478182_mir TaqMan® Assays by ThermoFisherScientific®), and RT-PCR kits for detecting hsa-miR-192, hsa-miR-320a-3p, hsa-miR- 320a-5p, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-642a-3p, hsa-miR-486-3p, and hsa-miR-629-5p (e.g., catalog # 4427975 or catalog # 4440886 by TaqMan® Assays by ThermoFisher Scientific®). The kits and assays for detecting the miRNA Cognitive Impairment Biomarkers include buffers, labeled nucleotides, reverse-transcription enzymes, polymerase enzymes, enzyme inhibitors, primers, probes, and control nucleic acids. METHOD EMBODIMENTS FOR PREDICTING RISK OF OR DETECTING A COGNITIVE IMPAIRMENT

[0102] In embodiments, the present disclosure includes methods for predicting risk of, or detecting, a cognitive impairment in a subject. The methods include applying expression characteristics of one or more biomarkers, such as Cognitive Impairment Biomarkers, in a sample from the subject to a prediction equation. MicroRNA

[0103] In embodiments, the present disclosure is directed to methods related to the use or detection of miRNA molecules as nucleic acid Cognitive Impairment Biomarkers as an indication of cognitive impairment in patients suspected of having or having neurological or a neurodegenerative disease. In embodiments, the present disclosure is directed to methods related to the use or detection of miRNA molecules as nucleic acid Cognitive Impairment Biomarkers as an indication of cognitive impairment in patients suspected of having or having MS.

[0104] In embodiments, nucleic acid Cognitive Impairment Biomarkers may include mutations, variants, or modifications to a sequence of a miRNA or a pre- miRNA or pri-miRNA without disrupting miRNA activity. As used herein, the term “functional variant” of a miRNA sequence refers to an oliginonucleotide sequence that varies from the reference miRNA sequence, but retains one or more functional characteristics of the reference miRNA (e.g. specific miRNA target indication). In some embodiments the “functional variants” refers to a miRNA that vary by one or two nucleotides, such as one or two substitutions, additions, deletions or combinations thereof. In some embodiments, a functional variant of a miRNA sequence retains all of the functional characteristics of the reference miRNA.In certain embodiments, a functional variant of a miRNA has a nucleobase sequence that is a least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the reference miRNA or precursor thereof over a region of about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or more nucleobases, or that the functional variant hybridizes to the complement of the reference miRNA or precursor thereof under stringent hybridization conditions. Accordingly, in certain embodiments the nucleobase sequence of a functional variant is capable of hybridizing to one or more target sequences of the reference miRNA.

[0105] It is understood that any nucleobase sequence set forth herein are independent of any modification to a sugar moiety, an internucleoside linkage, or a nucleobase. It is further understood that a nucleobase sequence including U's also encompasses the same nucleobase sequence wherein “U” is replaced by “T at one or more positions having “U.” Conversely, it is understood that a nucleobase sequence including T's also encompasses the same nucleobase sequence wherein “T; is replaced by “U at one or more positions having “T”.

[0106] Nucleobase sequences miRNAs and their corresponding stem-loop sequences described herein may be found in miRBase, an online searchable database of miRNA sequences and annotation, found at http_microrna_sanger_ac_uk. Entries in the miRBase Sequence database represent a predicted hairpin portion of a miRNA transcript (the stem-loop), with information on the location and sequence of the mature miRNA sequence. The miRNA stem-loop sequences in the database are not strictly precursor miRNAs (pre-miRNAs), and may in some instances include the pre-miRNA and some flanking sequence from the presumed primary transcript. The miRNA nucleobase sequences described herein encompass any version of the miRNA, including the sequences described in Release 10.0 of the miRBase sequence database and sequences described in any earlier Release of the miRBase sequence database. A sequence database release may result in the re-naming of certain miRNAs. A sequence database release may result in a variation of a mature miRNA sequence.

[0107] In embodiments, the one or more biomarkers include a nucleic acid having at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity to a nucleic acid sequence of any one of Cognitive Impairment Biomarker of SEQ ID NOs: 1-9 or function fragments thereof.

[0108] The one or more biomarkers can include one or more miRNA biomarkers and one or more protein biomarkers. The one or more biomarkers include miRNA biomarkers and protein biomarkers. For example, the one or more miRNA biomarkers include any one or any combination of miRNA listed in Tables 1-3. In some embodiments, the one or more miRNA biomarkers include any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, and hsa- miR-629-5p. In some embodiments, the one or more miRNA biomarkers include a combination of hsa-miR-486-3p and hsa-miR-629-3p.

[0109] The one or more protein biomarkers can include any one or any combination of GFAP, NfL, Abeta 40, Abeta 42, and the ratio of Abeta42 / Abeta40. Non-limiting examples of protein Cognitive Biomarkers include those shown in SEQ. ID NO.: 10-13, functional fragments, and combinations thereof.

[0110] In certain embodiments, the one or more biomarkers can include a combination of any one or more miRNA and any one or more protein biomarker, where the miRNA biomarker is selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR- 320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, and hsa-miR-629-5p, and the protein biomarker is selected from GFAP, NfL, Abeta 40, Abeta 42, and the ratio of Abeta42 / Abeta40. In some embodiments, the one or more biomarkers include a combination of hsa-miR-629-3p and NfL.

[0111] In certain embodiments, the methods include applying expression characteristics of one or more biomarkers in a sample from the subject and a disease score of the subject to a prediction equation. In certain embodiments, the disease score is an MMSE score, an EDSS score, or both.

[0112] In certain embodiments, the methods include applying expression characteristics of one or more biomarkers in a sample from the subject and the age of the subject to a prediction equation.

[0113] In certain embodiments, the methods include applying the expression characteristics of one of the one or more biomarkers and a disease score or with the age of the subject to a prediction equation, as set forth in FIG.5F.

[0114] In certain embodiments, the methods include applying the expression characteristics of Abeta 40, Abeta 42, or the ratio of Abeta42 / Abeta40 and the age of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of Abeta 40, Abeta 42, or the ratio of Abeta42 / Abeta40 and the disease score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of Abeta 40, Abeta 42, or the ratio of Abeta42 / Abeta40 and the MMSE score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of Abeta 40, Abeta 42, or the ratio of Abeta42 / Abeta40 and the EDSS score of the subject to a prediction equation.

[0115] In certain embodiments, the methods include applying the expression characteristics of GFAP and the age of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of GFAP and the disease score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of GFAP and the MMSE score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of GFAP and the EDSS score of the subject to a prediction equation.

[0116] In certain embodiments, the methods include applying the expression characteristics of NfL and the age of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of NfL and the disease score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of NfL and the MMSE score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of NfL and the EDSS score of the subject to a prediction equation.

[0117] In certain embodiments, the methods include applying the expression characteristics of hsa-miR-629-3p and the age of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristicsof hsa-miR-629-3p and the disease score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa-miR-629-3p and the MMSE score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa- miR-629-3p and the EDSS score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa- miR-629-3p and the expression characteristics of hsa-miR-486-3p of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa-miR-629-3p and the expression characteristics of Abeta40 of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa-miR-629-3p and the expression characteristics of NfL of the subject to a prediction equation.

[0118] In certain embodiments, the methods include applying the expression characteristics of hsa-miR-486-3p and the age of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa-miR-486-3p and the disease score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa-miR-486-3p and the MMSE score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa- miR-486-3p and the EDSS score of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa- miR-486-3p and the expression characteristics of Abeta40 of the subject to a prediction equation. In certain embodiments, the methods include applying the expression characteristics of hsa-miR-486-3p and the expression characteristics of NfL of the subject to a prediction equation.

[0119] The disclosed methods can include obtaining the expression characteristic of the one or more biomarkers. The expression characteristic can include a concentration or a fold change in expression of the one or more biomarkers in the samples. In some embodiments, the expression characteristic includes the concentration of the one or more biomarkers. In some embodiments, the expression characteristic includes the fold change in expression of the one or more biomarkers. The expression characteristic can be measured by any method suitable for measuringthe concentration or the fold change in expression of the one or more biomarkers. Suitable methods include immunoassays, (e.g., enzyme-linked immunosorbent assay (ELISA)), electrophoresis and immunoblotting assays, mass spectrometry, spectrophotometry, affinity chromatography, polymerase chain reaction (PCR), nucleic acid sequencing, nanopore sequencing, and nucleic acid probe hybridization.

[0120] The samples can include cerebrospinal fluid samples, blood, plasma, or sputum obtained from the subjects.

[0121] The subject can include a patient with multiple sclerosis. The subject can include a patient with multiple sclerosis without a detected or diagnosed cognitive impairment. The subject can include a patient with multiple sclerosis and an MMSE score of between about 23 and about 30. In some embodiments, the subject is free of symptoms of cognitive impairment and has unknown risk of developing cognitive impairment. In embodiments, the subject may include a patient including MS characterized as clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS), and / or relapsing MS (RMS). In embodiments, the subject may include a patient including MS characterized as PPMS and SPMS.

[0122] The disclosed methods apply the expression characteristics of the one or more biomarkers in a sample from the subject to a prediction equation to predict the risk of, or detect, the cognitive impairment in the subject. The prediction equation can include one or more prediction equations including a parametric ranking, non- parametric ranking, predictor screening, correlation screening, receiver operating characteristic (ROC) analysis, nominal logistic fits, nominal logistic analysis, and support vector machine analysis.

[0123] The miRNA enrichment function in FunRich can be used for miRNA targeting predictions. By combining the FunRich and differential analysis results from an mRNA differentially expressed profile, screened target genes related to CI in MS can be identified. The CI-related target genes can be further analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) through the DAVID database. The miRNA-mRNA regulatory network can be visualized using Cytoscape software (Version v.3.10.0).

[0124] The disclosed methods predict the risk of, or detect, the cognitive impairment in the subject at a specificity and / or sensitivity of at least 0.5. For example, the disclosed methods predict the risk of, or detect, the cognitive impairment in the subject at a specificity of about 0.55, about 0.6, about 0.65, about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The disclosed methods predict the risk of, or detect, the cognitive impairment in the subject at a sensitivity of about 0.55, about 0.6, about 0.65, about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99.

[0125] The prediction equation can predict the risk of, or detect, the cognitive impairment in the subject at the sensitivity of about 0.7 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.77 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.8 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.85 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.9 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.91 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.92 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99.The predicted risk can be at the sensitivity of about 0.93 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.93 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.94 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.95 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.96 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.97 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.98 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.99 and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99.

[0126] The prediction equation can predict the risk of, or detect, the cognitive impairment in the subject at the specificity of about 0.7 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.75 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.8 or greater and the sensitivityof about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.85 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.9 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.91 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.92 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.93 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.94 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.95 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.96 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.97 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.98 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99.The predicted risk can be at the specificity of about 0.99 and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99.

[0127] The subjects may have a probability of about 0.5 (about 50%) or greater for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.55 (about 55%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.6 (about 60%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.65 (about 65%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.7 (about 70%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.75 (about 75%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.8 (about 80%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.85 (about 85%) for developing cognitive impairment over a period of about a week, about a few weeks, about amonth, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.9 (about 90%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.95 (about 95%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. The subjects may have a probability of about 0.98 (about 98%) for developing cognitive impairment over a period of about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. Methods of Treating a Subject with Multiple Sclerosis for Cognitive Impairment The disclosed methods are useful for reducing cognitive impairment in individuals suffering from neurodegenerative-related cognitive impairment. In some embodiments, the neurodegenerative condition is multiple sclerosis. The treating can include providing cognition preserving intervention to the subject. The methods can include treating the subject when the subject with multiple sclerosis is free of symptoms of cognitive impairment and has a predicted high risk of developing cognitive impairment. The predicted high risk can be predicted at high level of sensitivity and / or specificity. For example, the subject may have a high risk, i.e., a probability of about 0.5 (about 50%) or greater for the subject with multiple sclerosis to develop cognitive impairment over a period of time. The period may be about a week, about a few weeks, about a month, about a few months, about six months, about nine months, about 12 months, about 1.25 years, about 1.5 years, about 1.75 years, about 2 years, or more. In embodiments the subject may include a patient including MS characterized as PPMS, SPMS, or RMS. In embodiments the subject may include a patient including MS characterized as PPMS or SPMS.

[0128] The methods of treating can include administering a cognition preserving intervention to the subject when expression characteristic of one or more biomarkers selected from miRNA biomarkers or miRNA and protein biomarkers in a sample from the subject is elevated relative to the expression characteristic of the same one or more biomarkers in a control population. The elevated expression characteristic of the one or more biomarkers in the subject with multiple sclerosis can include an expression characteristic that is at least 1.5 times higher than the expression characteristic of the one or more biomarkers in the control population. The expression characteristic can include a concentration or a fold change in expression of the one or more biomarkers in the sample. The expression characteristic can be at least about 1.5, at least about 2, at least about 2.5, at least about 3, at least about 3.5, at least about 4, at least about 4.5, at least about 5, at least about 5.5, at least about 6, at least about 6.5, at least about 7, at least about 7.5, at least about 8, at least about 8.5, at least about 9, at least about 9.5, or at least about 10 times higher than the expression characteristic of the one or more biomarkers in the control population.

[0129] The subject with multiple sclerosis for treatment with a cognition preserving intervention can include a subject who does not demonstrate cognitive impairment when tested with MMSE. The subject with multiple sclerosis for treatment with a cognition preserving intervention can include a subject with multiple sclerosis and MMSE score between about 23 and about 30. For example, the subject with multiple sclerosis for treatment with a cognition preserving intervention can include a subject with multiple sclerosis and MMSE score between about 23 and about 20, between about 19 and about 10, or between about 9 and about 30. The subject with multiple sclerosis for treatment with a cognition preserving intervention can include a subject with multiple sclerosis and MMSE score of about 23, about 22, about 21, about 20, about 19, about 18, about 17, about 16, about 15, about 14, about 13, about 12, about 11, about 10, about 9, about 8, about 7, about 6, about 5, about 4, about 3, about 2, about 1, or about 30. The control population can include a population of healthy subjects, a population of subjects without multiple sclerosis, or a population of subjects with multiple sclerosis having an MMSE score between about 24 and about 30.

[0130] The predicted risk can be at a high level of sensitivity and / or specificity and can include a sensitivity and / or specificity of at least about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. For example, the predicted risk can be at the sensitivity of about 0.7 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.77 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.8 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.85 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.9 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.91 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.92 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.93 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.93 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.94 or greater and the specificity of about 0.7, about 0.75, about0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.95 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.96 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.97 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.98 or greater and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the sensitivity of about 0.99 and the specificity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99.

[0131] The predicted risk can be at the specificity of about 0.7 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.75 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.8 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.85 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.9 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about0.99. The predicted risk can be at the specificity of about 0.91 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.92 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.93 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.94 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.95 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.96 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.97 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.98 or greater and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. The predicted risk can be at the specificity of about 0.99 and the sensitivity of about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99.

[0132] The methods can include treating a subject diagnosed with multiple sclerosis with cognition preserving intervention when the subject is free of symptoms of cognitive impairment.

[0133] The methods of treating can include:a) applying an expression characteristic of one or more biomarkers selected from miRNA biomarkers or miRNA and protein biomarkers in a sample from the subject to a prediction equation, b) detecting a high-risk cognitive impairment in the subject at a specificity and / or sensitivity of at least 0.5 from the prediction equation, and c) treating the subject with the cognition preserving intervention.

[0134] The cognition preserving interventions can include pharmacological treatments, cognitive rehabilitation, and physical and / or mental exercises. Examples of pharmacological treatments include therapeutic molecules, pharmaceutically active agents, or pharmaceutical compositions. Exemplary therapeutic molecules include ocrelizumab, donanemab-azbt, lecanemab-irmb, rituximab, ublituximab, immunosuppressants (such as Betaferon®, Rebif®, Avonex®), vitamins, and supplements.

[0135] In embodiments, preventative interventions may include providing a therapeutically effective amount of a drug sufficient to treat or alleviate a symptom of MS. Non-limiting examples include Baclofen for spasticity, Gabapentin for neuropathic pain such as burning feet in MS, Adderall is used for fatigue in MS, Xanax is used for sleep in MS, Celexa is used for depression in MS.

[0136] In embodiments, Disease Modifying Therapies (DMTs) may be used to modify MS as MS progresses or regresses. In embodiments, a DMT may be a drug or stem cell treatment. In embodiments, a DMT may include one or more of Alemtuzumab such as Lemtrada, interferon beta 1-α, dimethyl fumarate, cladribine, diroximel fumarate, fingolimod, Glatiramer acetate, HSCT, natalizumab, ocrelizumab, ofatumumab, ozanimod, peginterferon 1α, and combinations thereof and the like.

[0137] In embodiments, the therapeutic molecules, pharmaceutically active agents, or pharmaceutical compositions may be administered at a therapeutically effective dose. The therapeutically effective dose of the therapeutic molecules may be between about 0.05 mg / kg to about 100 mg / kg. For example, the therapeutically effective dose of the therapeutic molecules may be about 0.05 mg / kg, about 0.1 mg / kg, about 0.5 mg / kg, about 1 mg / kg, about 2.5 mg / kg, about 5 mg / kg, about 7.5 mg / kg, about 10 mg / kg, about 12.5 mg / kg, about 15 mg / kg, about 17.5 mg / kg, about 20 mg / kg, about 25 mg / kg, about 30 mg / kg, about 35 mg / kg, or about 40 mg / kg of thebody weight of the subject. The therapeutically effective dose of the therapeutic molecules may be about 0.05 mg, about 0.1 mg, about 0.5 mg, about 1 mg, about 5 mg, about 10 mg, about 20 mg, about 30 mg, about 40 mg, about 50 mg, about 60 mg, about 70 mg, about 80 mg, about 90 mg, about 100 mg, about 150 mg, about 200 mg, about 250 mg, about 300 mg, about 350 mg, about 400 mg, about 450 mg, about 500 mg, about 550 mg, about 600 mg, about 650 mg, about 700 mg, about 750 mg, about 800 mg, about 850 mg, about 900 mg, about 950 mg, or about 1000 mg. The therapeutically effective dose of the therapeutic molecules may be administered once a day, every two days, once a week, once every two weeks, once a month, once every two months, once every three months, once every four months, once every five months, or once every six months.

[0138] Cognition preserving interventions include cognitive rehabilitation with cognitive rehabilitation applications, such as digital twin, tele-cognitive-rehabilitation, and metaverse computer applications.

[0139] Methods for detecting the effects of treatment include obtaining patients’ MMSE scores, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, electroencephalography (EEG) tests, and posterior dominant rhythm (PDR) tests before and after treatment.

[0140] In some embodiments, the individual's cognitive performance improves after treatment with the method. Assessment of cognitive ability of an individual may involve a comparison with a healthy individual, e.g. a healthy individual of the same age, or at a certain point in the past, e.g.2 weeks, 1 month, 2 months, or to a reduction in cognitive ability compared to an individual's ability 3 months, 6 months, 1 year, 2 years, 5 years, or more than 10 years ago.

[0141] For example, the cognitive ability of an individual after treatment with the present methods may be, for example, 2 times or more, 5 times or more, 10 times or more, 15 times or more, 20 times or more, compared to the cognitive ability observed in the individual before treatment with the present method. The cognitive ability of an individual after treatment with the present method may improve by at least 50 times, 30 times or more, or 40 times or more, for example, 50 times or more, 60 times or more, 70 times or more, 80 times or more, 90 times or more, or 100 times or more. In some cases, treatment according to the presentmethods and compositions restores the cognitive performance of an individual suffering from MS-related cognitive decline, eg, to a level before symptoms of MS appeared, or MS staging. In some embodiments, treatment according to the present methods suppresses further cognitive decline. In some embodiments, treatment according to the present methods suppresses cognitive dysfunction.

[0142] According to some embodiments, the methods include monitoring the effects of a drug or treatment on a subject for the treatment of cognitive impairment.

[0143] The methods can further include assessment of cognitive impairment by comparing. Assessment of cognitive impairment may involve a comparison with a healthy individual, e.g. a healthy individual of the same age, or at a certain point in the past, e.g. 2 weeks, 1 month, 2 months, etc., or population; comparison of cognitive ability compared to an individual's ability 3 months, 6 months, 1 year, 2 years, 5 years, or more than 10 years ago; comparison to a subject or population of subjects with neurological or neurodegenerative disease having an MMSE score between about 24 and about 30, or a population of subjects with multiple sclerosis having an MMSE score between about 24 and about 30.

[0144] Those skilled in the art will recognize that there are well-known methods of assessing cognitive function. For example, and not by way of limitation, the methods include an assessment of cognitive function based on medical history, family history, physical and neurological examination by a clinician specializing in dementia and cognitive function, laboratory testing, and neuropsychological assessment. Further embodiments contemplated by the disclosure include assessment of the state of consciousness using, for example, the Glasgow Coma Scale (EMV), the Abbreviated Mental Test Score (AMTS) or the Mini-Mental State Examination (MMSE) (Folstein et al., J Psychiatr. Res 1975;12:1289-198), comprehensive assessment of higher functioning, and estimation of intracranial pressure by fundus examination. Methods Utilizing Thresholds

[0145] In some methods disclosed herein, a level of one or more Cognitive Impairment Biomarkers of the present disclosure can be compared to a threshold. Insome embodiments, methods disclosed herein include a comparison of a level of one or more Cognitive Impairment Biomarkers to respective thresholds. In some embodiments, methods disclosed herein include a comparison of a level of one or more Cognitive Impairment Biomarkers to reference thresholds. In embodiments, a suitable threshold is characterized as predetermined.

[0146] In embodiments, a reference threshold may be a threshold from a subject known or independently verified to have good neurological health, or from a subject known or independently verified to have poor neurological health, such as is the case for a subject having MS. In embodiments, a suitable reference threshold is characterized as predetermined. Alternately or in combination a subject’s Cognitive Impairment Biomarker profile is compared to a reference threshold determined from a plurality of subject of commonly known status (e.g., healthy, not diagnosed with MS, or diagnosed with MS). In some embodiments, a reference threshold is an average of known level of a Cognitive Impairment Biomarker from a plurality of subjects, or alternately is a range defined by the range of levels of a Cognitive Impairment Biomarkers of the present disclosure observed in reference subjects.

[0147] In more complex assessment approaches, a subject’s Cognitive Impairment Biomarker level is compared to a reference Cognitive Impairment Biomarker level constructed from a larger number of subjects of a common status (e.g., healthy, not diagnosed with neurodegenerative disease such as MS, or diagnosed with neurodegenerative disease such as MS), such as at least 10, at least 50, at least 100, at least 500, at least 1000 or more subjects. Often, reference subjects are evenly distributed in status between (1) healthy / not diagnosed with neurodegenerative disease such as MS and (2) diagnosed with neurodegenerative disease such as MS. Assessment includes in some cases iterative or simultaneous comparison of a subject’s Cognitive Impairment Biomarker level to a plurality of profiles of known status.

[0148] A plurality of known reference Cognitive Impairment Biomarker profiles (e.g., detected levels of one or more Cognitive Impairment Biomarkers in a reference sample) can also be used to train a computational assessment algorithm, e.g., a machine learning model, such that a single comparison between a subject’s Cognitive Impairment Biomarker profile and a reference Cognitive ImpairmentBiomarker profile provides an outcome that integrates or aggregates information from a large number of subjects of common known health status (e.g., healthy, not diagnosed with MS, or diagnosed with MS), such as at least 10, at least 50, at least 100, at least 500, at least 1000 or more individuals. Generation of such a reference Cognitive Impairment Biomarker profile can facilitate much faster assessment of a subject’s risk of cognitive impairment, or assessment using much less computational power.

[0149] A reference Cognitive Impairment Biomarker profile can be generated from a plurality of reference Cognitive Impairment Biomarker profiles through any of a number of computational approaches known to one of skill in the art. Machine learning models are readily constructed, for example, using any number of statistical programming languages such as R, scripting languages such as Python and associated machine learning packages, data mining software such as Weka or Java, Mathematica, Matlab or SAS.

[0150] A subject’s Cognitive Impairment Biomarker profile can be compared to a reference Cognitive Impairment Biomarker profile as generated above or otherwise by one of skill in the art, and an output assessment is generated. A number of output assessments are consistent with the disclosure herein. Output assessments include a single assessment, often narrowed by a sensitivity, specificity or sensitivity and specificity parameter, indicating a health status assessment (e.g., probability subject has MS, subject is not at risk of MS, subject is at risk of MS, subject has MS). Alternately or in combination, additional parameters are provided, such as the subject’s demographic factors (e.g., one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographic location) and / or the subject’s imaging-based biomarkers.

[0151] More specifically, in some embodiments, methods disclosed herein further include diagnosing a subject with Cognitive Impairment due to MS if the level of at least one of the one or more Cognitive Impairment Biomarkers described herein (e.g., Tables 1-3 miRNAs, protein Cognitive Impairment Biomarkers, or combinations thereof) that is detected is above a threshold value. In some embodiments, methods disclosed herein further include diagnosing a subject with cognitive impairment due to MS if the level of at least one of the one or more Cognitive Impairment Biomarkersthat is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9-fold greater than a threshold value. In some embodiments, methods disclosed herein further include diagnosing a subject with cognitive impairment due to MS if the level of each of the one or more Cognitive Impairment Biomarkers that is detected is above a threshold value. In some embodiments, methods disclosed herein further include diagnosing a subject with cognitive impairment due to MS that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9-fold greater than a threshold value.

[0152] In some embodiments, the methods disclosed herein further include recommending a subject for one or more neurological tests if the level of at least one or the one or more nucleic acid or protein Cognitive Impairment Biomarkers is above threshold value. In some such methods, the subject is recommended for one or more neurological tests if the level of at least one of the one or more nucleic acid Cognitive Impairment Biomarkers that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9-fold greater than a threshold value. In some embodiments, the methods disclosed herein further include recommending a subject for one or more neurological tests if the level of each of the one or more protein Cognitive Impairment Biomarker is above threshold value. In some such methods, the subject is recommended for one or more neurological tests if the level of each of the one or more nucleic acid and protein Cognitive Impairment Biomarkers that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9-fold greater than a threshold value.

[0153] In some embodiments, methods of detecting one or more protein Cognitive Impairment Biomarker (e.g., Cognitive Impairment Biomarker proteins) in a sample include contacting a sample with one or more antibody agents directed to the Cognitive Impairment Biomarker. In some embodiments, such methods also include contacting the sample with a first set of one or more detection agents. In some embodiments, the antibody agents are labeled with the first set of one or more detection agents. In some embodiments, the first set of one or more detection agents include one or more acridinium ester molecules. In embodiments, Acridinium ester (AE) molecules can be used to label proteins and nucleic acids. Acridinium-labeled proteins can be used for detection in immunoassays. Exposing AE to an alkaline H2O2(hydrogen peroxide) produces chemiluminscence. Light is emitted at a wavelength maximum in the range of 430 to 480 nm, depending on the specific AE variant. Such light can be detected, for example, by high-efficiency photomultiplier tubes.

[0154] In some embodiments, detecting binding between a Cognitive Impairment Biomarker and one or more antibody agents directed against the Cognitive Impairment Biomarker includes determining absorbance values or emission values for the first set of one or more detection agents. For example, the absorbance values are indicative of the level of binding (e.g., higher absorbance is indicative of more binding). In some embodiments, the absorbance values or emission values for the first set of one or more detection agents are above a threshold value. In some embodiments, the absorbance values or emission values for the first set of one or more detection agents is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9-fold greater than a threshold value. In some embodiments, the threshold value is an average of absorbance values or emission values determined for a second set of one or more detection agents that label two or more control samples. In some such embodiments, the second set of one or more detection agents is similar to or the same as the first set of one or more detection agents.

[0155] In one aspect, the present disclosure provides a method of detecting one or more Cognitive Impairment Biomarkers in a subject, said method including detecting whether one or more Cognitive Impairment Biomarker are present in a sample obtained from the subject according to the methods of detecting biological markers described above. In some embodiments, methods of detecting one or more Cognitive Impairment Biomarker include detecting binding between a Cognitive Impairment Biomarker and one or more anti- Cognitive Impairment Biomarker antibody agents. In some embodiments, detecting whether one or more Cognitive Impairment Biomarker are present in a sample obtained from the subject includes detecting a level of one or more Cognitive Impairment Biomarker present in a sample obtained from the subject. In some embodiments, the level of Cognitive Impairment Biomarker that is detected is above a threshold value for the respective Cognitive Impairment Biomarker(s). In some embodiments, the level of a Cognitive Impairment Biomarker that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9-fold greater than a threshold value.

[0156] In some embodiments, the present disclosure provides methods of detecting one or more one or more Cognitive Impairment Biomarker in a sample obtained from a subject. Cognitive Impairment Biomarker include the nucleic acid and protein biomarkers of the present disclosure. In some embodiments, control samples used in the methods described above include samples obtained from one or more subjects who do not have cognitive impairment due to MS.

[0157] In embodiments, a nucleic acid biomarker may be quantified by RT- PCR.

[0158] In embodiments, neurological tests that may be used according to the methods of the present disclosure include MMSE scores, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, electroencephalography (EEG) tests, and posterior dominant rhythm (PDR) tests.

[0159] In any of the above embodiments, a threshold value may be an average of values detected for two or more control samples. In some such embodiments, the values detected for the two or more control samples represent control levels for one or more nucleic acid and / or protein Cognitive Impairment Biomarkers. In some embodiments, the control samples include recombinant Cognitive Impairment Biomarkers such as nucleic acid sequences shown in SEQ ID NOS: 1-9 and protein sequences shown in various known UniProt and NCBI descriptors herein. In some embodiments, the two or more control samples are each a sample obtained from a subject who does not have cognitive impairment or MS. In some embodiments, the threshold value is a value reported in a standard table. Methods Utilizing Cognitive Impairment Profiles

[0160] In embodiments, an algorithm-based assay and associated information provided by the practice of any of the methods described herein can facilitate optimal treatment and decision making for subjects. For example, methods described herein can enable a physician or caretaker to identify patients who have a low likelihood of having cognitive impairment due to MS and therefore would not need treatment, would not need additional neurological tests, or would not need increased monitoring for cognitive impairment due to MS, or who have a high likelihood of having cognitive impairment due to MS, would need treatment, would need additional neurological tests, or would need increased monitoring for cognitive impairment.

[0161] A Cognitive Impairment Biomarker score can be determined by the application of a specific algorithm in some cases. In some embodiments, a Cognitive Impairment Biomarker score is quantitative. The algorithm used to calculate the Cognitive Impairment score in the methods disclosed herein may group the expression level values of a Cognitive Impairment Biomarker, or groups of Cognitive Impairment Biomarkers. The formation of a particular group of Cognitive Impairment Biomarkers, in addition, can facilitate the mathematical weighting of the contribution of various expression levels of Cognitive Impairment or Cognitive Impairment Biomarker subsets (for example classifier) to the quantitative score. Exemplary Cognitive Impairment Biomarkers and corresponding nucleic acids are miRNAs and sequences are listed in Tables 1-3 and 12. Protein biomarkers described herein, and one or more combinations of miRNA and protein biomarkers.

[0162] Methods described herein, as well as kits and compositions provided herein, can utilize an algorithm-based diagnostic assay for predicting if a subject from which the sample was obtained is at risk of, or is suffering from, cognitive impairment due to MS, selecting a subject from which the sample was obtained for one or more neurological tests, and / or selecting a subject from which the sample was obtained to receive one or more doses of a MS therapeutic agent or drug, prophylaxis, or the like.

[0163] Levels of one or more Cognitive Impairment Biomarkers, and optionally one or more demographic factors (e.g., one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographic location) and / or imaging-based biomarkers (e.g., brain or brainstem CT scans, MRI scans, and / or EEG tests), and / or MMSE score can be used alone or arranged into functional subsets to calculate a Cognitive Impairment Biomarker score that is used to predict if a subject from which the sample was obtained is at risk of or suffering from cognitive impairment, select a subject from which the sample was obtained for one or more neurological tests, and / or select a subject from which the sample was obtained to receive one or more doses a MS therapeutic agent or drug, prophylaxis, or the like.

[0164] Methods disclosed herein include using a Cognitive Impairment Biomarker profile to compute a Cognitive Impairment Biomarker score. In some embodiments, using a Cognitive Impairment Biomarker profile to compute a CognitiveImpairment Biomarker score includes applying an algorithm to the Cognitive Impairment Biomarker profile to compute a Cognitive Impairment Biomarker score. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a K nearest neighbors methodology, a generalized regression forward selection methodology, a generalized regression pruned forward selection methodology, a fit stepwise methodology, a generalized regression lasso methodology, a generalized regression elastic net methodology, a generalized regression ridge methodology, a nominal logistic methodology, a support vector machines methodology, a discriminant methodology, a naïve Bayes methodology, or a combination thereof. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a generalized regression lasso methodology, a generalized regression elastic net methodology, a generalized regression ridge methodology, a nominal logistic methodology, a support vector machines methodology, a discriminant methodology, or a combination thereof. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a support vector machines methodology, or a combination thereof.

[0165] In embodiments, a machine-learned algorithm may use data corresponding to one or more (e.g., two or more) demographic factors and a Cognitive Impairment Biomarker profile that includes data corresponding to levels of one or more Cognitive Impairment Biomarker in a sample from a subject. In some embodiments, the one or more Cognitive Impairment Biomarker include a nucleic acid Cognitive Impairment Biomarker of Tables 1-3 and 12 and a protein Cognitive Biomarker such as NfL or NEFL. In some embodiments, the two or more, three or more, four or more, five or more Cognitive Impairment Biomarkers of the present disclosure are included. In some embodiments, the one or more demographic factors include one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographic location. In some embodiments, the one or more demographic factors include age, biological sex, or both age and biological sex. In some embodiments, theone or more demographic factors is two demographic factors. In some embodiments, the two demographic factors are age and biological sex.

[0166] In embodiments, a machine-learned algorithm may use a Cognitive Impairment Biomarker profile that includes data corresponding to levels of one or more Cognitive Impairment Biomarkers in a sample from a subject and data corresponding to one or more imaging-based biomarkers for the subject, for example in further combination with data corresponding to one or more demographic factors for the subject. In embodiments, pattern recognition and / or segmentation and classification may be used to determine a state of an imaging-based biomarker from (e.g., a measurement corresponding to one or more imaging-based biomarkers) from one or more images (e.g., for one or more subjects). One or more images may be processed or pre-processed before determining a measurement or as part of determining a measurement for one or more imaging-based biomarkers. In some embodiments, data corresponding to an imaging-based biomarker (e.g., a measurement) is manually derived from one or more images, for example by a physician. A machine-learned algorithm may be used to determine a measurement for an imaging-based biomarker, for example by performing automated segmentation and classification. Data corresponding to one or more imaging-based biomarkers may be used to train a machine-learning algorithm, for example in combination with Cognitive Impairment Biomarker profiles and / or demographic factor(s). Data corresponding to one or more imaging-based biomarkers may include one or more images and / or one or more measurements.

[0167] In some embodiments, a machine-learned algorithm processes only data corresponding to one or more (e.g., two or more) demographic factors and a Cognitive Impairment Biomarker profile that includes data corresponding to levels of one or more, or two or more (e.g., only two) Cognitive Impairment Biomarkers to determine one or more outputs. In some embodiments, a machine-learned algorithm receives no other input than data corresponding to one or more (e.g., two or more) demographic factors and a Cognitive Impairment Biomarker profile that includes data corresponding to levels of two or more (e.g., only two) Cognitive Impairment Biomarker to determine one or more outputs.

[0168] In some embodiments, a method includes providing data corresponding to one or more demographic factors of a subject and a corresponding Cognitive Impairment Biomarker profile as input to a machine-learned algorithm. In some embodiments, the one or more demographic factors include biological sex, age, or both biological sex and age. The corresponding Cognitive Impairment Biomarker profile includes data for levels of one or more, or two or more Cognitive Impairment Biomarkers in a sample of a subject characterized by the demographic factor(s). The two or more Cognitive Impairment Biomarkers may be two Cognitive Impairment Biomarker (e.g., hsa-miR-629-3p and NfL) or three Cognitive Impairment Biomarkers (e.g., hsa-miR-629-3p, hsa-miR-486-3p, and NfL), for example. Performance (e.g., predictive power, sensitivity and / or selectivity, and / or classification accuracy) of a machine-learned algorithm may be improved by using a machine-learned algorithm that considers a combination of one or more, or two or more Cognitive Impairment Biomarkers with one or more (e.g., two or more) demographic factors, as compared to a machine-learned algorithm that does not consider any demographic factor.

[0169] Additional algorithms can be used in methods provided herein, and the algorithms provided above are merely exemplary of the types of algorithms that can be used to generate a Cognitive Impairment Biomarker score. In embodiments, a score can be calculated in a shorter duration compared to human calculation of the same score. Exemplary algorithms have been described, e.g., by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp.396-408 and pp.411-412; and Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, each of which is hereby incorporated by reference. Moreover, as indicated above, combinations of algorithms can be used in methods provided herein. For example, a boosted tree methodology can be a combination of a decision tree methodology and a boosting methodology. Further combinations are possible and are contemplated for use in methods provided herein. Exemplary algorithms that can be used in the methods provided herein are described below. Decision Tree

[0170] One methodology that can be used to calculate a Cognitive Impairment Biomarker score from a Cognitive Impairment Biomarker profile is a decision tree. A decision tree can be constructed using a training population andspecific data analysis algorithms. Decision trees are described generally by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp.395-396, which is hereby incorporated by reference. Tree-based methods partition the feature space into a set of rectangles, and then fit a model (like a constant) in each one.

[0171] A training population data can include Cognitive Impairment Biomarker profiles (e.g., including a level of one or more Cognitive Impairment Biomarkers in a sample) across a training set population. One specific algorithm that can be used to construct a decision tree is a classification and regression tree (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and Random Forests. CART, ID3, and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp.396-408 and pp.411-412, which is hereby incorporated by reference. CART, MART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, which is hereby incorporated by reference in its entirety. Random Forests are described in Breiman, 1999, “Random Forests—Random Features,” Technical Report 567, Statistics Department, U.C. Berkeley, September 1999, which is hereby incorporated by reference in its entirety.

[0172] An aim of a decision tree is to induce a classifier (a tree) from real- world example data. This tree can be used to classify unseen examples that have not been used to derive the decision tree. As such, a decision tree can be derived from training data. Exemplary training data contains data for a plurality of subjects (e.g., a training population). A Cognitive Impairment Biomarker profile can be provided and / or used for each respective subject. In some embodiments, training data includes one or more Cognitive Impairment Biomarker profiles for the training population.

[0173] The following algorithm describes an exemplary decision tree derivation: Tree (Examples,Class,Features) Create a root node If all Examples have the same Class value, give the root this label Else if Features is empty label the root according to the most common value Else begin Calculate the information gain for each FeatureSelect the Feature A with highest information gain and make this the root Feature For each possible value, v, of this Feature Add a new branch below the root, corresponding to A = v Let Examples(v) be those examples with A = v If Examples(v) is empty, make the new branch a leaf node labeled with the most common value among Examples Else let the new branch be the tree created by Tree(Examples(v),Class,Features - {A}) End

[0174] In univariate decision trees, each split is based on a feature value (e.g., a level) for a corresponding biomarker. Further, multivariate decision trees can be implemented in a method described herein. In some embodiments, a split is based on a feature value corresponding to a demographic factor, either alone or in combination with a feature value for a corresponding biomarker. For example, a split may be based on a combination of feature values corresponding to sex and level(s) of one or more biomarkers, to age and level(s) of one or more biomarkers, or to both age and sex and level(s) of one or more biomarkers. Multivariate decision trees are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp.408-409, which is hereby incorporated by reference. In such multivariate decision trees, some or all of the decisions include a linear combination of feature values (e.g., levels) for a plurality of Cognitive Impairment Biomarkers of a Cognitive Impairment profile. Such a linear combination can be trained using known techniques such as gradient descent on a classification or by the use of a sum-squared-error criterion.

[0175] As an illustrative example, the following expression is used: 0.05(X1) + 0.2(X2) < 500

[0176] In this example, X1 and X2 refer to two different features (e.g., levels) for two different Cognitive Impairment Biomarkers. To apply the methodology, the values of features X1 and X2 (e.g., as part of a Cognitive Impairment Biomarkers profile) are obtained from the measurements obtained from an unclassified subject. These values are then inserted into the equation. If a value of less than 500 iscomputed, then a first branch in the decision tree is taken. Otherwise, a second branch in the decision tree is taken.

[0177] Cognitive Impairment Biomarkers profiles may be used to train a machine-learning algorithm. Cognitive Impairment Biomarkers profiles may be from different subjects, a same subject at different times, or a combination thereof. Cognitive Impairment Biomarkers profiles may be associated with (e.g., labelled with) corresponding data indicative of health status. For example, a Cognitive Impairment Biomarkers profile for a subject used as training data may be labelled with data indicative of health status for the subject. Such corresponding data may be a probability or a score (e.g., a Cognitive Impairment Biomarker score). The health status may be a probability that a subject has a cognitive impairment such as cognitive impairment associated with MS, whether or not a subject is or is not at risk of cognitive impairment, or whether or not a subject does or does not have cognitive impairment due to MS (e.g., is healthy, not diagnosed with cognitive impairment due to MS, or diagnosed with MS). Health status may be determined manually, for example by a physician. Labelling Cognitive Impairment Biomarker profiles with corresponding data indicative of health status may be performed manually (e.g., by the physician who determines health status).

[0178] In embodiments, demographic factor data and / or imaging-based biomarkers may be used in combination with Cognitive Impairment Biomarker profiles to train a machine-learning algorithm. For example, a Cognitive Impairment Biomarker profile and data for one or more demographic factors and / or one or more imaging- based biomarkers for each of a set of subjects may be used to train a machine-learning algorithm. The set may be, for example, at least 10 subjects, at least 20 subjects, at least 50 subjects, at least 100 subjects, at least 200 subjects, at least 300 subjects, at least 500 subjects, at least 1,000 subjects, at least 1,500 subjects, at least 2,000 subjects, at least 5,000 subjects, or at least 10,000 subjects. In some embodiments, a combination of one or more demographic factors of age, sex, or both age and sex and a Cognitive Impairment Biomarker profile for each of a set of subjects is used for training a machine-learning algorithm. The Cognitive Impairment Biomarker profile may include data corresponding to the fold change levels of miRNA biomarkers and / or concentration of protein biomarkers of two or more Cognitive Impairment Biomarker.The Cognitive Impairment Biomarker may be selected from miRNA biomarkers hsa- miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa- miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and functional fragments or functional variants thereof, and from protein biomarkers GFAP, NfL, Abeta40, Abeta 42, functional fragments or functional variants thereof, and the ratio of Abeta42 / Abeta40.

[0179] In some embodiments, training a machine-learning algorithm includes determining one or more feature values. Each feature value may correspond to a Cognitive Impairment Biomarker. One or more feature values may be determined based on Cognitive Impairment Biomarker profiles and / or a linear combination thereof. One or more feature values may correspond to a level of one or more Cognitive Impairment Biomarkers. Training a machine-learning algorithm may include determining one or more decision rules based on Cognitive Impairment Biomarker profiles used as training data. The one or more decision rules may be further based on one or more demographic factors corresponding to subject(s) that the Cognitive Impairment Biomarker profiles are for. The one or more decision rules may be further based on one or more imaging-based biomarkers corresponding to subject(s) that the Cognitive Impairment Biomarker profiles are for. The one or more decision rules may be based one or more feature values (e.g., determined during training). The one or more decision rules may be used in one or more decision trees.

[0180] Bagging, boosting, and additive trees can be combined with a decision methodology to improve weak decision rules. These techniques are designed for, and usually applied to, decision trees, such as the decision trees described above. In some embodiments, a machine-learned algorithm may include at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees. In addition, such techniques can also be useful in decision rules developed using other types of data analysis algorithms such as linear discriminant analysis.

[0181] In bagging, a training set is sampled, generating random independent bootstrap replicates, constructing a decision rule on each of these, and aggregateing them by a simple majority vote in the final decision rule. See, for example, Breiman, 1996, Machine Learning 24, 123-140; and Efron & Tibshirani, An Introduction toBoostrap, Chapman & Hall, New York, 1993, which is hereby incorporated by reference in its entirety.

[0182] In boosting, decision rules are constructed on weighted versions of the training set, which are dependent on previous classification results. Initially, all features under consideration have equal weights, and the first decision rule is constructed on this data set. Then, weights are changed according to the performance of the decision rule. Erroneously classified features get larger weights, and the next decision rule is boosted on the reweighted training set. In this way, a sequence of training sets and decision rules is obtained, which is then combined by simple majority voting or by weighted majority voting in the final decision rule. See, for example, Freund & Schapire, “Experiments with a new boosting algorithm,” Proceedings 13th International Conference on Machine Learning, 1996, 148-156, which is hereby incorporated by reference in its entirety.

[0183] It has been found that machine-learned algorithms that are or are derived from decision tree methodologies perform unexpectedly well as compared to other types of algorithms (e.g., neural networks) when used to make determinations of health status using combinations of biomarkers, especially laboratory-based biomarkers (e.g., that are Cognitive Impairment Biomarker). Health status may include at least one of (e.g., two of, or all three of) (i) a state (e.g., stage) of a disease, disorder, or condition for the subject, (ii) whether the subject has or does not have a disease, disorder, or condition, and (iii) a probability that the subject has a disease, disorder or condition. A range of biomarkers could be used in this context, including protein biomarkers, (e.g., neurofilament biomarkers), and miRNA biomarkers. Bootstrapped forest algorithms perform particularly well within this class of methodologies, for example having high specificity and sensitivity. The nature of the decision rules learned and then employed as part of decision tree methodologies may contribute to the performance of such methodologies, for example when using biomarkers that are quantitative. Demographic factors may also be particularly well-suited to be used in decision-tree methodologies.

[0184] Measurement data used in the methods, systems, kits and compositions disclosed herein are optionally normalized. Normalization refers to a process to correct for example, differences in the amount of genes or protein levelsassayed and variability in the quality of the template used, to remove unwanted sources of systematic variation measurements involved in the processing and detection of genes or protein expression. Other sources of systematic variation are attributable to laboratory processing conditions.

[0185] In some instances, normalization methods are used for the normalization of laboratory processing conditions. Non-limiting examples of normalization of laboratory processing that may be used with methods of the disclosure include but are not limited to: accounting for systematic differences between the instruments, reagents, and equipment used during the data generation process, and / or the date and time or lapse of time in the data collection.

[0186] Assays can provide for normalization by incorporating the expression of certain normalizing standard genes or proteins, which do not significantly differ in expression levels under the relevant conditions, that is to say they are known to have a stabilized and consistent expression level in that particular sample type. Suitable normalization genes and proteins that can be used with the present disclosure include housekeeping genes. (See, for example, E. Eisenberg, et al., Trends in Genetics 19(7):362-365 (2003). In some applications, the normalizing biomarkers (genes and proteins), also referred to as reference genes, known not to exhibit meaningfully different expression levels in subjects with cognitive impairment as compared to control subjects without cognitive impairment. In some applications, it may be useful to add a stable isotope labeled standards which can be used and represent an entity with known properties for use in data normalization.

[0187] In other applications, a standard, fixed sample can be measured with each analytical batch to account for instrument and day-to-day measurement variability.

[0188] Machine learning algorithms for sub-selecting discriminating biomarkers and optionally subject characteristics, and for building classification models, are used in some methods and systems herein to determine clinical outcome scores. Examples of such algorithms are described above. These algorithms can aid in selection of important biomarker features and transform the underlying measurements into a score or probability relating to, for example, clinical outcome,disease risk, disease likelihood, presence or absence of disease, treatment response, and / or classification of disease status.

[0189] A machine-learned algorithm may output a Cognitive Impairment Biomarker score. A machine-learned algorithm may determine if a subject is at risk of or is suffering from cognitive impairment such as cognitive impairment due to MS. In some embodiments, an output of a machine-learned algorithm is a determination (e.g., a probability) of whether a subject is at risk of or is suffering from cognitive impairment. In some embodiments, a machine-learned algorithm determines whether a subject is at risk of or has cognitive impairment, for example by determining a probability that the subject has cognitive impairment or by determining a probability that the subject is at risk for developing cognitive impairment. A machine-learned algorithm may be a classifier for cognitive impairment. In some embodiments, a machine-learned algorithm can be used to classify whether or not a subject has cognitive impairment due to neurodegenerative disease e.g., MS, for example based on an Cognitive Impairment Biomarker profile for the subject. In some embodiments, the classifier or the classifying has a sensitivity and a specificity each of more than 80% (e.g., at least one or both of more than 90%).

[0190] In embodiments, a machine-learned model may be used to make a determination of whether a subject has or does not have cognitive impairment. For example, in some embodiments, a machine-learned algorithm outputs a probability that a subject has cognitive impairment. In general, and according to some embodiments, a determination that a subject has cognitive impairment may be made where it is determined that it is more likely that the subject has cognitive impairment than that the subject does not have cognitive impairment (i.e., a 0.5 or 50% probability cutoff), and vice versa (to determine when a subject does not have cognitive impairment). However, other probability cutoffs may be used instead. Sensitivity and specificity of a machine-learned algorithm may change when the probability cutoff is altered.

[0191] In embodiments, a Cognitive Impairment Biomarker score can be determined by comparing a subject-specific Cognitive Impairment Biomarker profile to a reference Cognitive Impairment Biomarker profile. A reference Cognitive Impairment Biomarker profile can be representative a known diagnosis. For example,a Cognitive Impairment Biomarker profile can represent a positive diagnosis of cognitive impairment. As another example, a reference Cognitive Impairment Biomarker profile can represent a negative diagnosis of cognitive impairment. In some cases, an increase in a score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some cases, a decrease in the quantitative score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management.

[0192] Results can be provided to a subject, a health care professional or other professional. Results are optionally accompanied by a heath recommendation, such as a recommendation to confirm or independently assess cognitive impairment risk, for example using one or more neurological or CNS tests.

[0193] A recommendation optionally includes information relevant to a treatment regimen, such as information indicating that a treatment regimen. Efficacy of a regimen can be assessed in some cases by comparison of a subject’s Cognitive Impairment Biomarker profile at a first time point, optionally prior to a treatment and a later second time point, optionally subsequent to a treatment instance. Cognitive Impairment Biomarker profiles can be compared to one another, each to a reference, or otherwise assessed so as to determine whether a treatment regimen demonstrates efficacy such that it should be continued, increased, replaced with an alternate regimen or discontinued because of its success in addressing cognitive impairment or associated signs and symptoms. Some assessments rely upon comparison of a subject’s Cognitive Impairment Biomarker profile at multiple time points, such as at least one time point prior to a treatment and at least one time point following a treatment. Cognitive Impairment Biomarker- profiles can be compared one to another or to at least one reference biomarker panel level or both to one another and to at least one reference biomarker panel level. Kits

[0194] Also provided by the present disclosure are kits including one or more compositions for detecting Cognitive Impairment Biomarker agents and instructions for use (e.g., for detecting the fold change of miRNA biomarkers and / or concentration of protein biomarkers). In some embodiments, the kit is used for an in vitro diagnostic assay to diagnose cognitive impairment (including cognitive impairment resulting from MS). In some embodiments, the kits of the disclosure further include an MS therapeutic agent such as a drug or DMT, or the like.

[0195] In some embodiments, the one or more compositions for detecting Cognitive Impairment Biomarker agents include antibody agents. In some embodiments, one or more of the antibody agents are labeled with a detectable moiety. In some embodiments, the kit further includes a detection agent (e.g., one or more acridinium ester molecules). In some embodiments, one or more of the antibody agents are labeled with one or more of the acridinium ester molecules. In some embodiments, the kit further includes one or more secondary antibody agents that specifically bind to one or more of the anti-Cognitive Impairment Biomarker antibody agents.

[0196] In some embodiments, the one or more compositions for detecting Cognitive Impairment Biomarker agents include nucleic acid probes. In some embodiments, at least a portion of each nucleic acid probe hybridizes to one or more portions of a nucleotide that encodes a Cognitive Impairment Biomarker. In embodiments, nucleotides that encode a Cognitive Impairment Biomarker can be DNA (e.g., cDNA) or RNA (e.g. mRNA). In some embodiments, the nucleic acid probes are labeled with one or more detection agents (e.g., wherein the detection agents indicate presence of nucleotides that are Cognitive Impairment Biomarkers (such as nucleic acid biomarkers) or encode a Cognitive Impairment Biomarker (such as protein Cognitive Impairment Biomarker)).

[0197] In some embodiments, the kit further includes one or more control samples. In some embodiments, the control samples include one or more Cognitive Impairment Biomarker standards. In some embodiments, a Cognitive Impairment Biomarker standard includes recombinant Cognitive Impairment Biomarker of the present disclosure, or combinations thereof. In some embodiments, a CognitiveImpairment Biomarker standard includes synthetic Cognitive Impairment Biomarker or combinations thereof.

[0198] In addition to the above, a kit can include other ingredients, such as a solvent or buffer, a stabilizer or a preservative, and / or an agent for treating a condition or disorder described herein. Alternatively, other ingredients can be included in a kit, but in different compositions or containers than the compositions for detecting Cognitive Impairment Biomarker agents. In such embodiments, a kit can include instructions for admixing the compositions for detecting Cognitive Impairment Biomarker agents and the other ingredients, or for using the compositions for detecting Cognitive Impairment Biomarker together with the other ingredients.

[0199] In certain embodiments, kits for use in accordance with the present disclosure may include, a reference or control sample(s), instructions for processing samples, performing tests on samples, instructions for interpreting the results, buffers and / or other reagents necessary for performing tests.

[0200] The present disclosure also provides that recognition that certain single Cognitive Impairment Biomarker can be helpful for detecting and / or diagnosing cognitive impairment due to MS. The present disclosure further provides the insight that particular combinations of Cognitive Impairment Biomarkers are especially useful for detecting and / or diagnosing MS. Thus, methods, compositions, and kits described herein can be used for assays to assess the risk of cognitive impairment due to MS, assess whether a subject should undergo further neurological or CNS tests, and / or diagnose cognitive impairment based on detection or measurement of Cognitive Impairment Biomarkers in a sample, e.g., a biological sample obtained from a subject.

[0201] Methods and kits provided herein detect Cognitive Impairment Biomarkers in a sample with a sensitivity and a specificity that renders the outcome of the test reliable enough to be medically actionable. Methods and kits described herein for detection and / or diagnosis of cognitive impairment in a subject detects cognitive impairment with a sensitivity greater than 75%, greater than 80%, greater than 85%, greater than 90%, greater than 95%, greater than 96%, greater than 97%, greater than 98%, greater than 99%, or about 100%. In some embodiments, methods and kits provided herein can detect cognitive impairment with a sensitivity that is between about 70%-100%, between about 80%-100%, or between about 90-100%. In someembodiments, methods and kits provided herein can detect cognitive impairment with a specificity greater than 70%, greater than 75%, greater than 80%, greater than 85%, greater than 90%, greater than 95%, greater than 96%, greater than 97%, greater than 98%, greater than 99%, or about 100%. In some embodiments, methods and kits provided herein can detect cognitive impairment with a specificity that is between about 50%-100%, between about 60%-100%, between about 70%-100%, between about 80%-100%, or between about 90-100%. In some embodiments, methods and kits provided herein can detect cognitive impairment with a sensitivity and a specificity that is 50% or greater, 60% or greater, 70% or greater, 75% or greater, 80% or greater, 85% or greater, 90% or greater. In some embodiments, methods and kits provided herein can detect cognitive impairment with a sensitivity and a specificity that is between about 50%-100%, between about 60%-100%, between about 70%-100%, between about 80%-100%, or between about 90-100%. Compositions

[0202] Also provided herein are compositions for detecting Cognitive Impairment Biomarkers. In some embodiments, a composition includes one or more Cognitive Impairment Biomarkers and one or more anti-Cognitive Impairment Biomarker agents. In some embodiments, the one or more Cognitive Impairment Biomarkers include glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, and characteristic fragments, functional fragments, or functional variants thereof. and the one or more anti-Cognitive Impairment Biomarker agents include an anti- GFAP agent, an anti- NfL agent, an anti- Abeta40 agent, an anti- Abeta42 agent, an anti-GRAP functional fragment_ agent, an anti- GRAP functional variant agent, an anti-NfL functional fragment agent, an anti-NfL functional variant agent, an anti- Abeta40 functional fragment agent, an anti- Abeta40 functional variant agent, an anti- Abeta42 functional fragment agent, an anti-Abeta42 functional variant agent.

[0203] In some embodiments, a composition includes a combination (e.g., one or more, two or more, three or more, four or more, five or more, etc.) of Cognitive Impairment Biomarkers and a corresponding combination of anti-Cognitive Impairment Biomarker agents. In some embodiments, one or more anti-Cognitive Impairment Biomarker agents in a composition provided herein include antibodyagents. In some embodiments, one or more of the antibody agents are labeled with a detectable moiety. In some embodiments, the composition further includes a detection agent (e.g., one or more acridinium ester molecules). In some embodiments, one or more of the antibody agents are labeled with one or more of the acridinium ester molecules. In some embodiments, the composition further includes one or more secondary antibody agents that specifically bind to one or more of the anti-Cognitive Impairment Biomarker antibody agents.

[0204] In some embodiments, one or more anti-Cognitive Impairment Biomarker agents in a composition provided herein include nucleic acid probes. In some embodiments, at least a portion of the one or more nucleic acid probes hybridizes to a portion of a nucleotide that encodes a Cognitive Impairment Biomarker (e.g., an miRNA biomarker selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR- 320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa- miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof). Nucleotides that encode a Cognitive Impairment Biomarker can be DNA (e.g., cDNA) or RNA (e.g. mRNA). In some embodiments, the nucleic acid probes are labeled with one or more detection agents (e.g., wherein the detection agents indicate presence of nucleotides that encode a Cognitive Impairment Biomarker).

[0205] In some embodiments, a composition includes one or more control samples. In some embodiments, the control samples include one or more Cognitive Impairment Biomarker standards. In some embodiments, a Cognitive Impairment Biomarker standard includes a recombinant Cognitive Impairment Biomarker. In some embodiments, a Cognitive Impairment Biomarker standard includes synthetic Cognitive Impairment Biomarker nucleic acids or proteins.

[0206] In addition to the above, a composition can include other ingredients, such as a solvent or buffer, a stabilizer or a preservative, and / or an agent for treating a condition or disorder described herein. Computer Systems

[0207] Methods described herein can be implemented in a computer system having a processor that executes specific instructions in a computer program. In some embodiments, a computer system may be arranged to output a Cognitive Impairment Biomarker score based on receiving a Cognitive Impairment Biomarker profile and / ora level of one or more Cognitive Impairment Biomarkers. Particularly, a computer program may include instructions for the system to select appropriate next steps, including additional medication (e.g., a therapeutic for MS or symptom of MS), a treatment, and / or additional testing (e.g., neurological or CNS tests) for a subject.

[0208] In some embodiments, the computer program may be configured such that the computer system can identify a subject for further testing (e.g., neurological tests), identify a subject as being at risk or having cognitive impairment, and / or identify a subject to receive medication based on received data (e.g., a Cognitive Impairment Biomarker profile) and use the data to calculate a Cognitive Impairment Biomarker score. A system may be able to rank-order identified next steps based on a Cognitive Impairment Biomarker profile with demographic factors and / or imaging-based biomarkers. A system may be able to adjust the rank ordering based on, e.g., a clinical response of a subject or of a family member of the subject who has or is suspected of having cognitive impairment due to MS.

[0209] FIG. 17 is a block diagram of a computer system 1100 that can be used in the operations described above, according to one embodiment. The system 1100 includes a processor 1110, a memory 1120, a storage device 1130 and an input / output device 1140. Each of the components 1110, 1120, 1130 and 1140 are interconnected using a system bus 1150. The system may include analyzing equipment 1160 for determining a level of one or more Cognitive Impairment Biomarkers in a sample.

[0210] The processor 1110 is capable of processing instructions for execution within the system 1100. In one embodiment, the processor 1110 is a single- threaded processor. In another embodiment, the processor 1110 is a multi-threaded processor. The processor 1110 is capable of processing instructions stored in the memory 1120 or on the storage device 1130, including for receiving or sending information through the input / output device 1140.

[0211] The memory 1120 stores information within the system 1100. In one embodiment, the memory 1120 is a computer-readable medium. In one embodiment, the memory 1120 is a volatile memory unit. In another embodiment, the memory 1120 is a non-volatile memory unit.

[0212] The storage device 1130 is capable of providing mass storage for the system 1100. In one embodiment, the storage device 1130 is a computer-readable medium.

[0213] The input / output device 1140 provides input / output operations for the system 1100. In one embodiment, the input / output device 1140 includes a keyboard and / or pointing device. In one embodiment, the input / output device 1140 includes a display unit for displaying graphical user interfaces.

[0214] The system 1100 can be used to build a database. FIG.18 shows a flow chart of a method 1200 for building a database for use in identifying a subject for further testing (e.g., neurological tests), identifying a subject as being at risk or having cognitive impairment, and / or identifying a subject to receive medication. Preferably, a method 1200 is performed in a system 1100. For example, a computer program product can include instructions that cause a processor 1110 to perform the steps of a method 1200 or a method 1300.

[0215] Method 1200 includes the following steps. Receiving, in step 1210, an subject’s Cognitive Impairment Biomarker Profile (e.g., levels of one or more Cognitive Impairment Biomarkers in a sample).

[0216] A computer program in the system 1100 may include instructions for presenting a suitable graphical user interface on input / output device 1140, and the graphical user interface may prompt the user to enter the levels 1170 using the input / output device 1140, such as a keyboard. Calculating, in step 1220, a Cognitive Impairment Biomarker score from a Cognitive Impairment Biomarker profile. As described herein, calculating, in step 1220, a Cognitive Impairment Biomarker score from (i) a Cognitive Impairment Biomarker profile and (ii) demographic factors and / or imaging-based biomarkers. Storing, in step 1230, a Cognitive Impairment Biomarker score. The system 1100 may store a Cognitive Impairment Biomarker score in the storage device 1130. Additionally or alternatively, a system 1100 may provide a readout including a Cognitive Impairment Biomarker score. A readout may also include proposed next steps for a subject and / or a confidence level associated with a Cognitive Impairment Biomarker score.

[0217] Method 1300 includes the following steps in FIG. 19. Detecting, in step 1310, levels of one or more Cognitive Impairment Biomarkers in a sample, e.g.,from a subject. Using, in step 1320, levels of one or more Cognitive Impairment Biomarkers to obtain a Cognitive Impairment Profile. Calculating, in step 1330, a Cognitive Impairment Biomarker score from a Cognitive Impairment Biomarker profile. As described herein, calculating, in step 1330, a Cognitive Impairment Biomarker score from (i) a Cognitive Impairment Biomarker profile and (ii) demographic factors and / or imaging-based biomarkers. Storing, in step 1340, a Cognitive Impairment Biomarker score. The system 1100 may store a Cognitive Impairment Biomarker score in the storage device 1130. Additionally or alternatively, a system 1100 may provide a readout including a Cognitive Impairment Biomarker score. A readout may also include proposed next steps for a subject and / or a confidence level associated with a Cognitive Impairment Biomarker score.

[0218] Additionally, non-transitory computer readable media containing executable instructions that when executed cause a processor to perform operations including a method as provided herein are provided. For example, a non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations including a method of 1200 or 1300 described above.

[0219] Illustrative embodiments of systems and methods disclosed herein were described above with reference to computations performed locally by a computing device. However, computations performed over a network are also contemplated. FIG.20 shows an illustrative network environment 2800 for use in the methods and systems described herein. In brief overview, referring now to FIG.20, a block diagram of an illustrative cloud computing environment 2800 is shown and described. The cloud computing environment 2800 may include one or more resource providers 2802a, 2802b, 2802c (collectively, 2802). Each resource provider 2802 may include computing resources. In some implementations, computing resources may include any hardware and / or software used to process data. For example, computing resources may include hardware and / or software capable of executing algorithms, computer programs, and / or computer applications. In some implementations, illustrative computing resources may include application servers and / or databases with storage and retrieval capabilities. Each resource provider 2802 may be connected to any other resource provider 2802 in the cloud computing environment2800. In some implementations, the resource providers 2802 may be connected over a computer network 2808. Each resource provider 2802 may be connected to one or more computing device 2804a, 2804b, 2804c (collectively, 2804), over the computer network 2800.

[0220] The cloud computing environment 2800 may include a resource manager 2806. The resource manager 2806 may be connected to the resource providers 2802 and the computing devices 2804 over the computer network 2808. In some implementations, the resource manager 2806 may facilitate the provision of computing resources by one or more resource providers 2802 to one or more computing devices 2804. The resource manager 2806 may receive a request for a computing resource from a particular computing device 2804. The resource manager 2806 may identify one or more resource providers 2802 capable of providing the computing resource requested by the computing device 2804. The resource manager 2806 may select a resource provider 2802 to provide the computing resource. The resource manager 2806 may facilitate a connection between the resource provider 2802 and a particular computing device 2804. In some implementations, the resource manager 2806 may establish a connection between a particular resource provider 2802 and a particular computing device 2804. In some implementations, the resource manager 2806 may redirect a particular computing device 2804 to a particular resource provider 2802 with the requested computing resource.

[0221] FIG.21 shows an example of a computing device 2900 and a mobile computing device 2950 that can be used in the methods and systems described in this disclosure. The computing device 2900 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device 2950 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.

[0222] The computing device 2900 includes a processor 2902, a memory 2904, a storage device 2906, a high-speed interface 2908 connecting to the memory 2904 and multiple high-speed expansion ports 2910, and a low-speed interface 2912connecting to a low-speed expansion port 2914 and the storage device 2906. Each of the processor 2902, the memory 2904, the storage device 2906, the high-speed interface 2908, the high-speed expansion ports 2910, and the low-speed interface 2912, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 2902 can process instructions for execution within the computing device 2900, including instructions stored in the memory 2904 or on the storage device 2906 to display graphical information for a GUI on an external input / output device, such as a display 2916 coupled to the high-speed interface 2908. In other implementations, multiple processors and / or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). Thus, as the term is used herein, where a plurality of functions are described as being performed by “a processor”, this encompasses embodiments wherein the plurality of functions are performed by any number of processors (e.g., one or more processors) of any number of computing devices (e.g., one or more computing devices). Furthermore, where a function is described as being performed by “a processor”, this encompasses embodiments wherein the function is performed by any number of processors (e.g., one or more processors) of any number of computing devices (e.g., one or more computing devices) (e.g., in a distributed computing system).

[0223] The memory 2904 stores information within the computing device 2900. In some implementations, the memory 2904 is a volatile memory unit or units. In some implementations, the memory 2904 is a non-volatile memory unit or units. The memory 2904 may also be another form of computer-readable medium, such as a magnetic or optical disk.

[0224] The storage device 2906 is capable of providing mass storage for the computing device 2900. In some implementations, the storage device 2906 may be or contain a computer-readable medium, such as a hard disk device, an optical diskdevice, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 2902), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices such as computer- or machine-readable mediums (for example, the memory 2904, the storage device 2906, or memory on the processor 2902).

[0225] The high-speed interface 2908 manages bandwidth-intensive operations for the computing device 2900, while the low-speed interface 2912 manages lower bandwidth-intensive operations. Such allocation of functions is an example only. In some implementations, the high-speed interface 2908 is coupled to the memory 2904, the display 2916 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 2910, which may accept various expansion cards (not shown). In the implementation, the low-speed interface 2912 is coupled to the storage device 2906 and the low-speed expansion port 2914. The low-speed expansion port 2914, which may include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or more input / output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

[0226] The computing device 2900 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 2920, or multiple times in a group of such servers. In addition, it may be implemented in a personal computer such as a laptop computer 2922. It may also be implemented as part of a rack server system 2924. Alternatively, components from the computing device 2900 may be combined with other components in a mobile device (not shown), such as a mobile computing device 2950. Each of such devices may contain one or more of the computing device 2900 and the mobile computing device 2950, and an entire system may be made up of multiple computing devices communicating with each other.

[0227] The mobile computing device 2950 includes a processor 2952, a memory 2964, an input / output device such as a display 2954, a communicationinterface 2966, and a transceiver 2968, among other components. The mobile computing device 2950 may also be provided with a storage device, such as a micro- drive or other device, to provide additional storage. Each of the processor 2952, the memory 2964, the display 2954, the communication interface 2966, and the transceiver 2968, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.

[0228] The processor 2952 can execute instructions within the mobile computing device 2950, including instructions stored in the memory 2964. The processor 2952 may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor 2952 may provide, for example, for coordination of the other components of the mobile computing device 2950, such as control of user interfaces, applications run by the mobile computing device 2950, and wireless communication by the mobile computing device 2950.

[0229] The processor 2952 may communicate with a user through a control interface 2958 and a display interface 2956 coupled to the display 2954. The display 2954 may be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 2956 may include appropriate circuitry for driving the display 2954 to present graphical and other information to a user. The control interface 2958 may receive commands from a user and convert them for submission to the processor 2952. In addition, an external interface 2962 may provide communication with the processor 2952, so as to enable near area communication of the mobile computing device 2950 with other devices. The external interface 2962 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

[0230] The memory 2964 stores information within the mobile computing device 2950. The memory 2964 can be implemented as one or more of a computer- readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory 2974 may also be provided and connected to the mobile computing device 2950 through an expansion interface 2972, which mayinclude, for example, a SIMM (Single In Line Memory Module) card interface. The expansion memory 2974 may provide extra storage space for the mobile computing device 2950, or may also store applications or other information for the mobile computing device 2950. Specifically, the expansion memory 2974 may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, the expansion memory 2974 may be provided as a security module for the mobile computing device 2950, and may be programmed with instructions that permit secure use of the mobile computing device 2950. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

[0231] The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as discussed below. In some implementations, instructions are stored in an information carrier and, when executed by one or more processing devices (for example, processor 2952), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 2964, the expansion memory 2974, or memory on the processor 2952). In some implementations, the instructions can be received in a propagated signal, for example, over the transceiver 2968 or the external interface 2962.

[0232] The mobile computing device 2950 may communicate wirelessly through the communication interface 2966, which may include digital signal processing circuitry where necessary. The communication interface 2966 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others. Such communication may occur, for example, through the transceiver 2968 using a radio-frequency. In addition, short-range communication may occur, such as using a Bluetooth®, Wi-Fi™, or othersuch transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 2970 may provide additional navigation- and location-related wireless data to the mobile computing device 2950, which may be used as appropriate by applications running on the mobile computing device 2950.

[0233] The mobile computing device 2950 may also communicate audibly using an audio codec 2960, which may receive spoken information from a user and convert it to usable digital information. The audio codec 2960 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 2950. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile computing device 2950.

[0234] The mobile computing device 2950 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 2680. It may also be implemented as part of a smart-phone 2682, personal digital assistant, or other similar mobile device.

[0235] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0236] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machineinstructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0237] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0238] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0239] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Exemplary Numbered Embodiments

[0240] Embodiment 1. A method for predicting risk of developing or detecting a cognitive impairment in a subject, the method including applying an expression characteristic of one or more biomarkers selected from miRNA biomarkersor miRNA , or characteristic fragments, functional fragments, or functional variants thereof and protein biomarkers or characteristic fragments, functional fragments, or functional variants thereof in a sample from the subject to a prediction equation and predicting the risk of or detecting the cognitive impairment from the prediction equation.

[0241] Embodiment 2. Embodiment 1, wherein the one or more biomarkers include one or more miRNA biomarkers and one or more protein biomarkers.

[0242] Embodiment 3. Embodiments 1 or 2, wherein the one or more miRNA biomarkers include any one or any combination of miRNA listed in Tables 1 and 3 or characteristic fragments, functional fragments, or functional variants thereof.

[0243] Embodiment 4. Embodiments 1-3, wherein the one or more miRNA biomarkers include any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof.

[0244] Embodiment 5. Embodiments 1-4, wherein the one or more protein biomarkers include any one or any combination of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40.

[0245] Embodiment 6. Embodiments 1-5, wherein the expression characteristic includes a concentration or a fold change in expression of the one or more biomarkers in the sample.

[0246] Embodiment 7. Embodiments 1-6, wherein the one or more biomarkers include a combination of at least one miRNA biomarker selected from hsa- miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa- miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof, and at least one protein biomarker selected from GFAP, NfL, Abeta 40, Abeta42, characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40.

[0247] Embodiment 8, Embodiments1-7, wherein the one or more miRNA biomarkers includes a combination of hsa-miR-486-3p and hsa-miR-629-3p.

[0248] Embodiment 9. Embodiments, 1-8 wherein the one or more biomarkers include a combination of miR-629-3p and NfL.

[0249] Embodiment 10. Embodiments, 1-9 wherein the subject includes a patient with multiple sclerosis.

[0250] Embodiment 11. Embodiments 1-10, wherein the sample includes a cerebrospinal fluid or plasma.

[0251] Embodiment 12. Embodiments, 1-11, wherein the prediction equation is selected from one or more prediction equations including: predetermined cutoff, a parametric ranking, non-parametric ranking, predictor screening, correlation screening, receiver operating characteristic (ROC) analysis, nominal logistic fits, nominal logistic analysis, and support vector machine analysis, deep learning, and the like.

[0252] Embodiment 13. Embodiments, 1-12 including treating the subject for cognitive impairment.

[0253] Embodiment 14. Embodiments 1-13 wherein treating includes providing cognition preserving intervention to the subject.

[0254] Embodiment 15. Embodiments 1-14, wherein the subject is free of symptoms of cognitive impairment and has a high risk of developing cognitive impairment.

[0255] Embodiment 16. A method of treating a subject with multiple sclerosis including administering a cognition preserving intervention to the subject when expression characteristic of one or more biomarkers selected from miRNA biomarkers or miRNA or characteristic fragments, functional fragments, or functional variants thereof and protein biomarkers or characteristic fragments, functional fragments, or functional variants thereof in a sample from the subject is elevated relative to the expression characteristic of the same one or more biomarkers in a control population.

[0256] Embodiment 17. Method embodiment 16, wherein the expression characteristic of the one or more biomarkers in the subject with multiple sclerosis is at least 1.5 times higher than the expression characteristic of the one or more biomarkers in the control population or is greater than a threshold value.

[0257] Embodiment 18. Method embodiment 16 or 17, wherein the expression characteristic includes a concentration or a fold change in expression of the one or more biomarkers in the sample.

[0258] Embodiment 19. Embodiment 16-18, wherein the cognition preserving intervention includes pharmacological treatment or rehabilitation.

[0259] Embodiment 20. Embodiments 16-19, wherein the subject with multiple sclerosis does not demonstrate cognitive impairment when tested with a mini mental state examination (MMSE).

[0260] Embodiment 21. Embodiments 16-20, wherein the control population includes healthy subjects, subjects with cognitive impairment but without multiple sclerosis, or subjects with multiple sclerosis having an MMSE score between about 24 and about 30.

[0261] Embodiment 22. Embodiments 16-21 further including: applying the expression characteristic of the one or more biomarkers of the subject with multiple sclerosis to a prediction equation, detecting a high risk of cognitive impairment in the subject from the prediction equation, and treating the subject with the cognition preserving intervention.

[0262] Embodiment 23. Embodiments 16-22, wherein the subject is free of symptoms of cognitive impairment at the time of treating.

[0263] Embodiment 24. Embodiments 16-23 further including repeating step a) after a period of time following treatment.

[0264] Embodiment 25. Embodiments 16-24, wherein the one or more biomarkers include one or more miRNA biomarkers and one or more protein biomarkers.

[0265] Embodiment 26. Embodiments 16-25, wherein the one or more miRNA biomarkers include any one or any combination of miRNA listed in Tables 1 and 3.

[0266] Embodiment 27, Embodiments 16-26, wherein the one or more miRNA biomarkers include any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof.

[0267] Embodiment 28. Embodiments, 16-27 wherein the one or more protein biomarkers includes any one or any combination of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, , characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40.

[0268] Embodiment 29. Embodiment 16-28, wherein the expression characteristic includes a fold change in expression of the one or more miRNA biomarkers in the sample.

[0269] Embodiment 30. Embodiments 16-29, wherein the one or more biomarkers includes a combination of at least one miRNA biomarker selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof, and at least one protein biomarker selected from GFAP, NfL, Abeta 40, Abeta42, and characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40.

[0270] Embodiment 31. Embodiments 16-30, wherein the one or more miRNA biomarkers includes a combination of miR-486-3p and miR-629-3p.

[0271] Embodiments 32. Embodiments 16-31, wherein the one or more biomarkers includes a combination of miR-629-3p and NfL.

[0272] Embodiments 33. Embodiments 16-32 wherein the sample includes cerebrospinal fluid or plasma.

[0273] Embodiments 34. Embodiments, 16-33, wherein the prediction equation is selected from one or more prediction equations comprising a parametric ranking, non-parametric ranking, predictor screening, correlation screening, receiver operating characteristic (ROC) analysis, nominal logistic fits, nominal logistic analysis,, support vector machine analysis, deep learning, and the like.

[0274] Embodiment 35. Embodiments 1-34, wherein the characteristic fragments of the one or more miRNA biomarkers or miRNA include fragments having between about 10 consecutive nucleotides and about 24 consecutive nucleotides of the nucleic acid sequence of the respective one or more miRNA biomarkers or miRNA;the functional fragments of the one or more miRNA biomarkers or miRNA includes fragments having at least about 90% of the entire length of the respective one or more miRNA biomarkers or miRNA; and / or the functional variants of the one or more miRNA biomarkers or miRNA include variants having between about 60% and about 99% sequence identity to the respective one or more miRNA biomarkers or miRNA.

[0275] Embodiment 36. Embodiments 1-34, wherein the characteristic fragment of the protein biomarker includes a fragment having between about 10 consecutive amino acids and about 50 consecutive amino acids of the amino acid sequence of the protein biomarker; the functional fragment of the protein biomarker includes a fragment having at least about 90% of the entire length of the protein biomarker; and / or the functional variants of protein biomarker includes a variant having between about 60% and about 99% sequence identity to the protein biomarker.

[0276] Embodiment 37. A kit for detection and monitoring of cognitive impairment, the kit including: a primer set for amplification of one or more miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof; and a probe for detection of one or more miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof.

[0277] Embodiment 38. The kit of Embodiment 37, wherein the probe includes a detection agent.

[0278] Embodiment 39. The kit of Embodiments 37-38, wherein the detection agent is selected from radionuclides, fluorescent dyes, chemiluminescent agents, bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals, metal nanoparticles, nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels, biotin, dioxigenin, haptens, proteins, and combinations thereof.

[0279] Embodiment 40. Embodiments 37-39, wherein the one or more miRNA biomarkers or miRNA include any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR- 374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments or functional variants thereof, and combinations thereof.

[0280] Embodiment 41. The kit of any one of embodiments 37-40, wherein the characteristic fragments of the one or more miRNA biomarkers or miRNA include fragments having between about 10 consecutive nucleotides and about 24 consecutive nucleotides of the nucleic acid sequence of the respective one or more miRNA biomarkers or miRNA.

[0281] Embodiment 42. The kit of any one of embodiments 37-40, wherein the functional fragments of the one or more miRNA biomarkers or miRNA include fragments having at least about 90% of the entire length of the respective one or more miRNA biomarkers or miRNA.

[0282] Embodiment 43. The kit of any one of embodiments 37-40, wherein the functional variants of the one or more miRNA biomarkers or miRNA include variants having between about 60% and about 99% sequence identity to the respective one or more miRNA biomarkers or miRNA.

[0283] Embodiment 44. The The kit of any one of embodiments 37-43 further including an antibody or an antibody fragment to a protein biomarker or to a characteristic fragment, functional fragment, or functional variant thereof.

[0284] Embodiment 45. The kit of embodiments 37-44, wherein the antibody or the antibody fragment includes a detection agent.

[0285] Embodiment 46, the kit of embodiments 37-45, wherein the detection agent is selected from radionuclides, fluorescent dyes, chemiluminescent agents, bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals, metal nanoparticles, nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels, biotin, dioxigenin, haptens, proteins, and combinations thereof.

[0286] Embodiment 47. The kit of any one of embodiments 44-46, wherein the protein biomarker includes any one of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, a characteristic fragment thereof, a functional fragment thereof, or a functional variant thereof.

[0287] Embodiment 48. The kit of any one of embodiments 44-47, wherein the characteristic fragment of the protein biomarker includes a fragment having between about 10 consecutive amino acids and about 50 consecutive amino acids of the amino acid sequence of the protein biomarker.

[0288] Embodiment 49. The kit of any one of embodiments 44-47, wherein the functional fragment of the protein biomarker includes a fragment having at least about 90% of the entire length of the protein biomarker.

[0289] Embodiment 50. The kit of any one of embodiments 44-47, wherein the functional variants of the protein biomarker include a variant having between about 60% and about 99% sequence identity to the protein biomarker.

[0290] Embodiment 51. The kit of any one of embodiments 37-50, wherein the CI is in a subject with neurological or neurodegenerative disease.

[0291] Embodiment 52. The kit of any one of embodiments 37-51, wherein the CI is in a subject with multiple sclerosis.

[0292] Embodiment 53. A composition for detection and monitoring of cognitive impairment, the composition including: a primer set for amplification of one or more miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof; and a probe for detection of one or more miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof.

[0293] Embodiment 54. The composition of embodiment 53, wherein the probe includes a detection agent.

[0294] Embodiment 55. The composition of claim 53, wherein the detection agent is selected from radionuclides, fluorescent dyes, chemiluminescent agents, bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals, metal nanoparticles, nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels, biotin, dioxigenin, haptens, proteins, and combinations thereof.

[0295] Embodiment 56. The composition of any one of embodiments 53-55, wherein the one or more miRNA biomarkers or miRNA include any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629- 3p, hsa-miR-629-5p, and characteristic fragments, functional fragments or functional variants thereof, and combinations thereof.

[0296] Embodiment 57. The composition of any one of embodiments 53-56, wherein the characteristic fragments of the one or more miRNA biomarkers or miRNA includes fragments having between about 10 consecutive nucleotides and about 24consecutive nucleotides of the nucleic acid sequence of the respective one or more miRNA biomarkers or miRNA.

[0297] Embodiment 58. The composition of any one of embodiments 53-57, wherein the functional fragments of the one or more miRNA biomarkers or miRNA includes fragments having at least about 90% of the entire length of the respective one or more miRNA biomarkers or miRNA.

[0298] Embodiment 59. The composition of any one of embodiments 53-57, wherein the functional variants of the one or more miRNA biomarkers or miRNA includes variants having between about 60% and about 99% sequence identity to the respective one or more miRNA biomarkers or miRNA.

[0299] Embodiment 60. The composition of any one of embodiments 53-59, wherein the CI is in a subject with neurological or neurodegenerative disease.

[0300] Embodiment 61. The composition of any one of claims 53-60, wherein the CI is in a subject with multiple sclerosis.

[0301] Embodiment 62. A composition for detection and monitoring of cognitive impairment, the composition comprising an antibody or an antibody fragment to a protein biomarker or to a characteristic fragment, functional fragment, or functional variant thereof.

[0302] Embodiment 63. The composition of embodiment 62, wherein the antibody or the antibody fragment includes a detection agent.

[0303] Embodiment 64. The composition of embodiments 62-63, wherein the detection agent is selected from radionuclides, fluorescent dyes, chemiluminescent agents, bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals, metal nanoparticles, nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels, biotin, dioxigenin, haptens, and proteins.

[0304] Embodiment 65. The composition of any one of embodiments 62-64, wherein the protein biomarker includes any one of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, a characteristic fragment thereof, a functional fragment thereof, or a functional variant thereof.

[0305] Embodiment 66. The composition of any one of embodiments 62-65, wherein the characteristic fragment of the protein biomarker includes a fragmenthaving between about 10 consecutive amino acids and about 50 consecutive amino acids of the amino acid sequence of the protein biomarker.

[0306] Embodiment 67. The composition of any one of embodiments 62-66, wherein the functional fragment of the protein biomarker includes a fragment having at least about 90% of the entire length of the protein biomarker.

[0307] Embodiment 68. The composition of any one of embodiments 62-67, wherein the functional variants of the protein biomarker include a variant having between about 60% and about 99% sequence identity to the protein biomarker.

[0308] Embodiment 69. The composition of any one of embodiments 62-68, wherein the CI is in a subject with neurological or neurodegenerative disease.

[0309] Embodiment 70. The composition of any one of embodiments 62-69, wherein the CI is in a subject with multiple sclerosis.

[0310] Embodiment 71. A method for detecting cognitive impairment, comprising: obtaining a fluid sample from a subject; sequencing the fluid sample to generate a first dataset corresponding the fluid sample, wherein the first dataset includes a set of expression characteristics respectively corresponding to a set of biomarkers; applying a first trained machine learning model to the first dataset to generate a cognitive impairment (CI) biomarker profile, wherein the CI biomarker profile includes a detected level of at least one CI biomarker; applying a second trained machine learning model to generate a CI biomarker score based on the CI biomarker profile; and generating a diagnostic report based on the CI biomarker profile.

[0311] Embodiment 72. The method of embodiment 71, wherein sequencing the fluid sample 7includes generating a set of images indicative of at least one image-based biomarker, and further comprising applying, to the set of images, at least one of automated segmentation and classification to generate the first dataset.

[0312] Embodiment 73. the method of embodiments 71 or embodiment 72, wherein the CI biomarker is at least one of miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof and protein biomarkers or characteristic fragments, functional fragments, or functional variants thereof.

[0313] Embodiment 74. The method of any one of embodiments 71-73, wherein the CI biomarker is a protein cognitive biomarker.

[0314] Embodiment 75. The method of any one of embodiments 71-74, wherein the CI biomarker is a nucleic acid cognitive biomarker.

[0315] Embodiment 76. The method of any one of embodiments 71-75, wherein the CI biomarker relates to a demographic factor.

[0316] Embodiment 77. The method of embodiment 76, wherein the demographic factor is at least one of: age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, geographic location, and combinations thereof.

[0317] Embodiment 78. The method of any one of embodiments 71-77, further comprising: training the first trained machine learning model using health data corresponding to a plurality of subjects, wherein the health data is indicative of a first set of biomarker indicators and expression characteristics.

[0318] Embodiment 79. The method of embodiment 78, wherein, for a first subject in the plurality of subjects, the health data includes a first biomarker indicator is associated with a first set of demographic factors.

[0319] Embodiment 80. The method of any one of embodiments 71-79, further comprising: retraining the first machine learning model using the CI biomarker profile.

[0320] Embodiment 81. The method of any one of embodiments 71-80, wherein the first trained machine learning model applies at least one of: a univariate decision tree, a multivariate decision tree, an additive tree, bagging, and boosting.

[0321] Embodiment 82. The method of any one of embodiments 71-81, wherein the first trained machine learning model determines one or more feature values, wherein a first feature value corresponds to a level of a first CI biomarker.

[0322] Embodiment 83. The method of embodiment 82, wherein the first trained machine learning model compares the first feature value to a threshold level associated with the first CI biomarker.

[0323] Embodiment 84. The method of any one of embodiments 71-83, wherein the second trained machine learning model determines the CI biomarker score based on a comparison with at least one reference CI biomarker profile.

[0324] Embodiment 85. The method of any one of embodiments 71-84, wherein the diagnostic report indicates an outcome associated with a treatment regimen.

[0325] Embodiment 86. The method of embodiment 85, wherein the outcome represents, with respect to the treatment regimen, a likelihood for one or more of: a poor clinical outcome, a positive clinical outcome, a high risk of disease, a low risk of disease, a complete response, a partial response, a stable disease, a non- response, and a recommended treatment for disease management.

[0326] Embodiment 87. The method of embodiment 85, wherein the treatment regimen relates to multiple sclerosis.

[0327] Embodiment 88. A system, comprising: at least one processor; and at least one memory communicatively coupled to the at least one processor and comprising computer-readable instructions that upon execution by the at least one processor cause the at least one processor to perform operations comprising: obtaining information indicative of a fluid sample from a subject; sequencing the information indicative of the fluid sample to generate a first dataset corresponding the fluid sample, wherein the first dataset includes a set of expression characteristics respectively corresponding to a set of biomarkers; applying a first trained machine learning model to the first dataset to generate a cognitive impairment (CI) biomarker profile, wherein the CI biomarker profile includes a detected level of at least one CI biomarker; applying a second trained machine learning model to generate a CI biomarker score based on the CI biomarker profile; and generating a diagnostic report based on the CI biomarker profile.

[0328] Embodiment 89. The system of embodiment 88, wherein the CI biomarker is a protein cognitive biomarker.

[0329] Embodiment 90. The system of embodiment 88 or embodiment 89, wherein the CI biomarker is a nucleic acid cognitive biomarker.

[0330] Embodiment 91. The system of any one of embodiments 88-90, wherein the diagnostic report relates to a treatment regimen for multiple sclerosis.

[0331] Embodiment 92. The system of any one of embodiments 88-91, wherein the fluid sample is at least one of: blood, blood plasma, serum, urine, and saliva.

[0332] Embodiment 93. The system of any one of embodiments 88-92, wherein the first dataset includes a set of expression characteristics respectively corresponding to a set of biomarkers.

[0333] Embodiment 94. The system of any one of embodiments 88-93, wherein sequencing the fluid sample includes utilizing at least one of: polymerase chain reaction (PCR), nucleic acid sequencing, nanopore sequencing, and nucleic acid probe hybridization.

[0334] Embodiment 95. A non-transitory computer readable medium comprising computer-executable instructions, which when executed cause: obtaining information indicative of a fluid sample from a subject; sequencing the information indicative of the fluid sample to generate a first dataset corresponding the fluid sample, wherein the first dataset includes a set of expression characteristics respectively corresponding to a set of biomarkers; applying a first trained machine learning model to the first dataset to generate a cognitive impairment (CI) biomarker profile, wherein the CI biomarker profile includes a detected level of at least one CI biomarker; applying a second trained machine learning model to generate a CI biomarker score based on the CI biomarker profile; and generating a diagnostic report based on the CI biomarker profile.

[0335] Embodiment 96. The non-transitory computer readable medium of embodiment 95, wherein the information indicative of the fluid sample is a set of images.

[0336] Embodiment 97. The non-transitory computer readable medium of embodiment 96, further comprising computer-executable instructions, which when executed cause: extracting, from the set of images, at least one image-based biomarker.

[0337] Embodiment 98. The non-transitory computer readable medium of embodiment 97, wherein extracting the at least one image-based biomarker includes applying, to the set of images, at least one of automated segmentation and classification to generate the first dataset.

[0338] Embodiment 99. The non-transitory computer readable medium of any one of embodiments 95-98, wherein the at least one CI biomarker is at least one of: a protein biomarker, a peptide biomarker, or combinations thereof.

[0339] Embodiment 100. A method comprising: receiving, by a processor of a computing device, a Cognitive Impairment (CI) biomarker profile comprising data corresponding to levels of one or more CI biomarkers in a sample from a subject; providing, by the processor, input to a machine-learned algorithm, the input comprising the CI biomarker profile; and determining, by the processor, a Cognitive Impairment Biomarker score for the subject with the machine-learned algorithm based on the input, wherein the one or more CI biomarkers includes at least one miRNA biomarker selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR- 629-5p, and characteristic fragments, functional fragments, or functional variants thereof; at least one protein biomarker selected from GFAP, NfL, Abeta 40, Abeta42, characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40; or any combination thereof.

[0340] Embodiment 101.A method comprising: receiving, by a processor of a computing device, a Cognitive Impairment (CI) biomarker profile comprising data corresponding to levels of one or more (CI) biomarkers in a sample from a subject; providing, by the processor, input to a machine-learned algorithm, the input comprising the CI biomarker profile; and determining, by the processor, whether the subject is at risk of or has symptoms cognitive impairment with the machine-learned algorithm based on the input, wherein the one or more CI biomarkers includes at least one miRNA biomarker selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa- miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof; at least one protein biomarker selected from GFAP, NfL, Abeta 40, Abeta42, characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40; or any combination thereof.

[0341] Embodiment 102. The method of claim 101, wherein determining whether the subject is at risk of or has (e.g., is suffering from) cognitive impairment with the machine-learned algorithm includes determining, by the processor, a probability that the subject has cognitive impairment.

[0342] Embodiment 103. The method of embodiments 101 or claim 102, comprising: receiving, by the processor, data corresponding to one or moredemographic factors for the subject; and providing, by the processor, the input to the machine-learned algorithm, wherein the input includes the data corresponding to the one or more demographic factors.

[0343] Embodiment 104. The method of embodiment 103, wherein the one or more demographic factors includes sex, age, or both sex, age, and combinations thereof.

[0344] Embodiment 105. The method of any one of embodiments 101- 104, comprising classifying, by the processor, the subject as having or not having cognitive impairment with the machine-learned algorithm.

[0345] Embodiment 106. The method of any one of embodiments 101- 105, wherein the machine-learned algorithm is a classifier for cognitive impairment.

[0346] Embodiment 107. The method of embodiment 105 or embodiment 106, wherein the classifier or the classifying has a sensitivity and a specificity each of more than 80%, optionally wherein at least one or both of sensitivity and specificity are more than 90%.

[0347] Embodiment 108. The method of any one of embodiments 100- 107, wherein the subject is a human subject.

[0348] Embodiment 109. The method of any one of embodiments 100- 108, wherein the sample includes blood, serum, plasma, or neurological or CNS tissue, and combinations thereof.

[0349] Embodiment 110. A non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations comprising the method of any one of embodiments 100-109.

[0350] Embodiment 111. A system comprising a non-transitory computer readable medium and a processor, wherein the non-transitory computer readable medium has the machine-learned algorithm stored thereon and executable instructions that, when executed by the processor, perform operations comprising the method of any one of embodiments 100-109.

[0351] Embodiment 112. A method of treating Mild Cognitive Impairment (MCI) or pre-MCI in a subject, the method comprising: measuring a level of cognitive impairment biomarkers in a bodily fluid collected from a subject, wherein the bodily fluid includes at least one of: blood, blood plasma, serum, urine, and saliva;measuring the level of a normalizer biomarker in the bodily fluid; determining a ratio of cognitive impairment biomarkers to the normalizer biomarker; and when the ratio is greater than a threshold value, administering a therapeutic or preventive treatment for neurodegeneration to the subject prior to an appearance of cognitive impairment.

[0352] Embodiment 113. The method of embodiment 112, wherein the subject does not show clinical symptoms of multiple sclerosis.

[0353] Embodiment 114. A method of monitoring effectiveness of a therapy or treatment strategy comprising using the biomarkers or a combined biomarker score or algorithm to track effectiveness of therapy or treatment strategy over time. EXAMPLES

[0354] For the disclosed Examples, the study population consisted of patients experiencing relapsing-remitting MS (RRMS) with CI, cognitively preserved (CP) RRMS, and matching healthy controls. Cognitive function of patients was assessed using the Mini-Mental State Examination.

[0355] Further examples investigated the correlations between such newly discovered molecular profiles and serum protein biomarkers already utilized for MS clinical trials (e.g., neurofilament light chain (NfL), chitinase-3 like-protein-1, chitinase- 3 like-protein-2, glial fibrillary acidic protein, vitamin D, and apolipoprotein E). The use of biomarker assays such as NfL to stratify patients with MS may help to better classify subsets of patients at highest risk for developing CI.Example 1. miRNAs and protein biomarker panel for prediction or early detection of CI in MS. Sample Selection

[0356] A study population included female patients 41 to 62 years of age experiencing RRMS with CI (RRMS-Mild CI), those with RRMS who were cognitively preserved (RRMS-No CI), and matching healthy controls (HC). Cognitive function of patients was assessed using the Mini-Mental State Examination (MMSE). MMSE score between about 24 and about 30 indicated no cognitive impairment. MMSE score of about 23 or lower indicated cognitive impairment.

[0357] Next Generation Sequencing (NGS) was used for differential expression and integrated network analysis to reveal the genes and pathways associated with CI in the patient cohort. Exosomal miRNAs associated with CI in PwMS were identified by performing miRNA profiling. Plasma protein markers including, e.g., glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), Abeta 40, Abeta 42, and the ratio of Abeta42 / Abeta40 were evaluated to provide a better understanding of the relationship between differentially regulated genes in CI and the most informative subset of quantitative immunoassays known for PwMS.

[0358] 1. The extra-cellular RNA processing toolkit (exceRpt) pipeline was used to analyze small RNA-seq raw data and is available from Github. The process includes adaptor removal, contaminant removal, alignment to the endogenous genome, transcriptome annotation, and alignment to the exogenous miRNA, rRNA, and genomic libraries. An analysis pipeline was used to analyze the RNA-seq raw data. The process involves quality control, alignments to human genome using STAR, and obtaining metrics for gene and transcript expression using FeatureCount. The differential gene expression analysis for small RNA was performed using DESeq2.

[0359] 2. It is important to predict target genes from associated miRNAs to indirectly elucidate the biological function and enrichment pathway. The miRNA enrichment function in FunRich can be used for miRNA targeting predictions.

[0360] 3. A comprehensive statistical analysis using appropriate statistical tests was performed for the identification of differentially expressed miRNA biomarkers. To address the issue of multiple testing, p-values were adjusted using the false discovery rate method. Clustering and dimensionality reduction techniques suchas Principal Component Analysis, were applied for unsupervised visualization of miRNA expression profiles. Results

[0361] Results from a study of miRNA profiling are provided. Cognitive function assessment used the Mini-Mental State Examination with a cutoff of ≤23 as criteria for CI. A total of 30 patient specimens were collected by PrecisionMed (Carlsbad, CA, USA) with 10 samples in each subgroup.

[0362] miRNAs with at least 2-fold expression changes and an FDR adjusted P value <0.05 were identified. Fifty-three miRNAs differentially expressed between the RRMS-No CI group and healthy controls. Thirty miRNAs were differentially expressed between the RRMS-Mild CI group and healthy controls. Notably, one miRNA was up- regulated in the RRMS-Mild CI group compared with the RRMS-No CI group (see FIGs.1A-1D).

[0363] Protein markers including GFAP, NfL, Abeta 40, Abeta 42, and the ratio of Abeta42 / Abeta40 were evaluated. The preliminary results for concentrations of GFAP and NfL in plasma showed a significant increase for both markers in patients with mild CI versus those without CI (see FIGs.2A and 2B).

[0364] The study results established that this patient selection strategy was sufficient for the miRNA component of testing, as well as for establishing statistically and practically significant group differences for two commonly used immunoassays for stratifying PwMS in clinical trials.

[0365] A multi-parametric algorithmic approach for predicting CI at early stages of disease found that differential expression of 5 miRNA markers was attributed to CI (FIG. 1D). Protein markers determined to be significantly higher in this study cohort also were used in an algorithmic analysis to provide multiple biomarkers for predicting CI at early stages (FIGs.5A-5F). Protein biomarker panel for detection of CI in MS.

[0366] This study analyzed the contribution of protein biomarkers NfL, GFAP, Abeta 40, and Abeta 42 to CI in MS.

[0367] An unexpectedly high contribution of amyloid beta 40 (AB40) was found as a factor in predicting cognitive impairment in MS (FIG.3C). The contributionof NfL was highest, but AB40 was second highest of the four biomarkers tested (FIGs. 3A-3C). miRNA biomarkers for detecting CI in MS.

[0368] This study analyzed the expression of miRNA biomarkers in samples taken from healthy controls (HC) and from subjects with RRMS and mild CI. A list of candidate miRNAs for detecting CI in MS is presented. The one or more miRNA biomarkers for detecting CI in MS are selected from the group of biomarkers shown in Table 1.Table 1. miRNA biomarkers for detecting CI in MS. uncorrected RRMS-mildCl 13 targets mi2Disease miRNA (only miRNAs vs ith d t d 7 0 2 6 - - - 2 d - - 1 - -hsa-miR- hsa-miR-151a- hsa-miR-192- hsa-miR-212- 125b-5p 5p 5p 3p h iR126 h iR151 h iR320 h iR1246 - - - - - - - - a - - - -hsa-miR-18a- hsa-miR-30a- hsa-miR-363- 5p 5p 3p h iR h iR315 h iR - - -hsa-miR-320d hsa-miR-625- 5p h iR335 h iR629hsa-miR- 519b-3p h iR532miRNA biomarkers with a fold change of greater than 1.5 for detecting CI in MS.

[0369] This example provides one or more biomarkers with a fold change of greater than 1.5 for detecting CI in MS that are selected from the group of biomarkers shown in Tables 2 and 3. Select miRNAs are presented in Table 4 and sequences for select miRNAs are presented in Table 12.

[0370] Select combinations of miRNA and protein biomarkers for predicting risk of developing, or detecting, a cognitive impairment in subjects with multiple sclerosis are shown in Table 10. For example, preferred combinations of miRNA and protein biomarkers can include a combination of hsa-miR-629-3p and NfL; a combination of hsa-miR-320c and NfL; a combination of hsa-miR-374a-3p and NfL; a combination of hsa-miR-486-3p and NfL; a combination of hsa-miR-629-3p and Abeta40; a combination of hsa-miR-320c and Abeta40; a combination of hsa-miR- 374a-3p and Abeta40; or a combination of hsa-miR-486-3p and Abeta40 for predicting risk of developing, or detecting, a cognitive impairment in subjects with multiple sclerosis.

[0371] Select combinations of miRNA biomarkers for predicting risk of developing, or detecting, a cognitive impairment in subjects with multiple sclerosis are shown in Table 11. For example, preferred combinations of miRNA biomarkers can include a combination of a combination of hsa-miR-486-3p and hsa-miR-629-3p; hsa-miR-320c and hsa-miR-629-3; a combination of hsa-miR-374a-3p and hsa-miR-629- 3p; or a combination of hsa-miR-642a and hsa-miR-629-3p for predicting risk of developing, or detecting, a cognitive impairment in subjects with multiple sclerosis.

[0372] Select combinations of miRNA and protein biomarkers for predicting risk of developing, or detecting, a cognitive impairment in subjects with multiple sclerosis also include combinations of miRNA and protein biomarkers that include a combination of hsa-miR-629-3p, HSA-miR-320c, and NfL; a combination of hsa-miR- 374a-3p, HSA-miR-320c, and NfL; a combination of hsa-miR-486-3p, HSA-miR-320c, and NfL; a combination of hsa-miR-629-3p, HSA-miR-320c, and Abeta40; a combination of hsa-miR-374a-3p, HSA-miR-320c, and Abeta40; or a combination of hsa-miR-486-3p, HSA-miR-320c, and Abeta40 for predicting risk of developing, or detecting, a cognitive impairment in subjects with multiple sclerosis.Table 2. miRNA with fold change greater than 1.5 in RRMS-noCI vs HC-uncorrected. uncorrected (only miRNAs with FC>1.5 RRMS-noCl vs HC-uncorrected padj <0.05, log2FC>+ / -0.58) 0 0 0 0 0 0 0 0 0 0hsa- 1 2021.50 2.37 0.5 4.0 0.00 0.0 3.13 11.0 miR- 9 1 0 0 122- 0 0 0 0 0 0 0 0 0 0 0hsa- 1 9721.57 0.69 0.2 2.8 0.00 0.0 1.56 23.0 miR- 4 2 3 0 139- 0 0 0 0 0 0 0 0 0 0hsa- 1 89.76 -1.35 0.4 - 0.01 0.0 1.47 34.0 miR- 9 2.7 3 0 181 - 4 0 0 0 0 0 0 0 0 0 0hsa- 1 1264.12 0.89 0.2 4.4 0.00 0.0 3.75 45.0 miR- 0 1 0 0 22-3 0 0 0 0 0 0 0 0 0 0 0 0hsa- 1 277.96 1.48 0.3 4.6 0.00 0.0 4.06 58.0 miR- 2 0 0 0 320d 0 0 0 0 0 0hsa- 1 756.00 -0.72 0.2 - 0.01 0.0 1.47 miR- 6 2.7 3 425- 1hsa- 1 10.90 2.82 1.0 2.7 0.01 0.0 1.47 miR- 4 2 3 519b-Table 3. miRNA with fold change greater than 1.5 in RRMS-mildCI vs HC-uncorrected. RRMS-mildCI vs HC-uncorrected uncorrected (only miRNAs with padj <0.05, log2FC>0.58)djhsa- 1 89.76 0.95 0.36 2.6 0.0077322 0.0 miR- 6 9 4 125hsa- 1 3039.40 -0.68 0.18 - 0.0002366 0.0 miR- 3.6 1 0 185- 829a- 5p h 1 147107 124 032 38 00001135 00hsa- 1 39.17 1.61 0.57 2.8 0.0043172 0.0 miR- 5 7 3 375hsa- 1 547.22 -0.72 0.26 - 0.0056581 0.0 miR- 2.7 3 625- 7Table 4. Select miRNAs. 13 targets FC>1.5 o s. s. s. s. s. s. s. s. s. s. s. s. s.FC>2 miRNA baseMe log2Fol lfcS stat pvalue padj Compariso an dChang E n s. s. s. s. s. s.Example 2. Data sets and development of miRNA biomarkers for prediction or early detection of CI in MS.

[0373] The data sets obtained for this study were from patients with RRMS without CI (RRMS-no CI), RRMS with mild CI (RRMS-Mild CI), and from healthy controls (HC). The characteristics of the patient group are presented in Table 5.

[0374] Four rows in data set were later timepoints for the same patients. Later timepoints were excluded from initial data analysis. Only first replicate was used for initial correlations and predictive factor analyses. The second replicate is used for robustness analysis.

[0375] Univariate simple statistics for the patients are presented in Table 6.

[0376] The data from the bivariate fit of MMSE Score to individual protein biomarkers is presented in FIGs. 4A-4D. The correlation between the protein biomarker and the disease score, such as MMSE score and EDSS score, is presented in in FIGs.5A-5F. Significant correlations included NfL and GFAP, Abeta 40 and 42, MMSE Score and Abeta 40, MMSE Score and NfL, and MMSE Score and EDSS Score (FIG.5F).

[0377] FIGs.6A and 6B are graphs depicting oneway analysis of NfL (FIG. 6A) and Abeta40 (FIG. 6B) with CI (Yes) or without CI (No). Means comparisons statistics are also provided.

[0378] FIGs. 7A-7D are tables depicting the predictor screening for biomarker proteins for EDSS Score (FIG.7A), MMSE Score (FIG.7B), MS (FIG.7C), and CI (FIG.7D). AB40 and NfL were the best predictors of CI.

[0379] Using k-fold cross validation resulted in a high performing classification model shown in Table 7 and in FIGs 8A-8C.

[0380] Varying the decision threshold produced reasonable sensitivity and specificity performance (FIGs.9A-9C).

[0381] The model was screened using 25% hold-back validation set. The training and validation parameters for the screening are presented in Table 8. The data are presented in FIGs.10A-10C.

[0382] Apart from protein biomarkers Abeta40 and NfL (FIGs.11A and 11B), two miRNAs were also significantly associated with CI. These were hsa-miR-486-3p and hsa-miR-629-3p (FIGs.11C and 11D).

[0383] Pairwise correlations with significant probability were detected for the following pairs: miR-486-3p and MMSE, miR-629-3p and MMSE, miR-486-3p and miR-629-3p, and NfL and MMSE.

[0384] The results are shown in FIG.12.

[0385] Nominal logistics model fitted the training and validation data nearly perfectly using both NfL and miR-629-3p (FIGs.13A-13C).

[0386] When CI prediction probability was plotted against the MMSE score, it identified one positive patient that was a far outlier based mainly on miRNA expression level (FIG. 14). Using a nominal logistics model, the performance of the model was very good when using both NfL and miR-629-3p (Table 9 and FIG. 15). Clinically relevant cutoffs could also be set for these two biomarkers.

[0387] The models developed and tested with the described data showed that the key predictors of CI were HSA-miR-629-3p, HSA-miR-320c, and NfL. The models also showed that a key predictor of MS is HSA-miR-98-5p (FIG.16A and 16B). Example 3. (Prophetic Example) Large data sets and development and validation of miRNA biomarkers for prediction or early detection of CI in MS.

[0388] These results will be further validated in an independent replication cohort, thus demonstrating the combined parameters for providing a reliable and reproducible predictor of early cognitive impairment in multiple sclerosis. The subjects included in the data set will be patients with RRMS without CI (RRMS-no CI), patients with RRMS with mild CI (RRMS-Mild CI), and healthy controls (HC). The number of patients for this study would be large enough to provide high power to the statistical analyses of the data generated from the study.

[0389] The first replicates of the biomarker concentration, fold change, MMSE score, and EDSS score data will be used for initial correlations and predictive factor analyses. The second replicates of the biomarker concentration, fold change, MMSE score, and EDSS score data will be used for robustness (i.e., validation) analysis.

[0390] The data from the bivariate fit of MMSE Score to individual protein biomarkers will be evaluated. The correlation between the protein biomarker and the disease score, such as MMSE score and EDSS score, will be evaluated.

[0391] The data from the bivariate fit of MMSE Score to individual miRNA biomarkers will be evaluated. The correlation between the miRNA biomarker and the disease score, such as MMSE score and EDSS score, will be evaluated.

[0392] The data from the bivariate fit of MMSE Score to individual protein and miRNA biomarker combinations will be evaluated. The correlation between the protein and miRNA biomarker combinations and the disease score, such as MMSE score and EDSS score, will be evaluated.

[0393] The data from the bivariate fit of MMSE Score to miRNA biomarker combinations will be evaluated. The correlation between the miRNA biomarker combinations and the disease score, such as MMSE score and EDSS score, will be evaluated.

[0394] The training and validation parameters for the screening will be similar to those presented in Table 8.

[0395] Exemplary models that may be used to develop and validate a prediction equation for high power prediction or early detection of CI in MS using biomarkers disclosed herein include: pairwise correlations, parametric ranking, non- parametric ranking, predictor screening, correlation screening, receiver operating characteristic (ROC) analysis, nominal logistic fits, nominal logistic analysis, support vector machine analysis, and other machine learning and deep learning approaches.Table 5. Characteristics of patient groups providing the data sets for development of miRNA biomarkers for or prediction or early detection of CI in MS. Patient Group MS Healthy RRMS- RRMS-Table 6. Univariate simple statistics for the patients. Univariate Simple Statistics orTable 7. Model screening for CI. Summary Across the Folds Method Details NaSum Validation Set Folds 00 00 33 00 00 00 33 33 00 33 00 33 33 33 00 33 33 33 00 33Naïve Bayes 5 5.2000 -32.10 0.35274 0.20629 0.8542 0.15333 a - N Trials Folds. Training Method Detail N Sum Entrop Miscla AUC RASE General rGeneralize 2 20.000 0.0000 0.2000 0.5000 0.4000 0.0000 d 0 0 0 R r i n l rBootstrap 6 6.0000 -0.052 0.3333 0.4375 0.4840 -0.095 Forest 0 9 G li 6 60000 0057 03333 06250 04842 0104Support 6 6.0000 -0.220 0.3333 0.8750 0.5138 -0.449 Vector 0 5 M hinTable 9. Sensitivity and Specificity of the nominal logistics (NL) model using NfL and miR-629-3p. Model Sens Spec Sens LCI Spec LCI TP TN FP FNTable 10. Select combinations of miRNA and protein biomarkers for predicting risk of developing, or detecting, a cognitive impairment in subjects with multiple sclerosis. Each X stands for a specific combination of a miRNA biomarker and a protein biomarker. Also, any one or more of the listed miRNAs can be combined with any one or more listed proteins to form biomarker combinations. Preferred biomarker combinations are shown in bold. Protein Bi k GFAP NfL Ab t Ab t R ti of 0hsa-miR-629-5p X X X X XTable 11. Select combinations of miRNA biomarkers for predicting risk of developing, or detecting, a cognitive impairment in subjects with multiple sclerosis. Each Y stands for a specific combination of a combination of miRNA biomarkers. Also, any one or more of the listed miRNAs can be combined with any other one or more listed miRNA to form biomarker combinations. Preferred biomarker combinations are shown in bold. miRNA Biomarkers hsa- hsa- hsa- hsa- hsa- hsa- hsa- hsa-Table 12. List of select miRNAs with associated accession numbers and sequences as listed in miRbase database. SEQ ID A i # D i ti S NOhsa-miR-374a-3p MIMAT0004688mature miRNA CUUAUCAGAUUGUAUUGUAAUU5While the foregoing is directed to embodiments of the present disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof.

Claims

What is Claimed:

1. A method for predicting risk of developing or detecting a cognitive impairment in a subject, the method comprising applying an expression characteristic of one or more biomarkers selected from miRNA biomarkers or miRNA , or characteristic fragments, functional fragments, or functional variants thereof and protein biomarkers or characteristic fragments, functional fragments, or functional variants thereof in a sample from the subject to a prediction equation and predicting the risk of or detecting the cognitive impairment from the prediction equation.

2. The method of claim 1, wherein the one or more biomarkers comprise one or more miRNA biomarkers and one or more protein biomarkers.

3. The method of claim 1 or 2, wherein the one or more miRNA biomarkers comprise any one or any combination of miRNA listed in Tables 1 and 3 or characteristic fragments, functional fragments, or functional variants thereof.

4. The method of any one of claims 1-3, wherein the one or more miRNA biomarkers comprise any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a- 3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof.

5. The method of any one of claims 1-4, wherein the one or more protein biomarkers comprises any one or any combination of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40.

6. The method any one of claims 1-5, wherein the expression characteristic comprises a concentration or a fold change in expression of the one or more biomarkers in the sample.

7. The method any one of claims 1-6, wherein the one or more biomarkers comprises a combination of at least one miRNA biomarker selected from hsa- miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486- 3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof, and at least one protein biomarker selected from GFAP, NfL, Abeta 40, Abeta42, characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40.

8. The method any one of claims 1-7, wherein the one or more miRNA biomarkers comprises a combination of hsa-miR-486-3p and hsa-miR-629-3p.

9. The method any one of claims 1-7, wherein the one or more biomarkers comprises a combination of miR-629-3p and NfL.

10. The method any one of claims 1-9, wherein the subject comprises a patient with multiple sclerosis.

11. The method any one of claims 1-10, wherein the sample comprises a cerebrospinal fluid or plasma.

12. The method any one of claims 1-11, wherein the prediction equation is selected from one or more prediction equations comprising , predetermined cutoff, a parametric ranking, non-parametric ranking, predictor screening, correlation screening, receiver operating characteristic (ROC) analysis, nominal logistic fits, nominal logistic analysis, and support vector machine analysis.

13. The method any one of claims 1-12 further comprising treating the subject for cognitive impairment.

14. The method of claim 13, wherein treating comprises providing cognition preserving intervention to the subject.

15. The method any one of claims 1-14, wherein the subject is free of symptoms of cognitive impairment and has a high risk of developing cognitive impairment.

16. A method of treating a subject with multiple sclerosis comprising administering a cognition preserving intervention to the subject when expression characteristic of one or more biomarkers selected from miRNA biomarkers or miRNA or characteristic fragments, functional fragments, or functional variants thereof and protein biomarkers or characteristic fragments, functional fragments, or functional variants thereof in a sample from the subject is elevated relative to the expression characteristic of the same one or more biomarkers in a control population.

17. The method of claim 16, wherein the expression characteristic of the one or more biomarkers in the subject with multiple sclerosis is at least 1.5 times higher than the expression characteristic of the one or more biomarkers in the control population or is greater than a threshold value.

18. The method claim 16 or 17, wherein the expression characteristic comprises a concentration or a fold change in expression of the one or more biomarkers in the sample.

19. The method of any one of claims 16-18, wherein the cognition preserving intervention comprises pharmacological treatment or rehabilitation.

20. The method of any one of claims 16-19, wherein the subject with multiple sclerosis does not demonstrate cognitive impairment when tested with a mini mental state examination (MMSE).

21. The method of any one of claims 16-20, wherein the control population comprises healthy subjects, subjects with cognitive impairment but without multiple sclerosis, or subjects with multiple sclerosis having an MMSE score between about 24 and about 30.

22. The method of any one of claims 16-21, further comprising: a) applying the expression characteristic of the one or more biomarkers of the subject with multiple sclerosis to a prediction equation, b) detecting a high risk of cognitive impairment in the subject from the prediction equation, and c) treating the subject with the cognition preserving intervention.

23. The method of any one of claims 16-22, wherein the subject is free of symptoms of cognitive impairment at the time of treating.

24. The method of claim 22 or 23 further comprising repeating step a) after a period of time following treatment.

25. The method of any one of claims 16-24, wherein the one or more biomarkers comprise one or more miRNA biomarkers and one or more protein biomarkers.

26. The method of any one of claims 16-25, wherein the one or more miRNA biomarkers comprise any one or any combination of miRNA listed in Tables 1 and 3.

27. The method of any one of claims 16-26, wherein the one or more miRNA biomarkers comprise any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a- 3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof.

28. The method of any one of claims 16-27, wherein the one or more protein biomarkers comprises any one or any combination of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40.

29. The method of any one of claims 16-28, wherein the expression characteristic comprises a fold change in expression of the one or more miRNA biomarkers in the sample.

30. The method of any one of claims 16-29, wherein the one or more biomarkers comprises a combination of at least one miRNA biomarker selected from hsa- miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa-miR-486- 3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments, or functional variants thereof, and at least one protein biomarker selected from GFAP, NfL, Abeta 40, Abeta42, and characteristic fragments, functional fragments, or functional variants thereof, and the ratio of Abeta42 / Abeta40.

31. The method of any one of claims 16-30, wherein the one or more miRNA biomarkers comprises a combination of miR-486-3p and miR-629-3p.

32. The method any one of claims 16-30, wherein the one or more biomarkers comprises a combination of miR-629-3p and NfL.

33. The method of any one of claims 16-32, wherein the sample comprises cerebrospinal fluid or plasma.

34. The method of any one of claims 16-33, wherein the prediction equation is selected from one or more prediction equations comprising a parametric ranking, non-parametric ranking, predictor screening, correlation screening, receiver operating characteristic(ROC) analysis, nominal logistic fits, nominal logistic analysis, and support vector machine analysis.

35. The method of any one of claims 1-34, wherein the characteristic fragments of the one or more miRNA biomarkers or miRNA comprise fragments having between about 10 consecutive nucleotides and about 24 consecutive nucleotides of the nucleic acid sequence of the respective one or more miRNA biomarkers or miRNA;the functional fragments of the one or more miRNA biomarkers or miRNA comprise fragments having at least about 90% of the entire length of the respective one or more miRNA biomarkers or miRNA; and the functional variants of the one or more miRNA biomarkers or miRNA comprise variants having between about 60% and about 99% sequence identity to the respective one or more miRNA biomarkers or miRNA.

36. The method of any one of claims 1-34, wherein the characteristic fragment of the protein biomarker comprises a fragment having between about 10 consecutive amino acids and about 50 consecutive amino acids of the amino acid sequence of the protein biomarker; the functional fragment of the protein biomarker comprises a fragment having at least about 90% of the entire length of the protein biomarker; and the functional variants of protein biomarker comprises a variant having between about 60% and about 99% sequence identity to the protein biomarker.

37. A kit for detection and monitoring of cognitive impairment, the kit comprising: a primer set for amplification of one or more miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof; and a probe for detection of one or more miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof.

38. The kit of claim 37, wherein the probe includes a detection agent.

39. The kit of claim 38, wherein the detection agent is selected from radionuclides, fluorescent dyes, chemiluminescent agents, bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals, metal nanoparticles, nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels, biotin, dioxigenin, haptens, and proteins.

40. The kit of any one of claims 37-39, wherein the one or more miRNA biomarkers or miRNA comprise any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa-miR-374a-3p, hsa- miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR-629-5p, and characteristic fragments, functional fragments or functional variants thereof.

41. The kit of any one of claims 37-40, wherein the characteristic fragments of the one or more miRNA biomarkers or miRNA comprise fragments having between about 10 consecutive nucleotides and about 24 consecutive nucleotides of the nucleic acid sequence of the respective one or more miRNA biomarkers or miRNA.

42. The kit of any one of claims 37-40, wherein the functional fragments of the one or more miRNA biomarkers or miRNA comprise fragments having at least about 90% of the entire length of the respective one or more miRNA biomarkers or miRNA.

43. The kit of any one of claims 37-40, wherein the functional variants of the one or more miRNA biomarkers or miRNA comprise variants having between about 60% and about 99% sequence identity to the respective one or more miRNA biomarkers or miRNA.

44. The kit of any one of claims 37-43 further comprising an antibody or an antibody fragment to a protein biomarker or to a characteristic fragment, functional fragment, or functional variant thereof.

45. The kit of claim 44, wherein the antibody or the antibody fragment includes a detection agent.

46. The kit of claim 45, wherein the detection agent is selected from radionuclides, fluorescent dyes, chemiluminescent agents, bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals, metal nanoparticles, nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels, biotin, dioxigenin, haptens, and proteins.

47. The kit of any one of claims 44-46, wherein the protein biomarker comprises any one of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta (Abeta) 40, Abeta 42, a characteristic fragment thereof, a functional fragment thereof, or a functional variant thereof.

48. The kit of any one of claims 44-47, wherein the characteristic fragment of the protein biomarker comprises a fragment having between about 10 consecutive amino acids and about 50 consecutive amino acids of the amino acid sequence of the protein biomarker.

49. The kit of any one of claims 44-47, wherein the functional fragment of the protein biomarker comprises a fragment having at least about 90% of the entire length of the protein biomarker.

50. The kit of any one of claims 44-47, wherein the functional variants of the protein biomarker comprises a variant having between about 60% and about 99% sequence identity to the protein biomarker.

51. The kit of any one of claims 37-50, wherein the CI is in a subject with neurological or neurodegenerative disease.

52. The kit of any one of claims 37-51, wherein the CI is in a subject with multiple sclerosis.

53. A composition for detection and monitoring of cognitive impairment, the composition comprising: a primer set for amplification of one or more miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof; and a probe for detection of one or more miRNA biomarkers or miRNA, or characteristic fragments, functional fragments, or functional variants thereof.

54. The composition of claim 53, wherein the probe includes a detection agent.

55. The composition of claim 54, wherein the detection agent is selected from radionuclides, fluorescent dyes, chemiluminescent agents, bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals, metal nanoparticles, nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels, biotin, dioxigenin, haptens, and proteins.

56. The composition of any one of claims 53-55, wherein the one or more miRNA biomarkers or miRNA comprise any one or any combination of miRNA biomarkers selected from hsa-miR-192-5p, hsa-miR-320a, hsa-miR-320c, hsa- miR-374a-3p, hsa-miR-486-3p, hsa-miR-642a-3p, hsa-miR-629-3p, hsa-miR- 629-5p, and characteristic fragments, functional fragments or functional variants thereof.

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