Neurofilament light chain biomarker compositions and methods of use thereof

Abstract

Methods, non-transitory computer-readable media, and computer systems for predicting heart disease with improved efficiency and accuracy are disclosed. In some examples, the techniques include obtaining a biomarker profile that includes a first level of one or more biomarkers in a first sample obtained from a subject. The one or more biomarkers include a neurofilament light chain (NfL). A first machine learning model is applied to the input to generate a biomarker score for the subject. The input includes at least a biomarker profile, and the biomarker score indicates a probability that the subject is at risk of a heart disease or is suffering from a heart disease. Then, via one or more communication networks and in response to the biomarker profile, an indication of the biomarker score or a heart disease determined from the biomarker score is output to a computing device.

Description

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 490,493, filed on March 15, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0002] The disclosed technology relates generally to molecular biology and cardiac health, and more particularly to neurofilament light chain biomarker compositions and methods of use thereof. Background Art

[0003] Heart disease is a significant health issue, as it is the leading cause of death in the United States for men, women, and people of most racial and ethnic groups. For example, in the United States, one person dies from cardiovascular disease every 34 seconds. In 2020, approximately 697,000 people in the United States died from heart disease, or 1 in every 5 deaths. From 2017 to 2018, heart disease cost the United States approximately $229 billion annually, including costs for healthcare services, medications, and lost productivity due to deaths.

[0004] Current methods for detecting heart disease are inconvenient, uncomfortable, and / or inaccurate.Therefore, there is a need in the art for compositions, kits, and methods for detecting heart disease with an acceptable level of specificity and in a manner that facilitates patient compliance. Summary of the Invention

[0005] The present disclosure provides methods for detecting and / or identifying cardiac disease (e.g., acute coronary syndrome, aortic aneurysm, aortic dissection, aortic stenosis, arrhythmia, atherosclerosis, atrial fibrillation, coronary artery disease (e.g., angina (e.g., stable angina or unstable angina) or heart attack), cardiac rhythm abnormalities, cardiomyopathy (e.g., dilated, hypertrophic, arrhythmogenic, or restrictive cardiomyopathy), carditis (e.g., endocarditis, infective endocarditis, myocarditis, pericarditis, Compositions, kits, methods, and computer systems for treating a variety of conditions, including heart disease, pancarditis or regurgitant carditis, congenital heart defect or disease, eosinophilic myocarditis, heart failure, heart murmur, valvular heart disease, valvular heart stenosis, hypertension, hypertensive heart disease, inflammatory cardiac hypertrophy, Kawasaki disease, myocardial infarction, Marfan syndrome, metabolic syndrome, peripheral arterial disease (PAD), rheumatic heart disease, thromboembolic disease, transthyretin amyloidosis (ATTR-CM), venous thrombosis, or valvular heart disease.

[0006] In addition, the present disclosure provides methods for classifying patients with heart disease compared to normal individuals. Additionally, the present disclosure recognizes that certain biomarkers (e.g., neurofilament light chain (NfL)) can help detect and / or diagnose heart disease, and / or help classify patients with heart disease. The present disclosure also recognizes that these biomarkers (e.g., NfL) in the compositions, kits, and methods can be useful for classifying, detecting, and / or diagnosing heart disease without the need to perform invasive or expensive tests, which represents a significant advancement in patient care. Specifically, heart disease can be detected and identified by methods of this technology that are more comfortable for the patient, cause less harm to the patient, and / or reduce the amount of time the patient needs to recover after detection and / or diagnosis.

[0007] In addition, the present disclosure provides that certain biomarkers (e.g., NfL) are useful for detecting heart disease with improved sensitivity. The present disclosure provides that certain biomarkers (e.g., NfL) are useful for detecting heart disease with improved selectivity. The present disclosure also provides that the combination of one or more demographic factors (particularly age and / or sex) and NfL performs surprisingly well in detecting heart disease with improved sensitivity and / or specificity. In addition, the present disclosure also provides that the combination of one or more imaging-based biomarkers (particularly left ventricular septal wall thickness and / or ejection fraction) and NfL is surprisingly beneficial in detecting heart disease with improved sensitivity and / or specificity.

[0008] The increased sensitivity achieved with the biomarkers described herein (e.g., NfL) reduces the number of false negatives obtained when detecting and / or identifying heart disease. The reduction in false negatives, in turn, will help ensure that more heart disease patients receive early treatment, which is crucial for reducing the signs, symptoms, and conditions associated with heart disease and promoting long-term survival in heart disease patients.

[0009] Thus, in some examples, this document describes a method for predicting heart disease implemented by a computer system. In some examples, this technology includes obtaining a biomarker profile that includes a first level of one or more biomarkers in a first sample obtained from a subject. The one or more biomarkers include neurofilament light chain (NfL). A first machine learning model is applied to the input to generate a biomarker score for the subject. The input includes at least the biomarker profile, and the biomarker score indicates the probability that the subject is at risk for or is experiencing heart disease. The biomarker score or an indication of heart disease determined from the biomarker score is then output to a computing device via one or more communication networks and in response to the biomarker profile.

[0010] In one example, the method further includes obtaining demographic data corresponding to one or more demographic factors of the subject from a computing device and via a communication network. The input in these examples further includes demographic data, and the demographic factors include at least one or more of the subject's gender or age. In another example, the input in these examples further includes obtaining one or more imaging-based biomarkers of the subject from a computing device and via a communication network. The input in these examples further includes imaging-based biomarkers, and the imaging-based biomarkers include at least left ventricular septal wall thickness or ejection fraction.

[0011] In yet other examples, to determine an imaging-based biomarker, one or more acquired images associated with the subject are analyzed to obtain one or more measurements. In these examples, the analysis includes one or more of pattern recognition performed on the acquired images, or segmentation and classification using the first machine learning model or the second machine learning model using one or more features extracted from the acquired images.

[0012] In some examples, the machine learning model is derived from bagging, boosting, or additive decision tree methodologies, neural boosting methods, bootstrap forest methodologies, boosted tree methodologies, or support vector machine methodologies. In other examples, the first machine learning model is a classifier for heart disease, and the method further includes classifying the subject as having or not having heart disease based on the biomarker score. In these examples, the indication of heart disease comprises a binary indication determined based on the classification. Optionally, one or more of the sensitivity or specificity of the classifier is greater than eighty percent.

[0013] In yet other examples, the subject is a human subject, and the first sample comprises one or more of blood, serum, plasma, or cardiac tissue. Additionally, in some examples, at least one of the following is determined based on the biomarker score: (i) the subject's cardiac disease status, (ii) whether the subject has cardiac disease, or (iii) a probability that the subject has cardiac disease.

[0014] In another example, in some examples, a first machine learning model can be trained using obtained training data, the training data including biomarker profiles of a plurality of subjects and health states of the subjects corresponding to the biomarker profiles. In these embodiments, each of the biomarker profiles includes levels of two or more biomarkers (including at least NfL).

[0015] In some examples, the first sample is obtained from the subject at a first time, and the method further includes determining that the subject is at risk for or is experiencing a heart attack when a second NfL level detected in a second sample obtained from the subject at a second time later than the first time exceeds the first NfL level by a threshold amount. In other examples, the first sample is obtained from the subject at a first time, and the method further includes determining whether the subject is at risk for or is experiencing a heart attack based on a change in the first NfL level, the change in the first NfL level being determined based on a comparison of the first NfL level with a second NfL level detected in a second sample obtained from the subject at the second time later than the first time.

[0016] In yet other examples, a first change in NfL level is compared to a second change in NfL, the second change in NfL being determined for a control subject over a first time period corresponding to the difference between the second time and the first time. In these embodiments, the control subject is within three years of the age of the subject and does not suffer from heart disease. In some examples, the subject is determined to be at risk for or experiencing heart disease based on the second change in NfL level determined over a second time period, during which a series of samples are obtained from the subject based on a third NfL level detected in each sample in the series of samples, wherein the series of samples includes the first sample.

[0017] The present disclosure also provides a non-transitory computer-readable medium comprising executable instructions that, when executed by a processor, cause the processor to perform the method disclosed herein.

[0018] The present disclosure also provides a computer system comprising a memory and a processor, wherein the memory comprises a trained machine learning model and has executable instructions stored thereon, which, when executed by the processor, cause the computer system to implement the method disclosed herein.

[0019] Two or more of the features described in this specification, including in this Summary, may then be combined to form embodiments of the present disclosure, regardless of whether or not specifically and explicitly described as separate combinations in this specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A block diagram of an exemplary computer system is depicted.

[0021] Figure 2 A flow chart depicts an exemplary method of identifying a subject as being at risk for or having heart disease for further testing and / or to receive medication.

[0022] Figure 3A flow chart depicting an exemplary method for generating a biomarker score indicative of heart disease risk.

[0023] Figure 4 is a block diagram of an example of a network environment.

[0024] Figure 5 A graph showing the performance of a machine learning model for classifying heart disease using various neurofilament light chains (NfL).

[0025] Figure 6 is a graph showing the correlation between NfL and age in subjects with various disease states.

[0026] definition Antibody agent: As used herein, the term "antibody agent" refers to an agent that specifically binds to a particular antigen. In some embodiments, the term encompasses any polypeptide or polypeptide complex that includes immunoglobulin structural elements sufficient to confer specific binding. Such polypeptides may be naturally occurring (e.g., produced by an organism in reaction with an antigen), or produced by recombinant engineering, chemical synthesis, or other artificial systems or methods. Exemplary antibody agents include, but are not limited to, human antibodies, primatized antibodies, chimeric antibodies, bispecific antibodies, humanized antibodies, conjugated antibodies (e.g., antibodies conjugated or fused to other proteins, radiolabels, cytotoxins), small modular immunopharmaceuticals ("SMIPs"), TM”), single-chain antibodies, camel-like antibodies, and antibody fragments. As used herein, the term “antibody agent” also includes intact monoclonal antibodies, polyclonal antibodies, single-domain antibodies (e.g., shark single-domain antibodies (e.g., IgNAR or fragments thereof)), multispecific antibodies formed from at least two intact antibodies (e.g., bispecific antibodies), and antibody fragments (as long as they exhibit the desired biological activity). The antibody agent may have an antibody constant region sequence that is characteristic of mouse, rabbit, primate, or human antibodies. In some embodiments, the term antibody agent encompasses stapled peptides. In some embodiments, the term antibody agent includes one or more antibody-like binding peptide mimetics, or one or more antibody-like binding scaffold proteins, monobodies, or adnectins. In some embodiments, an antibody agent is or includes a polypeptide whose amino acid sequence includes one or more structural elements, including a complementarity determining region (CDR). In some embodiments, an antibody agent is or includes a polypeptide whose amino acid sequence includes at least one CDR (e.g., at least one heavy chain CDR and / or at least one light chain CDR) that is substantially identical to that found in a reference antibody. In some embodiments, an antibody agent is or includes a polypeptide whose amino acid sequence includes structural elements, including an immunoglobulin variable domain. In some embodiments, an antibody agent is a polypeptide protein having a binding domain that is homologous or substantially homologous to an immunoglobulin binding domain. In some embodiments, an antibody agent may include a covalent modification (e.g., a glycan, a payload (e.g., a detectable moiety, a therapeutic moiety, a catalytic moiety, etc.), or other pendant groups (e.g., polyethylene glycol, etc.)).

[0027] Biomarker: As used herein, the terms "biomarker" or "biological marker," as used in the art, refer to an entity whose presence, level, or form correlates with a particular biological event or state of interest, such that it is considered a "marker" for that event or state. To give just a few examples, in some embodiments, a biomarker can be or include a marker for a particular disease state or the likelihood that a particular disease, disorder, or condition will develop, occur, or recur. In some embodiments, a biomarker can be or include a marker for a particular disease or treatment outcome or likelihood thereof. Thus, in some embodiments, a biomarker is predictive, prognostic, or diagnostic of a relevant biological event or state of interest. In some embodiments, a biomarker is a possible marker for a relevant biological event or state of interest. A biomarker can be an entity of any chemical class. For example, in some embodiments, a biomarker can 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 intracellular. In some embodiments, a biomarker is present in a specific tissue (e.g., cardiac tissue). In some embodiments, the biomarker is present extracellularly (e.g., secreted or otherwise produced or present extracellularly (e.g., in a body fluid such as blood, urine, tears, saliva, cerebrospinal fluid, etc.)).

[0028] Signature fragment: As used herein, the term "signature fragment" refers to a fragment of a biomarker (e.g., NfL) that is sufficient to identify the biomarker from which the fragment is derived. For example, in some embodiments, a "signature fragment" of a biomarker is a fragment comprising an amino acid sequence or a collection of amino acid sequences that collectively allow the biomarker from which the fragment is derived to be distinguished from other possible biomarkers, proteins, or polypeptides. In some embodiments, a signature fragment comprises at least 10, at least 20, at least 30, at least 40, or at least 50 amino acids, although other numbers of amino acids may also be used.

[0029] Gene product or expression product: As used herein, the term "gene product" generally refers to RNA transcribed from a gene (pre-processed and / or post-processed) or a polypeptide encoded by RNA transcribed from a gene (before and / or after modification).

[0030] Hybridization: As used herein, the term "hybridization" refers to the physical property of a single-stranded nucleic acid molecule (e.g., DNA or RNA) annealing to a complementary nucleic acid molecule. Hybridization can typically be assessed in a variety of contexts, including those where interacting nucleic acid molecules are studied in isolation or in the context of more complex systems (e.g., while covalently or otherwise associated with a support entity and / or in a biological system or cell). In some embodiments, hybridization can be detected by hybridization techniques such as in situ hybridization (ISH), microarrays, Northern blots, or Southern blots. In some embodiments, hybridization refers to 100% annealing between a single-stranded nucleic acid molecule and a complementary nucleic acid molecule. In some embodiments, annealing is less than 100% (e.g., at least 95%, at least 90%, at least 85%, at least 80%, at least 75%, or at least 70% of the single-stranded nucleic acid molecule anneals to the complementary nucleic acid molecule). Hybridization techniques and methods for assessing hybridization are well known in the art. See, e.g., Sambrook et al., 1989, Molecular Cloning: A Laboratory Manual, 2nd ed., Cold Spring Harbor Press, Plainview, NY, and Ausubel, FM et al., 1994, Current Protocols in Molecular Biology. John Wiley & Sons, Secaucus, NJ, the entire contents of each of which are incorporated herein by reference.

[0031] Detection agent: As used herein, the term "detection agent" refers to any element, molecule, functional group, compound, fragment, or moiety that can be detected. In some embodiments, the detection agent is provided or utilized alone. In some embodiments, the detection agent is provided and / or utilized in association with another agent. Examples of detection agents include, but are not limited to, various ligands, radionuclides (e.g., 3 H. 14 C. 18 F. 19 F. 32 P. 35 S. 135 I. 125 I. 123 I. 64 Cu, 187 Re、 111 In, 90 Y. 99m Tc, 177 Lu, 89Zr, etc.), fluorescent dyes, chemiluminescent agents (e.g., acridinium esters, stable dioxanes, etc.), bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductor nanocrystals (i.e., quantum dots), metal nanoparticles (e.g., gold, silver, copper, platinum, etc.), nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels (e.g., dyes, colloidal gold, etc.), biotin, digoxigenin, haptens, and proteins for which antisera or monoclonal antibodies are available.

[0032] Diagnostic Test: As used herein, the term "diagnostic test" is a step or series of steps that is performed or has been performed to obtain information useful in determining whether a patient has a disease, disorder, or condition and / or classifying the disease, disorder, or condition into a phenotypic category or any category that is meaningful regarding the prognosis of the disease, disorder, or condition or the likely response to treatment of the disease, disorder, or condition (generally or for any specific treatment). Similarly, as used herein, "diagnosing" means 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; the state, stage, or characteristics of the disease, disorder, or condition manifested by the subject; information regarding the nature or classification of the tumor; information regarding prognosis; and / or information useful in selecting an appropriate treatment or additional diagnostic test. Treatment options may include options for a specific therapeutic agent or other treatment modality (such as surgery, radiation therapy, etc.), options regarding whether to suspend or continue therapy, options regarding a dosing regimen (e.g., the frequency or level of one or more doses of a specific therapeutic agent or combination of therapeutic agents), etc. Additional diagnostic test options may include more specific tests for a given disease, disorder, or condition.

[0033] 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 a biological tissue or fluid. In some embodiments, a biological sample may include blood, blood cells, tissue or fine needle biopsy samples, cell-containing body fluids, free nucleic acids, cerebrospinal fluid, lymph, tissue biopsy specimens, surgical specimens, other body fluids, secretions, and / or excretions, and / or cells derived therefrom. In some embodiments, a biological sample includes cells obtained from an individual (e.g., a human or animal subject). In some embodiments, the obtained cells are or include cells from the individual from whom the sample was 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 biopsy (e.g., fine needle aspiration or tissue biopsy), surgery, or collection of body fluids (e.g., blood). In some embodiments, a sample is cardiac tissue obtained from a subject. In some embodiments, the term "sample" refers to a preparation obtained by processing a primary sample (e.g., by removing one or more components of the primary sample and / or by adding one or more reagents). For example, filtration using a semipermeable membrane can be used. As another example of sample processing, the sample can be a plasma sample treated with an anticoagulant (e.g., EDTA, heparin, or citrate). As another example of sample processing, the sample can be processed to isolate one or more proteins (e.g., by capturing the proteins with one or more antibodies). A "processed sample" can include, for example, nucleic acids or polypeptides extracted from the sample, or nucleic acids or polypeptides obtained by subjecting the primary sample to techniques such as mRNA amplification or reverse transcription, isolation of certain components, and / or purification.

[0034] Subject: As used herein, the term "subject" refers to an organism, such as a mammal (e.g., a human). In some embodiments, a human subject is an adult, adolescent, or pediatric subject. In some embodiments, the subject is at least 50 years old, at least 55 years old, at least 60 years old, at least 65 years old, at least 70 years old, at least 75 years old, or at least 80 years old, although other age thresholds may also be used. In some embodiments, the subject is suffering from a disease, disorder, or condition (e.g., a disease, disorder, or condition that can be treated as provided herein). In some embodiments, the subject is susceptible to the disease, disorder, or condition. In some embodiments, a susceptible subject is predisposed to and / or displays an increased risk of developing the disease, disorder, or condition (compared to the average risk observed in a reference subject or population). In some embodiments, the subject displays one or more symptoms of the disease, disorder, or condition. In some embodiments, the subject does not display specific symptoms (e.g., clinical manifestations of the disease) or characteristics of the disease, disorder, or condition. In some embodiments, the subject does not display any symptoms or characteristics of the disease, disorder, or condition. In some embodiments, the subject is a patient. In some embodiments, the subject is the individual to whom diagnostics and / or therapy are administered.

[0035] Therapeutically effective amount: As used herein, the term "therapeutically effective amount" refers to an amount that produces the desired effect for which it is administered. In some embodiments, the term "therapeutically effective amount" refers to an amount sufficient to treat the disease, disorder, and / or condition when administered according to a therapeutic dosing regimen to a population suffering from or susceptible to the disease, disorder, and / or condition. In some embodiments, a therapeutically effective amount is an amount that reduces the incidence and / or severity of one or more symptoms of the disease, disorder, and / or condition and / or delays its onset. As used herein, the term "therapeutically effective amount" does not require successful treatment in a specific individual. Rather, a therapeutically effective amount may be an amount that, when administered to a patient in need of such treatment, provides a specific desired pharmacological response in a substantial number of subjects. In some embodiments, reference to a therapeutically effective amount may refer to an amount measured in one or more specific tissues (e.g., tissues affected by the disease, disorder, or condition) or fluids (e.g., blood, saliva, serum, sweat, tears, urine, etc.). In some embodiments, a therapeutically effective amount of a particular agent or therapy may be formulated and / or administered in a single dose. In some embodiments, a therapeutically effective agent may be formulated and / or administered in multiple doses (eg, as part of a dosing regimen).

[0036] Threshold: As used herein, the term "threshold" refers to one or more values ​​used as a reference to obtain information about and / or classify a measurement result (e.g., a measurement result obtained in an assay). A threshold value can be determined based on one or more control samples. A threshold value can be determined before, simultaneously with, or after the measurement of interest is taken. In some embodiments, a threshold value can be a range of values. In some embodiments, a threshold value can be a value (or range of values) reported in a relevant field (e.g., a value found in a standard table).

[0037] Detailed Description of Certain Embodiments heart disease The methods and devices disclosed herein can be used, for example, to identify, diagnose, monitor, or treat heart disease. The heart disease can be or include acute coronary syndrome, aortic aneurysm, aortic dissection, aortic stenosis, arrhythmia, atherosclerosis, atrial fibrillation, coronary artery disease (e.g., angina (e.g., stable angina or unstable angina) or heart attack), abnormal heart rhythm, cardiomyopathy (e.g., dilated, hypertrophic, arrhythmogenic, or restrictive cardiomyopathy), carditis (e.g., endocarditis, infective endocarditis, myocarditis, pericarditis, pancarditis, or reflux carditis), congenital heart defect or disease, eosinophilic myocarditis, heart failure, heart murmur, heart valve disease, heart valve stenosis, hypertension, hypertensive heart disease, inflammatory cardiac hypertrophy, Kawasaki disease, myocardial infarction, Marfan syndrome, metabolic syndrome, peripheral arterial disease (PAD), rheumatic heart disease, thromboembolic disease, transthyretin amyloidosis (ATTR-CM), venous thrombosis, or valvular heart disease. The heart disease can be a cardiac disorder or a cardiac disorder. Heart disease can cause and / or exacerbate one or more neurological symptoms (e.g., neuropathy, such as, for example, peripheral neuropathy) in a subject. Heart disease can directly or indirectly lead to (e.g., cause) an increase in a subject's NfL level. This increase can occur regardless of whether the subject suffers from a neurological disease, disorder, or condition. In some embodiments, the subject's neurological condition (e.g., whether the subject suffers from one or more neurological diseases, disorders, or conditions) is taken into account when comparing, monitoring, or determining changes in a subject's NfL level over time, such as when determining whether the subject suffers from (e.g., is experiencing) or is at risk for heart disease. In some embodiments, heart disease is treated by administering a therapeutically effective amount of a drug or therapy for heart disease, such as an angiotensin-converting enzyme (ACE) inhibitor, angiotensin II receptor blocker, angiotensin receptor-neprilysin inhibitor, an anticoagulant or blood thinner, an antiplatelet agent, aspirin, a beta blocker, a calcium channel blocker, a cholestyramine transfer protein (CETP) inhibitor, a cholesterol absorption inhibitor, a digitalis preparation (e.g., a digitalis glycoside), a diuretic, dual antiplatelet therapy, a fibrate, niacin, a statin, a vasodilator, or a combination thereof.For example, the therapy can be apixaban, dabigatran, edoxaban, heparin, rivaroxaban, warfarin, aspirin, clopidogrel, dipyridamole, prasugrel, ticagrelor, benazepril, captopril, enalapril, fosinopril, moexipril, perindopril, quinapril, ramipril, trandolapril, azilsartan, candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan, valsartan, sacubitril, axapril / valsartan, acebutolol, atenolol, betaxolol, bisoprolol / hydrochlorothiazide, bisoprolol, metoprolol, nadolol, propranolol, sotamol, or valafil, carvedilol, labetalol hydrochloride, amlodipine, diltiazem, felodipine, nifedipine, nimodipine, nisoldipine, verapamil, atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, simvastatin, niacin, ezetimibe, ezetimibe / simvastatin, digoxin, acetazolamide, amiloride, bumetanide, chlorothiazide, chlorthalidone, furosemide, hydrochlorothiazide, indapamide, metolazone, spironolactone, torsemide, isosorbide dinitrate, isosorbide mononitrate, hydralazine, nitroglycerin, minoxidil, or a combination thereof.

[0038] Heart disease biomarkers The embodiments described herein offer numerous advantages over the prior art discussed herein. For example, the disclosed techniques are non-invasive, require minimal patient discomfort, are rapid to perform, and are relatively cost-effective. Thus, the techniques described herein offer advantages over these prior art techniques, including, but not limited to, providing non-invasive in vitro diagnostic (IVD) tests for cardiac disease, one or more specific in vitro biomarkers suitable for IVD testing, and alternatives to single-marker IVD tests that include more than one marker to effectively exclude or exclude candidates for more expensive and invasive procedures in disease diagnosis.

[0039] The present disclosure particularly relates to NfL as a biomarker for heart disease. In some embodiments, NfL as described herein is or includes an NfL protein, a nucleic acid sequence encoding NfL, a characteristic fragment thereof, and / or a variant thereof.

[0040] As provided herein, NfL includes gene products associated with NfL. For example, NfL can include proteins or nucleotides (e.g., RNA or mRNA). NfL also encompasses full-length proteins, as well as fragments (e.g., characteristic fragments) of NfL. In some embodiments, NfL includes fragments having an amino acid sequence that is identical to a continuous 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 the amino acid sequence provided in Table 1, although other numbers of amino acids may also be used. In some embodiments, NfL includes fragments 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% identity to the amino acid sequence provided in Table 1, although other percentages may also be used. In some embodiments, NfL comprises a nucleic acid fragment having a nucleic acid sequence that is identical to a consecutive span of at least 10 nucleic acids, at least 20 nucleic acids, at least 30 nucleic acids, at least 40 nucleic acids, at least 50 nucleic acids, at least 60 nucleic acids, at least 70 nucleic acids, at least 80 nucleic acids, at least 90 nucleic acids, or at least 100 nucleic acids of a nucleic acid sequence provided in Table 2, although other numbers of nucleic acids may also be used. In some embodiments, NfL comprises 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% identity to a nucleic acid sequence provided in Table 2, although other percentages may also be used. Variants or alternative forms of NfL include, for example, polypeptides encoded by any splice variant of the transcript encoding NfL.

[0041] Biomarkers contemplated herein also include truncated forms or polypeptide fragments of NfL as described herein. Truncated forms or polypeptide fragments of NfL may include N-terminal deletions or truncated forms and C-terminal deletions or truncated forms. Truncated forms or fragments of NfL may include fragments derived by any mechanism, such as, but not limited to, alternative transformation, exogenous proteolysis and / or endoproteolysis and / or degradation (e.g., by physical, chemical, and / or enzymatic proteolysis). Without limitation, a biomarker may comprise a truncation or fragment of NfL that may comprise 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%, or at least 99% of the amino acid sequence of the NfL protein, although other percentages may also be used.

[0042] In some cases, the fragment is truncated at the N-terminus or at the C-terminus by 1-20 amino acids, such as, for example, 1-15 amino acids, 1-10 amino acids, or 1-5 amino acids, compared to the corresponding mature, full-length NfL protein.

[0043] The NfL proteins of the present disclosure (e.g., NfL proteins or fragment(s) thereof) may also encompass modified forms of NfL, such as those carrying post-expression modifications, including but not limited to modifications such as phosphorylation, glycosylation, lipidation, methylation, selenocysteine ​​modification, cysteination, sulfonation, glutathionylation, acetylation, and / or oxidation of methionine to methionine sulfoxide or methionine sulfone.

[0044] In some embodiments, NfL can be a nucleotide (also referred to herein as a nucleic acid or polynucleotide). In some embodiments, the nucleotide can be RNA or DNA (e.g., cDNA). In some cases, the corresponding RNA or DNA can exhibit better diagnostic discrimination than the full-length protein.

[0045] Neurofilament light chain (NfL) Neurofilaments are components of the neuronal cytoskeleton that are particularly abundant in axons. Their functions include providing structural support and maintaining the size, shape, and caliber of axons. Neurofilaments consist of three subunits: neurofilament light chain (NfL), neurofilament medium chain, and neurofilament heavy chain. NfL levels in cerebrospinal fluid (CSF) and blood increase proportionally with the degree of axonal damage in a variety of neurological disorders, including inflammation, neurodegeneration, trauma, and cerebrovascular disease.

[0046] In some embodiments, NfL can be useful for detecting and diagnosing heart disease and is a biomarker for heart disease. In some embodiments, detection of NfL, characteristic fragments of NfL, and / or variants of NfL is used in methods for assessing a subject's risk for developing heart disease, diagnosing a subject with heart disease, or recommending that a subject undergo additional cardiomyopathy testing. In some embodiments, an anti-NfL agent (e.g., an anti-NfL antibody, probe, etc.) is used to detect NfL in a sample. In some embodiments, detection of nucleotides encoding NfL, nucleotides encoding characteristic fragments of NfL, and / or nucleotides encoding variants of NfL is used in methods for assessing a subject's risk for developing heart disease, diagnosing a subject with heart disease, or recommending that a subject undergo additional cardiomyopathy testing. In some embodiments, an anti-NfL nucleotide sequence agent (e.g., an anti-NfL nucleotide sequence antibody, probe, complementary nucleic acid, etc.) is used to detect nucleotides encoding NfL in a sample.

[0047] Exemplary amino acid sequences of NfL are included in Table 1 below.

[0048] Table 1: Exemplary NfL amino acid sequences .

[0049] Exemplary nucleic acid sequences of NfL are included in Table 2 below.

[0050] Table 2: Exemplary NfL nucleic acid sequences .

[0051] In some embodiments, additional biomarkers may be analyzed or assessed. In some embodiments, the biomarkers may include imaging-based biomarkers of the subject from which the sample was obtained (e.g., left ventricular septal wall thickness and / or ejection fraction). In some embodiments, the biomarkers may include factors including, but not limited to, demographic factors (e.g., one or more of age, weight, biological sex, race, BMI, medical history, risk factors, family history, or geographic location).

[0052] Exemplary Heart Disease Biomarker Compositions As provided herein, NfL can be a full-length protein or a fragment thereof. In some embodiments, the fragments of NfL are characteristic protein fragments. In some embodiments, NfL (e.g., in a sample) can include a subset of the full-length NfL protein and a subset of the characteristic protein fragments of NfL.

[0053] In some embodiments, NfL has a wild-type amino acid sequence. In some embodiments, NfL has a variant amino acid sequence (e.g., an amino acid sequence comprising one or more mutations). In some embodiments, a subset of NfL proteins has a wild-type amino acid sequence, and a subset of NfL proteins has a variant amino acid sequence.

[0054] As provided herein, NfL can be a full-length nucleotide sequence (e.g., DNA, cDNA, or RNA) encoding NfL or a fragment thereof. In some embodiments, the fragment of NfL is a characteristic nucleotide fragment. In some embodiments, NfL (e.g., in a sample) can include a subset of the full-length NfL nucleotide sequence (e.g., DNA, cDNA, or RNA) and a subset of the characteristic nucleotide fragments of NfL.

[0055] In some embodiments, NfL has a wild-type nucleic acid sequence. In some embodiments, NfL has a variant nucleic acid sequence, e.g., a nucleic acid sequence comprising one or more mutations. In some embodiments, a subset of NfL includes a wild-type nucleic acid sequence encoding NfL, and a subset includes a variant nucleic acid sequence encoding NfL.

[0056] Exemplary Methods Exemplary methods of the disclosed technology allow for earlier identification of more patients at risk for heart disease while minimizing the number of false negatives. In some embodiments, the methods disclosed herein provide the advantage of early screening for the presence of heart disease. In some embodiments, the methods disclosed herein can aid in the detection or diagnosis of heart disease (e.g., after genetic testing has ruled out heart disease). In some embodiments, the methods disclosed herein reduce or eliminate the need to initiate heart disease screening (e.g., using expensive and complex echocardiography, CMR, and / or scintigraphy procedures). In some embodiments, if the results of the methods disclosed herein are detection of possible heart disease in a subject, the subject can undergo subsequent confirmatory testing (e.g., echocardiography, cardiac magnetic resonance imaging (CMR), scintigraphy, and / or cardiac biopsy).

[0057] In some embodiments, the methods disclosed herein include determining a subject's risk for developing heart disease. In some embodiments, the methods disclosed herein include diagnosing a subject as having heart disease and obtaining a sample from the subject. In some embodiments, the methods disclosed herein include treating heart disease in a subject at risk of or suffering from heart disease. In some embodiments, the methods disclosed herein include determining that a patient does not have or is not at risk of developing heart disease.

[0058] In some embodiments, the methods disclosed herein include selecting a subject to receive one or more doses of a cardiac medication and obtaining a sample from the subject. In some embodiments, the methods disclosed herein include administering one or more doses of a cardiac medication to the subject. In some embodiments, the cardiac medication comprises an anticoagulant, an antiplatelet agent, an ACE inhibitor, an angiotensin II receptor blocker, an angiotensin receptor-neprilysin inhibitor, a beta blocker, a calcium channel blocker, a cholesterol-lowering drug, a digoxin preparation, a diuretic, a vasodilator, or a combination thereof.

[0059] In some embodiments, the methods disclosed herein include selecting a subject for one or more cardiomyopathy tests and obtaining a sample from the subject. In some embodiments, the one or more cardiomyopathy tests include echocardiography, advanced imaging methods, or both. In some embodiments, the advanced imaging methods include cardiac magnetic resonance imaging (CMR), scintigraphy, or both. In some embodiments, scintigraphy includes the use of a radioisotope conjugate, such as 99m Tc-pyrophosphate. In some embodiments, scintigraphy is performed using single photon emission computed tomography (SPECT).

[0060] The present disclosure provides a diagnostic test for heart disease characterized by detecting NfL according to the methods described and illustrated herein.

[0061] In some embodiments, methods of detecting, diagnosing, or identifying risk of heart disease as disclosed herein have one or more of the following benefits: improved sensitivity in identifying heart disease, improved specificity in identifying heart disease, improved accuracy in identifying heart disease, reduced time in diagnosing heart disease, and / or reduced cost in screening patients for heart disease.

[0062] In some embodiments, NfL can be used in in vitro diagnostic (IVD) or screening tests for heart disease conditions. In some embodiments, diagnostic tests as taught by the present disclosure detect the presence of NfL in a sample obtained from a subject. In some embodiments, diagnostic tests as disclosed herein can help detect or diagnose heart disease in a subject.

[0063] In some embodiments, the diagnostic tests disclosed herein are adapted for use in immunoassay platforms. In some embodiments, such immunoassay platforms include semi-automatic or automated immunoassay platforms. In some embodiments, the diagnostic tests disclosed herein are adapted for use in semi-automatic testing of one or more biomarkers.

[0064] In some embodiments, a diagnostic test as disclosed herein is an improved diagnosis of heart disease compared to standard techniques because the diagnostic test of the present disclosure includes one or more of the following benefits: improved sensitivity in identifying heart disease, improved specificity in identifying heart disease, improved accuracy in identifying heart disease, reduced time to diagnose heart disease, and / or reduced cost of screening patients for heart disease.

[0065] In some embodiments, the diagnostic test as disclosed herein can be a plasma-based screening assay. In some embodiments, by way of example only, the diagnostic test is adapted for use with a Siemens Atellica® system or a Siemens Advia Centaur® system.

[0066] In some embodiments, the methods provided herein include detecting the NfL levels present in the sample to obtain a biomarker spectrum, and using the biomarker spectrum to calculate a biomarker score. In some embodiments, the methods provided herein include detecting the NfL levels in the sample to obtain a biomarker spectrum, and using the biomarker spectrum and demographic factors to calculate a biomarker score. In some embodiments, the methods provided herein include detecting the NfL levels in the sample to obtain a biomarker spectrum, and using the biomarker spectrum and imaging-based biomarkers to calculate a biomarker score. In some embodiments, the methods provided herein include detecting the NfL levels in the sample to obtain a biomarker spectrum, and using the biomarker spectrum, demographic factors and imaging-based biomarkers to calculate a biomarker score.

[0067] In some embodiments, the methods provided herein include receiving NfL levels, demographic factors, and / or imaging-based biomarkers in a sample. In some embodiments, receiving comprises electronically receiving. In some embodiments, demographic factors include one or more of age, weight, biological sex, race, BMI, medical history, risk factors, family history, or geographic location. In some embodiments, imaging-based biomarkers include left ventricular septal wall thickness and / or ejection fraction.

[0068] In some embodiments, the methods described herein include using a biomarker score to select a subject for further testing for cardiomyopathy. In some embodiments, the methods described herein include using a biomarker score to select a subject to receive one or more doses of a cardiac medication. In some embodiments, the methods described herein include using a biomarker score to identify a subject as having or at risk for having heart disease.

[0069] In some embodiments, the methods described herein comprise comparing a biomarker score to a reference biomarker score. In some embodiments, the methods described herein comprise administering one or more doses of a cardiac medication to a subject. In some embodiments, the methods described herein comprise performing one or more cardiomyopathy tests on a subject.

[0070] Detecting NFL and obtaining biomarker profiles The methods provided herein include, among other things, assessing the level of NfL detected in a sample. Exemplary methods for detecting NfL levels are described herein. However, the NfL level can also be provided, for example, in electronic form, from a laboratory that has detected the NfL level in a sample.

[0071] In some embodiments, the present disclosure provides techniques for detecting, analyzing, and / or assessing NfL in a sample. In some embodiments, NfL is in a sample obtained from a subject, and a diagnosis or treatment decision is made based on such detection, analysis, and / or assessment.

[0072] In some embodiments, as described herein, the level of NfL encompasses the presence of NfL, the absence of NfL, the amount of NfL, the absolute amount of NfL, the relative amount of NfL, or the concentration of NfL.

[0073] Methods for detecting NfL include detecting biomarkers as proteins. Protein-based methods for detecting biomarkers include, for example, mass spectrometry (MS), immunoassays (e.g., immunoprecipitation), Western blots, ELISAs, immunohistochemistry, immunocytochemistry, flow cytometry, and / or immunoPCR.

[0074] In some embodiments, mass spectrometry includes MS, MS / MS, MALDI-TOF, electrospray ionization mass spectrometry (ESIMS), ESI-MS / MS, ESI-MS / (MS) n , matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), tandem liquid chromatography-mass spectrometry (LC-MS / MS) mass spectrometry, desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS), atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS) n , quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry. Typically, MS methods quantify fragments of biomarkers rather than full-length proteins. However, MS methods can be sufficient to determine the protein levels of biomarkers to an accuracy sufficient for the disclosed methods and / or assessments.

[0075] In some embodiments, the immunoassay can be a chemiluminescent immunoassay, a high-throughput and / or automated immunoassay platform, For example, the high-throughput and / or automated immunoassay platform can be used to analyze at least 240 tests per hour or at least 440 tests per hour, although any other number of tests per hour can also be used in other examples.

[0076] In some embodiments, a method for detecting NfL in a sample comprises contacting the sample with one or more antibodies directed against NfL. In some embodiments, the method further comprises contacting the sample with a first set of one or more detection agents. In some embodiments, the antibody agent is labeled with the first set of one or more detection agents. In some embodiments, the first set of one or more detection agents comprises one or more acridinium ester (AE) molecules.

[0077] AE molecules can be used to label proteins and nucleic acids. Acridine-labeled proteins can be used for detection in immunoassays. Exposure of AE to alkaline H2O2 (hydrogen peroxide) produces chemiluminescence. Depending on the specific AE variant, light is emitted with a maximum wavelength in the range of 430 to 480 nm. This light can be detected, for example, by high-efficiency photomultiplier tubes. Light emission is rapid, occurring within 1 to 5 seconds. The diversity of AE formats contributes to better assay performance, including improved sensitivity and robustness. AE molecules can be used to label small molecules, large analytes, and antibodies.

[0078] Additional methods for detecting biomarkers (e.g., NfL) include methods for detecting biomarkers that are nucleic acids. Nucleic acid-based methods for detecting NfL include performing nucleic acid amplification methods, such as polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), transcription-mediated amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence-based amplification (NASBA). In some embodiments, nucleic acid-based methods for detecting biomarkers include detecting hybridization between one or more nucleic acid probes and one or more nucleotides encoding NfL. In some embodiments, each nucleic acid probe is complementary to at least a portion of one of the one or more nucleotides encoding NfL. In some embodiments, the nucleotides encoding NfL comprise DNA (e.g., cDNA). In some embodiments, the nucleotides encoding NfL comprise RNA (e.g., mRNA).

[0079] Exemplary samples In some embodiments, the sample disclosed herein is a biological sample. In some embodiments, the biological sample is a blood sample (e.g., drawn from an artery or vein of a subject). The blood sample can be a whole blood sample, a plasma sample, or a serum sample. In some embodiments, the biological sample comprises cardiac tissue.

[0080] In some embodiments, the sample is obtained from a subject (e.g., via a biopsy). In some embodiments, the subject from whom the sample was obtained is assessed for heart disease. In some embodiments, the subject from whom the sample was obtained is suffering from or is at risk of developing heart disease.

[0081] Exemplary Subjects In some embodiments, the subject as disclosed herein is a mammal (e.g., a human). In some embodiments, the subject as disclosed herein is a biological male. In some embodiments, the subject as disclosed herein is a biological female. In some embodiments, the subject as disclosed herein is overweight. In some embodiments, the subject has a body mass index (BMI) of 25 or greater. In some embodiments, the subject has a body mass index (BMI) of 30 or greater, although another threshold BMI may also be used. In some embodiments, the subject is at least 50 years old, at least 55 years old, at least 60 years old, or at least 65 years old, although other age thresholds may also be used. Other types of subjects, subject characteristics, BMIs, and / or ages may also be used with the disclosed technology.

[0082] Exemplary Methods Using Thresholds In some methods disclosed herein, NfL levels can be compared to a threshold value. In some embodiments, the methods disclosed herein include comparing NfL levels to a corresponding threshold value. In some embodiments, the methods disclosed herein include comparing NfL levels to a reference threshold value.

[0083] The reference threshold can be a threshold from a subject known or independently verified to have good cardiac health, or a threshold from a subject known or independently verified to have poor cardiac health, such as in the case of a subject with heart disease. Alternatively, or in combination, the subject's biomarker profile can be compared to reference thresholds determined from subjects of multiple known states (e.g., healthy, undiagnosed with heart disease, or diagnosed with heart disease). In some embodiments, the reference threshold is an average of known NfL levels from multiple subjects, or alternatively, a range defined by the range of NfL levels observed in reference subjects.

[0084] In more complex assessment methods, a subject's biomarker level can be compared to a reference biomarker level constructed from a large number of subjects of known status (e.g., healthy, undiagnosed heart disease, or diagnosed heart disease), such as at least 10, at least 50, at least 100, at least 500, at least 1000, or more subjects, although another threshold number of subjects can also be used. In some examples, the status of the reference subjects can be evenly distributed between (1) healthy / undiagnosed heart disease and (2) diagnosed heart disease. In some cases, the assessment includes iterative or simultaneous comparison of the subject's biomarker (e.g., NfL) level to multiple profiles of known status.

[0085] Multiple known reference biomarker profiles (e.g., NfL levels detected in reference samples, demographic factors, and / or imaging-based biomarkers) can also be used to train a computational assessment algorithm (e.g., a machine learning model) such that a single comparison of a subject's biomarker profile to a reference biomarker profile provides a result that integrates or aggregates information from a large number of subjects of known health status (e.g., healthy, undiagnosed heart disease, or diagnosed heart disease), such as at least 10, at least 50, at least 100, at least 500, at least 1000, or more individuals, although other numbers of individuals can also be used. Generating such reference biomarker profiles can facilitate faster assessment of a subject's risk of heart disease and / or assessment using less computing power.

[0086] A reference biomarker profile can be generated from a plurality of reference biomarker profiles by any of a variety of computational methods. Machine learning models according to the present technology can be generated 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, etc.), or other similar software. TM or SAS) to construct.

[0087] The subject's biomarker profile can be compared to a reference biomarker profile (e.g., generated as explained above), and an output assessment can be generated. A variety of output assessments are consistent with the disclosure herein. The output assessments include a single assessment, optionally narrowed by sensitivity, specificity, or sensitivity and specificity parameters, indicating a health status assessment (e.g., the probability that the subject has heart disease, the subject is not at risk of heart disease, the subject is at risk of heart disease, the subject has heart disease). Alternatively or in combination, additional parameters are provided, such as the subject's demographic factors (e.g., one or more of age, weight, biological sex, race, BMI, medical history, risk factors, family history, and geographic location) and / or the subject's imaging-based biomarkers (e.g., left ventricular septal wall thickness and / or ejection fraction).

[0088] In some embodiments, the methods disclosed herein further comprise diagnosing the subject as having a heart disease when the detected level of NfL is above a threshold value. In some embodiments, the methods disclosed herein comprise diagnosing the subject as having a heart disease when the detected level of NfL is at least 1.3 times, at least 1.4 times, at least 1.5 times, at least 1.6 times, at least 1.7 times, at least 1.8 times, or at least 1.9 times the threshold value, although other factors of the threshold value may also be used.

[0089] In some embodiments, the methods disclosed herein include recommending that a subject undergo one or more cardiomyopathy tests when the NfL level is above a threshold value. In some such methods, the subject is recommended to undergo one or more cardiomyopathy tests when the detected NfL level is at least 1.3 times, at least 1.4 times, at least 1.5 times, at least 1.6 times, at least 1.7 times, at least 1.8 times, or at least 1.9 times the threshold value, although other factors of the threshold value may also be used.

[0090] As explained above, in some embodiments, a method for detecting NfL in a sample includes contacting the sample with one or more antibody agents directed against NfL. In some embodiments, the method further includes contacting the sample with a first set of one or more detection agents. In some embodiments, the antibody agent is labeled with the first set of one or more detection agents. In some embodiments, the first set of one or more detection agents includes one or more acridinium ester (AE) molecules.

[0091] AE molecules can be used to label proteins and nucleic acids. Acridine-labeled proteins can be used for detection in immunoassays. Exposure of AE to alkaline H2O2 (hydrogen peroxide) produces chemiluminescence. Depending on the specific AE variant, light is emitted at a maximum wavelength between 430 and 480 nm. This light can be detected, for example, by a high-efficiency photomultiplier tube.

[0092] In some embodiments, detecting the binding between NfL and one or more antibody agents for NfL includes determining the absorbance value or emission value of the first group of one or more detection agents. For example, the absorbance value indicates the level of binding (e.g., a higher absorbance indicates more binding). In some embodiments, the absorbance value or emission value of the first group of one or more detection agents is above a threshold value. In some embodiments, the absorbance value or emission value of the first group of one or more detection agents is at least 1.3 times, at least 1.4 times, at least 1.5 times, at least 1.6 times, at least 1.7 times, at least 1.8 times, or at least 1.9 times the threshold value, but other factors of the threshold value may also be used. In some embodiments, the threshold value is the average of the absorbance values ​​or emission values ​​determined for the second group of one or more detection agents that label two or more control samples. In some such embodiments, the second group of one or more detection agents are similar or identical to the first group of one or more detection agents.

[0093] In some embodiments, the methods disclosed herein further comprise diagnosing the subject with heart disease when the detected NfL level is above a threshold value. In some embodiments, the methods comprise diagnosing the subject with heart disease when the detected NfL level is at least 1.3 times, at least 1.4 times, at least 1.5 times, at least 1.6 times, at least 1.7 times, at least 1.8 times, or at least 1.9 times the threshold value, although other factors of the threshold value may also be used.

[0094] In some embodiments, the methods disclosed herein include diagnosing a subject as having a heart disease when the absorbance value or emission value of the first set of one or more detection agents is above a threshold value. In some embodiments, the methods include diagnosing a subject as having a heart disease when the absorbance value or emission value of the first set of one or more detection agents is at least 1.3 times, at least 1.4 times, at least 1.5 times, at least 1.6 times, at least 1.7 times, at least 1.8 times, or at least 1.9 times the threshold value, although other factors of the threshold value may also be used. In some embodiments, the threshold value is the average of the absorbance values ​​or emission values ​​determined for the 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 are similar or identical to the first set of one or more detection agents.

[0095] In some embodiments, the methods disclosed herein include recommending that a subject undergo one or more cardiomyopathy tests when the NfL level is above a threshold value. In some such methods, the subject is recommended to undergo one or more cardiomyopathy tests if the detected NfL level is at least 1.3 times, at least 1.4 times, at least 1.5 times, at least 1.6 times, at least 1.7 times, at least 1.8 times, or at least 1.9 times the threshold value, although other factors of the threshold value may also be used.

[0096] In some embodiments, the methods disclosed herein include recommending that a subject undergo one or more cardiomyopathy tests when the absorbance value or emission value of the first set of one or more detection agents is above a threshold value. In some such methods, the subject is recommended to undergo one or more cardiomyopathy tests when the absorbance value or emission value of the first set of one or more detection agents is at least 1.3 times, at least 1.4 times, at least 1.5 times, at least 1.6 times, at least 1.7 times, at least 1.8 times, or at least 1.9 times the threshold value, although other factors of the threshold value may also be used.

[0097] Cardiomyopathy tests that can be used according to the methods of the present disclosure include, for example, echocardiography or advanced imaging methods. In some embodiments, advanced imaging methods include cardiac magnetic resonance imaging (CMR) or scintigraphy (e.g., using a microscope such as 99m Tc-pyrophosphate radioisotope conjugates and / or single-photon emission computed tomography (SPECT).

[0098] In any of the embodiments described and illustrated herein, the threshold value can be the average of the values ​​detected for two or more control samples. In some such embodiments, the values ​​detected for the two or more control samples represent a control level of NfL. In some embodiments, the control samples include recombinant NfL. In some embodiments, the two or more control samples are each a sample obtained from a subject who has not had heart disease. In some embodiments, the threshold value is a value reported in a standard table.

[0099] Exemplary Methods Utilizing Biomarker Profiles The algorithm-based determinations and associated information provided by practicing any of the methods described herein can facilitate improved treatment and decision making for a subject. For example, the methods described herein can enable a physician or caregiver to identify a patient who has a low likelihood of having heart disease and, therefore, will not require treatment, will not require additional cardiac testing, or will not require increased monitoring for heart disease, or a patient who has a high likelihood of having heart disease and will require treatment, will require additional cardiac testing, or will require increased monitoring for heart disease.

[0100] In some cases, a biomarker score can be determined by applying a specific algorithm. In some embodiments, the biomarker score is quantitative. In some methods disclosed herein, the algorithm used to calculate the biomarker score can group the expression level values ​​of NfL or a biomarker panel that includes NfL. In addition, the formation of a specific biomarker panel can facilitate the mathematical weighting of the contribution of various expression levels of biomarkers or biomarker subsets (e.g., classifiers) to the quantitative score.

[0101] Some of the methods described herein, as well as the kits and systems provided herein, can utilize algorithm-based diagnostic assays to predict whether a subject from whom a sample was obtained is at risk for or experiencing a heart disease, to select a subject from whom a sample was obtained for one or more cardiomyopathy tests, and / or to select a subject from whom a sample was obtained to receive one or more doses of a cardiac medication.

[0102] NfL levels and optionally one or more demographic factors (e.g., one or more of age, weight, biological sex, race, BMI, medical history, risk factors, family history, and geographic location) and / or imaging-based biomarkers (e.g., left ventricular septal wall thickness and / or ejection fraction) can be used alone or arranged as functional subsets to calculate a biomarker score for predicting whether a subject from whom a sample was obtained is at risk for or has heart disease, selecting a subject from whom a sample was obtained for one or more cardiomyopathy tests, and / or selecting a subject from whom a sample was obtained to receive one or more doses of a cardiac medication.

[0103] Some methods disclosed herein include using a biomarker profile to calculate a biomarker score. In some embodiments, using a biomarker profile to calculate a biomarker score includes applying an algorithm to the biomarker profile to calculate the biomarker score. In some embodiments, the algorithm is an algorithm derived from a decision tree method, a neural boosting method, a guided clustering method, a boosted tree method, a K-nearest neighbor method, a generalized regression forward selection method, a generalized regression pruned forward selection method, a fitted stepwise method, a generalized regression lasso method, a generalized regression elastic net method, a generalized regression ridge method, a nominal logistic method, a support vector machine method, a discriminant method, a naive Bayes method, or a combination thereof. In some embodiments, the algorithm is a decision tree method, a neural boosting method, a guided clustering method, a boosted tree method, a generalized regression lasso method, a generalized regression elastic net method, a generalized regression ridge method, a nominal logistic method, a support vector machine method, a discriminant method, or a combination thereof, or an algorithm derived from the foregoing. In some embodiments, the algorithm is a decision tree method, a neural boosting method, a guided clustering method, a boosted tree method, a support vector machine method, or a combination thereof, or an algorithm derived from the foregoing.

[0104] The machine learning model can use data corresponding to one or more (e.g., two or more) demographic factors and a biomarker profile including data corresponding to NfL levels in a sample from a subject. In some embodiments, the one or more demographic factors include one or more of age, weight, biological sex, race, 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, the one or more demographic factors are two demographic factors. In some embodiments, the two demographic factors are age and biological sex.

[0105] In some embodiments, the method includes providing data corresponding to one or more demographic factors of the subject and a corresponding biomarker profile as input to a machine learning model. In some embodiments, the one or more demographic factors include biological sex, age, or both biological sex and age. The corresponding biomarker profile includes data on NfL levels in a sample from the subject characterized by (one or more) demographic factors. Compared to a machine learning model that does not consider any demographic factors, the performance of the machine learning model (e.g., predictive ability, sensitivity and / or selectivity, and / or classification accuracy) can be improved by using a machine learning model that considers NfL in combination with one or more (e.g., two or more) demographic factors.

[0106] Additional algorithms can be used for the methods provided herein, and the algorithms provided herein are merely examples of algorithm types that can be used to generate biomarker scores. For example, exemplary algorithms have been described 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, the entire contents of each of which are incorporated herein by reference. In addition, as indicated above, a combination of algorithms can be used in the methods provided herein. For example, a boosted tree method can be a combination of a decision tree method and a boosted method. Other combinations are possible and are contemplated for use in the methods provided herein. Exemplary algorithms that can be used for the methods provided herein are further described below.

[0107] Decision Tree One method that can be used to calculate biomarker scores from biomarker profiles is a decision tree. A training population and a specific data analysis algorithm can be used to construct a decision tree. Decision trees are generally described by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, NY, pp. 395-396. Tree-based methods partition the feature space into a set of rectangles and then fit a model (e.g., a constant) to each rectangle.

[0108] The training population data may include a biomarker profile across the training population (e.g., including NfL levels in the sample). A specific algorithm that can be used to construct a decision tree is 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. CART, MART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9. Random forests are described in Breiman, 1999, “Random Forests—Random Features,” Technical Report 567, Statistics Department, UC Berkeley, September 1999, the entire contents of which are hereby incorporated by reference herein.

[0109] The purpose of a decision tree is to induce a classifier (i.e., 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. Thus, a decision tree can be derived from training data. Exemplary training data includes data from multiple subjects (e.g., a training population). A biomarker profile can be provided and / or used for each corresponding subject. In some embodiments, the training data includes the biomarker profile of the training population.

[0110] Example decision tree The following algorithm describes an exemplary derivation of a decision tree: Create the root node If all examples have the same class value, the root is given that label Otherwise, if the feature is empty, the roots are labeled according to the most common value Otherwise, start Calculate the information gain for each feature Select feature A with the highest information gain and make it the root feature For every possible value v of this feature Add a new branch below the root, which corresponds to A = v Let examples(v) be those examples where A = v If example (v) is empty, make the new branch the leaf node labeled with the most common value in the example Otherwise, let new branch be the tree created by: tree(example(v),class,feature-{A}) Finish.

[0111] In a univariate decision tree, each segmentation is based on the characteristic value (e.g., level) of the corresponding biomarker. In addition, multivariate decision trees can be implemented in the methods described herein. In some embodiments, segmentation is based on characteristic values ​​corresponding to demographic factors, either alone or in combination with characteristic values ​​of corresponding biomarkers. For example, segmentation can be based on a combination of characteristic values ​​corresponding to (one or more) gender and level of one or more biomarkers, (one or more) age and level of one or more biomarkers, or (one or more) age and level of one or more biomarkers and both gender and level. Multivariate decision trees are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 408-409. In such a multivariate decision tree, some or all of the decisions include linear combinations of characteristic values ​​(e.g., levels) of multiple biomarkers of the spectrum. Such linear combinations can be trained using known techniques such as gradient descent on classification or by using a sum-of-squares error criterion.

[0112] As an illustrative example, consider the following equation: 0.05(X1) + 0.2(X2) < 500. In this example, X1 and X2 refer to two different features (e.g., levels) of two different biomarkers (e.g., including NfL). To apply this method, values ​​for features X1 and X2 are obtained from measurements obtained from unclassified subjects (e.g., as part of a biomarker profile). These values ​​are then inserted into the equation. If a value less than 500 is calculated, the first branch of the decision tree is taken. Otherwise, the second branch of the decision tree is taken.

[0113] Biomarker profiles can be used to train machine learning models. Biomarker profiles can be from different subjects, the same subject at different times, or a combination thereof. Biomarker profiles can be associated with (e.g., labeled with) corresponding data indicating a health state. For example, a biomarker profile of a subject used as training data can be labeled with data indicating the subject's health state. This corresponding data can be a probability or a score (e.g., a biomarker score). The health state can be the probability that the subject has heart disease, whether the subject is at risk for heart disease, or whether the subject has heart disease (e.g., the subject is healthy, not diagnosed with heart disease, or diagnosed with heart disease). The health state can be manually determined by a physician, for example. Labeling the biomarker profile with corresponding data indicating a health state can be performed manually (e.g., by the physician determining the health state) or automatically based on stored correlations.

[0114] Demographic factor data can be used in combination with biomarker profiles to train machine learning models. For example, biomarker profiles and data for one or more demographic factors of each subject in a group of subjects can be used to train a machine learning model. The group can 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, but in other examples, another number of subjects can also be used. In some embodiments, a combination of one or more demographic factors and biomarker profiles of age, sex, or both age and sex for each subject in a group of subjects is used to train a machine learning model. The biomarker profile can include data corresponding to NfL levels.

[0115] In some embodiments, training a machine learning model includes determining one or more feature values. Each feature value may correspond to a biomarker (e.g., NfL). The one or more feature values ​​may be determined based on a biomarker profile and / or a linear combination thereof. The one or more feature values ​​may correspond to NfL levels. Training a machine learning model may include determining one or more decision rules based on the biomarker profile used as training data. The one or more decision rules may be based on one or more demographic factors corresponding to the subject(s) associated with the biomarker profile. The one or more decision rules may be based on 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.

[0116] Bagging, boosting, and additive tree methods can be combined with decision methods to improve weak decision rules. These techniques are designed for use with decision trees, such as those described herein. In some embodiments, the machine learning model can include at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees, but any number of decision trees can be used. In addition, this technique can also be used for decision rules developed using other types of data analysis algorithms, such as linear discriminant analysis.

[0117] In bagging, the training set is sampled, random independent bootstrapped replicates are generated, a decision rule is constructed on each of these, and they are aggregated into a final decision rule by simple majority voting. See, for example, Breiman, 1996, Machine Learning 24, 123-140; and Efron & Tibshirani, An Introduction to Boostrap, Chapman & Hall, New York, 1993, the entire contents of which are hereby incorporated by reference.

[0118] In the boosting method, a decision rule is constructed on a weighted version of the training set, which depends on the previous classification results. Initially, all features considered have equal weights, and the first decision rule is constructed on this data set. Then, the weights are changed according to the performance of the decision rule. Features that are misclassified receive a larger weight, and the next decision rule is promoted on the reweighted training set. In this way, a sequence of training sets and decision rules is obtained, which are then combined in the final decision rule by majority voting or by weighted majority voting. See, for example, Freund & Schapire, "Experiments with a new boosting algorithm," Proceedings 13th International Conference on Machine Learning, 1996, 148-156, the entire contents of which are hereby incorporated by reference.

[0119] The measurement result data used in the technology disclosed herein can optionally be normalized. Normalization refers to the process of correcting for differences in the amount of gene or protein levels measured and the variability in the quality of the template used to remove unwanted sources of systematic variation in measurement results, such as those related to the processing and detection of gene or protein expression. Other sources of systematic variation can be attributed to laboratory processing conditions.

[0120] In some cases, the normalization method is used to standardize laboratory processing conditions. Non-limiting examples of standardization of laboratory processes that can be used with this technique include, but are not limited to, taking into account systematic differences between instruments, reagents, and / or equipment used during the data generation process, and / or the date and / or time or time lapse in data collection.

[0121] The assay can provide normalization by combining the expression of certain normalized standard genes or proteins that do not differ significantly in expression levels under relevant conditions and are known to have stable and consistent expression levels in that particular sample type. Suitable normalized 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), the entire contents of which are hereby incorporated by reference. In some instances, the normalized biomarkers (genes and / or proteins) (also referred to as reference genes) are known to not exhibit significantly different expression levels in subjects with heart disease compared to control subjects without heart disease. In some examples, it may be useful to add stable isotope labeled standards that can be used and represent entities with known properties for data normalization. In other examples, a fixed sample of the standard can be measured with each analytical batch to account for instrument and day-to-day measurement variability.

[0122] In some methods and systems herein, machine learning models are used to reselect discriminative biomarkers and optional subject characteristics and to build classification models to determine clinical outcome scores. Examples of such algorithms are described above. These algorithms can help select important biomarker features and transform the basic measurements into scores or probabilities related to, for example, clinical outcome, disease risk, disease likelihood, presence or absence of disease, treatment response, and / or classification of disease state.

[0123] The machine learning model can output a biomarker score. The machine learning model can determine whether the subject is at risk for or experiencing heart disease. In some embodiments, the output of the machine learning model is a determination of whether the subject is at risk for or experiencing heart disease. The machine learning model can be a classifier for heart disease. In some embodiments, the machine learning model can be used to classify whether a subject has heart disease, for example, based on the subject's biomarker profile. In some embodiments, the classifier or classification has a sensitivity and specificity each greater than 80% (e.g., at least one or both greater than 90%).

[0124] The biomarker score can be determined by comparing the subject-specific biomarker profile with a reference biomarker profile. The reference biomarker profile can represent a known diagnosis. For example, the biomarker profile can represent a positive diagnosis of heart disease. As another example, the reference biomarker profile can represent a negative diagnosis of heart disease. In some examples, an increase in the score indicates an increased likelihood of one or more of the following: poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, no response, or recommended treatment for disease management. In some examples, a decrease in the quantitative score indicates an increased likelihood of one or more of the following: poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, no response, or recommended treatment for disease management.

[0125] A biomarker profile from a subject that is similar to a reference biomarker profile often indicates an increased likelihood of one or more of the following: a poor clinical outcome, a good clinical outcome, a high risk of disease, a low risk of disease, a complete response, a partial response, stable disease, no response, or a recommended treatment for disease management. In some applications, a dissimilar biomarker profile between a subject and a reference indicates an increased likelihood of one or more of the following: a poor clinical outcome, a good clinical outcome, a high risk of disease, a low risk of disease, a complete response, a partial response, stable disease, no response, or a recommended treatment for disease management.

[0126] The results can be provided to the subject, a healthcare professional, or other professional. The results are optionally accompanied by health recommendations, such as a recommendation to use one or more cardiomyopathy tests to confirm or independently assess heart disease risk, for example.

[0127] The recommendation may optionally include information related to the treatment regimen. In some cases, the efficacy of the regimen can be assessed by comparing the biomarker profiles of the subject at a first time point (optionally before treatment) and a later second time point (optionally after the treatment instance). The biomarker profiles can be compared to each other, each compared to a reference, or otherwise assessed to determine whether the treatment regimen demonstrates efficacy, such that the treatment regimen should be continued, increased, replaced with an alternative, or discontinued due to its successful resolution of the heart disease or associated signs and symptoms. Some assessments rely on comparing the biomarker profiles of the subject at multiple time points (such as at least one time point before treatment and at least one time point after treatment). The biomarker profiles can be compared to each other or to at least one reference biomarker panel level, or both.

[0128] Treatment of transthyretin amyloid cardiomyopathy In some embodiments, the present disclosure includes a method for selecting a patient for treatment with a cardiac medication, the method comprising the step of detecting NfL levels in a sample obtained from the subject.

[0129] In some embodiments, the present disclosure includes methods for treating heart disease in a subject at risk for or experiencing heart disease, the methods comprising administering to the subject a therapeutically effective amount of a cardiac medication. In some embodiments, the subject expresses NfL levels above a threshold. In some embodiments, the method further comprises determining that the subject expresses NfL levels above the threshold. In some embodiments, prior to administering, it is determined that the subject expresses NfL levels above the threshold. In some embodiments, the cardiac medication is an angiotensin-converting enzyme (ACE) inhibitor, an angiotensin II receptor blocker, an angiotensin receptor-neprilysin inhibitor, an anticoagulant or blood thinner, an antiplatelet agent, aspirin, a beta blocker, a calcium channel blocker, a cholestyramine transfer protein (CETP) inhibitor, a cholesterol absorption inhibitor, a digitalis preparation (e.g., a digitalis glycoside), a diuretic, dual antiplatelet therapy, a fibrate, niacin, a statin, a vasodilator, or a combination thereof. For example, cardiac medications can include apixaban, dabigatran, edoxaban, heparin, rivaroxaban, warfarin, aspirin, clopidogrel, dipyridamole, prasugrel, ticagrelor, benazepril, captopril, enalapril, fosinopril, moexipril, perindopril, quinapril, ramipril, trandolapril, azilsartan, candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan, valsartan, sacubitril, axapril / valsartan, acebutolol, atenolol, betaxolol, bisoprolol / hydrochlorothiazide, bisoprolol, metoprolol, nadolol, propranolol. valafil, sotalol, carvedilol, labetalol hydrochloride, amlodipine, diltiazem, felodipine, nifedipine, nimodipine, nisoldipine, verapamil, atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, simvastatin, niacin, ezetimibe, ezetimibe / simvastatin, digoxin, acetazolamide, amiloride, bumetanide, chlorothiazide, chlorthalidone, furosemide, hydrochlorothiazide, indapamide, metolazone, spironolactone, torsemide, isosorbide dinitrate, isosorbide mononitrate, hydralazine, nitroglycerin, minoxidil, or a combination thereof.

[0130] In some embodiments, the biomarkers disclosed herein can be used to screen patients for effective therapies for heart disease. In some embodiments, the therapy for heart disease is an ACE inhibitor, an angiotensin II receptor blocker, an angiotensin receptor-neprilysin inhibitor, an anticoagulant or blood thinner, an antiplatelet agent, aspirin, a beta blocker, a calcium channel blocker, a CETP inhibitor, a cholesterol absorption inhibitor, a digitalis preparation (e.g., a digitalis glycoside), a diuretic, dual antiplatelet therapy, a fibrate, niacin, a statin, a vasodilator, or a combination thereof. For example, cardiac medications can include apixaban, dabigatran, edoxaban, heparin, rivaroxaban, warfarin, aspirin, clopidogrel, dipyridamole, prasugrel, ticagrelor, benazepril, captopril, enalapril, fosinopril, moexipril, perindopril, quinapril, ramipril, trandolapril, azilsartan, candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan, valsartan, sacubitril, axapril / valsartan, acebutolol, atenolol, betaxolol, bisoprolol / hydrochlorothiazide, bisoprolol, metoprolol, nadolol, propranolol. valafil, sotalol, carvedilol, labetalol hydrochloride, amlodipine, diltiazem, felodipine, nifedipine, nimodipine, nisoldipine, verapamil, atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, simvastatin, niacin, ezetimibe, ezetimibe / simvastatin, digoxin, acetazolamide, amiloride, bumetanide, chlorothiazide, chlorthalidone, furosemide, hydrochlorothiazide, indapamide, metolazone, spironolactone, torsemide, isosorbide dinitrate, isosorbide mononitrate, hydralazine, nitroglycerin, minoxidil, or a combination thereof.

[0131] Exemplary kits The present disclosure also provides kits comprising one or more anti-NfL agents and instructions for use (e.g., for therapeutic, prophylactic, or diagnostic purposes). In some embodiments, the kits are used in in vitro diagnostic assays to diagnose heart disease. In some embodiments, the kits of the present disclosure further comprise a cardiac medication.

[0132] In some embodiments, the one or more anti-NfL agents include an antibody agent. In some embodiments, one or more of the antibody agents are labeled with a detectable moiety. In some embodiments, the kit 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 bind to one or more of the anti-NfL antibody agents.

[0133] In some embodiments, the one or more anti-NfL agents comprise nucleic acid probes. In some embodiments, at least a portion of each nucleic acid probe hybridizes to one or more portions of nucleotides encoding NfL. The nucleotides encoding NfL 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., where the detection agent indicates the presence of nucleotides encoding NfL).

[0134] In some embodiments, the kit includes one or more control samples. In some embodiments, the control samples include one or more standards. In some embodiments, the standards include recombinant NfL. In some embodiments, the standards include synthetic NfL nucleic acid.

[0135] Kits according to the disclosed technology may also include other ingredients, such as solvents or buffers, stabilizers or preservatives, and / or agents for treating conditions or disorders. Alternatively, the other ingredients may be included in the kit, but in a different composition or container than the anti-NfL agent. In such embodiments, the kit may include instructions for mixing the anti-NfL agent and the other ingredients or for using the anti-NfL agent with the other ingredients. In some embodiments, kits for use according to the present disclosure may include (one or more) reference or control samples, instructions for processing the samples, performing tests on the samples, instructions for interpreting the results, and / or buffers and / or other reagents required to perform the tests.

[0136] Single biomarkers can be helpful in detecting and / or diagnosing heart disease as explained herein. This disclosure also provides the recognition that NfL is particularly useful for detecting and / or diagnosing heart disease. Thus, the methods, compositions, and kits described herein can be used in assays to assess the risk of heart disease, assess whether a subject should undergo further cardiac testing, and / or diagnose heart disease based on detecting or measuring NfL in a sample (e.g., a biological sample obtained from a subject).

[0137] The methods and kits provided herein can detect heart disease in a sample with medically feasible sensitivity and specificity so that the results of the test are reliable enough. The methods and kits described herein for detecting and / or diagnosing heart disease in a subject can detect heart disease with a sensitivity of 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, the methods and kits provided herein can detect heart disease with a sensitivity of between about 70%-100%, between about 80%-100% or between about 90%-100%. In some embodiments, the methods and kits provided herein can detect heart disease with a specificity of 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, the methods and kits provided herein can detect heart disease with a specificity of between about 50%-100%, between about 60%-100%, between about 70%-100%, between about 80%-100%, or between about 90%-100%. In some embodiments, the methods and kits provided herein can detect heart disease with a sensitivity and specificity of 50% or greater, 60% or greater, 70% or greater, 75% or greater, 80% or greater, 85% or greater, or 90% or greater. In some embodiments, the methods and kits provided herein can detect heart disease with a sensitivity and specificity of between about 50%-100%, between about 60%-100%, between about 70%-100%, between about 80%-100%, or between about 90%-100%.

[0138] Exemplary compositions Compositions are also provided herein. In some embodiments, the compositions include NfL and one or more anti-NfL agents. In some embodiments, the one or more anti-NfL agents in the compositions provided herein include an antibody agent. In some embodiments, one or more of the antibody agents are labeled with a detectable moiety. In some embodiments, the compositions include 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 compositions include one or more secondary antibody agents that bind to one or more of the anti-NfL antibody agents.

[0139] In some embodiments, one or more anti-NfL agents in the compositions provided herein comprise nucleic acid probes. In some embodiments, at least a portion of each nucleic acid probe hybridizes to one or more portions of nucleotides encoding NfL. Nucleotides encoding NfL 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., where the detection agent indicates the presence of nucleotides encoding NfL).

[0140] In some embodiments, the composition includes one or more control samples. In some embodiments, the control samples include one or more standards. In some embodiments, the standards include recombinant NfL. In some embodiments, the standards include synthetic NfL nucleic acid. For example, compositions according to this technology can include other ingredients such as solvents or buffers, stabilizers or preservatives, and / or agents for treating conditions or disorders.

[0141] Exemplary Computer System Figure 1 1 is a block diagram of a computer system 1100 that can be used for the operations described and illustrated herein and disclosed 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 can include analytical equipment 1160 for determining NfL levels in a sample.

[0142] The methods described herein can be implemented by a computer system 1100 having a processor 1110 that executes specific instructions in a computer program stored in a memory 1120. In some embodiments, the computer system 1100 can be configured to output a biomarker score based on the received biomarker profile and / or NfL level, demographic factors, and / or imaging-based biomarkers. The computer program can include instructions for the computer system 1100 to select appropriate next steps, including additional medications and / or additional testing (e.g., cardiomyopathy testing) for the subject.

[0143] In some embodiments, the computer program can be configured such that the computer system 1100 can identify subjects for further testing (e.g., cardiomyopathy testing), identify subjects as being at risk for or suffering from heart disease, and / or identify subjects for medication based on received data (e.g., biomarker profiles) and using the data to calculate biomarker scores. The computer system 100 according to this technology can prioritize the next steps of identification based on the biomarker profiles as well as demographic factors and / or imaging-based biomarkers. The computer system 1100 can adjust the ranking based on, for example, the clinical response of subjects or family members who have or are suspected of having heart disease.

[0144] Thus, the processor 1110 is capable of processing instructions for execution within the computer 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 instructions for receiving or sending information through the input / output device 1140.

[0145] Memory 1120 stores information within system 1100. In one embodiment, memory 1120 is a computer-readable medium. In one embodiment, memory 1120 is a volatile memory unit. In another embodiment, memory 1120 is a non-volatile memory unit (e.g., a non-transitory computer-readable medium). Storage device 1130 can provide mass storage for computer system 1100. In one embodiment, storage device 1130 is a volatile or non-volatile computer-readable medium. Therefore, the non-transitory computer-readable medium of memory 1120 can contain executable instructions that, when executed by processor 1110, cause processor 1110 to perform operations including the methods provided herein. For example, a non-transitory computer-readable medium can contain executable instructions that, when executed by processor 1110, cause processor 1110 to perform operations including methods 1200 or 1300.

[0146] Input / output device 1140 provides input / output operations for computer system 1100. In one embodiment, input / output device 1140 includes a keyboard and / or a pointing device. In one embodiment, input / output device 1140 includes a display unit for displaying a graphical user interface. In some examples, input / output device 1140 is a touch screen.

[0147] In some examples, computer system 1100 can be used to build a database. Figure 2A flow chart is shown of an exemplary method 1200 for generating a database for identifying subjects for further testing (e.g., cardiomyopathy testing), identifying subjects as being at risk for or suffering from heart disease, and / or identifying subjects to receive medication. Any of the methods described and illustrated herein, including method 1200 and / or method 1300, can be performed in whole or in part by computer system 1100. For example, a computer program product can include instructions for causing processor 1110 to perform the steps of method 1200 or method 1300.

[0148] In step 1205 in this example, the computer system 1100 trains a machine learning model or obtains a trained machine learning model. In some examples, the machine learning model is trained using training data that includes biomarker profiles of subjects and the health status of the subjects corresponding to the biomarker profiles (e.g., healthy, experiencing heart disease, etc.). In this example, each of the biomarker profiles includes levels of two or more biomarkers, including at least NfL levels. In some examples, the training data also includes demographic data and / or imaging-based biomarkers associated with each of the subjects and their corresponding biomarker profiles and health status.

[0149] In some examples, the machine learning model is derived from a bagging method, a boosting method or an additive decision tree method, a neural boosting method, a bootstrap forest method, a boosted tree method, or a support vector machine method, although other methods and / or machine learning algorithms or models may also be used in other examples. In some examples, the machine learning model is a classifier for heart disease that is trained to provide an output indicating whether a subject has heart disease (e.g., based on an output biomarker score), as explained in more detail below. Thus, the machine learning model is trained to utilize pattern and vector search, for example, to associate input data associated with a subject in the training data with the subject's health status, thereby analyzing the probability that the subject is at risk for or is experiencing heart disease, as also explained in more detail below.

[0150] In step 1210, computer system 1100 obtains a biomarker profile for a subject (e.g., levels of one or more biomarkers, including at least NfL levels, in a sample obtained from the subject) from a computing device (e.g., computer device 2404) via one or more communication networks (e.g., network 2408). In some examples, the machine learning model is trained on training data associated with a human subject and / or the biomarker profile obtained in step 1210 is associated with the human subject. In these examples, the sample may include one or more of blood, serum, plasma, or cardiac tissue, although other types of subjects and / or samples may also be used in other examples. Alternatively, the sample may be obtained, for example, via analytical equipment 1160.

[0151] Optionally, computer system 1100 may also obtain additional data about the subject corresponding to the obtained biomarker profile, including demographic data and / or imaging-based biomarkers. For example, the demographic data may correspond to one or more demographic factors of the subject (e.g., age, gender, etc.), although any other demographic factors, including those identified above, may also be used. Additionally, the imaging-based biomarkers may include at least left ventricular septal wall thickness or ejection fraction, although other imaging-based biomarkers may also be used.

[0152] In the example where imaging-based biomarkers are obtained in step 1210, the computer system 1100 can be configured to determine the imaging-based biomarkers by analyzing one or more acquired images associated with the subject to obtain one or more measurements. For example, the analysis can include pattern recognition performed on the acquired images and / or segmentation and classification of one or more features extracted from the acquired images, for example, but other types of analysis can also be used in other examples. Optionally, the segmentation and classification can be performed by a first machine learning model or a second machine learning model trained to extract features from images.

[0153] In some examples, the computer program in the computer system 1100 may include instructions for presenting a suitable graphical user interface (GUI) on the input / output device 1140, and the GUI may prompt the user to enter the NfL level using the input / output device 1140 (such as a keyboard). In other examples, the computer system 1100 may provide an application programming interface (API) or other integration with a third-party service or other device for obtaining a subject's biomarker profile, demographic data, and / or imaging-based biomarkers.

[0154] In step 1220, the computer system 1100 applies the machine learning model trained in step 1205 to the input to generate a biomarker score for the subject. The input may include at least the biomarker profile received in step 1210, but demographic data and / or imaging-based biomarkers received in step 1210 may also form part of the input to the machine learning model. The biomarker score in this example indicates the probability that the subject is at risk for or is experiencing heart disease. Thus, in some examples, the biomarker score is effectively calculated or determined based on the subject's biomarker profile, demographic data or factors, and / or imaging-based biomarkers. The biomarker score can be a binary value indicating that the subject is classified into one of two categories. Alternatively, the biomarker score can include a range or can be a value within a range, and other types of biomarker scores can also be generated in step 1220.

[0155] In step 1230, the computer system 1100 provides the biomarker score and / or an indication of heart disease derived therefrom to the computing device via the communication network and in response to the biomarker profile, and optionally stores the calculated biomarker score. For example, the computer system 1100 may store the biomarker score in the storage device 1130.

[0156] In some examples, the indication of heart disease includes a binary indication determined based on a machine learning model classifying the subject into one of two categories, corresponding to having or not having heart disease. Optionally, one or more of the sensitivity or specificity of the classifier in these examples is greater than eighty percent, although another percentage may be used or required before the machine learning model is deployed in production. In other examples, the computer system 1100 may use a biomarker score to determine at least one of: (i) the subject's heart disease status, (ii) whether the subject has heart disease (e.g., a heart disorder or condition), or (iii) a probability that the subject has heart disease.

[0157] Additionally or alternatively, the computer system 1100 can provide a readout (e.g., via the input / output device 1140) that includes a biomarker score and / or an indication of heart disease (e.g., a likelihood or probability that the subject is experiencing or is at risk of heart disease, as determined from the biomarker score). The readout can include a recommended next step for the subject associated with the received biomarker profile and / or a confidence level associated with the calculated biomarker score. For example, a biomarker score within a first range or exceeding a first threshold can be associated with a next step of retesting after a recommended time period has elapsed to provide a comparative analysis to determine trends in NfL levels that may indicate an increased risk of heart disease, as explained in more detail below with reference to method 1300.

[0158] In another example, a biomarker score within a second range or exceeding a second threshold (e.g., above the first threshold) can be associated with a next step of confirmatory testing (e.g., echocardiography, cardiac magnetic resonance imaging (CMR), scintigraphy, and / or cardiac biopsy). In this example, the biomarker score may indicate that the subject may be experiencing a heart attack. Other types of indicia and suggested next steps may also be used in other examples.

[0159] exist Figure 3 In the example method 1300 illustrated in FIG, in step 1310, the computer system 1100 detects biomarker levels in a sample (e.g., from a subject), including at least NfL levels. For example, the NfL levels can be detected using the analysis equipment 1160 of the computer system 1100, but other methods for detecting biomarker levels can also be used in other examples.

[0160] In step 1320 , the computer system 1100 uses the NfL level to obtain a biomarker profile for the subject corresponding to the sample.

[0161] In step 1330, the computer system 1100 calculates a biomarker score based on the biomarker profile. The biomarker score can be calculated based on (i) the biomarker profile and (ii) demographic factors and / or imaging-based biomarkers. In some examples, a trained machine learning model can be used to generate a biomarker score for the subject, as explained in more detail above with reference to method 1200.

[0162] In step 1340, the computer system 1100 stores the calculated biomarker score, such as in the storage device 1130, for example. Additionally or alternatively, the computer system 1100 can provide a readout including the biomarker score, such as via the input / output device 1140, for example. The readout can also include suggested next steps for the subject associated with the received biomarker profile and / or the confidence level associated with the calculated biomarker score, as explained in more detail above.

[0163] In some examples, the sample from which the biomarker level is detected in step 1310 is a second sample or a subsequent sample in a series of samples that is obtained from the subject and analyzed according to method 1200 or 1300. In this example, a biomarker score and / or an indication of a heart disease can be generated based on a comparison of the NfL level detected in the second sample obtained from the subject at a second time later than the first time the first sample was obtained, with the NfL level detected from the first sample. In some examples, a biomarker score and / or an indication of a heart disease is generated based on the NfL level detected from the second sample exceeding the NfL level detected from the first sample (e.g., exceeding by a threshold amount, which can be, for example, any amount, percentage, or factor).

[0164] In other examples, computer system 1100 is configured to compare the change in NfL levels between the two samples to a second change in NfL determined for a control subject during a first time period corresponding to the difference between the times the two samples were obtained. In these examples, the control subject is optionally within three years of the subject and does not have heart disease, although other age differences may also be used.

[0165] In yet other examples, the computer system 1100 can be configured to analyze trends in NfL levels detected from a series of samples obtained from the subject to generate a biomarker score in step 1330 and / or generate an indication of heart disease in step 1340. By way of example only, a gradual increase in NfL over time can indicate the type of damage that accumulates as cerebral perfusion decreases due to decreased cardiovascular function. In other examples, other methods for generating a biomarker score in step 1330 and / or generating an indication of heart disease in step 1340 can also be used.

[0166] Illustrative embodiments of the disclosed technology may be executed by computer system 1100 locally or over a network. Figure 4An illustrative network environment 2400 is shown for use with an example of the technology described herein. Network environment 2400 may include one or more resource providers 2402a, 2402b, and 2402c (collectively, 2402). Each resource provider 2402 may include computing resources. In some embodiments, computing resources may include any hardware (e.g., a computing resource may be computer system 1100) and / or software for processing data according to any of the methods described herein, including methods 1200 and 1300. For example, computing resources may include hardware and / or software capable of executing algorithms, computer programs, and / or computer applications. In some embodiments, illustrative computing resources may include application servers and / or databases with storage and retrieval capabilities. Each resource provider 2402 may be connected to any other resource provider 2402 in network environment 2400. In some embodiments, resource providers 2402 may be connected via a computer network 2408. Each resource provider 2402 may be connected via a computer network 2408 to one or more computing devices 2404a, 2404b, and 2404c (collectively, 2404).

[0167] Network environment 2400 may include a resource manager 2406. Resource manager 2406 may connect to resource providers 2402 and computing devices 2404 via a computer network 2408. In some implementations, resource manager 2406 may facilitate the provision of computing resources by one or more resource providers 2402 to one or more computing devices 2404. Resource manager 2406 may receive a request for computing resources from a particular computing device 2404. Resource manager 2406 may identify one or more resource providers 2402 that can provide the computing resources requested by computing device 2404. Resource manager 2406 may select a resource provider 2402 to provide the computing resources. Resource manager 2406 may facilitate connections between resource providers 2402 and a particular computing device 2404. In some implementations, resource manager 2406 may establish a connection between a particular resource provider 2402 and a particular computing device 2404. In some implementations, resource manager 2406 may redirect a particular computing device 2404 to a particular resource provider 2402 that has the requested computing resources.

[0168] Example Example 1: Machine Learning Model Performance Results A machine learning model was trained and evaluated for its performance in classifying subjects as having or not having heart disease. Biomarker profiles, including NfL measurements, were obtained for a population of subjects of varying race / ethnicity, sex, age, and geographic location. The population included subjects diagnosed with heart disease and normal subjects. A bootstrapped forest machine learning model was trained and evaluated using k-fold cross-validation (k=5). Results were presented in Figure 6 Shown is a box plot of the area under the receiver operating characteristic (ROC) curve (AUC) on the y-axis (with 95% confidence interval diamonds shown). Figure 5 The figure shows that, averaged across training folds, the NfL-based machine learning model is able to distinguish patients with heart disease from those without.

[0169] Example 2: Relationship between NfL and age Biomarker profiles including NfL levels were obtained for a population of subjects with different cardiac statuses: subjects had ATTR-CM, non-ATTR-CM HF-PEF, or normal cardiac function. Figure 6 The figure shows that NfL levels in the blood increase with age in subjects with ATTR-CM and non-ATTR-CM HF-PEF, but do not increase with age in subjects with normal cardiac function, at least in older subjects (i.e., approximately 60 years of age and older). Therefore, changes in NfL levels over time can be used to predict whether an individual has heart disease and / or monitor the progression of heart disease. For example, a subject's NfL levels can be monitored periodically (such as annually) for comparison. Monitoring can begin after a specific age at which NfL levels are expected to increase over time, such as at least 40 years of age, at least 50 years of age, at least 60 years of age, or at least 65 years of age, although other threshold ages can also be used.

[0170] Although various illustrative embodiments incorporating the principles of the present teachings have been disclosed, the present teachings are not limited to the disclosed embodiments. Rather, this application is intended to cover any variations, uses, or adaptations of the present teachings, and to employ their general principles. Furthermore, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which these teachings pertain.

[0171] In the above detailed description, reference is made to the accompanying drawings, which form a part thereof. In the accompanying drawings, similar reference numerals typically identify similar components, unless the context indicates otherwise. The illustrative embodiments described in this disclosure are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the various features of the present disclosure, as generally described herein and illustrated in the accompanying drawings, may be arranged, substituted, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein.

[0172] Aspects of the present technical solution are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the technical solution. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0173] These computer-readable program instructions can be provided to a processor of a special-purpose computer or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device create instructions for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that can direct the computer, programmable data processing device, and / or other equipment to operate in a specific manner, such that the computer-readable storage medium having the instructions stored therein comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0174] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, such that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0175] As used herein, the terms "worker," "algorithm," "system," "module," "engine," or "architecture" (if used herein) are not intended to limit any particular implementation for implementing and / or performing the actions, steps, processes, etc. attributable to and / or performed thereby. An algorithm, system, module, engine, and / or architecture may be, but is not limited to, software, hardware, and / or firmware, or any combination thereof, that performs a specific function, including but not limited to any use of general-purpose and / or special-purpose processors in conjunction with appropriate software loaded or stored in a machine-readable memory and executed by the processor. Furthermore, unless otherwise specified, any name associated with a particular algorithm, system, module, and / or engine is for convenience of reference and is not intended to limit a particular implementation. Additionally, any functionality attributable to an algorithm, system, module, engine, and / or architecture may be equivalently performed by multiple algorithms, systems, modules, engines, and / or architectures, which may be incorporated into and / or combined with the functionality of another algorithm, system, module, engine, and / or architecture of the same or different type, or distributed across one or more algorithms, systems, modules, engines, and / or architectures in various configurations.

[0176] The flowcharts and block diagrams in the various figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present technical solutions. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may not occur in the order noted in the figures. For example, depending on the functions involved, two blocks shown in succession may actually be executed substantially simultaneously, or the blocks may sometimes be executed in the reverse order. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions or implements a combination of dedicated hardware and computer instructions.

[0177] A second action can be said to be "responsive to" a first action, regardless of whether the second action is directly or indirectly caused by the first action. A second action can occur at a substantially later time than the first action and still be responsive to the first action. Similarly, a second action can be said to be responsive to the first action even if intervening actions occur between the first and second actions, and even if one or more of the intervening actions directly cause the second action to be performed. For example, if a first action sets a flag, and a third action later initiates a second action whenever the flag is set, the second action can be responsive to the first action.

[0178] The present disclosure is not limited to the specific embodiments described in this application, which are intended to serve as illustrations of various features. As will be apparent to those skilled in the art, many modifications and variations can be made without departing from its spirit and scope. In addition to those listed herein, functionally equivalent methods and devices within the scope of the present disclosure will become apparent to those skilled in the art based on the foregoing description. It should be understood that the present disclosure is not limited to specific methods, reagents, compounds, compositions or biological systems, which can certainly vary. It should also be understood that the terms used herein are only for the purpose of describing specific embodiments and are not intended to be restrictive.

[0179] With respect to the use of substantially any plural and / or singular terms herein, those skilled in the art can translate from the plural to the singular and / or from the singular to the plural as appropriate to the context and / or application. For clarity, various singular / plural arrangements may be expressly set forth herein.

[0180] Those skilled in the art will understand that, in general, the terms used herein are generally intended to be "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "comprising" should be interpreted as "including but not limited to," etc.). Although various compositions, methods, and apparatuses are described in terms of "including" various components or steps (interpreted as "including but not limited to"), the compositions, methods, and apparatuses may also "consist essentially of" or "consist of" the various components and steps, and such terms should be interpreted as defining substantially closed groups of members.

[0181] As used herein, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Nothing in this disclosure should be construed as an admission that the embodiments described in this disclosure are not entitled to antedate such disclosure by virtue of prior invention.

[0182] In addition, even if specific numbers are explicitly recited, those skilled in the art will recognize that such recitation should be interpreted as meaning at least the recited number (e.g., the most basic recitation of "two statements" without other modifiers means at least two statements, or two or more statements). Moreover, in those instances where a convention similar to "at least one of A, B, and C, etc." is used, such construction is generally intended to be in the sense that one skilled in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). In those instances where a convention similar to "at least one of A, B, or C, etc." is used, such construction is generally intended to be in the sense that one skilled in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). Those skilled in the art will also understand that virtually any disjunctive word and / or phrase presenting two or more alternative terms (whether in the specification, sample examples, or figures) should be understood to contemplate the possibility of including one, either, or both of the terms. For example, the phrase "A or B" will be understood to include the possibility of "A" or "B" or "A and B."

[0183] In addition, where features of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0184] As will be understood by those skilled in the art, for any and all purposes, such as in providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations thereof. Any listed range can be easily identified as fully describing and enabling the same range to be decomposed into at least equal halves, one-third, one-quarter, one-fifth, one-tenth, etc. As non-limiting examples, each range discussed herein can be easily decomposed into lower one-third, middle one-third, and upper one-third, etc. As will be understood by those skilled in the art, all languages ​​such as "up to," "at least," etc. include the enumerated numbers and refer to the ranges that can subsequently be decomposed into subranges, as discussed above. Finally, as will be understood by those skilled in the art, ranges include each individual member. Therefore, for example, a group with 1-3 components refers to a group with 1, 2, or 3 components. Similarly, a group with 1-5 components refers to a group with 1, 2, 3, 4, or 5 components, etc.

[0185] The various and other features and functions disclosed above, or alternatives thereof, may be combined into many other different systems or applications. Various currently unforeseen or unanticipated substitutions, modifications, variations, or improvements may subsequently be made by those skilled in the art, each of which is also intended to be encompassed by the disclosed embodiments.

Claims

1. A method for predicting heart disease, the method being implemented by a computer system and comprising: obtaining a biomarker profile comprising first levels of one or more biomarkers in a first sample obtained from a subject, wherein the one or more biomarkers comprise neurofilament light chain (NfL); applying a first machine learning model to inputs to generate a biomarker score for the subject, wherein the inputs include at least the biomarker profile, and the biomarker score indicates a probability that the subject is at risk for or experiencing heart disease; as well as The biomarker score or an indication of heart disease determined based on the biomarker score is output to a computing device via one or more communication networks.

2. The method of claim 1 , further comprising obtaining demographic data corresponding to one or more demographic factors of the subject from the computing device and via the communication network, wherein The input further includes the demographic data, and the demographic factors include at least one or more of the subject's gender or age.

3. The method of any one of claims 1-2, further comprising obtaining one or more imaging-based biomarkers of the subject from the computing device and via the communication network, wherein The input further includes the imaging-based biomarker, and the imaging-based biomarker includes at least left ventricular septal wall thickness or ejection fraction.

4. The method according to claim 3, further comprising: To determine the imaging-based biomarker, one or more acquired images associated with the subject are analyzed to obtain one or more measurements, wherein the analyzing comprises one or more of the following: pattern recognition performed on the acquired image; or Segmentation and classification using a first machine learning model or a second machine learning model of one or more features extracted from the obtained image.

5. The method according to any one of claims 1 to 4, wherein The first machine learning model is derived from a bagging method, a boosting method, or an additive decision tree method, a neural boosting method, a bootstrapped forest method, a boosted tree method, or a support vector machine method.

6. The method according to any one of claims 1 to 5, wherein The first machine learning model is a classifier for heart disease, and the method further includes classifying the subject as having or not having heart disease based on the biomarker score, wherein the indication of heart disease comprises a binary indication determined based on the classification, and one or more of a sensitivity or a specificity of the classifier is greater than eighty percent.

7. The method according to any one of claims 1 to 6, wherein The subject is a human subject, and the first sample comprises one or more of blood, serum, plasma, or cardiac tissue.

8. The method of any one of claims 1-7, further comprising determining at least one of the following based on the biomarker score: (i) the subject's heart disease status, (ii) whether the subject has heart disease, or (iii) the probability that the subject has heart disease.

9. The method according to any one of claims 1 to 8, further comprising training a first machine learning model using the obtained training data, the training data comprising biomarker profiles of a plurality of subjects and health states of the subjects corresponding to the biomarker profiles, wherein Each of the biomarker profiles includes levels of two or more biomarkers, including at least NfL.

10. The method according to any one of claims 1 to 9, wherein A first sample is obtained from the subject at a first time, and the method further comprises determining that the subject is at risk for or suffering from heart disease when a second NfL level detected in a second sample obtained from the subject at a second time later than the first time exceeds the first NfL level by a threshold amount.

11. The method according to any one of claims 1 to 10, wherein A first sample is obtained from the subject at a first time, and the method further includes determining whether the subject is at risk for or suffering from heart disease based on a first NfL level change, the first NfL level change being determined based on a comparison of the first NfL level with a second NfL level detected in a second sample obtained from the subject at a second time later than the first time.

12. The method of claim 11, further comprising comparing the first change in NfL level to a second change in NfL, the second change in NfL determined for a control subject over a first time period corresponding to a difference between the second time and the first time, wherein The control subject is within three years of the subject's age and does not suffer from heart disease.

13. The method of any one of claims 1-12, further comprising determining that the subject is at risk for or experiencing a heart disease based on a change in a second NfL level determined over a second time period, during which a series of samples are obtained from the subject based on a third NfL level detected in each sample in the series, wherein The series of samples includes a first sample.

14. A non-transitory computer-readable medium comprising executable instructions that, when executed by a processor, cause the processor to carry out the method according to any one of claims 1 to 13.

15. A computer system comprising a memory and a processor, wherein: The memory includes a first machine learning model and has executable instructions stored thereon that, when executed by the processor, cause the computer system to perform the method according to any one of claims 1-13.

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