Biomarker composition and method of use thereof

Abstract

This disclosure provides methods and kits for identifying and treating individuals at risk of or suffering from amyloid-trans-tyretin cardiomyopathy. Generally, the detection or measurement of one or more biomarkers, such as TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof, aids in identifying amyloid-trans-tyretin cardiomyopathy. This disclosure also provides methods for selecting patients for the treatment of amyloid-trans-tyretin cardiomyopathy, for example, with trans-tyretin stabilizers.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims the benefit of U.S. Provisional Patent Application No. 63 / 490,481, filed on 15 March 2023, the disclosure of which is incorporated herein by reference in its entirety.

[0002] Sequence listing cross-references This application includes a computer-readable sequence listing file titled "ATTRSeqList.xml," created on February 23, 2024, which is 12 KB in size. This computer-readable format is incorporated herein by reference.

[0003] This disclosure generally pertains to the fields of molecular biology and cardiovascular health. [Background technology]

[0004] Amyloid-trans-tiretin cardiomyopathy (TTR-CM) is a rare condition resulting from the misfolding of the trans-tiretin protein (TTR), leading to the deposition of amyloid fibrils in cardiac tissue and ultimately to heart failure. Therefore, early treatment of TTR-CM is crucial for improving the prognosis in affected individuals. However, screening for wild-type trans-tiretin amyloidosis (ATTRwt) involves multiple costly and invasive procedures that are not particularly specific to TTR-CM (including TTR-CM resulting from wild-type ATTR amyloidosis), which can lead to delayed treatment and problems. Furthermore, there are no non-invasive in vitro screening tests to detect and / or identify amyloid-trans-tiretin cardiomyopathy (TTR-CM) resulting from wild-type ATTR amyloidosis (ATTRwt). [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] The difficulty in identifying TTR-CM caused by ATTRwt using current methods may be a contributing factor to the general underdiagnosis of this disease. Therefore, there is a need in the art for compositions, kits, and methods to detect TTR-CM, particularly TTR-CM caused by wild-type ATTR amyloidosis, with acceptable levels of specificity. Furthermore, there is a constant need for compositions, assays, apparatus, and methods to test, identify, or stage individuals or populations with misfolded TTR proteins. [Means for solving the problem]

[0006] This disclosure provides compositions, kits, and methods for detecting and / or identifying TTR-CM, particularly TTR-CM resulting from wild-type ATTR amyloidosis. Furthermore, this disclosure provides methods for classifying TTR-CM patients (including TTR-CM resulting from wild-type ATTR amyloidosis) compared to individuals experiencing normal conditions or other cardiac conditions. Furthermore, this disclosure is the first to recognize that certain biomarkers (e.g., ATTR biomarkers, e.g., troponin I (TnI), pyruvate kinase muscle isoform 1 (PKM1), pyruvate kinase muscle isoform 2 (PKM2), N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), retinol-binding protein 4 (RBP4), decorin (DCN), tissue metalloproteinase inhibitor 2 (TIMP2), SPARC-related modular calcium binding 2 (SMOC-2), neurofilament light chain (NfL), and combinations thereof) can help detect and / or diagnose TTR-CM, and / or help classify patients with TTR-CM. This disclosure further recognizes that these biomarkers in compositions, kits, and methods (e.g., ATTR biomarkers, e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) may be useful for classifying, detecting, and / or diagnosing TTR-CM (including TTR-CM resulting from wild-type ATTR amyloidosis) without the need to perform invasive or costly tests. This represents a significant advance in patient care, as TTR-CM resulting from ATTRwt can be detected and identified in a way that is more comfortable for the patient, causes less harm to the patient, and / or reduces the amount of time the patient needs to recover after the detection and / or diagnostic method.

[0007] Furthermore, this disclosure provides that certain biomarkers (e.g., ATTR biomarkers, e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) are useful for detecting TTR-CM with improved sensitivity. In particular, this disclosure provides that TnI, RBP4, and NT-proBNP are an unexpectedly excellent combination for detecting TTR-CM with improved sensitivity and / or specificity. This disclosure further provides other combinations of ATTR biomarkers that are particularly useful for detecting TTR-CM with improved sensitivity and / or specificity. Such combinations include, for example: (1) TnI, RBP4, and TIMP2; (2) TnI, NT-proBNP, and DCN; (3) TnI, NT-proBNP, and TIMP2; and (4) TnI, RBP4, and DCN. This disclosure further provides that a combination of one or more demographic factors, particularly age and / or sex, and two or more ATTR biomarkers, particularly TnI and RBP4, and optionally further NT-proBN, yields unexpectedly favorable results in detecting TTR-CM with improved sensitivity and / or specificity. In some embodiments, a combination of one or more demographic factors and two or more ATTR biomarkers can be used to detect TTR-CM as effectively as a combination of three or more ATTR biomarkers. A combination of one or more demographic factors and two or more ATTR biomarkers can be used, for example, when data (e.g., levels) for a third ATTR biomarker are unavailable or assays for a third ATTR biomarker are not reproducible.

[0008] The increased sensitivity achieved using the ATTR biomarkers described herein reduces the number of false negatives obtained when detecting and / or identifying TTR-CM. This reduction in false negatives, in turn, helps ensure that more TTR-CM patients receive earlier treatment, which is crucial for reducing TTR-CM-related signs, symptoms, and conditions, and promotes long-term survival for TTR-CM patients.

[0009] Among many methods, this disclosure provides a method comprising the step of detecting the respective levels of two or more trans tyretin amyloidosis (ATTR) biomarkers in a sample. In some embodiments, the sample is obtained from a subject.

[0010] The disclosure also provides a method comprising (a) detecting the levels of two or more ATTR biomarkers in a sample obtained from a subject to obtain an ATTR biomarker profile, and (b) using the ATTR biomarker profile to calculate an ATTR biomarker score using a computer.

[0011] In some embodiments of the methods provided herein, two or more ATTR biomarkers include (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), (v) retinol-binding protein 4 (RBP4), (vi) tissue metalloproteinase inhibitor 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

[0012] In some embodiments, two or more ATTR biomarkers are TnI and PKM1, or include these. In some embodiments, two or more ATTR biomarkers are TnI and PKM2, or include these. In some embodiments, two or more ATTR biomarkers are TnI, PKM1, and PKM2, or include these.

[0013] In some embodiments, two or more ATTR biomarkers are TnI, PKM2, NT-proBNP, and RBP4, or include these. In some embodiments, two or more ATTR biomarkers are TnI, PKM2, and NT-proBNP, or include these. In some embodiments, two or more ATTR biomarkers are TnI, PKM2, and RBP4, or include these. In some embodiments, two or more ATTR biomarkers are TnI, PKM1, and NT-proBNP, or include these. In some embodiments, two or more ATTR biomarkers are TnI, PKM1, PKM2, NT-proBNP, and RBP4, or include these. In some embodiments, two or more ATTR biomarkers are TnI, PKM1, PKM2, NT-proBNP, and RBP4, or include these. In some embodiments, two or more ATTR biomarkers are TnI, PKM1, PKM2, and NT-proBNP, or include these. In some embodiments, two or more ATTR biomarkers are TnI, PKM1, PKM2, and RBP4, or include these. In some embodiments, two or more ATTR biomarkers are RBP4, SMOC-2, and TnI, or include these. In some embodiments, two or more ATTR biomarkers are DCN, NT-proBNP, and TnI, or include these. In some embodiments, two or more ATTR biomarkers are DCN, RBP4, and TnI, or include these. In some embodiments, two or more ATTR biomarkers are NT-proBNP, SMOC-2, and TnI, or include these. In some embodiments, two or more ATTR biomarkers are NT-proBNP, TIMP2, and TnI, or include these. In some embodiments, two or more ATTR biomarkers are NT-proBNP and TnI, or include these. In some embodiments, two or more ATTR biomarkers are DCN and TnI, or include these. In some embodiments, two or more ATTR biomarkers are DCN, TIMP2, and TnI, or include these.In some embodiments, two or more ATTR biomarkers are RBP4 and TnI, or include these. In some embodiments, two or more ATTR biomarkers are RBP4, TIMP2 and TnI, or include these. In some embodiments, two or more ATTR biomarkers are DCN, SMOC-2 and TnI, or include these. In some embodiments, two or more ATTR biomarkers are TIMP2 and TnI, or include these. In some embodiments, two or more ATTR biomarkers are SMOC-2, TIMP2 and TnI, or include these. In preferred embodiments, two or more ATTR biomarkers are TnI and RBP4, more preferably NT-proBNP, RBP4 and TnI, or include these.

[0014] In some embodiments, two or more ATTR biomarkers do not include PKM1. In some embodiments, two or more ATTR biomarkers do not include PKM2. In some embodiments, two or more ATTR biomarkers do not include either PKM1 or PKM2.

[0015] In some embodiments, two or more ATTR biomarkers do not contain SMOC-2. In some embodiments, two or more ATTR biomarkers do not contain DCN. In some embodiments, two or more ATTR biomarkers do not contain either SMOC-2 or DCN.

[0016] In some embodiments, the step of using an ATTR biomarker profile to computer-calculate the score of an ATTR biomarker includes the step of applying an algorithm to the ATTR biomarker profile to computer-calculate the score of an ATTR biomarker. In some embodiments, the algorithm is or derived from decision trees, neural boosting, bootstrap forests, boost trees, K-nearest neighbors, generalized regression pruned forward selection methodology, fit stepwise methodology, generalized regression lasso, generalized regression elastic nets, generalized regression ridges, nominal logistic regression, support vector machines, discriminant methods, naive Bayes, or a combination thereof. In some embodiments, the algorithm is a decision tree, neural boost, bootstrap forest, boost tree, support vector machine, or a combination thereof, or derived from these.

[0017] In some embodiments, the methods described herein include determining, using an ATTR biomarker score, whether a subject from whom a sample was obtained is at risk of or has transthyretin amyloid cardiomyopathy (TTR-CM). In some embodiments, the methods described herein include diagnosing a subject with TTR-CM using an ATTR biomarker score. In some embodiments, the methods described herein include determining, using an ATTR biomarker score, whether a subject from whom a sample was obtained is selected for one or more cardiomyopathy tests. In some embodiments, the methods described herein include determining, using an ATTR biomarker score, whether a subject from whom a sample was obtained is selected to receive one or more doses of a TTR stabilizer.

[0018] In some embodiments, the subject is a human subject.

[0019] In some embodiments, the sample includes blood, serum, plasma, or heart tissue.

[0020] The present disclosure also provides a non-transitory computer-readable medium. In some embodiments, the non-transitory computer-readable medium includes executable instructions that, when executed, cause a processor to perform operations including the methods described herein.

[0021] Furthermore, the present disclosure provides a composition. In some embodiments, the composition includes one or more ATTR biomarkers. In some embodiments, the one or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or combinations thereof. In some embodiments, the one or more ATTR biomarkers are or include TnI, RBP4, and NT-proBNP.

[0022] In some embodiments, the composition comprises one or more anti-ATTR biomarker drugs. In some embodiments, one or more anti-ATTR biomarker drugs are or comprise an anti-TnI drug, an anti-PKM1 drug, an anti-PKM2 drug, an anti-NT-proBNP drug, an anti-RBP4 drug, an anti-TIMP2 drug, an anti-NfL drug, or a combination thereof. In some embodiments, one or more anti-ATTR biomarker drugs are or comprise an anti-TnI drug and an anti-RBP4 drug. In some embodiments, one or more anti-ATTR biomarker drugs are or comprise an anti-TnI drug, an anti-RBP4 drug, and an anti-NT-proBNP drug.

[0023] This disclosure further provides kits. In some embodiments, the kit includes one or more ATTR biomarkers. In some embodiments, one or more ATTR biomarkers are TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof, or include these. In some embodiments, one or more ATTR biomarkers are TnI and RBP4, or include these. In some embodiments, one or more ATTR biomarkers are TnI, RBP4, and NT-proBNP, or include these.

[0024] In some embodiments, the kit includes one or more anti-ATTR biomarker drugs. In some embodiments, one or more anti-ATTR biomarker drugs are or include anti-TnI drugs, anti-PKM1 drugs, anti-PKM2 drugs, anti-NT-proBNP drugs, anti-RBP4 drugs, anti-TIMP2 drugs, anti-NfL drugs, or a combination thereof. In some embodiments, one or more anti-ATTR biomarker drugs are or include anti-TnI drugs and anti-RBP4 drugs. In some embodiments, one or more anti-ATTR biomarker drugs are or include anti-TnI drugs, anti-RBP4 drugs and anti-NT-proBNP drugs.

[0025] In some embodiments, the kit includes instructions for use.

[0026] In some embodiments, the kit comprises one or more anti-ATTR biomarker drugs, and one or more anti-ATTR biomarkers comprises one or more antibody drugs. In some embodiments, one or more of the antibody drugs are labeled with a detectable portion.

[0027] In some embodiments, the kit includes one or more control samples. In some embodiments, the control samples include one or more ATTR biomarker standards.

[0028] This disclosure provides the use of the kit described herein. In some embodiments, the kit can be used in an in vitro diagnostic assay for diagnosing TTR-CM in a subject. [Brief explanation of the drawing]

[0029] [Figure 1] This figure shows the patient cohort used in the exemplary methods described herein. [Figure 2] This figure shows an overview of the data obtained from the exemplary methods described herein. [Figure 3] Figure 3A is a diagram containing graphs showing the performance of individual sensitivities and specificities determined by TIMP2. Figure 3B is a diagram containing graphs showing the performance of individual sensitivities and specificities determined by TnI. [Figure 4] Figure 4A is a diagram containing graphs showing the performance of individual sensitivities and specificities determined by PKM. Figure 4B is a diagram containing graphs showing the performance of individual sensitivities and specificities determined by RBP4. [Figure 5] Figure 5A is a diagram containing graphs showing the performance of individual sensitivities and specificities determined by RBP. Figure 5B is a diagram containing graphs showing the performance of individual sensitivities and specificities determined by DCN. [Figure 6]Figure 6A is a diagram containing graphs showing the individual sensitivity and specificity performance determined by NT-proBNP. Figure 6B is a diagram containing graphs showing the individual sensitivity and specificity performance determined by SMOC-2. [Figure 7] This figure includes bar graphs showing the effects of representative biomarkers on each NYHA class. [Figure 8-1] This figure includes a table showing the results of screening using the exemplary biomarkers described herein, including an evaluation of the regression fit used. [Figure 8-2] Continuation of Figure 8-1. [Figure 9] This figure includes a table summarizing the data obtained when a selected subset of biomarkers was used in various predictive models. [Figure 10] This figure shows the optimization of detection cutoff values ​​for assays measuring PKM, TIMP2, LIMS-1, C3, and A11. [Figure 11] This is a diagram illustrating an exemplary block diagram of computer system 1100. [Figure 12] This is a diagram illustrating an exemplary flowchart of Method 1200. [Figure 13] This is a diagram illustrating an exemplary flowchart of Method 1300. [Figure 14-1] This figure includes a table showing the signal-to-noise ratio (S / N) of the selected markers. [Figure 14-2] Continuation of Figure 14-1. [Figure 14-3] Continuation of Figure 14-2. [Figure 14-4] Continuation of Figure 14-3. [Figure 14-5] Continuation of Figure 14-4. [Figure 14-6] Continuation of Figure 14-5. [Figure 14-7] Continuation of Figure 14-6. [Figure 14-8] Continuation of Figure 14-7. [Figure 14-9] Continuation of Figure 14-8. [Figure 14-10] Continuation of Figure 14-9. [Figure 14-11] Continuation of Figure 14-10. [Figure 14-12] Continuation of Figure 14-11. [Figure 14-13] Continuation of Figure 14-12. [Figure 14-14] Continuation of Figure 14-13. [Figure 14-15] Continuation of Figure 14-14. [Figure 14-16] Continuation of Figure 14-15. [Figure 14-17] Continuation of Figure 14-16. [Figure 14-18] Continuation of Figure 14-17. [Figure 14-19] Continuation of Figure 14-18. [Figure 15] This graph shows plots illustrating the performance of machine learning-based algorithms in classifying TTR-CM using various ATTR biomarkers, and in some cases, one or more demographic factors. [Figure 16] This graph shows plots illustrating the performance of machine learning-based algorithms in classifying TTR-CM using various ATTR biomarkers, and in some cases, one or more demographic factors. [Figure 17] This graph shows plots illustrating the performance of machine learning-based algorithms in classifying TTR-CM using various ATTR biomarkers, and in some cases, one or more demographic factors. [Figure 18] This graph shows plots illustrating the performance of machine learning-based algorithms in classifying TTR-CM using various ATTR biomarkers, and in some cases, one or more demographic factors. [Figure 19] This graph shows plots illustrating the performance of machine learning-based algorithms in classifying TTR-CM using various ATTR biomarkers, and in some cases, one or more demographic factors. [Figure 20]This graph shows plots illustrating the performance of machine learning-based algorithms in classifying TTR-CM using various ATTR biomarkers, and in some cases, one or more demographic factors. [Figure 21] This graph shows plots illustrating the performance of machine learning-based algorithms in classifying TTR-CM using various ATTR biomarkers, and in some cases, one or more demographic factors. [Figure 22] Figures 15-21 are tables showing the patient cohorts used to train and analyze the machine learning algorithms whose performance is demonstrated. [Figure 23] This graph shows plots illustrating the performance of various biomarkers, including a bootstrap Mori machine learning model that uses a combination of ATTR biomarkers (TnI, NT-proBNP, and RBP4), when predicting whether a subject has TTR-CM. [Figure 24] This graph shows plots illustrating the relative contributions and proportions of various individual biomarkers. [Figure 25] This graph shows plots illustrating the relative contributions and proportions of various individual biomarkers. [Figure 26] This graph shows plots illustrating the performance of various biomarkers, including a bootstrap Mori machine learning model that uses combinations of ATTR biomarkers (TnI, NT-proBNP, and RBP4), when categorizing subjects as TTR-CM, non-TTR-CM with HF-PEF, or normal. [Figure 27] This graph shows plots illustrating the performance of various biomarkers, including a bootstrap Mori machine learning model using a combination of ATTR biomarkers (TnI, NT-proBNP, and RBP4), when predicting New York Heart Association (NYHA) class. [Figure 28] This is a block diagram of an example network environment for use in the methods and systems described herein. [Figure 29]This is a block diagram of an example of a computer computing device and a mobile computer computing device. [Modes for carrying out the invention]

[0030] definition Antibody Drugs: As used herein, the term “antibody drug” refers to a drug that specifically binds to a particular antigen. In some embodiments, the term encompasses any polypeptide or polypeptide complex containing immunoglobulin structural elements sufficient to provide specific binding. Such polypeptides may be naturally occurring (e.g., produced by organisms that react to antigens) or produced by recombinant operations, chemical synthesis, or other artificial systems or methods. Exemplary antibody drugs include, but are not limited to, human antibodies, primate-like antibodies, chimeric antibodies, bispecific antibodies, humanized antibodies, conjugated antibodies (e.g., antibodies conjugated or fused with other proteins, radiolabeled, or cytotoxic substances), Small Modular ImmunoPharmaceuticals ("SMIPs"); single-chain antibodies; cameloid antibodies; and antibody fragments. As used herein, the term “antibody drug” includes intact monoclonal antibodies, polyclonal antibodies, single-domain antibodies (e.g., shark single-domain antibodies (e.g., IgNAR or its fragments)), multispecific antibodies formed from at least two intact antibodies (e.g., bispecific antibodies), and antibody fragments, provided they exhibit the desired biological activity. Antibody drugs may have antibody constant region sequences characteristic of mouse, rabbit, primate, or human antibodies. In some embodiments, the term encompasses staple peptides. In some embodiments, the term encompasses one or more antibody-like conjugated peptide mimetic drugs. In some embodiments, the term encompasses one or more antibody-like conjugated scaffold proteins. In some embodiments, the term encompasses monobodies or adnectins.In many embodiments, the antibody drug is a polypeptide whose amino acid sequence contains one or more structural elements recognized by those skilled in the art as complementarity-determining regions (CDRs), or comprises such polypeptide; in some embodiments, the antibody drug is a polypeptide whose amino acid sequence contains at least one CDR (e.g., at least one heavy-chain CDR and / or at least one light-chain CDR) substantially identical to that found in a reference antibody, or comprises such polypeptide. In some embodiments, the antibody drug is a polypeptide whose amino acid sequence contains structural elements recognized by those skilled in the art as immunoglobulin variable domains, or comprises such polypeptide. In some embodiments, the antibody drug is a polypeptide protein having a binding domain that is homologous to, or largely homologous to, an immunoglobulin-binding domain. In some embodiments, the antibody drug may include covalent modifications (e.g., glycans, payloads (e.g., detectable moieties, therapeutic moieties, catalytic moieties, etc.) or other pendant groups (e.g., attachments such as polyethylene glycol).

[0031] Biomarker: The terms “biomarker” or “biological marker” are used herein, not inconsistently with their use in the art, to refer to entities whose presence, level, or form correlates with a particular biological event or situation of interest, and which are consequently considered “markers” of that event or situation. To give a few examples, in some embodiments, a biomarker may be, or may contain, a marker for a particular pathological condition, or for the likelihood of a particular disease, disorder, or condition occurring, developing, or recurring. In some embodiments, a biomarker may be, or may contain, a marker for a particular disease or treatment outcome, or the likelihood thereof. Thus, in some embodiments, a biomarker is a prediction of the relevant biological event or situation of interest, in some embodiments, a biomarker is a prognostic sign of the biological event or situation, and in some embodiments, a biomarker is a diagnosis of the biological event or situation. In some embodiments, a biomarker is a possible biomarker of the relevant biological event or situation of interest. A biomarker may be an entity of any class of chemical substances. For example, in some embodiments, a biomarker may be, or may contain, nucleic acids, polypeptides, small molecules, or combinations thereof. In some embodiments, the biomarker is a cell surface marker. In some embodiments, the biomarker is intracellular. In some embodiments, the biomarker is found in a specific tissue (e.g., cardiac tissue). In some embodiments, the biomarker is found extracellularly (e.g., secreted, or otherwise produced or present extracellularly in body fluids such as blood, urine, tears, saliva, cerebrospinal fluid, etc.).

[0032] As described herein, in some embodiments, the biomarker is an ATTR biomarker. As used herein, “ATTR biomarker” refers to a biological marker for ATTR amyloidosis or TTR-CM. In some embodiments, one or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. To avoid misunderstanding, an ATTR biomarker may be or may contain a gene product associated with a particular biomarker listed. For example, depending on the context, “TnI” refers to a nucleotide encoding TnI or a characteristic fragment thereof, as well as the TnI protein or a characteristic fragment thereof.

[0033] Characteristic Fragment: The term “characteristic fragment” refers to a fragment of a biomarker (e.g., an ATTR biomarker) that is sufficient to identify the biomarker from which the fragment originated. For example, in some embodiments, the “characteristic fragment” of a biomarker includes an amino acid sequence or a set of amino acid sequences that together enable the biomarker from which the fragment originated to be distinguished from other possible biomarkers, proteins, or polypeptides. In some embodiments, the characteristic fragment includes at least 10, at least 20, at least 30, at least 40, or at least 50 amino acids.

[0034] Gene product or expression product: As used herein, the term “gene product” generally refers to RNA transcribed from a gene (before and / or after processing) or polypeptides encoded by RNA transcribed from a gene (before and / or after modification).

[0035] Hybridization: The term "hybridization" refers to the physical properties of a single-stranded nucleic acid molecule (e.g., DNA or RNA) for annealing to a complementary nucleic acid molecule. Hybridization can be evaluated in a variety of contexts, including when interacting nucleic acid molecules are studied independently or in more complex system contexts (e.g., while covalently or otherwise bound to a carrier entity and / or within a biological system or cell). In some embodiments, hybridization can be detected by hybridization techniques, such as techniques selected from the group consisting of in situ hybridization (ISH), microarrays, Northern blotting, and Southern blotting. In some embodiments, hybridization refers to 100% annealing between the single-stranded nucleic acid molecule and the 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%, at least 70% of the single-stranded nucleic acid molecule anneals to the complementary nucleic acid molecule). Hybridization techniques and methods for evaluating hybridization are well known in the art. For example, see Sambrook et al., 1989, Molecular Cloning: A Laboratory Manual, 2nd edition, Cold Spring Harbor Press, Plainview, NY. Those skilled in the art understand how to estimate and adjust the stringency of hybridization conditions so that sequences with at least a desired level of complementarity stably hybridize, while those with lower complementarity do not. For examples of hybridization conditions and parameters, see, for example, Sambrook et al., 1989, Molecular Cloning: A Laboratory Manual, 2nd edition, Cold Spring Harbor Press, Plainview, NY; Ausubel, FM et al., 1994, Current Protocols in Molecular Biology. John Wiley & Sons, Secaucus, NJ.

[0036] 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 together with (e.g., conjugated to) 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, 89 Zr, etc.), fluorescent dyes, chemiluminescent agents (e.g., acridinum esters, stabilized dioxetanes, 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, dioxigenin, haptens, and proteins for which antisera or monoclonal antibodies are available.

[0037] Diagnostic Tests: As used herein, “diagnostic tests” are any one or more steps or steps taken to determine whether a patient has a disease, disorder or condition, and / or to obtain information useful in classifying a disease, disorder or condition into any category that is significant in terms of phenotypic categories, prognosis of the disease, disorder or condition, or possible response to treatment of the disease, disorder or condition (either general treatment or any specific treatment). Similarly, “diagnosis” means providing any type of diagnostic information, but not limited to, information useful in determining whether a subject has or is likely to develop a disease, disorder or condition, the present state, stage, or characteristics of a disease, disorder or condition manifested in the subject, information about the nature or classification of a tumor, information about prognosis, and / or information useful in selecting appropriate treatment or further diagnostic tests. Treatment selection may include selection of specific therapeutic agents or other treatments, such as surgery or radiation therapy, selection of whether to withhold or provide therapy, and selection of administration regimens (e.g., frequency or level of a specific therapeutic agent or combination of therapeutic agents in one or more doses). Selection of further diagnostic tests may include more specific tests for a given disease, disorder or condition.

[0038] Sample: As used herein, the term “sample” means a biological sample obtained from or derived from a human subject, as described herein. In some embodiments, a biological sample includes biological tissue or fluid. In some embodiments, a biological sample may include blood; blood cells; tissue or fine-needle biopsy samples; cell-containing fluids; suspended nucleic acids; cerebrospinal fluid; lymph; tissue biopsy specimens; surgical specimens; other fluids, secretions and / or excretions; and / or cells derived therefrom. In some embodiments, a biological sample includes cells obtained from an individual, for example, from a human or animal subject. In some embodiments, the cells obtained are cells derived from the individual from which the sample is obtained, or include such cells. In some embodiments, a sample is a “primary sample” obtained directly from the source of interest by any suitable means. For example, in some embodiments, a primary biological sample is obtained by a method selected from the group consisting of biopsy (e.g., fine-needle aspiration or tissue biopsy), surgery, or collection of a body fluid (e.g., blood). In some embodiments, a sample is cardiac tissue obtained from a subject. In some embodiments, as will be apparent from the context, 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 agents to the primary sample). For example, this is by filtration using a semipermeable membrane. Another example of sample processing is that the sample may be a plasma sample, treated with an anticoagulant selected from the group consisting of EDTA, heparin, and citrate. Another example of sample processing is that the sample may be processed to isolate one or more proteins (e.g., by capturing proteins with one or more antibodies). A “processed sample” may include, for example, nucleic acids or polypeptides extracted from the sample or obtained by subjecting the primary sample to techniques such as mRNA amplification or reverse transcription, isolation and / or purification of certain components.

[0039] Subject: As used herein, the term “subject” refers to a living organism, e.g., a mammal (e.g., human). In some embodiments, a human subject is an adult, adolescent, or child subject. In some embodiments, a subject is at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, or at least 80 years of age. In some embodiments, a subject suffers from a disease, disorder, or condition, e.g., a disease, disorder, or condition that can be treated as provided herein. In some embodiments, a subject is susceptible to a disease, disorder, or condition; in some embodiments, a susceptible subject is prone to a disease, disorder, or condition and / or exhibits an increased risk of developing a disease, disorder, or condition (compared to the mean risk observed in a reference subject or population). In some embodiments, a subject exhibits one or more symptoms of a disease, disorder, or condition. In some embodiments, a subject does not exhibit any specific symptoms or characteristics of a disease, disorder, or condition (e.g., clinical symptoms of a disease). In some embodiments, a subject does not exhibit any symptoms or characteristics of a disease, disorder, or condition. In some embodiments, a subject is a patient. In some embodiments, the subject is an individual who is being diagnosed and / or receiving treatment.

[0040] Threshold: As used herein, the term “threshold” refers to a value used as a reference to classify the results of information acquisition and / or measurements, e.g., the results of measurements acquired in an assay. A threshold may be determined based on one or more control samples. A threshold may be determined before the measurement of interest is performed, simultaneously with the measurement of interest is performed, or after the measurement of interest is performed. In some embodiments, a threshold may be a range of values. In some embodiments, a threshold may be a value (or range of values) reported in the relevant field (e.g., a value found in a standard table).

[0041] Detailed description of a particular embodiment Amyloid trans tyretin cardiomyopathy Trans thyroretin (TTR) is a transport protein found in serum and cerebrospinal fluid that transports the thyroid hormones thyroxine (T4) and retinol-binding proteins. The liver secretes TTR into the bloodstream, and the choroid plexus secretes TTR into the cerebrospinal fluid. TTR is produced as a homotetrameric complex. However, TTR can undergo conformational changes and aggregate into abnormal amyloid forms that can lead to pathological conditions.

[0042] ATTR amyloidosis is characterized by the deposition of amyloid fibrils derived from the trans tiretin (TTR) protein in various organs and tissues. For example, misfolding of the TTR protein can lead to the deposition of amyloid fibrils in cardiac tissue, resulting in cardiomyopathy (referred herein to as “amyloid-trans tiretin cardiomyopathy” or “TTR-CM”). Clinical manifestations of TTR-CM include increased ventricular wall thickness and heart failure.

[0043] There are three types of ATTR amyloidosis: (1) familial polymyloid neuropathy (FAP), (2) familial amyloid cardiomyopathy (FAC), and (3) senile systemic amyloidosis, also known as wild-type ATTR amyloidosis (ATTRwt). Familial polymyloid neuropathy (FAP) affects the nervous system, as well as the heart and sometimes the kidneys and eyes. Symptoms of FAP may include peripheral neuropathy, autonomic neuropathy, and heart failure. Familial amyloid cardiomyopathy (FAC) affects the heart and may manifest as carpal tunnel syndrome. FAP and FAC are hereditary conditions caused by mutations in the TTR gene that lead to the production of abnormal ("variant") TTR. More than 100 different mutations have been observed in the TTR gene, many of which lead to the production of variant TTRs that can misfold into mutated amyloid fibrils and cause aggregation of amyloid deposits in tissues. Most affected individuals are heterozygotes; therefore, both mutant TTR and wild-type TTR may be present in the aggregates. For the purposes of this specification, hereditary cardiomyopathy resulting from mutant or variant forms of TTR is referred to as familial amyloid cardiomyopathy (abbreviated as ATTRm). Wild-type ATTR amyloidosis (ATTRwt) is a slowly progressive, non-hereditary (sporadic) disease. Individuals with ATTRwt have no mutations in the TTR gene, and their amyloid fibrils consist of wild-type TTR. Symptoms of ATTRwt include heart failure and, in some individuals, carpal tunnel syndrome. ATTRwt occurs more frequently in elderly subjects aged 65 years or older.

[0044] While the exact prevalence is unknown, the incidence of ATTRm is roughly estimated at 0.4 cases / 1 million people / year in the United States, while ATTRwt has at least twice as high an incidence. However, autopsy studies of patients over 80 years of age have shown a prevalence of approximately 25% for ATTRwt, suggesting that the disease is largely underdiagnosed, and that the actual prevalence is much higher, especially in the elderly population. The difficulty of testing a large number of individuals with costly and invasive tests and procedures is a possible contributing factor to the considerable underdiagnosis of TTR-CM.

[0045] Current diagnostic methods for amyloid-trans tyretin cardiomyopathy TTR-CM is currently diagnosed using a series of different tests starting with echocardiography (Gertz, MA et al., JACC 66:2452~2466, 2015). This technique is used to check general cardiac function and look for structural abnormalities (Ashley, EA and Niebauer, J., Cardiology Explained, London: Remedica, Chapter 4, 2004), and as a screening for cardiac amyloidosis, which is manifested by ventricular thickening and hypertrophy. While this test cannot distinguish between hypertrophic cardiomyopathy and hypertensive cardiomyopathy, one study (Ashley 2004) has shown relatively good specificity (e.g., 82%) in differentiating cardiac amyloidosis from cardiac hypertrophy. However, because there are more than one form of cardiac amyloidosis (e.g., light chain amyloidosis (AL)), this test is not specific for TTR-CM.

[0046] Patients who test positive on echocardiography are then tested by magnetic resonance imaging (CMR) (Gertz, MA et al., JACC 66:2452~2466, 2015; Krishnamurthy, R. et al., Current Cardiology Reviews, 9:185~190, 2013; Doltra, A. et al., Curr Cardiol Rev.9(3):185~90, 2013). This technique uses gadolinium contrast, which can be concentrated on areas of cardiac cell damage (e.g., myocardial infarction) or areas where the extracellular space is increased due to scarring or amyloid deposits (Krishnamurthy, R. et al., Current Cardiology Reviews, 9:185~190, 2013). This technique can differentiate between hypertensive cardiomyopathy and hypertrophic cardiomyopathy better than echocardiography, and in one study, it showed greater specificity than AL for detecting ATTR amyloidosis (Ashley 2004). However, this test alone is not a definitive test for diagnosing TTR-CM.

[0047] Another imaging method for detecting cardiac amyloidosis is, 99m This is a scintigraphy technique that uses radioactive isotope conjugates such as Tc-pyrophosphate (Bokhari et al., Circ Cardiovasc Imaging. 6(2):195~201, 2013). It is performed using single-photon emission computed tomography (SPECT) to obtain 3D images, and although the radioactive tracer is not specific to cardiac tissue, it is useful for detecting areas of poor blood flow that occur in pathological cardiac tissue. In one study, this technique has been shown to distinguish ATTR amyloidosis from AL amyloidosis with high specificity and sensitivity (100% and 97%, respectively).

[0048] Currently, the most definitive test for diagnosing TTR-CM, typically performed after obtaining a positive score using the aforementioned tests, is cardiac biopsy followed by immunochemical staining. Polyclonal antibodies against kappa or light chain amyloid deposits in cardiac tissue are used for the detection of AL, while polyclonal antibodies against trans tyretin deposits are used for the detection of TTR-CM (Crotty, TB et al., Cardiovascular Pathology 4:39-42, 1995).

[0049] Finally, antibodies against trans tiretin (TTR) have been previously disclosed, and the application of such antibodies in the diagnosis of amyloid diseases such as ATTRwt has been described (WO2014 / 124334A2 and WO2016 / 120811). A disadvantage of using such anti-TTR antibodies for the diagnosis of cardiomyopathy, such as cardiomyopathy caused by ATTRwt, is that TTR may not be recognized in some patients in misfolded or aggregated situations, leading to false-negative test results and underdiagnosis. For example, both WO2014 / 124334A2 and WO2016 / 120811 rely on exposure to epitopes within specific amino acid residues of TTR; however, such epitopes may not be readily accessible in all ATTR amyloidosis patients, depending on the non-natural form of TTR. Wild-type TTR, mutant TTR, or mixed TTR tetramers can dissociate, misfold, aggregate, and / or form fibrils in ATTR amyloidosis. Such different morphologies may not be readily detectable by anti-TTR antibodies. Furthermore, the gene encoding TTR has been reported to have many different mutations associated with ATTR amyloidosis. Therefore, anti-TTR antibodies may fail to recognize the disease in many patients.

[0050] ATTR biomarkers The embodiments described herein offer several advantages over the conventional techniques discussed herein. For example, the techniques of this disclosure are non-invasive, require minimal patient discomfort, are rapid to perform, and are relatively cost-effective. Accordingly, the techniques described herein offer advantages over these conventional techniques, including, but are not limited to, providing: a non-invasive in vitro diagnostic (IVD) test for TTR-CM caused by ATTRwt; one or more specific in vitro biomarkers suitable for use in an IVD test; and an alternative to a single-marker IVD test, including one or more markers that effectively rule in or rule out candidates for more expensive and invasive procedures in the diagnosis of the disease.

[0051] This disclosure relates in particular to biomarkers for ATTR amyloidosis and TTR-CM. Such biomarkers are referred to herein as “ATTR biomarkers.” ATTR biomarkers may include, for example, TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, characteristic fragments thereof, and / or variants thereof. In some embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, TIMP2, characteristic fragments thereof, and / or variants thereof, or include them. In some embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, and TIMP2, or include them. In some embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, DCN, characteristic fragments thereof, and / or variants thereof, or include them. In some embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, and DCN, or include them. In some embodiments, one or more ATTR biomarkers described herein are TnI, NT-proBNP, TIMP2, characteristic fragments thereof, and / or variants thereof, or include them. In some embodiments, one or more ATTR biomarkers described herein are TnI, NT-proBNP and TIMP2, or include them. In some embodiments, one or more ATTR biomarkers described herein are TnI, NT-proBNP, DCN, characteristic fragments thereof, and / or variants thereof, or include them. In some embodiments, one or more ATTR biomarkers described herein are TnI, NT-proBNP and DCN, or include them.In preferred embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, NT-proBNP, characteristic fragments thereof, and / or variants thereof, or include them. In particularly preferred embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, and NT-proBNP, or include them. In some embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, characteristic fragments thereof, and / or variants thereof, or include them.

[0052] As provided herein, ATTR biomarkers include gene products associated with specific biomarkers listed. For example, ATTR biomarkers may include, for example, proteins or nucleotides (e.g., RNA, e.g., mRNA). ATTR biomarkers also include full-length proteins and fragments (e.g., characteristic fragments) of the ATTR biomarker. For example, in some embodiments, ATTR biomarkers may include proteins listed in Table 1. In some embodiments, ATTR biomarkers include fragments having amino acid sequences 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 from the amino acid sequences provided in Table 1. In some embodiments, ATTR biomarkers include 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 with the amino acid sequences provided in Table 1. A variant or alternative form of the biomarker includes, for example, a polypeptide encoded by any splice variant of the transcript encoding the disclosed biomarker.

[0053] The biomarkers intended herein also include any truncated form or polypeptide fragment of any of the proteins described herein. The truncated form or polypeptide fragment of a protein may include forms in which the N-terminus is removed or truncated, and forms in which the C-terminus is removed or truncated. The truncated form or fragment of a protein may include fragments resulting from any mechanism, for example, but not limited to, selective translation, exo and / or endoproteolysis, and / or degradation by physical, chemical and / or enzymatic proteolysis. Biomarkers may also include, but are not limited to, truncated or fragmented forms of proteins, polypeptides, or peptides, which may include 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 ATTR biomarker protein.

[0054] In some cases, the fragments have only 1 to 20 amino acids truncated at the N-terminus and / or C-terminus compared to the corresponding mature full-length ATTR biomarker protein, such as 1 to 15 amino acids, 1 to 10 amino acids, or just 1 to 5 amino acids.

[0055] Any ATTR biomarker proteins of this disclosure, such as peptides, polypeptides, or proteins and fragments thereof, may also encompass, but are not limited to, modifications of the markers, peptides, polypeptides, or proteins and fragments, including, but not limited to, modifications such as phosphorylation, glycosylation, lipidation, methylation, selenocystination, cysteination, sulfonation, glutathioneation, acetylation, and / or oxidation of methionine to methionine sulfoxide or methionine sulfone.

[0056] In some embodiments, the ATTR biomarker has different isoforms. While only one or more isoforms may be disclosed herein, all isoforms of the ATTR biomarker are intended for use in the disclosed techniques.

[0057] In some embodiments, the ATTR biomarker may be a nucleotide. In some embodiments, the nucleotide may be RNA or DNA. In some cases, the corresponding RNA or DNA may exhibit better discriminative power in diagnosis compared to the full-length protein.

[0058] Exemplary ATTR biomarkers for use with the technologies provided herein are briefly listed below.

[0059] Troponin I (TnI) Troponins are a group of proteins found in skeletal and cardiac muscle fibers. One function of troponins is to regulate muscle contraction. Three types of troponin proteins are known: troponin C, troponin I, and troponin T. Together, the three types of troponins form a complex. Within the complex, troponin C binds to calcium ions. This binding causes contraction by leading to a conformational change in troponin I. Troponin I binds to actin in thin muscle filaments, holding the actin-tropomyosin complex in place. Troponin T anchors the troponin complex to tropomyosin, a muscle fiber structure.

[0060] Previous analyses have shown that while there is little to no difference in troponin C between skeletal and cardiac muscle, the morphologies of troponin I and troponin T are understood to differ between skeletal and cardiac muscle. Normally, troponin is present in the blood in very small to undetectable amounts. However, if there is damage to cardiomyocytes, troponin is released into the blood. Higher concentrations of troponin I in the blood generally correlate with greater damage to cardiac tissue.

[0061] In some embodiments of this disclosure, TnI is an ATTR biomarker. In some embodiments, the detection of TnI, characteristic fragments of TnI, and / or variants of TnI is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject with TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, TnI is detected in a sample using an anti-TnI drug (e.g., an anti-TnI antibody drug, a probe, etc.). In some embodiments, the detection of a nucleotide encoding TnI, a nucleotide encoding a characteristic fragment of TnI, and / or a nucleotide encoding a variant of TnI is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject with TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, the nucleotide encoding TnI is detected in a sample using an anti-TnI nucleotide sequence drug (e.g., an anti-TnI nucleotide sequence antibody drug, a probe, a complementary nucleic acid, etc.).

[0062] N-terminal hormone precursor type B natriuretic peptide (NT-proBNP) B-type natriuretic peptide (BNP) is a hormone produced in the heart. BNP is a small cyclic peptide secreted by the heart to regulate blood pressure and fluid balance. N-terminal (NT)-hormone precursor BNP (NT-proBNP or NT-proBNP) is an inactive hormone precursor released from the same molecule that produces BNP. In particular, BNP is stored in the ventricular membrane granules in its precursor form (proBNP) and is secreted primarily from there. Once released from the heart in response to ventricular volume expansion or pressure overload, the 76-amino acid N-terminal (NT) fragment (NT-proBNP) is rapidly cleaved by the enzymes choline and furin to release the active 32-amino acid peptide (BNP).

[0063] In some embodiments of this disclosure, NT-proBNP is an ATTR biomarker. In some embodiments, the detection of NT-proBNP, characteristic fragments of NT-proBNP, and / or variants of NT-proBNP is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject with TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, NT-proBNP is detected in a sample using an anti-NT-proBNP agent (e.g., an anti-NT-proBNP antibody, probe, etc.). In some embodiments, the detection of nucleotides encoding NT-proBNP, nucleotides encoding characteristic fragments of NT-proBNP, and / or nucleotides encoding variants of NT-proBNP is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject with TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding NT-proBNP are detected in a sample using an anti-NT-proBNP nucleotide sequence agent (e.g., an anti-NT-proBNP nucleotide sequence antibody, probe, complementary nucleic acid, etc.).

[0064] Retinol-binding protein 4 (RBP4) Retinol-binding protein 4 (RBP4) is a transporter protein for retinol (vitamin A alcohol). RBP4 has a molecular weight of approximately 21 kDa and is encoded in the RBP4 gene in humans. It is synthesized primarily in the liver, though not exclusively. RBP4 delivers retinol from its storage site in the liver to peripheral tissues. In plasma, the RBP-retinol complex interacts with trans tiretin, which prevents its loss through filtration via the renal glomeruli. Vitamin A deficiency blocks the secretion of this binding protein post-translation, resulting in incomplete delivery and supply to epithelial cells. Circulating RBP4 has previously been proposed as a way to differentiate ATTRm from non-amyloid heart failure (Arvanitis, M. et al., JAMA Cardiol., 2017). However, this prior study only examined ATTRm caused by a specific mutation in RBP4 (the substitution of isoleucine to valine at codon 122 of the TTR gene (V122I)), and did not investigate whether RBP4 can be more commonly used as a biomarker for TTR-CM, whether evaluated alone or in conjunction with other possible biomarkers.

[0065] This disclosure provides the recognition that RBP4 can be used as an ATTR biomarker. Accordingly, in some embodiments of this disclosure, RBP4 is an ATTR biomarker. In some embodiments, the detection of RBP4, characteristic fragments of RBP4, and / or variants of RBP4 is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, RBP4 is detected in a sample using an anti-RBP4 agent (e.g., an anti-RBP4 antibody, probe, etc.). In some embodiments, the detection of nucleotides encoding RBP4, nucleotides encoding characteristic fragments of RBP4, and / or nucleotides encoding variants of RBP4 is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding RBP4 are detected in a sample using an anti-RBP4 nucleotide sequence agent (e.g., an anti-RBP4 nucleotide sequence antibody, probe, complementary nucleic acid, etc.).

[0066] Pyruvate kinase muscle isoforms 1 and 2 (PKM1 and PKM2) Pyruvate kinase is an enzyme that catalyzes the final step of glycolysis by catalyzing the transfer of a phosphate group from phosphoenolpyruvate (PEP) to adenosine diphosphate (ADP), yielding pyruvate and ATP. In vertebrates, there are four tissue-specific isozymes of pyruvate kinase: L (liver), R (red blood cells), muscle isoform 1 (muscle, heart, and brain), and muscle isoform 2 (early fetal tissue and most adult tissues). Pyruvate kinase proteins can form dimers and tetramers.

[0067] The PKM gene encodes the muscle isoform 1 isozyme and the muscle isoform 2 isozyme (PKM1 and PKM2). Exons 9 and 10 of the PKM gene contain sequences for the muscle isoform 1 and muscle isoform 2 isozymes, respectively. There are at least 14 splice variants of PKM, including one non-coding variant. Among the splice variants of PKM are PKM1 and PKM2, which are produced by differential splicing and differ by only 22 amino acids at the carboxyl terminus. Since the amino acid sequences of PKM1 and PKM2 share the same region, certain fragments of PKM1 and PKM2 are characteristic fragments of both PKM1 and PKM2, and certain fragments (e.g., fragments derived from the 22 amino acids at the carboxyl terminus) are characteristic fragments of either PKM1 or PKM2. Furthermore, since the amino acid sequences of PKM1 and PKM2 share the same region, certain anti-PKM1 drugs also detect PKM2, and vice versa.

[0068] In some embodiments of this disclosure, PKM (e.g., PKM1 and / or PKM2) is an ATTR biomarker. In some embodiments, the detection of PKM, characteristic fragments of PKM, and / or variants of PKM is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject with TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, PKM is detected in a sample using an anti-PKM agent (e.g., an anti-PKM antibody, probe, etc.). In some embodiments, the detection of nucleotides encoding PKM, nucleotides encoding characteristic fragments of PKM, and / or nucleotides encoding variants of PKM is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject with TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding PKM are detected in a sample using an anti-PKM nucleotide sequence agent (e.g., an anti-PKM nucleotide sequence antibody, probe, complementary nucleic acid, etc.).

[0069] In some embodiments of this disclosure, PKM1 is an ATTR biomarker. In some embodiments, the detection of PKM1, characteristic fragments of PKM1, and / or variants of PKM1 is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as having TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, PKM1 is detected in a sample using an anti-PKM1 drug (e.g., an anti-PKM1 antibody drug, a probe, etc.). In some embodiments, the detection of nucleotides encoding PKM1, nucleotides encoding characteristic fragments of PKM1, and / or nucleotides encoding variants of PKM1 is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as having TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding PKM1 are detected in a sample using an anti-PKM1 nucleotide sequence drug (e.g., an anti-PKM1 nucleotide sequence antibody drug, a probe, a complementary nucleic acid, etc.).

[0070] In some embodiments of this disclosure, PKM2 is an ATTR biomarker. In some embodiments, the detection of PKM2, characteristic fragments of PKM2, and / or variants of PKM2 is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as having TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, PKM2 is detected in a sample using an anti-PKM2 drug (e.g., an anti-PKM2 antibody drug, a probe, etc.). In some embodiments, the detection of nucleotides encoding PKM2, nucleotides encoding characteristic fragments of PKM2, and / or nucleotides encoding variants of PKM2 is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as having TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding PKM2 are detected in a sample using an anti-PKM2 nucleotide sequence drug (e.g., an anti-PKM2 nucleotide sequence antibody drug, a probe, a complementary nucleic acid, etc.).

[0071] Tissue metalloproteinase inhibitor 2 (TIMP2) Tissue metalloproteinase inhibitor 2 (TIMP2) is the gene that encodes the TIMP2 protein. The TIMP2 gene is encoded by five exons spanning 83kb of genomic DNA. The five prime ends of the TIMP2 gene contain several regulatory elements, including Sp1 binding sites, AP2 binding sites, AP1 binding sites, and PEA3 binding sites.

[0072] The TIMP2 gene is a member of the TIMP gene family. Proteins encoded by genes in the TIMP gene family inhibit matrix metalloproteinases (MMPs), a group of peptidases involved in the degradation of the extracellular matrix. TIMP2 also has the ability to directly suppress endothelial cell proliferation. TIMP2 has been shown to suppress tumor metastasis.

[0073] In some embodiments of this disclosure, TIMP2 is an ATTR biomarker. In some embodiments, the detection of TIMP2, characteristic fragments of TIMP2, and / or variants of TIMP2 is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as having TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, TIMP2 is detected in a sample using an anti-TIMP2 agent (e.g., an anti-TIMP2 antibody, probe, etc.). In some embodiments, the detection of nucleotides encoding TIMP2, nucleotides encoding characteristic fragments of TIMP2, and / or nucleotides encoding variants of TIMP2 is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as having TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding TIMP2 are detected in a sample using an anti-TIMP2 nucleotide sequence agent (e.g., an anti-TIMP2 nucleotide sequence antibody, probe, complementary nucleic acid, etc.).

[0074] Neurofilament light chain (NfL) Neurofilaments are cytoskeletal components of neurons, particularly abundant in axons. The functions of neurofilaments include providing structural support and maintaining axonal size, shape, and diameter. Neurofilaments consist of three subunits: neurofilamentous light chains (NfLs), neurofilamentous medium chains, and neurofilamentous heavy chains. NfL levels increase in cerebrospinal fluid (CSF) and blood in proportion to the degree of axonal damage in various neurological disorders, including inflammatory diseases, neurodegenerative diseases, traumatic diseases, and cerebrovascular diseases. While NfLs are used as a biomarker for neurodegenerative disorders, their association with other diseases and conditions, including cardiac conditions, has not been fully explored.

[0075] As described herein, in some embodiments, neurofilamentous light chains (NfLs) may be useful for the detection and diagnosis of TTR-CM. In some embodiments, NfLs are ATTR biomarkers. In some embodiments, the detection of NfLs, characteristic fragments of NfLs, and / or variants of NfLs is used in methods to assess the risk of a subject developing TTR-CMs, to diagnose a subject with TTR-CMs, or to recommend a subject for further cardiomyopathy testing. In some embodiments, NfLs are detected in a sample using anti-NfL agents (e.g., anti-NfL antibody drugs, probes, etc.). In some embodiments, the detection of nucleotides encoding NfLs, nucleotides encoding characteristic fragments of NfLs, and / or nucleotides encoding variants of NfLs is used in methods to assess the risk of a subject developing TTR-CMs, to diagnose a subject with TTR-CMs, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding NfLs are detected in a sample using anti-NfL nucleotide sequence agents (e.g., anti-NfL nucleotide sequence antibody drugs, probes, complementary nucleic acids, etc.).

[0076] Decorin (DCN) Decorin (DCN) is a proteoglycan with an average molecular weight of 90–140 kilodaltons (kDa). It belongs to the small leucine-rich proteoglycan (SLRP) family and contains a protein core with a leucine repeat along with either chondroitin sulfate (CS) or dermatan sulfate (DS) glycosaminoglycan (GAG) chains. DCN is a component of connective tissue and binds to type I collagen fibrils. DCN also acts as a ligand for various cytokines and growth factors by directly or indirectly interacting with corresponding signaling molecules involved in cell proliferation, differentiation, growth, adhesion, and metastasis. DCN plays a particularly crucial role in the proliferation, propagation, pro-inflammatory processes, and antigen fibril formation of cancer cells.

[0077] In some embodiments of this disclosure, DCN is an ATTR biomarker. In some embodiments, the detection of DCN, characteristic fragments of DCN, and / or variants of DCN is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as having TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, DCN is detected in a sample using an anti-DCN agent (e.g., an anti-DCN antibody, probe, etc.). In some embodiments, the detection of nucleotides encoding DCN, nucleotides encoding characteristic fragments of DCN, and / or nucleotides encoding variants of DCN is used in a method to assess the risk of a subject developing TTR-CM, to diagnose a subject as having TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding DCN are detected in a sample using an anti-DCN nucleotide sequence agent (e.g., an anti-DCN nucleotide sequence antibody, probe, complementary nucleic acid, etc.).

[0078] SPARC-related modular calcium binding 2 (SMOC-2) SPARC-associated modular calcium-binding protein 2 (SMOC-2), formerly known as SMAP2 (smooth muscle-associated protein 2), is a 55 kDa glycoprotein that is a member of the SPARC family of matrix cell proteins. SMOC-2 promotes cell cycle progression by signaling through integrin-binding kinase (ILK) to upregulate cyclin D1. When expressed in the endothelial extracellular matrix, it enhances growth factor-induced angiogenesis. SMOC-2 expression is upregulated during neointima-forming, promoting the proliferation and migration of vascular smooth muscle. In the skin, it promotes the attachment and migration of keratinocytes. SMOC-2 can also inhibit proteases in the lungs and arteries. SMOC-2 has been proposed as a biomarker for several cancers.

[0079] In some embodiments, SPARC-associated modular calcium-bound 2 (SMOC2) is an ATTR biomarker. In some embodiments, the detection of SMOC2, characteristic fragments of SMOC2, and / or variants of SMOC2 is used in methods to assess a subject's risk of developing TTR-CM, to diagnose a subject with TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, SMOC2 is detected in a sample using an anti-SMOC2 agent (e.g., an anti-SMOC2 antibody, probe, etc.). In some embodiments, the detection of nucleotides encoding SMOC2, nucleotides encoding characteristic fragments of SMOC2, and / or nucleotides encoding variants of SMOC2 is used in methods to assess a subject's risk of developing TTR-CM, to diagnose a subject with TTR-CM, or to recommend a subject for further cardiomyopathy testing. In some embodiments, nucleotides encoding SMOC2 are detected in a sample using an anti-SMOC2 nucleotide sequence agent (e.g., an anti-SMOC2 nucleotide sequence antibody, probe, complementary nucleic acid, etc.).

[0080] Exemplary amino acid sequences of certain ATTR biomarkers disclosed herein are included in Table 1 below.

[0081] [Table 1-1] [Table 1-2] [Table 1-3]

[0082] In some embodiments, further markers can be analyzed or evaluated. In some such embodiments, the further markers include misfolded or aggregated tranthyretin (TTR).

[0083] In some embodiments, but not limited to, further factors may be considered, including demographic factors of the subjects from which the sample was obtained (e.g., one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographical location) and / or image-based biomarkers [e.g., posterior wall thickness, septal thickness (e.g., left ventricular septal thickness), or ejection fraction].

[0084] Exemplary combinations of ATTR biomarkers As provided herein, each ATTR biomarker may be a full-length protein or a fragment thereof. In some embodiments, the fragment of the ATTR biomarker is a characteristic fragment. In some embodiments, the ATTR biomarker is a full-length ATTR biomarker protein. In some embodiments, the ATTR biomarker is a characteristic fragment of the ATTR biomarker. In some embodiments, a subset of the ATTR biomarker is a full-length ATTR biomarker protein, and a subset of the ATTR biomarker is a characteristic fragment of the ATTR biomarker.

[0085] In some embodiments, the ATTR biomarker has a wild-type amino acid sequence. In some embodiments, the ATTR biomarker has a variant amino acid sequence, for example, an amino acid sequence containing one or more mutations. In some embodiments, each ATTR biomarker has a wild-type amino acid sequence. In some embodiments, each ATTR biomarker has a variant amino acid sequence. In some embodiments, a subset of the ATTR biomarkers each has a wild-type amino acid sequence, and a subset of the ATTR biomarkers each has a variant amino acid sequence.

[0086] In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes the combinations of ATTR biomarkers listed below in Tables 2 to 5. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes the combinations of ATTR biomarkers listed below in Table 2. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes the combinations of ATTR biomarkers listed below in Table 3. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes the combinations of ATTR biomarkers listed below in Table 4. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes the combinations of ATTR biomarkers listed below in Table 5.

[0087] In some embodiments, the ATTR biomarker combination includes one or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, the ATTR biomarker combination includes two or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, the ATTR biomarker combination includes three or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, the ATTR biomarker combination includes four or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, the ATTR biomarker combination includes five or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, the ATTR biomarker combination includes six or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, the ATTR biomarker combination includes seven or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, the ATTR biomarker combination includes eight or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, the combination of ATTR biomarkers includes TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, or NfL.

[0088] In some embodiments, the ATTR biomarker combination includes TnI. In some embodiments, the ATTR biomarker combination is TnI and PKM1, or includes these. In some embodiments, the ATTR biomarker combination is TnI and PKM2, or includes these. In some embodiments, the ATTR biomarker combination is TnI, PKM1, and PKM2, or includes these. In some embodiments, the ATTR biomarker combination is TnI and RBP4, or includes these. In some embodiments, the ATTR biomarker combination further includes NT-proBNP, RBP4, or both. In some embodiments, the ATTR biomarker combination is TnI, RBP4, and NT-proBNP, or includes these. In some embodiments, the ATTR biomarker combination further includes TIMP2, NfL, or both. In some embodiments, the ATTR biomarker combination further includes NT-proBNP, TIMP2, or both.

[0089] In some embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, and TIMP2, or include these. In some embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, and DCN, or include these. In some embodiments, one or more ATTR biomarkers described herein are TnI, NT-proBNP, and TIMP2, or include these. In some embodiments, one or more ATTR biomarkers described herein are TnI, NT-proBNP, and DCN, or include these. In particular, in some embodiments, one or more ATTR biomarkers described herein are TnI and RBP4, or include these. In particular, in some embodiments, one or more ATTR biomarkers described herein are TnI, RBP4, and NT-proBNP, or include these.

[0090] In some embodiments, the ATTR biomarker combination includes NT-proBNP. In some embodiments, the ATTR biomarker combination further includes TnI, PKM1, PKM2, RBP4, or a combination thereof. In some embodiments, the ATTR biomarker combination further includes TIMP2, NfL, or both. In some embodiments, the ATTR biomarker combination includes or is TnI, RBP4, and NT-proBNP. In some embodiments, the ATTR biomarker combination includes or is TnI, TIMP2, and NT-proBNP.

[0091] In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM2, NT-proBNP, and RBP4. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, PKM2, and NT-proBNP, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, PKM2, and RBP4, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, PKM1, and NT-proBNP, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, PKM1, and RBP4, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, PKM1, PKM2, NT-proBNP, and RBP4, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, PKM1, PKM2, and NT-proBNP, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, PKM1, PKM2, and RBP4, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is RBP4, SMOC-2, and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is DCN, NT-proBNP, and TnI, or includes these.In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is DCN, RBP4, and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is NT-proBNP, SMOC-2, and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is NT-proBNP, TIMP2, and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is NT-proBNP and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is DCN and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is DCN, TIMP2, and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is RBP4 and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is RBP4, TIMP2, and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is DCN, SMOC-2, and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TIMP2 and TnI, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is SMOC-2, TIMP2, and TnI, or includes these.In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, TIMP2, and RBP4, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, DCN, and RBP4, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, DCN, and NT-proBNP, or includes these. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, TIMP2, and NT-proBNP, or includes these. In preferred embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI and RBP4, or includes these. In preferred embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) is TnI, NT-proBNP, and RBP4, or includes these.

[0092] Exemplary combinations of ATTR biomarkers consistent with this disclosure are included in Table 2 below. Combinations of ATTR biomarkers, including those listed in Table 2 below, are also disclosed herein.

[0093] [Table 2]

[0094] Exemplary combinations of two ATTR biomarkers that are consistent with this disclosure are included in Table 3 below. Combinations of ATTR biomarkers, including those listed in Table 3 below, are also disclosed herein.

[0095] [Table 3-1] [Table 3-2]

[0096] Exemplary combinations of three ATTR biomarkers that are consistent with this disclosure are included in Table 4 below. Combinations of ATTR biomarkers, including those listed in Table 4 below, are also disclosed herein.

[0097] [Table 4-1] [Table 4-2] [Table 4-3]

[0098] Exemplary combinations of four ATTR biomarkers that are consistent with this disclosure are included in Table 5 below. Combinations of ATTR biomarkers, including those listed in Table 5 below, are also disclosed herein.

[0099] [Table 5-1] [Table 5-2] [Table 5-3] [Table 5-4]

[0100] In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) does not include PKM1. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) does not include PKM2. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) does not include either PKM or PKM2.

[0101] In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) does not include SMOC-2. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) does not include DCN. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) does not include either SMOC-2 or DCN.

[0102] method The methods disclosed herein minimize the number of false negatives while enabling earlier identification of more patients at risk of TTR-CM. In some embodiments, the methods disclosed herein offer the advantage of early screening for the presence of ATTRwt. In some embodiments, the methods disclosed herein can assist in the detection or diagnosis of ATTRwt after genetic testing has ruled out ATTRm. In some embodiments, the methods disclosed herein reduce or eliminate the need to initiate screening for TTR-CM using costly and complex processes such as echocardiography, followed by CMR and scintigraphy. In some embodiments, if the methods disclosed herein detect the possibility of TTR-CM in a subject, the subject may undergo subsequent confirmatory tests, such as echocardiography, magnetic resonance imaging (CMR), scintigraphy, and / or cardiac biopsy.

[0103] In some embodiments, the methods disclosed herein are for determining the risk of developing amyloid trans tyretin cardiomyopathy (TTR-CM) in a subject. In some embodiments, the methods disclosed herein are for diagnosing a subject with TTR-CM, where the sample is obtained from the subject. In some embodiments, the methods disclosed herein are for treating TTR-CM in a subject who is at risk of developing TTR-CM or who has TTR-CM. In some embodiments, the methods disclosed herein are for determining whether a patient does not have TTR-CM or is not at risk of developing TTR-CM.

[0104] In some embodiments, TTR-CM arises from wild-type trans tyretin amyloidosis (ATTRwt). In some embodiments, subjects are negative for familial amyloid cardiomyopathy (ATTRm) by genetic testing.

[0105] In some embodiments, the method disclosed herein is a method for selecting a subject to receive one or more doses of a TTR stabilizer, the sample being obtained from the subject. In some embodiments, the method disclosed herein includes the step of administering one or more doses of a TTR stabilizer to the subject.

[0106] In some embodiments, the methods disclosed herein are methods for selecting subjects for one or more cardiomyopathy tests, wherein the samples are obtained from the subjects. In some embodiments, one or more cardiomyopathy tests include echocardiography, advanced imaging methods, or both. In some embodiments, the advanced imaging methods include magnetic resonance imaging (CMR), scintigraphy, or both. In some embodiments, scintigraphy includes the use of radioisotope conjugates such as 99mTc-pyrophosphate. In some embodiments, scintigraphy is performed using single-photon emission computed tomography (SPECT).

[0107] This disclosure provides a diagnostic test for TTR-CM (including TTR-CM caused by wild-type ATTR amyloidosis) characterized by the detection of a biomarker by the method described above.

[0108] In some embodiments, a method for detecting, diagnosing, or identifying the risk of TTR-CM as taught herein is an improved method compared to standard techniques, in that the method herein provides one or more of the following benefits: improved sensitivity for identifying TTR-CM, improved specificity for identifying TTR-CM, improved accuracy for identifying TTR-CM, reduced time to diagnosis of TTR-CM, and / or reduced cost of screening patients for TTR-CM.

[0109] In some embodiments, the biomarkers disclosed herein can be used in in vitro diagnostic (IVD) or screening tests for amyloid trans-tyretin cardiomyopathy status. In some embodiments, the diagnostic tests taught herein detect whether one or more biomarkers are present in a sample obtained from a subject. In some embodiments, the diagnostic tests taught herein can assist in the detection or diagnosis of TTR-CM (including TTR-CM resulting from wild-type ATTR amyloidosis) in a subject.

[0110] In some embodiments, the diagnostic tests taught by this disclosure are suitable for an immunoassay platform. In some embodiments, such an immunoassay platform includes a semi-automated or automated immunoassay platform. In some embodiments, the diagnostic tests taught by this disclosure are suitable for a semi-automated test of one or more biomarkers.

[0111] In some embodiments, the diagnostic tests taught by this disclosure are improved diagnostics of TTR-CM compared to standard techniques, in that the diagnostic tests of this disclosure include one or more of the following benefits: improved sensitivity for identifying TTR-CM, improved specificity for identifying TTR-CM, improved accuracy for identifying TTR-CM, reduced time to diagnosis of TTR-CM, and / or reduced cost of screening patients for TTR-CM.

[0112] In some embodiments, the diagnostic tests disclosed herein may be plasma-based screening assays. In some embodiments, the diagnostic tests are suitable for, for example, the Siemens Atellica® system or the Siemens Advia Centaur® system.

[0113] As described in more detail below, in some embodiments, the methods provided herein include the step of detecting the respective levels of two or more trans tyretin amyloidosis (ATTR) biomarkers present in a sample.

[0114] In some embodiments, the method provided herein includes the steps of detecting the levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample to obtain an ATTR biomarker profile, and using the ATTR biomarker profile to computer-based calculation of an ATTR biomarker score. In some embodiments, the method provided herein includes the steps of detecting the levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample to obtain an ATTR biomarker profile, and using the ATTR biomarker profile and demographic factors to computer-based calculation of an ATTR biomarker score. In some embodiments, the method provided herein includes the steps of detecting the levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample to obtain an ATTR biomarker profile, and using the ATTR biomarker profile and image-based biomarkers to computer-based calculation of an ATTR biomarker score. In some embodiments, the methods provided herein include the steps of detecting the respective levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample to obtain an ATTR biomarker profile, and the steps of using the ATTR biomarker profile, demographic factors, and image-based biomarkers to computer-calculate an ATTR biomarker score.

[0115] In some embodiments, the method provided herein includes a step of receiving the respective levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample. In some embodiments, the receiving step includes a step of receiving electronically.

[0116] In some embodiments, the method provided herein includes the steps of obtaining an ATTR biomarker profile using the respective levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample, and calculating an ATTR biomarker score using the ATTR biomarker profile on a computer. In some embodiments, the method provided herein includes the steps of obtaining an ATTR biomarker profile using the respective levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample, and calculating an ATTR biomarker score using the ATTR biomarker profile and demographic factors on a computer. In some embodiments, the method provided herein includes the steps of obtaining an ATTR biomarker profile using the respective levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample, and calculating an ATTR biomarker score using the ATTR biomarker profile and image-based biomarkers on a computer. In some embodiments, the methods provided herein include the steps of obtaining an ATTR biomarker profile using the respective levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample, and calculating an ATTR biomarker score by computer using the ATTR biomarker profile, demographic factors, and image-based biomarkers.

[0117] In some embodiments, demographic factors include one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographical location. In some embodiments, image-based biomarkers include one or more of the following: posterior wall thickness, septal thickness (e.g., left ventricular septal thickness), and ejection fraction.

[0118] In some embodiments, the method herein includes the step of selecting subjects for further cardiomyopathy testing using an ATTR biomarker score. In some embodiments, the method herein includes the step of selecting subjects for receiving one or more doses of a TTR stabilizer using an ATTR biomarker score. In some embodiments, the method herein includes the step of identifying subjects as having or being at risk of having TTR-CM using an ATTR biomarker score.

[0119] In some embodiments, the method described herein includes the step of comparing the score of an ATTR biomarker with the score of a reference ATTR biomarker. In some embodiments, the method described herein includes the step of administering one or more doses of a TTR stabilizer to a subject. In some embodiments, the method described herein includes the step of performing one or more cardiomyopathy tests on a subject.

[0120] Further explanation of the exemplary methods disclosed herein is provided below.

[0121] Detection of ATTR biomarkers and acquisition of ATTR biomarker profiles. Among the many methods available, those provided herein involve evaluating the level of one or more ATTR biomarkers in a sample. The level of an ATTR biomarker can be detected in the sample. Exemplary methods for detecting the level of one or more ATTR biomarkers are described herein. However, the level of an ATTR biomarker can also be provided, for example, electronically, by the laboratory that detected the level of one or more ATTR biomarkers in the sample.

[0122] In some embodiments, the Disclosure provides techniques for detecting, analyzing, and / or evaluating one or more ATTR biomarkers in a sample. In some embodiments, one or more ATTR biomarkers are present in a sample obtained from a subject; in some embodiments, a diagnostic or therapeutic decision is made based on such detection, analysis, and / or evaluation. In some embodiments, the biomarkers to be detected, analyzed, or evaluated are one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof).

[0123] In some embodiments, the Disclosure provides a method for detecting the level of one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) in a sample. The levels of ATTR biomarkers described herein include the presence or absence of an ATTR biomarker, the amount of an ATTR biomarker, the absolute amount of an ATTR biomarker, the relative amount of an ATTR biomarker, or the concentration of an ATTR biomarker.

[0124] In some embodiments, the method provided herein includes the step of detecting the level of each ATTR biomarker in the sample for each combination listed in Tables 2 to 5. In some embodiments, the method provided herein includes the step of detecting the level of each ATTR biomarker in the sample for each combination listed in Table 2. In some embodiments, the method provided herein includes the step of detecting the level of each ATTR biomarker in the sample for each combination listed in Table 3. In some embodiments, the method provided herein includes the step of detecting the level of each ATTR biomarker in the sample for each combination listed in Table 4. In some embodiments, the method provided herein includes the step of detecting the level of each ATTR biomarker in the sample for each combination listed in Table 5.

[0125] In some embodiments, the methods provided herein include detecting the level of one or more ATTR biomarkers in a sample, where one or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the methods provided herein include detecting the level of two or more ATTR biomarkers in a sample, where two or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the methods provided herein include detecting the level of three or more ATTR biomarkers in a sample, where three or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the method provided herein includes the step of detecting the level of each of four or more ATTR biomarkers in a sample, wherein the four or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the method provided herein includes the step of detecting the level of each of five or more ATTR biomarkers in a sample, wherein the five or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the method provided herein includes the step of detecting the level of each of six or more ATTR biomarkers in a sample, wherein the six or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof.In some embodiments, the method provided herein includes the step of detecting the level of each of seven or more ATTR biomarkers in a sample, the seven or more ATTR biomarkers being TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, and NfL. In some embodiments, the method provided herein includes the step of detecting the level of each of eight or more ATTR biomarkers in a sample, the eight or more ATTR biomarkers being TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, and NfL. In some embodiments, the method provided herein includes the step of detecting the level of each of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, and NfL in a sample.

[0126] In some embodiments, the method provided herein includes the step of detecting the level of TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the levels of TnI and PKM1 in the sample. In some embodiments, the method provided herein includes the step of detecting the levels of TnI and PKM2 in the sample. In some embodiments, the method provided herein includes the step of detecting the levels of TnI, PKM1 and PKM2 in the sample. In some embodiments, the method provided herein further includes the step of detecting the levels of NT-proBNP, RBP4 or both in the sample. In some embodiments, the method provided herein further includes the step of detecting the levels of TIMP2, NfL or both in the sample.

[0127] In some embodiments, the method provided herein includes the step of detecting the level of NT-proBNP in a sample. In some embodiments, the method provided herein further includes the step of detecting the levels of TnI, PKM1, PKM2, RBP4, or a combination thereof in a sample. In some embodiments, the method provided herein further includes the step of detecting the levels of TIMP2, NfL, or both in a sample.

[0128] In some embodiments, the method provided herein includes the step of detecting the respective levels of TnI, PKM2, NT-proBNP, and RBP4 in a sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of TnI, PKM1, PKM2, and RBP4 in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of NT-proBNP, RBP4, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of RBP4, SMOC-2, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of DCN, NT-proBNP, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of DCN, RBP4, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of NT-proBNP, SMOC-2, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of NT-proBNP, TIMP2, and TnI in the sample.In some embodiments, the method provided herein includes the step of detecting the respective levels of NT-proBNP and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of DCN and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of DCN, TIMP2, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of RBP4 and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of RBP4, TIMP2, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of DCN, SMOC-2, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of TIMP2 and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of SMOC-2, TIMP2, and TnI in the sample.

[0129] In some embodiments, the method provided herein includes the step of detecting the respective levels of NT-proBNP, RBP4, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of DCN, NT-proBNP, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of DCN, RBP4, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of NT-proBNP, TIMP2, and TnI in the sample. In some embodiments, the method provided herein includes the step of detecting the respective levels of RBP4, TIMP2, and TnI in the sample.

[0130] Methods for detecting biomarkers (e.g., ATTR biomarkers) include methods for detecting biomarkers as proteins. Protein-based biomarker detection methods include, for example, mass spectrometry (MS), immunoassays (e.g., immunoprecipitation), Western blotting, ELISA, immunohistochemical tests, immunocytochemical tests, flow cytometry, and / or immunoPCR.

[0131] In some embodiments, mass spectrometry is performed using MS, MS / MS, MALDI-TOF, electrospray ionization mass spectrometry (ESIMS), ESI-MS / MS, or 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), silicon desorption / ionization (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 This includes quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry. Often, MS approaches quantify fragments of biomarkers rather than full-length proteins. However, MS approaches may be sufficient to determine the protein level of biomarkers to a degree of accuracy sufficient for the disclosed methods and / or evaluations.

[0132] In some embodiments, the immunoassay may be a chemiluminescent immunoassay. In some embodiments, the immunoassay may be a high-throughput and / or automated immunoassay platform. For example, a 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.

[0133] In some embodiments, a method for detecting a biomarker as a protein in a sample includes contacting the sample with one or more antibody drugs against the biomarker of interest. In some embodiments, such a method also includes contacting the sample with a first set of one or more detection drugs. In some embodiments, the antibody drug is labeled with the first set of one or more detection drugs. In some embodiments, the first set of one or more detection drugs comprises one or more acridinium ester molecules.

[0134] Acridinium ester (AE) molecules can be used to label proteins and nucleic acids. Acridinium-labeled proteins can be used for detection in immunoassays. When AEs are exposed to alkaline H2O2 (hydrogen peroxide), chemiluminescence occurs. Depending on the specific AE variant, the light is emitted at a maximum wavelength in the range of 430–480 nm. Such light can be detected, for example, by a high-efficiency photomultiplier tube. The photoluminescence is rapid and completes in 1–5 seconds. The diversity of AE morphologies contributes to better assay performance, including improved sensitivity and robustness. AE molecules can be used to label small molecules, large analytes, and antibodies.

[0135] Further methods for detecting biomarkers include methods for detecting biomarkers as nucleic acids. Nucleic acid-based biomarker detection methods include performing nucleic acid amplification methods, such as polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), transcription amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence-based amplification (NASBA). In some embodiments, a nucleic acid-based biomarker detection method includes the step of detecting hybridization between one or more nucleic acid probes and one or more nucleotides encoding the biomarker of interest. In some embodiments, the nucleic acid probes are complementary to at least a portion of one or more nucleotides encoding the biomarker of interest. In some embodiments, the nucleotides encoding the biomarker of interest include DNA (e.g., cDNA). In some embodiments, the nucleotides encoding the biomarker of interest include RNA (e.g., mRNA).

[0136] In some embodiments, the methods provided herein detect the levels of one or more ATTR biomarkers (e.g., two or more, three or more, four or more, or five or more ATTR biomarkers) to obtain an ATTR biomarker profile. In some embodiments, the ATTR biomarker profile includes the levels of each of the ATTR biomarkers being evaluated. In some embodiments, the ATTR biomarker profile includes the levels of each of the ATTR biomarkers detected, for example, as described herein.

[0137] sample In some embodiments, the samples disclosed herein are biological samples. In some embodiments, the biological sample is, for example, a blood sample collected from an artery or vein of the subject. The blood sample may be a whole blood sample, a plasma sample, or a serum sample. In some embodiments, the biological sample includes cardiac tissue.

[0138] In some embodiments, the sample is obtained from the subject. In some embodiments, the subject from which the sample was obtained is evaluated for TTR-CM. In some embodiments, the subject from which the sample was obtained has amyloid trans tyretin cardiomyopathy (TTR-CM) or is at risk of developing it.

[0139] In some embodiments, the methods disclosed herein include the step of obtaining a biological sample from a subject. In some embodiments, the step of obtaining a biological sample from a subject includes blood collection. In some embodiments, the step of obtaining a biological sample from a subject includes the step of performing a biopsy.

[0140] In some embodiments of the methods disclosed herein, the sample is provided, for example, by a medical professional.

[0141] subject In some embodiments, the subject matter disclosed herein is a mammal. In some embodiments, the mammal is a human.

[0142] In some embodiments, the subject disclosed herein is a biological male. In some embodiments, the subject disclosed herein is a biological female.

[0143] In some embodiments, the subjects disclosed herein are overweight. In some embodiments, the subjects have a body mass index (BMI) of 25 or higher. In some embodiments, the subjects have a body mass index (BMI) of 30 or higher.

[0144] In some embodiments, the subject is at least 50 years old. In some embodiments, the subject is at least 55 years old. In some embodiments, the subject is at least 60 years old. In some embodiments, the subject is at least 65 years old.

[0145] Method using thresholds Several methods disclosed herein allow the level of one or more ATTR biomarkers to be compared to a threshold. In some embodiments, the methods disclosed herein include comparing the level of one or more ATTR biomarkers to their respective thresholds. In some embodiments, the methods disclosed herein include comparing the level of one or more ATTR biomarkers to a reference threshold.

[0146] The reference threshold may be from subjects known to have good cardiac health or independently verified, or from subjects known to have poor cardiac health or independently verified, such as subjects with TTR-CM. Alternately or in combination, the ATTR biomarker profile of a subject is compared to a reference threshold determined from multiple subjects of a common known state (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM). In some embodiments, the reference threshold is the average of known levels of ATTR biomarkers from multiple subjects, or alternately, a range defined by the range of ATTR biomarker levels observed in the reference subject.

[0147] In more complex assessment approaches, the subject's ATTR biomarker levels are compared to reference ATTR biomarker levels constructed from a larger number of subjects with a common condition (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM), e.g., at least 10, at least 50, at least 100, at least 500, at least 1000 or more subjects. Often, the reference subjects are evenly distributed between (1) healthy / not diagnosed with TTR-CM and (2) diagnosed with TTR-CM. In some cases, the assessment involves repeated or concurrent comparisons of the subject's ATTR biomarker levels with multiple profiles of known conditions.

[0148] Multiple known reference ATTR biomarker profiles (e.g., detection levels of one or more ATTR biomarkers in a reference sample) can also be used to train computer-aided assessment algorithms, such as machine learning models, so that a single comparison between the subject's ATTR biomarker profile and the reference ATTR biomarker profile provides a result that integrates or aggregates information from a large number of subjects with common known health conditions (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM), e.g., at least 10, at least 50, at least 100, at least 500, at least 1000 or more individuals. Generating such reference ATTR biomarker profiles can facilitate faster assessment of a subject's risk for TTR-CM, or assessment using less computing power.

[0149] A reference ATTR biomarker profile can be generated from multiple reference ATTR biomarker profiles by any of several computer computation approaches known to those skilled in the art. Machine learning models can be easily constructed, for example, using any number of statistical programming languages, e.g., R; scripting languages, e.g., Python; and associated machine learning packages, data mining software, e.g., Weka or Java, Mathematica, Matlab or SAS.

[0150] The subject's ATTR biomarker profile can be compared to a reference ATTR biomarker profile generated as described above, or otherwise by a person skilled in the art, to generate an output assessment. Several output assessments are consistent with the present disclosures herein. The output assessment includes a single assessment, often narrowed down by sensitivity, specificity, or sensitivity and specificity parameters, indicating a health status assessment (e.g., the probability that the subject has TTR-CM, the subject is not at risk of TTR-CM, the subject is at risk of TTR-CM, the subject has TTR-CM). Alternating or in combination, further parameters such as the subject's demographic factors (e.g., one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographical location) and / or the subject's image-based biomarkers (e.g., left ventricular septum thickness and / or ejection fraction) are provided.

[0151] More specifically, in some embodiments, the method disclosed herein further includes the step of diagnosing a subject with amyloid-trans-tyretin cardiomyopathy (TTR-CM) if the level of at least one of the detected ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) is above a threshold. In some embodiments, the method disclosed herein further includes the step of diagnosing a subject with amyloid-trans-tyretin cardiomyopathy (TTR-CM) if the level of at least one of the detected ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than a threshold. In some embodiments, the methods disclosed herein further include the step of diagnosing a subject with amyloid-trans-tyretin cardiomyopathy (TTR-CM) if the level of each of the detected one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) is above a threshold. In some embodiments, the methods disclosed herein further include the step of diagnosing a subject with amyloid-trans-tyretin cardiomyopathy (TTR-CM) if the level of each of the detected one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than a threshold.

[0152] In some embodiments, the methods disclosed herein further include a step of recommending a subject for one or more cardiomyopathy tests if the level of at least one or more ATTR biomarkers is above a threshold. In some such methods, a subject is recommended for one or more cardiomyopathy tests if the level of at least one of the detected ATTR biomarkers is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than the threshold. In some embodiments, the methods disclosed herein further include a step of recommending a subject for one or more cardiomyopathy tests if the level of each of the one or more ATTR biomarkers is above a threshold. In some such methods, a subject is recommended for one or more cardiomyopathy tests if the level of each of the detected ATTR biomarkers is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than the threshold.

[0153] As described above, in some embodiments, a method for detecting one or more ATTR biomarkers (e.g., ATTR biomarker proteins) in a sample includes the step of contacting the sample with one or more antibody drugs against the ATTR biomarker. In some embodiments, such a method also includes the step of contacting the sample with a first set of one or more detection drugs. In some embodiments, the antibody drug is labeled with the first set of one or more detection drugs. In some embodiments, the first set of one or more detection drugs includes one or more acridinium ester molecules.

[0154] Acridinium ester (AE) molecules can be used to label proteins and nucleic acids. Acridinium-labeled proteins can be used for detection in immunoassays. When AE is exposed to alkaline H2O2 (hydrogen peroxide), chemiluminescence occurs. Depending on the specific AE variant, the light is emitted at a maximum wavelength in the range of 430-480 nm. Such light can be detected, for example, by a high-efficiency photomultiplier tube.

[0155] In some embodiments, the step of detecting binding between an ATTR biomarker and one or more antibody drugs against the ATTR biomarker includes the step of determining absorbance or emission values ​​for a first set of one or more detection drugs. For example, the absorbance value indicates the level of binding (e.g., higher absorbance indicates more binding). In some embodiments, the absorbance or emission value for the first set of one or more detection drugs is above a threshold. In some embodiments, the absorbance or emission value for the first set of one or more detection drugs is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than the threshold. In some embodiments, the threshold is the average of the absorbance or emission values ​​determined for a second set of one or more detection drugs labeling two or more control samples. In some such embodiments, the second set of one or more detection drugs is similar to or the same as the first set of one or more detection drugs.

[0156] In one embodiment, the Disclosure provides a method for detecting one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) in a subject, and for detecting whether one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) are present in a sample obtained from the subject, in accordance with the method for detecting the above-mentioned biological markers. In some embodiments, the method for detecting one or more ATTR biomarkers includes the step of detecting binding between the ATTR biomarker and one or more anti-ATTR biomarker antibody drugs. In some embodiments, the step of detecting whether one or more ATTR biomarkers are present in a sample obtained from the subject includes the step of detecting the level of one or more ATTR biomarkers present in the sample obtained from the subject. In some embodiments, the level of the detected ATTR biomarker is above a threshold for each ATTR biomarker. In some embodiments, the level of the detected ATTR biomarker is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than the threshold.

[0157] In some embodiments, the disclosure provides a method for detecting one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) in a sample obtained from a subject. Biological markers for amyloid trans-tyretin cardiomyopathy may include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the control sample used in the above method includes a sample obtained from one or more subjects that do not have ATTR amyloidosis and / or TTR-CM.

[0158] In some embodiments, the method disclosed herein further includes the step of diagnosing a subject with amyloid trans-tyretin cardiomyopathy (TTR-CM) if the level of at least one or more detectable ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) exceeds a threshold. In some embodiments, the method includes the step of diagnosing a subject with TTR-CM if the level of at least one or more detectable ATTR biomarkers is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than a threshold.

[0159] In some embodiments, the method disclosed herein further includes the step of diagnosing a subject with amyloid trans-tyretin cardiomyopathy (TTR-CM) if the absorbance or emission value for a first set of one or more detection agents exceeds a threshold. In some embodiments, the method includes the step of diagnosing a subject with TTR-CM if the absorbance or emission value for a first set of one or more detection agents is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than the threshold. In some embodiments, the threshold is the average of the absorbance or emission values ​​determined for a second set of one or more detection agents labeling two or more control samples. In some such embodiments, the second set of one or more detection agents is similar to or the same as the first set of one or more detection agents.

[0160] In some embodiments, the methods disclosed herein further include the step of recommending a subject for one or more cardiomyopathy tests if the level of at least one or more ATTR biomarkers is above a threshold. In some such methods, a subject is recommended for one or more cardiomyopathy tests if the level of at least one or more detectable ATTR biomarkers is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than a threshold.

[0161] In some embodiments, the methods disclosed herein further include the step of recommending a subject for one or more cardiomyopathy tests if the absorbance or emission value for a first set of one or more detection agents exceeds a threshold. In some such methods, a subject is recommended for one or more cardiomyopathy tests if the absorbance or emission value for a first set of one or more detection agents is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than the threshold.

[0162] Cardiomyopathy tests that can be used according to the methods of this disclosure include echocardiography or advanced imaging methods. In some embodiments, the advanced imaging method is cardiac magnetic resonance imaging (CMR) or scintigraphy (e.g., 99mThis includes using radioisotope conjugates such as Tc-pyrophosphate and / or using single-photon emission computed tomography (SPECT). In some embodiments, the methods disclosed herein further include recommending a subject for cardiac biopsy if the level of at least one or more detectable ATTR biomarkers is above a threshold and the subject tests positive for cardiomyopathy in one or more cardiomyopathy tests. In some such embodiments, the biopsy tissue is tested for cardiomyopathy by immunochemical staining. For example, the immunochemical staining includes the use of one or more antibody drugs against kappa or lambda light chain amyloid deposits and / or one or more antibody drugs against trans tyretin deposits in cardiac tissue. If the presence of trans tyretin deposits in cardiac tissue is indicated by immunochemical staining, the subject can be diagnosed with amyloid-trans tyretin cardiomyopathy (TTR-CM).

[0163] In any of the embodiments described above, the threshold may be the average of the values ​​detected for two or more control samples. In some such embodiments, the values ​​detected for two or more control samples represent the control level for one or more ATTR biomarkers. In some embodiments, the control samples include recombinant ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof). In some embodiments, the two or more control samples are each samples obtained from subjects that do not have TTR-CM. In some embodiments, the threshold is a value reported in a standard table.

[0164] How to use the ATTR biomarker profile The algorithm-based assays and associated information provided by implementing any of the methods described herein can facilitate optimal treatment and decision-making for a subject. For example, the methods described herein may enable a physician or caregiver to identify patients who are unlikely to have TTR-CM and therefore will not require treatment, further cardiac testing, or increased monitoring for TTR-CM, or patients who are likely to have TTR-CM and will require treatment, further cardiac testing, or increased monitoring for TTR-CM.

[0165] The score of an ATTR biomarker can, in some cases, be determined by the application of a specific algorithm. In some embodiments, the score of an ATTR biomarker is quantitative. The algorithms used to calculate the score of an ATTR biomarker in the methods disclosed herein can group the expression level values ​​of an ATTR biomarker or a group of ATTR biomarkers. Furthermore, by forming specific groups of ATTR biomarkers, it is possible to mathematically weight the contributions of various expression levels of an ATTR biomarker or a subset of ATTR biomarkers (e.g., classifiers) to the quantitative score.

[0166] Exemplary ATTR biomarkers and their corresponding amino acid sequences are listed in Table 1. Exemplary combinations of ATTR biomarkers are listed in Tables 2-5.

[0167] The methods described herein, as well as the kits and systems provided herein, may utilize algorithm-based diagnostic assays to predict whether a sampled subject is at risk of or has TTR-CM, to select a sampled subject for one or more cardiomyopathy tests, and / or to select a sampled subject for receiving one or more doses of a TTR stabilizer.

[0168] The levels of one or more ATTR biomarkers, as well as optionally one or more demographic factors (e.g., one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographical location), and / or image-based biomarkers (e.g., left ventricular septal thickness and / or ejection fraction), can be used alone or in conjunction with a functional subset to calculate ATTR biomarker scores used to select subjects for sampling for one or more cardiomyopathy tests, and / or to select subjects for receiving one or more doses of TTR stabilizers, in order to predict whether a sampled subject is at risk of or has TTR-CM.

[0169] The methods disclosed herein include the step of using an ATTR biomarker profile to compute a score for an ATTR biomarker on a computer. In some embodiments, the step of using an ATTR biomarker profile to compute a score for an ATTR biomarker on a computer includes the step of applying an algorithm to the ATTR biomarker profile to compute a score for an ATTR biomarker on a computer. In some embodiments, the algorithm is or derived from decision trees, neural boosting, bootstrap forests, boost trees, K-nearest neighbors, generalized regression variable augmentation, generalized regression pruning variable augmentation, fit stepwise, generalized regression lasso, generalized regression elastic net, generalized regression ridge, nominal logistic regression, support vector machine, discriminant regression, naive Bayes, or a combination thereof. In some embodiments, the algorithm is a decision tree, neural boost, bootstrap forest, boost tree, generalized regression lasso, generalized regression elastic network, generalized regression ridge, nominal logistic regression, support vector machine, discriminant, or a combination thereof, or derived from these.

[0170] A machine learning-based algorithm can use an ATTR biomarker profile that includes data corresponding to one or more (e.g., two or more) demographic factors and data corresponding to the levels of one or more ATTR biomarkers in a sample from a subject. In some embodiments, two or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof. In some embodiments, two or more ATTR biomarkers include TnI and RBP4. In some embodiments, two or more ATTR biomarkers further include NT-proBNP, TIMP2, or both NT-proBNP and TIMP2. In some embodiments, two or more ATTR biomarkers is three or more ATTR biomarkers. In some embodiments, two or more ATTR biomarkers is two or three ATTR biomarkers. In some embodiments, two or more ATTR biomarkers is four ATTR biomarkers. In some embodiments, two or more ATTR biomarkers is only two ATTR biomarkers. In some embodiments, one or more demographic factors include one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographical location. In some embodiments, one or more demographic factors include age, biological sex, or both age and biological sex. In some embodiments, one or more demographic factors are two demographic factors. In some embodiments, the two demographic factors are age and biological sex.

[0171] A machine learning-based algorithm can be used to further combine an ATTR biomarker profile, which includes data corresponding to the levels of one or more ATTR biomarkers in a sample from a subject and image-based data corresponding to one or more biomarkers about the subject, with data corresponding to one or more demographic factors about the subject, for example. For example, septal thickness and TnI can be used in combination with each other (even without using a machine learning-based algorithm). In some embodiments, the ATTR biomarker includes data corresponding to the levels of two or more ATTR biomarkers. In some embodiments, the two or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof. In some embodiments, the two or more ATTR biomarkers include TnI and RBP4. In some embodiments, the two or more ATTR biomarkers further include NT-proBNP, TIMP2, or both NT-proBNP and TIMP2. In some embodiments, the two or more ATTR biomarkers are three or more ATTR biomarkers. In some embodiments, two or more ATTR biomarkers are two or three ATTR biomarkers. In some embodiments, two or more ATTR biomarkers are four ATTR biomarkers. In some embodiments, two or more ATTR biomarkers are only two ATTR biomarkers. In some embodiments, one or more image-based biomarkers are derived from one or more images (e.g., video) of the subject. Images of the subject can be obtained from any suitable source, including, for example, ultrasound, echocardiography, tomography, optical coherence tomography (OCT), angiography, magnetic resonance imaging (MRI), ventricular contrast, and nuclear medicine. In some embodiments, the image-based biomarkers are measurements derived from one or more images. In some embodiments, the image-based biomarkers include septal thickness (e.g., left ventricular septum thickness), posterior wall thickness, ejection fraction, or a combination thereof.Pattern recognition and / or segmentation and classification can be used to determine the status of an image-based biomarker from one or more images (e.g., measurements corresponding to one or more image-based biomarkers) (e.g., for one or more subjects). One or more images can be processed or preprocessed before determining measurements for one or more image-based biomarkers, or as part of determining measurements. In some embodiments, the data (e.g., measurements) corresponding to the image-based biomarkers is manually derived from one or more images, for example, by a physician. Measurements for image-based biomarkers can be determined using machine learning algorithms, for example, by performing automated segmentation and classification. Machine learning algorithms can be trained using data corresponding to one or more image-based biomarkers, for example, in combination with ATTR biomarker profiles and / or demographic factors. The data corresponding to one or more image-based biomarkers may include one or more images and / or one or more measurements.

[0172] In some embodiments, the machine learning algorithm processes only ATTR biomarker profiles, which include data corresponding to one or more (e.g., two or more) demographic factors and data corresponding to the levels of two or more (e.g., only two) ATTR biomarkers, in order to determine one or more outputs. In some embodiments, the machine learning algorithm accepts no other inputs than ATTR biomarker profiles, which include data corresponding to one or more (e.g., two or more) demographic factors and data corresponding to the levels of two or more (e.g., only two) ATTR biomarkers, in order to determine one or more outputs.

[0173] In some embodiments, the method includes the step of providing a machine-trained algorithm as input data corresponding to one or more demographic factors of the subject and a corresponding ATTR biomarker profile. In some embodiments, one or more demographic factors include biological sex, age, or both biological sex and age. The corresponding ATTR biomarker profile includes data on the levels of two or more ATTR biomarkers in a sample of the subject characterized by the demographic factors. The two or more ATTR biomarkers may be, for example, two ATTR biomarkers (e.g., TnI and RBP4) or three ATTR biomarkers (e.g., TnI, RBP4, and NT-proBNP). The performance of the machine-trained algorithm (e.g., predictive power, sensitivity and / or selectivity, and / or classification accuracy) can be improved by using a machine-trained algorithm that considers two or more ATTR biomarkers and one or more (e.g., two or more) demographic factors compared to a machine-trained algorithm that does not consider any demographic factors.

[0174] Further algorithms can be used in the manner provided herein, and the algorithms provided above are merely examples of the types of algorithms that can be used to generate scores for ATTR biomarkers. Exemplary algorithms are described, for example, in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 396-408 and 411-412; and in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, each of which is incorporated herein by reference. Furthermore, as previously stated, combinations of algorithms can be used in the manner provided herein. For example, the boosted tree method may be a combination of the decision tree method and the boosting method. Further combinations are possible and intended for use in the manner provided herein. Exemplary algorithms that can be used in the manner provided herein are listed below.

[0175] Decision tree One methodological framework that can be used to calculate ATTR biomarker scores from ATTR biomarker profiles is the decision tree. Decision trees can be constructed using a training population and specific data analysis algorithms. Decision trees are commonly described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which are incorporated herein by reference. The tree-based method divides the feature space into a series of rectangles, and then fits a model (such as constants) into each rectangle.

[0176] Training population data may include ATTR biomarker profiles (e.g., levels of one or more ATTR biomarkers in the sample) across the entire training set population. One specific algorithm that can be used to construct decision trees is Classification and Regression Tree (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and Random Forest. CART, ID3, and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 396-408 and 411-412, and are incorporated herein by reference. CART, MART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, and are incorporated herein by reference in their entirety. Random forests are described in Breiman, 1999, "Random Forests—Random Features," Technical Report 567, Statistics Department, UC Berkeley, September 1999, and their entire content is incorporated herein by reference.

[0177] The purpose of a decision tree is to derive a classifier (tree) from real-world example data. This tree can be used to classify unseen examples that were not used to derive the decision tree. Thus, the decision tree can be derived from training data. Exemplary training data includes data for multiple subjects (e.g., training populations). An ATTR biomarker profile can be provided and / or used for each of these subjects. In some embodiments, the training data includes an ATTR biomarker profile for the training population.

[0178] The following algorithm illustrates an exemplary decision tree derivation: Tree (e.g., class, feature) Create a root node If all examples have the same class value, give the root this label. Otherwise, if the feature is empty, label the route according to the most common value. Otherwise, start the following: Calculate the information gain for each feature. Select feature A, which has the highest information gain, and make it the root feature. For each possible value of this feature, v Add a new branch under the root corresponding to A=v. Let example (v) be an example where A = v. If example (v) is empty, make the new branch a leaf node labeled with the most common value in the example. Otherwise, the new branch will be the tree created by the following Tree (e.g., (v), class, feature - {A}) end

[0179] In a univariate decision tree, each split is based on a feature value (e.g., level) for the corresponding biomarker. Furthermore, multivariate decision trees can be implemented using the methods described herein. In some embodiments, splits are based on features corresponding to demographic factors, either individually or in combination with features for the corresponding biomarkers. For example, splits may be based on levels for sex and one or more biomarkers, levels for age and one or more biomarkers, or combinations of features corresponding to both age and sex and levels for one or more biomarkers. A multivariate decision tree is described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 408-409, which is incorporated herein by reference. In such a multivariate decision tree, some or all decisions involve a linear combination of features (e.g., levels) for multiple ATTR biomarkers in an ATTR profile. Such a linear combination can be trained using known techniques such as gradient descent in classification, or by the use of a sum-squared-error criterion.

[0180] As an explanatory example, the following formula is used: 0.05(X1) + 0.2(X2) < 500

[0181] In this example, X1 and X2 refer to two different features (e.g., levels) for two different ATTR biomarkers. To apply this methodology, the values ​​of features X1 and X2 (e.g., as part of an ATTR biomarker profile) are obtained from measurements taken from unclassified subjects. These values ​​are then inserted into the formula. If a value less than 500 is calculated by the computer, the first branch of the decision tree is then selected. Otherwise, the second branch of the decision tree is selected.

[0182] ATTR biomarker profiles can be used to train machine learning algorithms. ATTR biomarker profiles may come from different subjects, the same subject at different times, or a combination of these. ATTR biomarker profiles may be associated with corresponding data indicating health status (e.g., they may be labeled with such data). For example, the ATTR biomarker profiles of subjects used as training data may be labeled with data indicating the subject's health status. Such corresponding data may be probabilities or scores (e.g., ATTR biomarker scores). Health status may be the probability that a subject has TTR-CM, whether a subject is at risk of TTR-CM, or whether a subject has TTR-CM (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM). Health status can be determined manually, for example, by a physician. Labeling of ATTR biomarker profiles with corresponding data indicating health status can be done manually (e.g., by a physician determining health status).

[0183] Demographic factor data and / or image-based biomarkers can be used in combination with ATTR biomarker profiles to train machine learning algorithms. For example, a machine learning algorithm can be trained using an ATTR biomarker profile and data for one or more demographic factors and / or one or more image-based biomarkers for each of a set of subjects. The set could be, for example, at least 10 subjects, at least 20 subjects, at least 50 subjects, at least 100 subjects, at least 200 subjects, at least 300 subjects, at least 500 subjects, at least 1,000 subjects, at least 1,500 subjects, at least 2,000 subjects, at least 5,000 subjects, or at least 10,000 subjects. In some embodiments, a combination of one or more demographic factors—age, sex, or both age and sex—and an ATTR biomarker profile for each of the set of subjects is used to train a machine learning algorithm. An ATTR biomarker profile can include data corresponding to levels of two or more ATTR biomarkers. ATTR biomarkers may be, or may include, TnI and RBP4, for example, NT-proBNP, TIMP2, or a combination of both NT-proBNP and TIMP2.

[0184] In some embodiments, training a machine learning algorithm may include determining one or more features. Each feature may correspond to an ATTR biomarker. One or more features may be determined based on ATTR biomarker profiles and / or linear combinations thereof. One or more features may correspond to levels of ATTR biomarkers. Training a machine learning algorithm may include determining one or more rules of decision based on ATTR biomarker profiles used as training data. One or more rules of decision may further be based on one or more demographic factors corresponding to the subject with respect to the ATTR biomarker profiles. One or more rules of decision may further be based on one or more image-based biomarkers corresponding to the subject with respect to the ATTR biomarker profiles. One or more rules of decision may be based on one or more features (e.g., determined during training). One or more rules of decision may be used in one or more decision trees.

[0185] To improve weak decision rules, bagging, boosting, and additive trees can be combined with decision methodologies. These techniques are designed for and typically applied to decision trees such as those described above. In some embodiments, machine learning-based algorithms may include at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees. Furthermore, such techniques may also be useful for decision rules developed using other types of data analysis algorithms, such as linear discriminant analysis.

[0186] In bagging, a training set is sampled to generate randomly independent bootstrap replicas, a decision rule is constructed based on each of these, and these are then combined by simple majority voting in the final decision rule. See, for example, Breiman, 1996, Machine Learning 24, 123-140; and Efron & Tibshirani, An Introduction to Boostrap, Chapman & Hall, New York, 1993, the entire content of which is incorporated herein by reference.

[0187] In boosting, decision rules are constructed using a weighted version of the training set that relies on previous classification results. Initially, all features under consideration have equal weights, and the first decision rule is constructed on this dataset. The weights are then varied according to the performance of the decision rule. Misclassified features are given greater weights, and the next decision rule is boosted again with the weighted training set. In this way, a series of training sets and decision rules are obtained, which are then combined in the final decision rule by simple or weighted majority voting. The entire content is incorporated herein by reference, see, for example, "Experiments with a new boosting algorithm," Proceedings 13th International Conference on Machine Learning, 1996, pp. 148-156.

[0188] Decision tree algorithms, or machine-learned algorithms derived from decision tree algorithms, have been found to perform unexpectedly well compared to other types of algorithms (e.g., neural networks) when used to determine health status using biomarkers, particularly combinations of laboratory-based biomarkers (e.g., ATTR biomarkers). Health status can include (i) the status of the subject's disease, disorder, or condition (e.g., stage), (ii) whether the subject has the disease, disorder, or condition, and (iii) at least one (e.g., two, or all three) of the probabilities that the subject has the disease, disorder, or condition. A variety of biomarkers can be used in this context, including protein biomarkers, enzyme biomarkers, peptide biomarkers, or intermediate filament biomarkers (e.g., neurofilament biomarkers). Bootstrap forest algorithms, for example, perform particularly well within this class of methodologies, possessing high specificity and sensitivity. The nature of the decision rules, learned and then used as part of a decision tree, can contribute to the performance of such methodologies, for example, when using quantitative biomarkers. Demographic factors may also be particularly well suited for use in decision tree algorithms. These findings are not limited to specific cases of TTR-CM, nor are they necessarily limited to cardiac disease, disability, or condition in general.

[0189] The measurement data used in the methods, systems, kits, and compositions disclosed herein may be normalized. Normalization refers to the process of correcting, for example, differences in the amount of assayed gene or protein levels and variability in the quality of the templates used to eliminate unwanted sources of systematic variability in the processing and detection of gene or protein expression. Other sources of systematic variability may be due to processing conditions in the laboratory.

[0190] In some cases, normalization methods are used to normalize processing conditions in a laboratory. Non-limiting examples of normalization of laboratory processing that can be used in the methods of this disclosure include, but are not limited to, taking into account systematic differences between instruments, reagents, and equipment used in the data generation process, and / or between data and time or time elapsed in data acquisition.

[0191] The assay can provide normalization by incorporating the expression of a specific normalization standard gene or protein known to have no significant difference in expression levels under relevant conditions, i.e., to have a stable and constant expression level in that particular sample type. Suitable normalization genes and proteins that can be used in this disclosure include housekeeping genes (see, for example, E. Eisenberg et al., Trends in Genetics 19(7):362-365 (2003)). In some applications, it is known that normalization biomarkers (genes and proteins), also called reference genes, do not show a significant difference in expression levels in subjects with TTR-CM compared to control subjects without TTR-CM. In some applications, it may be useful to add stable isotope-labeled standards that can be used for data normalization and correspond to entities with known properties for use in data normalization.

[0192] In other applications, standard immobilized samples can be measured for each analytical batch to account for instrument and daily measurement variability.

[0193] Machine learning algorithms for subselecting identifying biomarkers and, optionally, target characteristics, and for constructing classification models, are used in some of the methods and systems herein to determine clinical outcome scores. Examples of such algorithms are described above. These algorithms can help in the selection of important biomarker features and can convert underlying measurements into scores or probabilities relating to, for example, clinical outcomes, disease risk, disease likelihood, disease presence, treatment response, and / or disease status classifications.

[0194] A machine learning-based algorithm can output a score for ATTR biomarkers. The algorithm can determine whether a subject is at risk of or has TTR-CM. In some embodiments, the output of the machine learning-based algorithm is a determination (e.g., probability) of whether a subject is at risk of or has TTR-CM. In some embodiments, the algorithm determines whether a subject is at risk of or has TTR-CM by, for example, determining the probability that the subject has TTR-CM, or determining the probability that the subject is at risk of developing TTR-CM. The machine learning-based algorithm can be a classifier for TTR-CM. In some embodiments, the algorithm can be used to classify whether a subject has TTR-CM based, for example, on the subject's ATTR biomarker profile. In some embodiments, the classifier or classification has sensitivity and specificity greater than 80% (e.g., at least one or both greater than 90%).

[0195] A machine learning model can be used to determine whether a subject has TTR-CM. For example, in some embodiments, the machine learning algorithm outputs the probability that the subject has TTR-CM. Generally, and according to some embodiments, the decision that a subject has TTR-CM can be made when it is determined that the subject is more likely to have TTR-CM than not to have TTR-CM (i.e., a probability cutoff of 0.5 or 50%), and vice versa (to determine if the subject does not have TTR-CM). However, other probability cutoffs may be used instead. The sensitivity and specificity of the machine learning algorithm may change if the probability cutoff is changed.

[0196] The ATTR biomarker score can be determined by comparing a target-specific ATTR biomarker profile with a reference ATTR biomarker profile. The reference ATTR biomarker profile may represent a known diagnosis. For example, the ATTR biomarker profile may represent a positive diagnosis of TTR-CM. As an alternative example, the reference ATTR biomarker profile may represent a negative diagnosis of TTR-CM. In some cases, an increase in the score indicates a higher likelihood of one or more of the following: poor clinical outcome, good clinical outcome, high disease risk, low disease risk, complete response, partial response, stable disease, non-response, and recommendations for treatment for disease management. In some cases, a decrease in the quantitative score indicates a higher likelihood of one or more of the following: poor clinical outcome, good clinical outcome, high disease risk, low disease risk, complete response, partial response, stable disease, non-response, and recommendations for treatment for disease management.

[0197] An ATTR biomarker profile from a control that is similar to the reference ATTR biomarker profile often indicates a higher likelihood of one or more of the following: poor clinical outcome, good clinical outcome, high disease risk, low disease risk, complete response, partial response, stable disease, non-response, and recommendations for treatment for disease management. In some applications, an ATTR biomarker profile that is not similar between the subject and the reference indicates a higher likelihood of one or more of the following: poor clinical outcome, good clinical outcome, high disease risk, low disease risk, complete response, partial response, stable disease, non-response, and recommendations for treatment for disease management.

[0198] The results can be provided to the subject, healthcare professionals, or other specialists. The results may, in some cases, be accompanied by health recommendations, such as recommendations to confirm or independently assess the risk of TTR-CM using one or more cardiomyopathy tests.

[0199] Recommendations may include information related to the treatment regimen, e.g., information indicating the treatment regimen. The effectiveness of the regimen may be assessed by comparing the subject's ATTR biomarker profile at an early point in time, sometimes before treatment, and at a later second point in time, sometimes after treatment. ATTR biomarker profiles may be compared to each other, to each reference, or otherwise assessed to determine whether the treatment regimen should be continued, increased, replaced with an alternative regimen, or discontinued because it has demonstrated efficacy and has successfully addressed TTR-CM or associated signs and symptoms. Some assessments rely on comparing the subject's ATTR biomarker profile at multiple points in time, such as at least one point in time before treatment and at least one point in time after treatment. ATTR biomarker profiles may be compared to each other and to at least one reference biomarker panel level.

[0200] Treatment of amyloid trans tyretin cardiomyopathy Therapeutic approaches to ATTR amyloidosis include reducing TTR production, inhibiting or reducing TTR aggregation, inhibiting or reducing TTR fibril or amyloid formation, reducing or removing TTR deposits, and stabilizing a non-toxic conformation of TTR (e.g., tetrameric form). In some embodiments, the methods disclosed herein further include detecting the level of one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) in a sample obtained from a subject, and administering an effective amount of a trans tiretin (TTR) stabilizer to the diagnosed subject. In some embodiments, the TTR stabilizer is tafamidis.

[0201] In some embodiments, the Disclosure includes a method for selecting a patient for treatment with a TTR stabilizer, comprising the step of detecting the level of one or more ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof) in a sample obtained from the subject.

[0202] In some embodiments, the Disclosure includes a method for treating TTR-CM in a subject at risk of or suffering from TTR-CM, the method comprising administering a therapeutically effective dose of a TTR stabilizer to the subject, wherein the subject expresses an ATTR biomarker or ATTR biomarker gene product at a threshold level. In some embodiments, a method for treating TTR-CM in a subject at risk of or suffering from TTR-CM comprises administering a therapeutically effective dose of a TTR stabilizer to the subject, wherein the subject expresses one or more ATTR biomarkers or ATTR biomarker gene products at a threshold level. In some embodiments, the method further includes determining that the subject expresses an ATTR biomarker or ATTR biomarker gene product at a threshold level. In some embodiments, it is determined prior to administration that the subject expresses an ATTR biomarker or ATTR biomarker gene product at a threshold level.

[0203] In some embodiments, the biomarkers disclosed herein can be used to screen patients for effective therapies for ATTR amyloidosis and TTR-CM. In some embodiments, the therapy for TTR-CM is a stabilizer of the TTR tetramer. Examples of TTR stabilizers include tafamidis and diflunisal.

[0204] Another therapeutic approach is to reduce total TTR production. For example, antisense oligonucleotide (ASO)-based therapies and RNA interference (RNAi) are therapeutic approaches to reduce total TTR. ISIS-TTR RxThis is an ASO-based therapy that causes the destruction of both wild-type and mutant TTR transcripts. Other possible therapeutic agents include ALN-TTR02 (patisiran), ALN-TTRsc (Revurisan), doxycycline, tauroursodeoxycholic acid (TUDCA), a combination of doxycycline and TUDCA, epigallocatechin gallate (EGCG), curcumin, or resveratrol. Antibodies that target TTR can also be used as therapies, for example, by antibody-mediated inhibition of TTR aggregation and fibrillation; antibody-mediated stabilization of non-toxic conformations of TTR (e.g., tetrameric form); or antibody-mediated clearance of aggregated TTR, oligomeric TTR, or monomeric TTR. Furthermore, antibodies against TTR can be conjugated or attached to therapeutic agents, for example, to target TTR.

[0205] kit The Disclosure also provides a kit comprising one or more anti-ATTR biomarker drugs and instructions for use (e.g., therapeutic, prophylactic, or diagnostic use). In some embodiments, the kit is used for an in vitro diagnostic assay to diagnose TTR-CM (including TTR-CM resulting from wild-type ATTR amyloidosis). In some embodiments, the kit of the Disclosure further comprises a TTR stabilizer (e.g., tafamidis).

[0206] In some embodiments, one or more anti-ATTR biomarker drugs include antibody drugs. In some embodiments, one or more antibody drugs are labeled with a detectable portion. In some embodiments, the kit further includes a detection agent (e.g., one or more acridinium ester molecules). In some embodiments, one or more antibody drugs are labeled with one or more acridinium ester molecules. In some embodiments, the kit further includes one or more secondary antibody drugs that specifically bind to one or more anti-ATTR biomarker antibody drugs.

[0207] In some embodiments, one or more anti-ATTR biomarker drugs comprise nucleic acid probes. In some embodiments, at least a portion of each nucleic acid probe hybridizes to one or more nucleotides encoding an ATTR biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof). The nucleotides encoding the ATTR biomarker may 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., the detection agents indicate the presence of nucleotides encoding the ATTR biomarker).

[0208] In some embodiments, the kit further includes one or more control samples. In some embodiments, the control samples include one or more ATTR biomarker standards. In some embodiments, the ATTR biomarker standards include recombinant ATTR biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof). In some embodiments, the ATTR biomarker standards include synthetic ATTR biomarker nucleic acids (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof).

[0209] In addition to the above, the kit may include other components, such as solvents or buffers, stabilizers or preservatives, and / or agents for treating the conditions or disorders described herein. Alternatively, other components may be included in the kit but in a different composition or container from the anti-ATTR biomarker drug. In such embodiments, the kit may include instructions for mixing the anti-ATTR biomarker drug with the other components, or for using the anti-ATTR biomarker together with the other components.

[0210] In certain embodiments, a kit for use in accordance with this disclosure may include a reference or control sample, instructions for processing the sample and performing tests on the sample, instructions for interpreting the results, buffers and / or other reagents required to perform the tests.

[0211] This disclosure also provides the recognition that certain single ATTR biomarkers may be useful in detecting and / or diagnosing ATTR amyloidosis or TTR-CM. Furthermore, this disclosure provides the insight that certain combinations of ATTR biomarkers are particularly useful in detecting and / or diagnosing ATTR amyloidosis or TTR-CM. Therefore, the methods, compositions, and kits described herein can be used in assays to assess the risk of TTR-CM, to determine whether a subject should undergo further cardiac testing, and / or to diagnose TTR-CM based on the detection or measurement of ATTR biomarkers in a sample, e.g., a biological sample obtained from a subject.

[0212] The methods and kits provided herein can detect TTR-CM in a sample with sensitivity and specificity that makes the test results medically reliable. The methods and kits provided herein for the detection and / or diagnosis of TTR-CM of a target can detect TTR-CM with sensitivity greater than 75%, greater than 80%, greater than 85%, greater than 90%, greater than 95%, greater than 96%, greater than 97%, greater than 98%, greater than 99%, or about 100%. In some embodiments, the methods and kits provided herein can detect TTR-CM with sensitivity between about 70% and 100%, between about 80% and 100%, or between about 90% and 100%. In some embodiments, the methods and kits provided herein can detect TTR-CM with specificity greater than 70%, greater than 75%, greater than 80%, greater than 85%, greater than 90%, greater than 95%, greater than 96%, greater than 97%, greater than 98%, greater than 99%, or about 100%. In some embodiments, the methods and kits provided herein can detect TTR-CM with specificity between approximately 50% and 100%, between approximately 60% and 100%, between approximately 70% and 100%, between approximately 80% and 100%, or between approximately 90% and 100%. In some embodiments, the methods and kits provided herein can detect TTR-CM with sensitivity and specificity of 50% or more, 60% or more, 70% or more, 75% or more, 80% or more, 85% or more, or 90% or more. In some embodiments, the methods and kits provided herein can detect TTR-CM with sensitivity and specificity between approximately 50% and 100%, between approximately 60% and 100%, between approximately 70% and 100%, between approximately 80% and 100%, or between approximately 90% and 100%.

[0213] composition Compositions are also provided herein. In some embodiments, a composition comprises one or more ATTR biomarkers and one or more anti-ATTR biomarker drugs. In some embodiments, one or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof, and one or more anti-ATTR biomarker drugs include anti-TnI drugs, anti-PKM1 drugs, anti-PKM2 drugs, anti-NT-proBNP drugs, anti-RBP4 drugs, anti-TIMP2 drugs, anti-NfL drugs, or a combination thereof.

[0214] In some embodiments, the composition comprises a combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, five or more, etc.) and a corresponding combination of anti-ATTR biomarker drugs.

[0215] In some embodiments, the composition comprises two or more ATTR biomarkers and two or more anti-ATTR biomarker drugs. In some embodiments, the two or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof, and the two or more anti-ATTR biomarker drugs include an anti-TnI drug, an anti-PKM1 drug, an anti-PKM2 drug, an anti-NT-proBNP drug, an anti-RBP4 drug, an anti-TIMP2 drug, an anti-NfL drug, or a combination thereof.

[0216] In some embodiments, the composition comprises three or more ATTR biomarkers and three or more anti-ATTR biomarker drugs. In some embodiments, the three or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the three or more anti-ATTR biomarker drugs include an anti-TnI drug, an anti-PKM1 drug, an anti-PKM2 drug, an anti-NT-proBNP drug, an anti-RBP4 drug, an anti-TIMP2 drug, an anti-NfL drug, or a combination thereof.

[0217] In some embodiments, the composition comprises four or more ATTR biomarkers and four or more anti-ATTR biomarker drugs. In some embodiments, the four or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the four or more anti-ATTR biomarker drugs include an anti-TnI drug, an anti-PKM1 drug, an anti-PKM2 drug, an anti-NT-proBNP drug, an anti-RBP4 drug, an anti-TIMP2 drug, an anti-NfL drug, or a combination thereof.

[0218] In some embodiments, the composition comprises five or more ATTR biomarkers and five or more anti-ATTR biomarker drugs. In some embodiments, the five or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combination thereof. In some embodiments, the five or more anti-ATTR biomarker drugs include an anti-TnI drug, an anti-PKM1 drug, an anti-PKM2 drug, an anti-NT-proBNP drug, an anti-RBP4 drug, an anti-TIMP2 drug, an anti-NfL drug, or a combination thereof.

[0219] In some embodiments, the composition includes a combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) and a corresponding combination of anti-ATTR biomarker drugs. In some embodiments, the composition includes the combinations of ATTR biomarkers and anti-ATTR biomarker drugs shown in Tables 2-5. In some embodiments, the composition includes the combinations of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) and anti-ATTR biomarker drugs shown in Table 2 and a corresponding combination of anti-ATTR biomarker drugs. In some embodiments, the combinations of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) and anti-ATTR biomarker drugs shown in Table 3 and a corresponding combination of anti-ATTR biomarker drugs. In some embodiments, the combinations of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) and anti-ATTR biomarker drugs shown in Table 4 and a corresponding combination of anti-ATTR biomarker drugs. In some embodiments, the combinations of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) and the corresponding combinations of anti-ATTR biomarker drugs are as shown in Table 5.

[0220] In some embodiments, the composition comprises TnI and an anti-TnI drug. In some embodiments, the composition comprises TnI, PKM1, an anti-TnI drug, and an anti-PKM1 drug. In some embodiments, the composition comprises TnI, PKM2, an anti-TnI drug, and an anti-PKM2 drug. In some embodiments, the composition comprises TnI, PKM1, PKM2, an anti-TnI drug, an anti-PKM1 drug, and an anti-PKM2 drug. In some embodiments, the composition comprises NT-proBNP, RBP4, or both, and an anti-NT-proBNP drug, an anti-RBP4 drug, or both. In some embodiments, the composition comprises TIMP2, NfL, or both, and an anti-TIMP2 drug, an anti-NfL drug, or both.

[0221] In some embodiments, the composition comprises NT-proBNP and an anti-NT-proBNP agent. In some embodiments, the composition comprises TnI, PKM1, PKM2, RBP4 or a combination thereof and an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, an anti-RBP4 agent or a combination thereof. In some embodiments, the composition comprises TIMP2, NfL or both and an anti-TIMP2 agent, an anti-NfL agent or both.

[0222] In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM2, NT-proBNP, RBP4, an anti-TnI drug, an anti-PKM2 drug, an anti-NT-proBNP drug, and an anti-RBP4 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM2, NT-proBNP, an anti-TnI drug, an anti-NT-proBNP drug, and an anti-RBP4 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM2, RBP4, an anti-TnI drug, an anti-PKM2 drug, and an anti-RBP4 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM1, NT-proBNP, an anti-TnI drug, an anti-PKM1 drug, and an anti-NT-proBNP drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM1, RBP4, an anti-TnI drug, an anti-PKM1 drug, and an anti-RBP4 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM1, PKM2, NT-proBNP, RBP4, an anti-TnI drug, an anti-PKM1 drug, an anti-PKM2 drug, an anti-NT-proBNP drug, and an anti-RBP4 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM1, PKM2, NT-proBNP, an anti-TnI drug, an anti-PKM1 drug, an anti-PKM2 drug, and an anti-NT-proBNP drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TnI, PKM1, PKM2, RBP4, an anti-TnI drug, an anti-PKM1 drug, an anti-PKM2 drug, and an anti-RBP4 drug.In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes NT-proBNP, RBP4, TnI, an anti-TnI drug, an anti-NT-proBNP drug, and an anti-RBP4 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes RBP4, SMOC-2, TnI, an anti-TnI drug, an anti-RBP4 drug, and an anti-SMOC-2 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes DCN, NT-proBNP, TnI, an anti-TnI drug, an anti-NT-proBNP drug, and an anti-DCN drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes DCN, RBP4, TnI, an anti-TnI drug, an anti-RBP4 drug, and an anti-DCN drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes NT-proBNP, SMOC-2, TnI, an anti-TnI drug, an anti-NT-proBNP drug, and an anti-SMOC-2 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes NT-proBNP, TIMP2, TnI, an anti-TnI drug, an anti-NT-proBNP drug, and an anti-TIMP2 drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes NT-proBNP, TnI, an anti-TnI drug, and an anti-NT-proBNP drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes DCN, TnI, an anti-TnI drug, and an anti-DCN drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes DCN, TIMP2, TnI, an anti-TnI drug, an anti-TIMP2 drug, and an anti-DCN drug. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes RBP4, TnI, an anti-TnI drug, and an anti-RBP4 drug.In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes RBP4, TIMP2, TnI, anti-TnI drugs, anti-RBP4 drugs, and anti-TIMP2 drugs. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes DCN, SMOC-2, TnI, anti-TnI drugs, anti-SMOC-2 drugs, and anti-DCN drugs. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes TIMP2, TnI, anti-TnI drugs, and anti-TIMP2 drugs. In some embodiments, the combination of ATTR biomarkers (e.g., one or more, two or more, three or more, four or more, etc.) includes SMOC-2, TIMP2, TnI, anti-TnI drugs, anti-SMOC-2 drugs, and anti-TIMP2 drugs.

[0223] In some embodiments, one or more anti-ATTR biomarker drugs in the compositions provided herein include antibody drugs. In some embodiments, one or more antibody drugs are labeled with a detectable portion. In some embodiments, the kit further includes a detection agent (e.g., one or more acridinium ester molecules). In some embodiments, one or more antibody drugs are labeled with one or more acridinium ester molecules. In some embodiments, the kit further includes one or more secondary antibody drugs that specifically bind to one or more anti-ATTR biomarker antibody drugs.

[0224] In some embodiments, one or more anti-ATTR biomarker drugs 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 an ATTR biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL or combinations thereof). The nucleotides encoding the ATTR biomarker may be DNA (e.g., cDNA) or RNA (e.g., mRNA). In some embodiments, the nucleic acid probe is labeled with one or more detection agents (e.g., the detection agent indicates the presence of nucleotides encoding the ATTR biomarker).

[0225] In some embodiments, the composition comprises one or more control samples. In some embodiments, the control sample comprises one or more ATTR biomarker standards. In some embodiments, the ATTR biomarker standard comprises a recombinant ATTR biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL or combinations thereof). In some embodiments, the ATTR biomarker standard comprises synthetic ATTR biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL or combinations thereof) nucleic acid.

[0226] In addition to the above, the composition can include other components, such as solvents or buffers, stabilizers or preservatives, and / or agents for treating the conditions or disorders described herein.

[0227] Computer system The methods described herein can be performed on a computer system having a processor that executes specific instructions in a computer program. In some embodiments, the computer system can be configured to output an ATTR biomarker score based on the reception of an ATTR biomarker profile and / or levels of two or more ATTR biomarkers. In particular, the computer program may include instructions for the system to select appropriate next steps, such as further pharmaceuticals (e.g., TTR stabilizers), treatments, and / or further tests (e.g., cardiomyopathy tests) for the subject.

[0228] In some embodiments, a computer program can be configured to enable a computer system to identify subjects for further testing (e.g., cardiomyopathy testing), identify subjects as being at risk of or having TTR-CM, and / or patients for receiving medication (e.g., TTR stabilizers), and to calculate ATTR biomarker scores using the data, based on received data (e.g., ATTR biomarker profiles). The system may be able to rank the identified next steps based on the ATTR biomarker profiles along with demographic factors and / or image-based biomarkers. The system may adjust the ranking based, for example, on the clinical response of a subject, or a subject or family member of a subject who has or is suspected of having TTR-CM.

[0229] Figure 11 is a block diagram of a computer system 1100 that can be used in the above operation according to one embodiment. The system 1100 includes a processor 1110, memory 1120, storage device 1130, and input / output device 1140. Each of the components 1110, 1120, 1130, and 1140 is interconnected using a system bus 1150. The system may include an analytical instrument 1160 for determining the level of one or more ATTR biomarkers in a sample.

[0230] The processor 1110 can process instructions for execution within the system 1100. In one embodiment, the processor 1110 is a single-threaded processor. In another embodiment, the processor 1110 is a multi-threaded processor. The processor 1110 can process instructions stored in memory 1120 or storage device 1130, including those for receiving or transmitting information through input / output devices 1140.

[0231] Memory 1120 stores information within the 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.

[0232] The storage device 1130 can provide large-capacity storage to the system 1100. In one embodiment, the storage device 1130 is a computer-readable medium.

[0233] The input / output device 1140 provides input / output operations to the system 1100. In one embodiment, the input / output device 1140 includes a keyboard and / or a pointing device. In one embodiment, the input / output device 1140 includes a display unit for displaying a graphical user interface.

[0234] System 1100 can be used to build a database. Figure 12 shows a flowchart of Method 1200 for building a database to be used to identify subjects for further testing (e.g., cardiomyopathy testing), subjects as being at risk of or having TTR-CM, and / or patients for receiving medication (e.g., TTR stabilizers). Preferably, Method 1200 is performed in System 1100. For example, a computer program product may include instructions to cause Processor 1110 to perform the steps of Method 1200 or Method 1300.

[0235] Method 1200 includes the following steps: In step 1210, receive the ATTR biomarker profile of the subject (e.g., the levels of one or more ATTR biomarkers in the sample). A computer program in System 600 may include instructions for presenting a suitable graphical user interface on an input / output device 640, the graphical user interface may prompt the user to input levels 670 using the input / output device 640, e.g., a keyboard. In step 1220, calculate the ATTR biomarker scores from the ATTR biomarker profile. As described herein, in step 1220, calculate the ATTR biomarker scores from (i) the ATTR biomarker profile and (ii) demographic factors and / or image-based biomarkers. In step 1230, store the ATTR biomarker scores. System 600 may store the ATTR biomarker scores in a storage device 630. In addition, or instead, System 600 may provide readout information including the ATTR biomarker scores. The readout information may also include the next steps proposed for the subject and / or confidence levels related to the ATTR biomarker scores.

[0236] Method 1300 includes the following steps: Step 1310 detects the level of one or more ATTR biomarkers in a sample, for example, from a subject. Step 1320 obtains an ATTR biomarker profile using the levels of one or more ATTR biomarkers. Step 1330 calculates the ATTR biomarker score from the ATTR biomarker profile. As described herein, step 1330 calculates the ATTR biomarker score from (i) the ATTR biomarker profile and (ii) demographic factors and / or image-based biomarkers. Step 1340 stores the ATTR biomarker score. System 600 can store the ATTR biomarker score in storage device 630. In addition, or instead, System 600 can provide readout information including the ATTR biomarker score. The readout information may also include the next steps proposed for the subject and / or confidence levels related to the ATTR biomarker score.

[0237] Furthermore, a non-temporary computer-readable medium is provided which contains executable instructions that cause a processor to perform an operation including the methods provided herein at runtime. For example, the non-temporary computer-readable medium containing executable instructions causes a processor to perform an operation including the methods 1200 or 1300 described above at runtime.

[0238] Exemplary embodiments of the systems and methods disclosed herein have been described above in relation to computer computing performed locally by a computer device. However, computer computing performed over a network is also intended. Figure 28 shows an exemplary network environment 2800 for use in the methods and systems described herein. Briefly, referring here to Figure 28, a block diagram of the exemplary cloud computing environment 2800 is shown and described. The cloud computing environment 2800 may include one or more resource providers 2802a, 2802b, 2802c (collectively, 2802). Each resource provider 2802 may include a computer computing resource. In some implementations, the computer computing resource may include any hardware and / or software used to process data. For example, the computer computing resource may include hardware and / or software capable of running algorithms, computer programs and / or computer applications. In some implementations, the exemplary computer computing resource may include an application server and / or database having storage and retrieval capabilities. Each resource provider 2802 may be connected to any other resource provider 2802 in the cloud computing environment 2800. In some implementations, resource providers 2802 may be connected on the computer network 2808. Each resource provider 2802 may be connected on the computer network 2808 to one or more computer devices 2804a, 2804b, 2804c (collectively, 2804).

[0239] The cloud computing environment 2800 may include a resource manager 2806. The resource manager 2806 may be connected to resource providers 2802 and computing devices 2804 on a computer network 2808. In some implementations, the resource manager 2806 can facilitate the provision of computing resources to one or more computing devices 2804 by one or more resource providers 2802. The resource manager 2806 can receive requests for computing resources from specific computing devices 2804. The resource manager 2806 can identify one or more resource providers 2802 that can provide the computing resources requested by computing devices 2804. The resource manager 2806 can select resource providers 2802 to provide computing resources. The resource manager 2806 can facilitate connections between resource providers 2802 and specific computing devices 2804. In some implementations, the resource manager 2806 can establish connections between specific resource providers 2802 and specific computing devices 2804. In some implementations, the resource manager 2806 can redirect a specific computer device 2804 to a specific resource provider 2802 that has the requested computing resources.

[0240] Figure 29 shows examples of computer computing devices 2900 and mobile computer computing devices 2950 that can be used in the manner and systems described herein. Computer computing device 2900 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Mobile computer computing device 2950 is intended to represent various forms of mobile devices, such as personal digital assistants, mobile phones, smartphones, and other similar computer computing devices. The components shown herein, their connections and relationships, and their functions are intended to be illustrative only and not to limit.

[0241] The computer calculator 2900 includes a processor 2902, memory 2904, storage device 2906, a high-speed interface 2908 connected to memory 2904 and several high-speed expansion ports 2910, and a low-speed interface 2912 connected to a low-speed expansion port 2914 and storage device 2906. Each of the processor 2902, memory 2904, storage device 2906, high-speed interface 2908, high-speed expansion port 2910, and low-speed interface 2912 is interconnected using various buses and can be mounted on a common motherboard or in other ways as needed. The processor 2902 can process instructions for execution within the computer calculator 2900, including instructions stored in memory 2904 or storage device 2906 to present graphical information for a GUI on an external input / output device such as a display 2916 connected to the high-speed interface 2908. In other implementations, multiple processors and / or multiple buses can be used, along with multiple memories and several types of memory, as needed. Furthermore, multiple computer devices may be connected to each device that provides a portion of the required operations (for example, as a server bank, a group of blade servers, or a multiprocessor system). Therefore, where the term is used herein, if multiple functions are described as being performed by "processors," this includes embodiments in which multiple functions are performed by any number of processors (e.g., one or more processors) of any number of computer devices (e.g., one or more computer devices).Furthermore, where a function is described as being performed by a “processor,” this includes embodiments in which the function is performed by any number of processors (e.g., one or more processors) of any number of computer devices (e.g., one or more computer devices) (e.g., in a distributed computing system).

[0242] Memory 2904 stores information within the computer calculator 2900. In some implementations, memory 2904 is one or more volatile memory units. In some implementations, memory 2904 is one or more non-volatile memory units. Memory 2904 may also be another form of computer-readable medium, such as a magnetic disk or an optical disk.

[0243] The storage device 2906 can provide large-capacity storage to the computer computing device 2900. In some implementations, the storage device 2906 may be, or include, numerous devices, including computer-readable media such as hard disk drives, optical disk drives, flash memory or other similar solid-state storage devices, or storage networks or other devices in a configuration. Instructions may be stored in the information carrier. When an instruction is executed by one or more processing units (e.g., processor 2902), it performs one or more methods, such as the methods described above. Instructions may also be stored in one or more storage devices, such as computers or machine-readable media (e.g., memory 2904, storage device 2906 or memory on processor 2902).

[0244] The high-speed interface 2908 manages bandwidth-intensive operations of the computer unit 2900, while the low-speed interface 2912 manages slower bandwidth-intensive operations. Such functional assignments are merely examples. In some implementations, the high-speed interface 2908 is connected to memory 2904, a display 2916 (e.g., via a graphics processor or accelerator), and a high-speed expansion port 2910 that can accept various expansion cards (not shown). In this implementation, the low-speed interface 2912 is connected to storage device 2906 and the low-speed expansion port 2914. The low-speed expansion port 2914, which can include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet), may be connected, for example, via a network adapter to one or more input / output devices, e.g., a keyboard, pointing device, scanner, or networking device, e.g., a switch or router.

[0245] The computer calculator 2900 can be implemented in several different forms, as shown in the figure. For example, it can be implemented as a standard server 2920, or multiple times in a group of such servers. Furthermore, it can be implemented as a personal computer, for example, a laptop computer 2922. It can also be implemented as part of a rack server system 2924. Alternatively, the components of the computer calculator 2900 can be combined with other components of a mobile device (not shown), such as a mobile computer calculator 2950. Each of such devices may contain one or more computer calculators 2900 and mobile computer calculators 2950, ​​and the entire system may consist of multiple computer calculators communicating with each other.

[0246] The mobile computer computing device 2950 includes, among other components, a processor 2952, a memory 2964, an input / output device such as a display 2954, a communication interface 2966, and a transceiver 2968. The mobile computer computing device 2950 can also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the processor 2952, the memory 2964, the display 2954, the communication interface 2966, and the transceiver 2968 are interconnected using various buses, and some of the components can be mounted on a common motherboard or otherwise as needed.

[0247] The processor 2952 can execute instructions within the mobile computer computing device 2950, including instructions stored in the memory 2964. The processor 2952 can be implemented as a chipset of chips that includes a plurality of separate analog and digital processors. The processor 2952 can provide, for example, coordination of other components of the mobile computer computing device 2950, such as control of the user interface, execution of applications by the mobile computer computing device 2950, and wireless communication by the mobile computer computing device 2950.

[0248] The processor 2952 can communicate with the user via a control interface 2958 and a display interface 2956 connected to the display 2954. The display 2954 may be, for example, a TFT (Thin Film Transistor Liquid Crystal) display, an OLED (Organic Light Emitting Diode) display, or other suitable display technology. The display interface 2956 may include appropriate circuitry for driving the display 2954 to present graphical and other information to the user. The control interface 2958 can receive commands from the user and translate them for transmission to the processor 2952. Furthermore, the external interface 2962 can provide communication with the processor 2952 to enable short-range communication between the mobile computer calculator 2950 and other devices. The external interface 2962 may provide, for example, wired communication in some implementations and wireless communication in other implementations, and multiple interfaces may be used.

[0249] Memory 2964 stores information within the mobile computer 2950. Memory 2964 can be implemented as one or more computer-readable media, one or more volatile memory units, or one or more non-volatile memory units. An expansion memory 2974 can also be provided, which can be connected to the mobile computer 2950 via an expansion interface 2972, which may include a SIMM (Single In-Line Memory Module) card interface, for example. The expansion memory 2974 can provide extra storage space to the mobile computer 2950, ​​or it can store applications or other information of the mobile computer 2950. In particular, the expansion memory 2974 may contain instructions to perform or supplement the above processes, and may also contain secure information. For example, the expansion memory 2974 can be provided as a security module for the mobile computer 2950 and can be programmed with instructions that enable secure use of the mobile computer 2950. Furthermore, secure applications can be provided via a SIMM card along with additional information, such as storing identification information on the SIMM card in a hack-proof manner.

[0250] The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as described below. In some implementations, instructions are stored on an information carrier and executed by one or more processing units (e.g., processor 2952) in one or more ways, such as those described above. Instructions may also be stored in one or more storage devices, such as one or more computers or machine-readable media (e.g., memory 2964, extended memory 2974, or memory on processor 2952). In some implementations, instructions may be received by propagated signals, for example, through transceiver 2968 or external interface 2962.

[0251] The mobile computer 2950 can communicate wirelessly via a communication interface 2966, which may include digital signal processing circuits if necessary. The communication interface 2966 can provide communication under various modes or protocols, such as GSM voice calls (Pan-European Digital Mobile Telephony System), SMS (Short Message Service), EMS (Extended Messaging Service) or MMS messaging (Multimedia Messaging Service), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Purpose Packet Radio Service). Such communication may occur, for example, via a transceiver 2968 using radio frequencies. Furthermore, narrow-area communication may occur, for example, using Bluetooth®, Wi-Fi®, or other such transceivers (not shown). Furthermore, the GPS (Global Positioning System) receiver module 2970 can provide additional navigation and location-related radio data to the mobile computer 2950, ​​which can be used as needed by applications running on the mobile computer 2950.

[0252] The mobile computer 2950 can also communicate audibly using an audio codec 2960 that can receive information spoken by the user and convert it into usable digital information. Furthermore, the audio codec 2960 can generate audible sound for the user, for example, through the speaker of the mobile computer 2950's handset. Such sound may include sounds from a voice call, recorded sounds (e.g., voice messages, music files), and sounds generated by applications running on the mobile computer 2950.

[0253] The mobile computer calculator 2950 can be implemented in several different forms, as shown in the figure. For example, it can be implemented as a mobile phone 2980. It can also be implemented as part of a smartphone 2982, a personal digital assistant, or other similar mobile device.

[0254] Various implementations of the systems and techniques described herein can be realized in digital electronic circuits, integrated circuits, specially designed ASICs (application-specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations may be dedicated or general-purpose and may include implementations in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor that may be connected to a storage system, at least one input device and at least one output device to receive data and instructions from and to transmit data and instructions to the storage system, at least one input device and at least one output device.

[0255] These computer programs (also known as programs, software, software applications, or code) contain machine instructions to a programmable processor and may be implemented in high-level procedural and / or object-oriented programming languages, as well as in assembly / machine languages. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, device, and / or apparatus (e.g., magnetic disks, optical disks, memory, programmable logic devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including machine-readable medium that receives machine instructions as machine-readable signals. The term machine-readable signal refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0256] To provide user interaction, the systems and techniques described herein may be implemented in a computer having a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) and a keyboard and pointing device (e.g., a mouse or trackball) to which the user can provide input to the computer. Other types of devices may also be used to provide user interaction; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, voice, or tactile input.

[0257] The systems and techniques described herein may be implemented in a computer computing system that includes backend components (e.g., as a data server), middleware components (e.g., an application server), or frontend components (e.g., a client computer having a graphical user interface or web browser that allows a user to interact with the implementation of the systems and techniques described herein), or any combination of such backend, middleware, or frontend components. The components of the system may be interconnected by digital data communications (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0258] Computer systems can include clients and servers. Clients and servers are generally geographically distant from each other and typically interact via a communication network. The client-server relationship arises from computer programs running on each computer that have a client-server relationship with one another.

[0259] Numbered exemplary embodiments Embodiment 1. A method comprising the step of detecting the respective levels of two or more trans tiretin amyloidosis (ATTR) biomarkers in a sample, wherein the two or more ATTR biomarkers include (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), (v) retinol-binding protein 4 (RBP4), (vi) tissue metalloproteinase inhibitor 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

[0260] Embodiment 2. The method according to Embodiment 1, wherein the levels of three or more ATTR biomarkers in a sample are detected, and the three or more ATTR biomarkers include (i) TnI, (ii) PKM1, (iii) PKM2, (iv) NT-proBNP, (v) RBP4, (vi) TIMP2, (vii) NfL, or (viii) a combination thereof.

[0261] Embodiment 3. The method according to Embodiment 1 or 2, wherein the levels of four or more ATTR biomarkers in a sample are detected, and the four or more ATTR biomarkers include (i) TnI, (ii) PKM1, (iii) PKM2, (iv) NT-proBNP, (v) RBP4, (vi) TIMP2, (vii) NfL, or (viii) a combination thereof.

[0262] Embodiment 4. A method for detecting the levels of two or more ATTR biomarkers in a sample, wherein the two or more ATTR biomarkers include TnI.

[0263] Embodiment 5. The method according to Embodiment 4, wherein the two or more ATTR biomarkers include TnI and PKM1.

[0264] Embodiment 6. The method according to Embodiment 4, wherein the two or more ATTR biomarkers include TnI and PKM2.

[0265] Embodiment 7. The method according to Embodiment 4, wherein the two or more ATTR biomarkers include TnI, PKM1, and PKM2.

[0266] Embodiment 8. The method according to any one of Embodiments 4 to 7, wherein two or more ATTR biomarkers further include NT-proBNP, RBP4, or both.

[0267] Embodiment 9. The method according to any one of Embodiments 4 to 8, wherein two or more ATTR biomarkers further include TIMP2, NfL, or both.

[0268] Embodiment 10. A method for detecting the levels of two or more ATTR biomarkers in a sample, wherein the two or more ATTR biomarkers include NT-proBNP.

[0269] Embodiment 11. The method according to any one of Embodiments 4 to 7, wherein two or more ATTR biomarkers further include TnI, PKM1, PKM2, RBP4, or a combination thereof.

[0270] Embodiment 12. The method according to any one of Embodiments 4 to 8, wherein two or more ATTR biomarkers further include TIMP2, NfL, or both.

[0271] Embodiment 13. A method, (a) a step of detecting the levels of two or more ATTR biomarkers in a sample to obtain an ATTR biomarker profile; and (b) A process of using the ATTR biomarker profile to computer-calculate the ATTR biomarker score. Methods that include...

[0272] Embodiment 14. The method according to Embodiment 13, wherein two or more ATTR biomarkers include (i) TnI, (ii) PKM1, (iii) PKM2, (iv) NT-proBNP, (v) RBP4, (vi) TIMP2, (vii) NfL, or (viii) a combination thereof.

[0273] Embodiment 15. The method according to Embodiment 13 or 14, wherein the levels of three or more ATTR biomarkers in a sample are detected, and the three or more ATTR biomarkers include (i) TnI, (ii) PKM1, (iii) PKM2, (iv) NT-proBNP, (v) RBP4, (vi) TIMP2, (vii) NfL, or (viii) a combination thereof.

[0274] Embodiment 16. The method according to any one of Embodiments 13 to 15, wherein the levels of four or more ATTR biomarkers in a sample are detected, and the four or more ATTR biomarkers include (i) TnI, (ii) PKM1, (iii) PKM2, (iv) NT-proBNP, (v) RBP4, (vi) TIMP2, (vii) NfL, or (viii) a combination thereof.

[0275] Embodiment 17. The method according to any one of Embodiments 13 to 16, wherein two or more ATTR biomarkers include TnI.

[0276] Embodiment 18. The method according to any one of Embodiments 13 to 17, wherein two or more ATTR biomarkers include TnI and PKM1.

[0277] Embodiment 19. The method according to any one of Embodiments 13 to 18, wherein two or more ATTR biomarkers include TnI and PKM2.

[0278] Embodiment 20. The method according to any one of Embodiments 13 to 19, wherein two or more ATTR biomarkers include TnI, PKM1, and PKM2.

[0279] Embodiment 21. The method according to any one of Embodiments 17 to 20, wherein two or more ATTR biomarkers further include NT-proBNP, RBP4, or both.

[0280] Embodiment 22. The method according to any one of Embodiments 17 to 21, wherein two or more ATTR biomarkers further include TIMP2, NfL, or both.

[0281] Embodiment 23. The method according to any one of Embodiments 13 to 16, wherein two or more ATTR biomarkers include NT-proBNP.

[0282] Embodiment 24. The method according to Embodiment 23, wherein two or more ATTR biomarkers further include TnI, PKM1, PKM2, RBP4, or a combination thereof.

[0283] Embodiment 25. The method according to Embodiment 23 or 24, wherein two or more ATTR biomarkers further include TIMP2, NfL, or both.

[0284] Embodiment 26. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, PKM1, NT-proBNP, and RBP4.

[0285] Embodiment 27. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, PKM1, and RBP4.

[0286] Embodiment 28. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, PKM1, and NT-proBNP.

[0287] Embodiment 29. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, PKM2, NT-proBNP, and RBP4.

[0288] Embodiment 30. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, PKM2, and RBP4.

[0289] Embodiment 31. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, PKM2, and NT-proBNP.

[0290] Embodiment 32. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, PKM1, PKM2, NT-proBNP, and RBP4.

[0291] Embodiment 33. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, PKM1, PKM2, and RBP4.

[0292] Embodiment 34. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, NT-proBNP, and RBP4.

[0293] Embodiment 35. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, NT-proBNP, and TIMP2.

[0294] Embodiment 36. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI and NT-proBNP.

[0295] Embodiment 37. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI and RBP4.

[0296] Embodiment 38. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, RBP4, and TIMP2.

[0297] Embodiment 39. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI and TIMP2.

[0298] Embodiment 40. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, NT-proBNP, and DCN.

[0299] Embodiment 41. The method according to Embodiment 1, wherein two or more ATTR biomarkers include or consist of TnI, RBP4, and DCN.

[0300] Embodiment 42. The method according to any one of Embodiments 1-3, 4, 6, 8, 9, 10-12, 13-17, 19 and 21-25, wherein two or more ATTR biomarkers do not include PKM1.

[0301] Embodiment 43. The method according to any one of Embodiments 1-3, 4, 5, 8, 9, 10-12, 13-18 and 21-25, wherein two or more ATTR biomarkers do not include PKM2.

[0302] Embodiment 44. The method according to any one of Embodiments 1-3, 4, 8, 9, 10-12, 13-17 and 21-25, wherein the two or more ATTR biomarkers do not include PKM1 or PKM2.

[0303] Embodiment 45. The method according to any one of Embodiments 1 to 44, wherein two or more ATTR biomarkers do not include SMOC-2.

[0304] Embodiment 46. The method according to any one of Embodiments 1-39 and 42-44, wherein two or more ATTR biomarkers do not include DCN.

[0305] Embodiment 47. The method according to any one of Embodiments 1 to 39 and 42 to 44, wherein the two or more ATTR biomarkers do not include SMOC-2 or DCN.

[0306] Embodiment 48. A method for determining the risk of developing amyloid trans tyretin cardiomyopathy (TTR-CM) in a subject, wherein the sample is obtained from the subject, according to any one of Embodiments 1 to 47.

[0307] Embodiment 49. A method for diagnosing a subject as TTR-CM, wherein the sample is obtained from the subject, according to any one of Embodiments 1 to 47.

[0308] Embodiment 50. A method according to any one of Embodiments 1 to 47, which is a method for treating TTR-CM in a subject who is at risk of or has TTR-CM.

[0309] Embodiment 51. The method according to any one of Embodiments 48 to 50, wherein TTR-CM is derived from wild-type trans tyretin amyloidosis (ATTRwt).

[0310] Embodiment 52. The method according to any one of Embodiments 48 to 51, wherein the subject is negative for familial amyloid cardiomyopathy (ATTRm) as determined by genetic testing.

[0311] Embodiment 53. A method for selecting a subject to receive one or more doses of a TTR stabilizer, wherein the sample is obtained from the subject, according to any one of Embodiments 1 to 47.

[0312] Embodiment 54. The method according to Embodiment 53, further comprising the step of administering one or more doses of a TTR stabilizer to a target.

[0313] Embodiment 55. A method for selecting subjects for one or more cardiomyopathy tests, wherein the sample is obtained from the subjects, according to any one of Embodiments 1 to 47.

[0314] Embodiment 56. The method according to Embodiment 55, wherein one or more cardiomyopathy tests include echocardiography, advanced imaging methods, or both.

[0315] Embodiment 57. The method according to Embodiment 56, wherein the advanced imaging method includes magnetic resonance imaging (CMR), scintigraphy, or both.

[0316] Embodiment 58. Scintigraphy is 977 The method according to Embodiment 57, which includes the use of a radioisotope conjugate such as Tc-pyrophosphate.

[0317] Embodiment 59. The method according to Embodiment 57 or 58, wherein scintigraphy is performed using single-photon emission computed tomography (SPECT).

[0318] Embodiment 60. A method according to any one of Embodiments 1 to 47, which is a method for determining that a patient does not have TTR-CM or is not at risk of developing TTR-CM.

[0319] Embodiment 61. The method according to any one of Embodiments 1 to 59, wherein the sample is a biological sample or includes a biological sample.

[0320] Embodiment 62. The method according to Embodiment 61, wherein the biological sample comprises blood, serum, plasma, or cardiac tissue.

[0321] Embodiment 63. The method according to any one of Embodiments 1 to 62, wherein the sample is obtained from the subject.

[0322] Embodiment 64. The method according to Embodiment 63, wherein the subject is a human.

[0323] Embodiment 65. The method according to Embodiment 63 or 64, relating to a person who has amyloid trans tyretin cardiomyopathy (TTR-CM) or is at risk of developing it.

[0324] Embodiment 66. The method according to any one of Embodiments 48 to 65, wherein the subject is at least 65 years of age.

[0325] Embodiment 67. The method according to any one of Embodiments 1 to 66, wherein the step of detecting the levels of two or more ATTR biomarkers in a sample includes the step of detecting the presence, absence, level, or genotype of each of the two or more ATTR biomarkers in the sample.

[0326] Embodiment 68. The method according to any one of Embodiments 1 to 67, wherein the step of detecting the levels of two or more ATTR biomarkers in the sample includes the step of performing mass spectrometry.

[0327] Embodiment 69. The method according to any one of Embodiments 1 to 68, wherein the step of detecting the levels of two or more ATTR biomarkers in a sample includes the step of measuring chemiluminescence.

[0328] Embodiment 70. The method according to any one of Embodiments 1 to 69, wherein the step of detecting the levels of two or more ATTR biomarkers in a sample includes the step of detecting two or more nucleotides encoding two or more ATTR biomarkers in a sample.

[0329] Embodiment 71. The method according to Embodiment 70, wherein the step of detecting two or more nucleotides encoding two or more ATTR biomarkers includes the step of performing a nucleic acid amplification method.

[0330] Embodiment 72. The method according to Embodiment 71, wherein the nucleic acid amplification method is selected from the group consisting of polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), transcription amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence-based amplification (NASBA).

[0331] Embodiment 73. The method according to any one of Embodiments 70 to 72, wherein the step of detecting two or more nucleotides encoding two or more ATTR biomarkers includes the step of detecting hybridization between two or more nucleic acid probes and two or more nucleotides encoding two or more ATTR biomarkers.

[0332] Embodiment <93. The method according to Embodiment 73, wherein two or more nucleic acid probes are each complementary to at least a portion of one of two or more nucleotides encoding two or more ATTR biomarkers.

[0333] Embodiment 75. The method according to any one of Embodiments 70 to 74, wherein the nucleotides encoding two or more ATTR biomarkers are DNA.

[0334] Embodiment 76. The method according to Embodiment 75, wherein the DNA is cDNA.

[0335] Embodiment 77. The method according to any one of Embodiments 70 to 74, wherein the nucleotides encoding two or more ATTR biomarkers are RNA.

[0336] Embodiment 78. The method according to any one of Embodiments 1 to 77, wherein the step of detecting the levels of two or more ATTR biomarkers in a sample includes the step of performing an immunoassay.

[0337] Embodiment 79. The method according to Embodiment 78, wherein the immunoassay is a chemiluminescent immunoassay.

[0338] Embodiment 80. The method according to Embodiment 78 or 79, wherein the immunoassay is selected from the group consisting of immunoprecipitation; Western blotting; ELISA; immunohistochemical testing; immunocytochemical testing; flow cytometry; and immunoPCR.

[0339] Embodiment 81. The method according to any one of Embodiments 78 to 80, wherein the immunoassay is an ELISA.

[0340] Embodiment 82. The method according to any one of Embodiments 78-81, wherein the immunoassay is a high-throughput and / or automated immunoassay platform.

[0341] Embodiment 83. The method according to any one of Embodiments 1 to 81, wherein the step of detecting the levels of two or more ATTR biomarkers in the sample includes the step of contacting the sample with two or more anti-ATTR biomarker antibody drugs.

[0342] Embodiment 84. The method according to any one of Embodiments 1 to 83, wherein the step of detecting the levels of two or more ATTR biomarkers in a sample obtained from a subject includes the step of bringing the sample into contact with two or more detection agents.

[0343] Embodiment 85. The method according to Embodiment 84, wherein two or more anti-ATTR biomarker antibody drugs are labeled with two or more detection agents.

[0344] Embodiment 86. The method according to Embodiment 84 or 85, wherein the two or more detection agents are two or more acridinium ester molecules.

[0345] Embodiment 87. The method according to any one of Embodiments 1 to 86, wherein the step of detecting the levels of two or more ATTR biomarkers in a sample includes the step of detecting the binding between two or more ATTR biomarkers and two or more anti-ATTR biomarker antibody drugs.

[0346] Embodiment 88. The method according to Embodiment 87, wherein the step of detecting the binding between two or more ATTR biomarkers and two or more anti-ATTR biomarker antibody drugs includes the step of performing an immunocytochemical test (ICC).

[0347] Embodiment 89. The method according to Embodiment 87 or 88, wherein the step of detecting binding between two or more ATTR biomarkers and two or more anti-ATTR biomarker antibody drugs includes the step of determining the absorbance or emission value of at least one ATTR biomarker.

[0348] Embodiment 90. The method according to Embodiments 1 to 89, wherein the step of detecting the levels of two or more ATTR biomarkers in a sample includes the step of detecting the level of each of the two or more ATTR biomarkers, and further includes the step of comparing the level of each of the two or more ATTR biomarkers with a threshold for each ATTR biomarker, wherein the level of each of the one or more ATTR biomarkers detected is above the threshold for each ATTR biomarker.

[0349] Embodiment 91. The method of Embodiment 90, further comprising the step of determining whether a subject has TTR-CM or is at risk of developing TTR-CM if the level of each of one or more ATTR biomarkers exceeds a threshold for each ATTR biomarker.

[0350] Embodiment 92. The method of Embodiment 90 or 91, further comprising the step of diagnosing a subject as TTR-CM when the level of each of one or more ATTR biomarkers exceeds a threshold for each ATTR biomarker.

[0351] Embodiment 93. The method according to any one of Embodiments 90 to 92, further comprising the step of recommending a subject for one or more cardiomyopathy tests if the level of each of one or more ATTR biomarkers is above a threshold for each ATTR biomarker.

[0352] Embodiment 94. The method according to any one of Embodiments 90 to 93, wherein the level of each of one or more ATTR biomarkers is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 times greater than the threshold for each ATTR biomarker.

[0353] Embodiment 95. The method according to any one of Embodiments 90 to 94, wherein the threshold for the ATTR biomarker is the average of the values ​​for the ATTR biomarker detected for two or more control samples.

[0354] Embodiment 96. The method according to Embodiment 95, wherein two or more control samples are each samples obtained from subjects that do not have ATTR amyloidosis.

[0355] Embodiment 97. The method according to any one of Embodiments 90 to 94, wherein the threshold for the ATTR biomarker is a value reported in a standard table.

[0356] Embodiment 98. The method according to any one of Embodiments 90 to 94, further comprising the step of recommending a subject for cardiac biopsy if the level of each of one or more ATTR biomarkers is above a threshold, wherein the subject tests positive for cardiomyopathy in one or more cardiomyopathy tests.

[0357] Embodiment 99. The method according to any one of Embodiments 13 to 89, wherein the step of using an ATTR biomarker profile to calculate a score for an ATTR biomarker by computer includes the step of applying an algorithm to the ATTR biomarker profile to calculate a score for an ATTR biomarker by computer, wherein the algorithm is a decision tree algorithm, a neural boost algorithm, a bootstrap forest algorithm, a boost tree algorithm, a K-nearest neighbor algorithm, a generalized regression variable augmentation algorithm, a generalized regression pruning variable augmentation algorithm, a fit stepwise algorithm, a generalized regression lasso algorithm, a generalized regression elastic net algorithm, a generalized regression ridge algorithm, a nominal logistic algorithm, a support vector machine algorithm, a discrimination algorithm, or a naive Bayes algorithm.

[0358] Embodiment 100. The method according to any one of Embodiments 13 to 89 and 99, wherein the step of using an ATTR biomarker profile to calculate a score for an ATTR biomarker by computer includes the step of applying an algorithm to the ATTR biomarker profile to calculate a score for an ATTR biomarker by computer, wherein the algorithm is a decision tree algorithm, a neural boost algorithm, a bootstrap forest algorithm, a boost tree algorithm, a generalized regression lasso algorithm, a generalized regression elastic net algorithm, a generalized regression ridge algorithm, a nominal logistic algorithm, a support vector machine algorithm, or a discriminant algorithm.

[0359] Embodiment 101. The method according to any one of Embodiments 13-89, 99, and 100, wherein the step of using an ATTR biomarker profile to calculate a score for an ATTR biomarker by computer includes the step of applying an algorithm to the ATTR biomarker profile to calculate a score for an ATTR biomarker by computer, wherein the algorithm is a decision tree algorithm, a neural boost algorithm, a bootstrap forest algorithm, a boost tree algorithm, or a support vector machine algorithm.

[0360] Embodiment 102. The method according to any one of Embodiments 13-89 and 99-101, further comprising the step of determining whether a subject from which a sample was obtained is at risk of or has TTR-CM, using an ATTR biomarker score.

[0361] Embodiment 103. The method according to any one of Embodiments 13-89 and 99-102, further comprising the step of determining whether a subject from which a sample was obtained is selected for one or more cardiomyopathy tests using an ATTR biomarker score.

[0362] Embodiment 104. The method according to any one of Embodiments 13-89 and 99-103, further comprising the step of determining whether a subject from which a sample was obtained is selected to receive one or more doses of a TTR stabilizer, using an ATTR biomarker score.

[0363] Embodiment 105. The method according to any one of Embodiments 1 to 104, further comprising immunochemical staining of a biopsy tissue derived from the subject.

[0364] Embodiment 106. The method according to Embodiment 90, wherein the biopsy tissue includes cardiac biopsy tissue.

[0365] Embodiment 107. The method according to Embodiment 90 or 91, wherein immunochemical staining comprises the use of two or more antibody drugs against kappa or lambda light chain amyloid deposits and / or two or more antibody drugs against trans tyretin deposits in cardiac tissue.

[0366] Embodiment 108. The method according to any one of Embodiments 90 to 92, further comprising the step of diagnosing a subject as TTR-CM when immunochemical staining indicates the presence of trans tyretin deposits in cardiac tissue.

[0367] Embodiment 109. (a) A step of detecting the levels of two or more ATTR biomarkers in a sample obtained from a subject to obtain an ATTR biomarker profile; and (b) A process of using the ATTR biomarker profile to computer-calculate the ATTR biomarker score. A method including, Two or more ATTR biomarkers include (i) TnI, (ii) PKM1, (iii) PKM2, (iv) NT-proBNP, (v) RBP4, (vi) TIMP2, (vii) NfL, or (viii) a combination thereof. The method for calculating ATTR biomarker scores using an ATTR biomarker profile includes the step of applying an algorithm to the ATTR biomarker profile to calculate ATTR biomarker scores using a computer, wherein the algorithm is a decision tree algorithm, a neural boost algorithm, a bootstrap forest algorithm, a boost tree algorithm, or a support vector machine algorithm.

[0368] Embodiment 110. The method according to Embodiment 109, wherein the subject is a human subject.

[0369] Embodiment 111. The method according to Embodiment 109 or 110, wherein the subject is at least 65 years of age.

[0370] Embodiment 112. The method according to any one of Embodiments 109 to 111, wherein the sample comprises blood, serum, plasma, or cardiac tissue.

[0371] Embodiment 113. The method according to any one of Embodiments 109 to 112, further comprising the step of determining whether a subject from which a sample was obtained is at risk of or has TTR-CM, using an ATTR biomarker score.

[0372] Embodiment 114. The method according to any one of Embodiments 109 to 113, further comprising the step of determining the risk of developing amyloid trans-tyretin cardiomyopathy (TTR-CM) in a subject using an ATTR biomarker score.

[0373] Embodiment 115. The method according to any one of Embodiments 109 to 114, further comprising the step of diagnosing a subject with TTR-CM using an ATTR biomarker score.

[0374] Embodiment 116. The method according to Embodiment 115, wherein TTR-CM is derived from wild-type trans tyretin amyloidosis (ATTRwt).

[0375] Embodiment 117. The method according to any one of Embodiments 109 to 116, further comprising the step of determining whether a subject from which a sample was obtained is selected for one or more cardiomyopathy tests, using an ATTR biomarker score.

[0376] Embodiment 118. The method according to Embodiment 117, wherein one or more cardiomyopathy tests include echocardiography, advanced imaging methods, or both.

[0377] Embodiment 119. The method according to Embodiment 118, wherein the advanced imaging method includes magnetic resonance imaging (CMR), scintigraphy, or both.

[0378] Embodiment 120. Scintigraphy is 99mThe method according to Embodiment 119, including the use of a radioisotope conjugate such as Tc-pyrophosphate.

[0379] Embodiment 121. The method according to Embodiment 119 or 120, wherein scintigraphy is performed using single-photon emission computed tomography (SPECT).

[0380] Embodiment 122. The method according to any one of Embodiments 109 to 121, further comprising the step of determining whether a subject from which a sample was obtained is selected to receive one or more doses of a TTR stabilizer, using an ATTR biomarker score.

[0381] Embodiment 123. The method according to Embodiment 122, further comprising the step of administering one or more doses of a TTR stabilizer to a target.

[0382] Embodiment 124. The method according to any one of Embodiments 13 to 123, comprising the step of computer-calculating a score for an ATTR biomarker using one or more demographic factors (for example, the application of an algorithm includes the step of using one or more demographic factors in an algorithm).

[0383] Embodiment 125. The method according to Embodiment 124, wherein one or more demographic factors include or consist of age and / or sex.

[0384] Embodiment 126. A non-temporary computer-readable medium comprising executable instructions that cause a processor to perform an operation comprising the method described in any one of Embodiments 1 to 125 at runtime.

[0385] Embodiment 127. (a) One or more ATTR biomarkers comprising (i) TnI, (ii) PKM1, (iii) PKM2, (iv) NT-proBNP, (v) RBP4, (vi) TIMP2, (vii) NfL, or (viii) a combination thereof; and (b) One or more anti-ATTR biomarkers, including (i) anti-TnI drugs, (ii) anti-PKM1 drugs, (iii) anti-PKM2 drugs, (iv) anti-NT-proBNP drugs, (v) anti-RBP4 drugs, (vi) anti-TIMP2 drugs, (vii) anti-NfL drugs, or (viii) combinations thereof. A composition containing the following:

[0386] Embodiment 128. (a) One or more ATTR biomarkers including TnI, and (b) One or more anti-ATTR biomarkers, including anti-TnI drugs A composition containing the following:

[0387] Embodiment 129. (a) One or more ATTR biomarkers include TnI and PKM1, (b) One or more anti-ATTR biomarker drugs include anti-TnI drugs and anti-PKM1 drugs. The composition according to Embodiment 128.

[0388] Embodiment 130. (a) One or more ATTR biomarkers include TnI and PKM2, (b) One or more anti-ATTR biomarker drugs include anti-TnI drugs and anti-PKM2 drugs. The composition according to Embodiment 128.

[0389] Embodiment 131. (a) One or more ATTR biomarkers include TnI, PKM1 and PKM2, (b) One or more anti-ATTR biomarker drugs include anti-TnI drugs, anti-PKM1 drugs and anti-PKM2 drugs, The composition according to Embodiment 128.

[0390] Embodiment 132. (a) One or more ATTR biomarkers further include NT-proBNP, RBP4, or both. (b) One or more anti-ATTR biomarker drugs further include anti-NT-proBNP drugs, anti-RBP4 drugs, or both. The composition according to any one of embodiments 127 to 131.

[0391] Embodiment 133. (a) One or more ATTR biomarkers further include TIMP2, NfL, or both. (b) One or more anti-ATTR biomarker drugs further include anti-TIMP2 drugs, anti-NfL drugs, or both. The composition according to any one of embodiments 127 to 132.

[0392] Embodiment 134. (a) One or more ATTR biomarkers including NT-proBNP, and (b) One or more anti-ATTR biomarkers, including anti-NT-proBNP drugs A composition containing the following:

[0393] Embodiment 135. (a) One or more ATTR biomarkers further include (i) TnI, (ii) PKM1, (iii) PKM2, (iv) RBP4, (v) TIMP2, (vi) NfL, or (vii) a combination thereof. (b) One or more anti-ATTR biomarker drugs further include (i) anti-TnI drugs, (ii) anti-PKM1 drugs, (iii) anti-PKM2 drugs, (iv) anti-RBP4 drugs, (v) anti-TIMP2 drugs, (vi) anti-NfL drugs, or (vii) combinations thereof. The composition described in Embodiment 134.

[0394] Embodiment 136. (a) One or more ATTR biomarkers include NT proBNP, TnI and RBP, (b) One or more anti-ATTR biomarker drugs include anti-NT proBNP drugs, anti-TnI drugs and anti-RBP drugs, The composition according to Embodiment 134 or 135.

[0395] Embodiment 137. A composition according to any one of Embodiments 127, 128, 130, and 132-136, wherein one or more ATTR biomarkers do not contain PKM1, and one or more anti-ATTR biomarker drugs do not contain an anti-PKM1 drug.

[0396] Embodiment 138. A composition according to any one of Embodiments 127-129 and 132-136, wherein one or more ATTR biomarkers do not contain PKM2, and one or more anti-ATTR biomarker drugs do not contain an anti-PKM2 drug.

[0397] Embodiment 139. The composition according to any one of Embodiments 127, 128, and 132-136, wherein one or more ATTR biomarkers do not include PKM1 or PKM2, and one or more anti-ATTR biomarker drugs do not include anti-PKM1 drugs or anti-PKM2 drugs.

[0398] Embodiment 140. A kit for detecting TTR-CM, (a) (i) anti-TnI drugs, (ii) anti-PKM1 drugs, (iii) anti-PKM2 drugs, (iv) anti-NT-proBNP drugs, (v) anti-RBP4 drugs, (vi) anti-TIMP2 drugs, (vii) anti-NfL drugs, or (viii) one or more anti-ATTR biomarker drugs including combinations thereof; and (b) instructions for use. A kit that includes this.

[0399] Embodiment 141. A kit for detecting TTR-CM, (a) One or more anti-ATTR biomarker drugs, including anti-TnI drugs; and (b) Instructions for use A kit that includes this.

[0400] Embodiment 142. The kit according to Embodiment 141, wherein one or more anti-ATTR biomarker drugs include an anti-TnI drug and an anti-PKM1 drug.

[0401] Embodiment 143. The kit according to Embodiment 141, wherein one or more anti-ATTR biomarker drugs include an anti-TnI drug and an anti-PKM2 drug.

[0402] Embodiment 144. The kit according to Embodiment 141, wherein one or more anti-ATTR biomarker drugs include an anti-TnI drug, an anti-PKM1 drug, and an anti-PKM2 drug.

[0403] Embodiment 145. A kit according to any one of Embodiments 141 to 144, wherein one or more anti-ATTR biomarker drugs further comprise an anti-NT-proBNP drug, an anti-RBP4 drug, or both.

[0404] Embodiment 146. A kit according to any one of Embodiments 141 to 145, wherein one or more anti-ATTR biomarker drugs further comprise an anti-TIMP2 drug, an anti-NfL drug, or both.

[0405] Embodiment 147. (a) One or more anti-ATTR biomarker drugs comprising an anti-NT-proBNP drug; and (b) Instructions for use A kit that includes this.

[0406] Embodiment 148. The kit according to Embodiment 147, wherein one or more anti-ATTR biomarker drugs further include (i) an anti-TnI drug, (ii) an anti-PKM1 drug, (iii) an anti-PKM2 drug, (iv) an anti-RBP4 drug, (v) an anti-TIMP2 drug, (vi) an anti-NfL drug, or (vii) a combination thereof.

[0407] Embodiment 149. The kit according to Embodiment 147 or 148, wherein one or more anti-ATTR biomarker drugs include an anti-NT-proBNP drug, an anti-TnI drug, and an anti-RBP drug.

[0408] Embodiment 150. The method according to any one of Embodiments 140, 141, 143 and 145-149, wherein one or more ATTR biomarkers do not contain PKM1, and one or more anti-ATTR biomarker drugs do not contain anti-PKM1 drugs.

[0409] Embodiment 151. The method according to any one of Embodiments 140-142 and 141-145, wherein one or more ATTR biomarkers do not contain PKM2, and one or more anti-ATTR biomarker drugs do not contain anti-PKM2 drugs.

[0410] Embodiment 152. The method according to any one of Embodiments 140, 141, and 145-149, wherein one or more ATTR biomarkers do not include PKM1 or PKM2, and one or more anti-ATTR biomarker drugs do not include anti-PKM1 drugs or anti-PKM2 drugs.

[0411] Embodiment 153. The kit according to any one of Embodiments 140 to 152, further comprising a TTR stabilizer.

[0412] Embodiment 154. A kit according to any one of Embodiments 140 to 153, comprising one or more anti-ATTR biomarker drugs, or one or more antibody drugs.

[0413] Embodiment 155. A kit according to any one of Embodiments 140 to 154, comprising one or more anti-ATTR biomarker drugs, or one or more nucleic acid probes.

[0414] Embodiment 156. The kit according to Embodiment 154 or 155, wherein one or more antibody drugs are labeled with a detectable portion.

[0415] Embodiment 157. The kit according to Embodiments 154-156, further comprising one or more secondary antibody drugs that specifically bind to one or more anti-ATTR biomarker antibody drugs.

[0416] Embodiment 158. The kit according to Embodiment 157, wherein one or more anti-ATTR biomarker antibody drugs and / or secondary antibody drugs are linked to an enzyme.

[0417] Embodiment 159. The kit according to Embodiments 140-158, further comprising a detection agent.

[0418] Embodiment 160. The kit according to Embodiment 159, wherein the detection agent is a substrate of the enzyme or contains said substrate.

[0419] Embodiment 161. The kit according to Embodiment 159, wherein the detection agent is one or more acridinium ester molecules or comprises said acridinium ester molecules.

[0420] Embodiment 162. The kit according to Embodiment 157, wherein one or more anti-ATTR biomarker antibody drugs and / or secondary antibody drugs are labeled with one or more acridinium ester molecules.

[0421] Embodiment 163. The kit according to any one of Embodiments 155 to 162, wherein one or more anti-ATTR biomarker nucleic acid probes are complementary to one or more nucleotides encoding an ATTR biomarker.

[0422] Embodiment 164. The kit according to any one of Embodiments 155 to 163, wherein at least a portion of each anti-ATTR biomarker nucleic acid probe hybridizes to one or more nucleotides encoding an ATTR biomarker.

[0423] Embodiment 165. The kit according to any one of Embodiments 163 and 164, wherein the nucleotide encoding one or more ATTR biomarkers is DNA.

[0424] Embodiment 166. The kit according to Embodiment 165, wherein the DNA is cDNA.

[0425] Embodiment 167. The kit according to any one of Embodiments 163 and 164, wherein the nucleotide encoding one or more ATTR biomarkers is RNA.

[0426] Embodiment 168. The kit according to Embodiments 155-167, wherein one or more anti-ATTR biomarker nucleic acid probes are labeled with one or more detection agents.

[0427] Embodiment 169. The kit according to Embodiment 168, wherein the detection agent indicates the presence of nucleotides encoding one or more ATTR biomarkers.

[0428] Embodiment 170. The kit according to Embodiments 140-169, further comprising one or more control samples.

[0429] Embodiment 171. The kit according to Embodiment 170, wherein the control sample comprises one or more ATTR biomarker standards.

[0430] Embodiment 172. The kit according to Embodiment 171, wherein one or more ATTR biomarker standards include recombinant ATTR biomarkers.

[0431] Embodiment 173. The kit according to Embodiment 171, wherein one or more ATTR biomarker standards include a synthetic ATTR biomarker nucleic acid.

[0432] Embodiment 174. Use of kits according to Embodiments 140-173 in an in vitro diagnostic assay for diagnosing TTR-CM in a subject.

[0433] Embodiment 175. A method, wherein the method is a) A step of receiving an ATTR biomarker profile, which includes data corresponding to the levels of two or more (e.g., only two) ATTR biomarkers in a sample from a subject, using the processor of a computer; b) The process of providing input, including the TTR biomarker profile, to a machine learning algorithm via a processor; and c) The process by which the processor determines the score of the target ATTR biomarker using a machine learning algorithm based on the input. The ATTR biomarkers include (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), (v) retinol-binding protein 4 (RBP4), (vi) tissue metalloproteinase inhibitor 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof. method.

[0434] Embodiment 176. A method, wherein the method is a) A step of receiving an ATTR biomarker profile, which includes data corresponding to the levels of two or more (e.g., only two) ATTR biomarkers in a sample from a subject, using the processor of a computer; b) The process of providing an input, including the ATTR biomarker profile, to a machine learning algorithm via a processor; and c) A process in which the processor uses a machine learning algorithm based on the input to determine whether the subject is at risk of or has (e.g., is suffering from) trans tiretin amyloid cardiomyopathy (TTR-CM). The ATTR biomarkers include (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), (v) retinol-binding protein 4 (RBP4), (vi) tissue metalloproteinase inhibitor 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof. method.

[0435] Embodiment 177. The method according to Embodiment 176, wherein the step of determining whether a subject is at risk of or has TTR-CM using a machine learning algorithm includes the step of determining the probability that the subject has TTR-CM using a processor.

[0436] Embodiment 178. The method according to any one of Embodiments 175 to 177, wherein two or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof.

[0437] Embodiment 179. The method according to any one of Embodiments 175 to 178, wherein two or more ATTR biomarkers include TnI and RBP4.

[0438] Embodiment 180. The method according to Embodiment 179, further comprising two or more ATTR biomarkers, NT-proBNP, TIMP2, or both NT-proBNP and TIMP2.

[0439] Embodiment 181. The method according to any one of Embodiments 175 to 180, wherein the two or more ATTR biomarkers are three or more ATTR biomarkers.

[0440] Embodiment 182. The method according to any one of Embodiments 175 to 181, wherein the two or more ATTR biomarkers are two ATTR biomarkers or three ATTR biomarkers.

[0441] Embodiment 183. The method according to any one of Embodiments 175 to 182, wherein the two or more ATTR biomarkers are four ATTR biomarkers.

[0442] Embodiment 184. a) A step of receiving data corresponding to one or more demographic factors about a subject by a processor; and b) The process of providing a machine learning algorithm with input containing data corresponding to one or more demographic factors, via a processor. The method according to any one of embodiments 175 to 183, including the method described above.

[0443] Embodiment 184. The method according to Embodiment 183, wherein one or more demographic factors include sex, age, or both sex and age.

[0444] Embodiment 185. a) A step of receiving data corresponding to one or more image-based biomarkers about a subject by a processor; and b) The process by which the processor provides input, including data corresponding to image-based biomarkers, to a machine learning algorithm. A method according to any one of 175-184, including the method described in 175-184.

[0445] Embodiment 186. The method according to Embodiment 185, wherein one or more image-based biomarkers include septal thickness (e.g., left ventricular septal thickness), posterior wall thickness, or ejection fraction.

[0446] Embodiment 187. The method according to Embodiment 185 or Embodiment 186, further comprising the step of a processor determining data corresponding to one or more image-based biomarkers from one or more images.

[0447] Embodiment 188. The method according to Embodiment 187, wherein the step of determining data corresponding to one or more image-based biomarkers from one or more images includes the step of a processor determining measurements from one or more images [for example, by the processor performing pattern recognition on one or more images, and / or by the processor segmenting and classifying one or more features from one or more images using a machine learning algorithm].

[0448] Embodiment 189. The method according to any one of Embodiments 175 to 188, wherein the machine learning algorithm is a decision tree, neural boost, bootstrap forest, boost tree, or support vector machine, or derived therefrom.

[0449] Embodiment 190. The method according to any one of Embodiments 175 to 189, wherein the machine learning-prepared algorithm is a bootstrap Mori algorithm.

[0450] Embodiment 191. The method according to any one of Embodiments 175 to 190, wherein the machine learning algorithm is a decision tree or derived from a decision tree.

[0451] Embodiment 192. The method according to Embodiment 191, wherein the decision tree method is a machine-learned algorithm, which is a bagged, boosted, or additive tree method (for example, the machine-learned algorithm includes at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees).

[0452] Embodiment 193. The method according to any one of Embodiments 175 to 192, wherein the score of the ATTR biomarker is the output from a machine learning algorithm (for example, including the step of a processor outputting the score of the ATTR biomarker from a machine learning algorithm).

[0453] Embodiment 194. The method according to any one of Embodiments 175 to 193, further comprising the step of determining whether a subject from which a sample was obtained is at risk of or has trans tiretin amyloid cardiomyopathy (TTR-CM) using an ATTR biomarker score.

[0454] Embodiment 195. The method according to any one of Embodiments 175 to 194, comprising the step of a processor using a machine learning algorithm to determine whether a subject is at risk of or has trans tiretin amyloid cardiomyopathy (TTR-CM) (for example, the output of the machine learning algorithm is a determination (e.g., probability) of whether the subject is at risk of or has TTR-CM).

[0455] Embodiment 196. The method according to any one of Embodiments 175 to 195, further comprising the step of classifying objects as having or not having TTR-CM using a machine learning algorithm by a processor.

[0456] Embodiment 197. The method according to any one of Embodiments 175 to 196, wherein the machine-learned algorithm is a classifier for TTR-CM.

[0457] Embodiment 198. The method according to Embodiment 196 or Embodiment 197, wherein the classifier or classification has a sensitivity and specificity of more than 80% each (for example, at least one or both are more than 90%).

[0458] Embodiment 199. The method according to any one of Embodiments 175 to 198, wherein the subject is a human subject.

[0459] Embodiment 200. The method according to any one of Embodiments 175 to 199, wherein the sample comprises blood, serum, plasma, or cardiac tissue.

[0460] Embodiment 201. The method according to any one of Embodiments 175 to 200, wherein two or more ATTR biomarkers do not include PKM1.

[0461] Embodiment 202. The method according to any one of Embodiments 175 to 201, wherein two or more ATTR biomarkers do not include PKM2.

[0462] Embodiment 203. The method according to any one of Embodiments 175 to 202, wherein the two or more ATTR biomarkers do not include PKM1 or PKM2.

[0463] Embodiment 204. The method according to any one of Embodiments 175 to 203, wherein two or more ATTR biomarkers do not contain SMOC-2.

[0464] Embodiment 205. The method according to any one of Embodiments 175 to 204, wherein two or more ATTR biomarkers do not contain DCN.

[0465] Embodiment 206. The method according to any one of Embodiments 175 to 205, wherein the two or more ATTR biomarkers do not include SMOC-2 or DCN.

[0466] Embodiment 207. A non-temporary computer-readable medium comprising executable instructions causing a processor to perform an operation comprising the method described in any one of Embodiments 175 to 206 at runtime.

[0467] Embodiment 208. A system comprising a non-temporary computer-readable medium and a processor, wherein the non-temporary computer-readable medium has machine learning algorithms stored therein and executable instructions that, when executed by the processor, perform operations including the method described in any one of Embodiments 175 to 206.

[0468] Embodiment 209. A method comprising the steps of: receiving, by a processor of a computer device, ATTR biomarker profiles, each containing data corresponding to the levels of two or more (e.g., only two) ATTR biomarkers, and corresponding data indicating the health status of each ATTR biomarker profile; and training a machine learning algorithm by the processor using the ATTR biomarker profiles and the corresponding data indicating the health status, wherein the two or more ATTR biomarkers include (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), (v) retinol-binding protein 4 (RBP4), (vi) tissue metalloproteinase inhibitor 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

[0469] Embodiment 210. The method according to Embodiment 209, wherein training includes the step of determining one or more features for corresponding ATTR biomarkers of two or more types based on ATTR biomarker profiles and / or linear combinations thereof, via an algorithm, by a processor.

[0470] Embodiment 211. The method according to Embodiment 210, wherein training includes the step of a processor determining one or more decision rules in one or more decision trees based on one or more features and / or linear combinations thereof via an algorithm.

[0471] Embodiment 212. The method according to Embodiment 210 or Embodiment 211, wherein each of one or more features corresponds to the level of a corresponding one of two or more ATTR biomarkers.

[0472] Embodiment 213. The method according to any one of Embodiments 209 to 212, wherein training includes the step of a processor determining one or more decision rules based on the ATTR biomarker profile via an algorithm.

[0473] Embodiment 214. The method according to any one of Embodiments 209 to 213, wherein the ATTR biomarker profile is labeled with corresponding data.

[0474] Embodiment 215. The method according to any one of Embodiments 209 to 214, wherein at least two of the ATTR biomarker profiles are for different subjects (for example, each ATTR biomarker profile includes data corresponding to the levels of two or more ATTR biomarkers for a unique subject).

[0475] Embodiment 216. The method according to any one of Embodiments 209 to 215, wherein at least two ATTR biomarker profiles are for the same subject at different time points.

[0476] Embodiment 217. The method according to any one of Embodiments 209 to 216, wherein the health status is the probability that the subject has TTR-CM, whether the subject is at risk of TTR-CM, or whether the subject has TTR-CM (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM).

[0477] Embodiment 218. The method according to any one of Embodiments 209 to 217, wherein the corresponding data indicating health status is a probability or a score (e.g., a score for an ATTR biomarker).

[0478] Embodiment 219. The method according to any one of Embodiments 209 to 218, wherein the machine learning algorithm is derived from a decision tree, neural boost, bootstrap forest, boost tree, or support vector machine.

[0479] Embodiment 220. The method according to any one of Embodiments 209 to 219, wherein the machine learning-prepared algorithm is a bootstrap Mori algorithm.

[0480] Embodiment 221. The method according to any one of Embodiments 209 to 220, wherein the machine learning-prepared algorithm is derived from a decision tree.

[0481] Embodiment 222. The method according to Embodiment 221, wherein the decision tree method is a bagging, boosting, or additive tree method (for example, the machine-learned algorithm includes at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees).

[0482] Embodiment 223. The method according to any one of Embodiments 209 to 222, wherein the two or more ATTR biomarkers are three or more ATTR biomarkers.

[0483] Embodiment 224. The method according to any one of Embodiments 209 to 223, wherein the two or more ATTR biomarkers are two ATTR biomarkers or three ATTR biomarkers.

[0484] Embodiment 225. The method according to any one of Embodiments 209 to 224, wherein the two or more ATTR biomarkers are four ATTR biomarkers.

[0485] Embodiment 226. The method according to any one of Embodiments 209 to 225, comprising the steps of: the processor receiving data corresponding to one or more demographic factors for each subject corresponding to an ATTR biomarker profile (for example, for each subject to which an ATTR biomarker profile corresponds); and the processor training a machine learning algorithm based on the data corresponding to one or more demographic factors in combination with the ATTR biomarker profile.

[0486] Embodiment 227. The method according to Embodiment 226, wherein one or more demographic factors include age, sex, or both age and sex.

[0487] Embodiment 228. a) The process of the processor receiving data corresponding to one or more image-based biomarkers for each subject corresponding to the ATTR biomarker profile; and b) The process of training a machine learning algorithm by a processor based on image-based data corresponding to one or more biomarkers, combined with ATTR biomarker profiles (e.g., data corresponding to one or more demographic factors). A method according to any one of embodiments 209 to 227, including the method described above.

[0488] Embodiment 229. The method according to Embodiment 228, wherein one or more image-based biomarkers include septal thickness (e.g., left ventricular septal thickness), posterior wall thickness, or ejection fraction.

[0489] Embodiment 230. The method according to any one of Embodiments 209 to 229, wherein two or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof.

[0490] Embodiment 231. The method according to any one of Embodiments 209 to 230, wherein two or more ATTR biomarkers include TnI and RBP4.

[0491] Embodiment 232. The method according to Embodiment 231, further comprising two or more ATTR biomarkers, NT-proBNP, TIMP2, or both NT-proBNP and TIMP2.

[0492] Embodiment 233. The method according to any one of Embodiments 209 to 232, wherein, after training, the machine learning algorithm is a trained algorithm that is a classifier for TTR-CM.

[0493] Embodiment 234. The method according to Embodiment 233, wherein the classifiers each have a sensitivity and specificity of more than 80% (for example, at least one or both are more than 90%).

[0494] Embodiment 235. A non-temporary computer-readable medium comprising executable instructions causing a processor to perform an operation comprising the method described in any one of Embodiments 208 to 234 at runtime.

[0495] Embodiment 236. A system comprising a non-temporary computer-readable medium and a processor, wherein the non-temporary computer-readable medium has machine learning algorithms stored therein and executable instructions that, when executed by the processor, perform operations including the method described in any one of Embodiments 209 to 234.

[0496] Embodiment 237. A method, the method is a) A process of receiving data corresponding to two or more biomarkers about a subject using a computer processor; b) The process of providing the input, including the data, to a machine learning algorithm using a processor; and c) The process by which the processor determines the health status of the subject using a machine learning algorithm based on the input. Includes, Machine learning-based algorithms are derived from decision tree methods.

[0497] Embodiment 238. A method, a) A process of receiving training inputs, including data corresponding to two or more biomarkers and corresponding data indicating health status, into a computer processor; and b) The process by which the processor uses the input to train a machine learning algorithm based on the decision tree method. Methods that include...

[0498] Embodiment 239. The method according to Embodiment 237 or Embodiment 238, wherein the decision tree method is a bootstrap forest method.

[0499] Embodiment 240. The method according to any one of Embodiments 237 to 239, wherein the data corresponding to two or more biomarkers are data corresponding to the levels of two or more biomarkers (for example, for subjects, or for at least one subject if used for training).

[0500] Embodiment 241. The method according to any one of Embodiments 237 to 240, wherein the health status comprises (i) the status of the disease, disability or condition of the subject (e.g., stage of the disease), (ii) whether the subject has the disease, disability or condition, and (iii) at least one (e.g., two or all three) of the probabilities that the subject has the disease, disability or condition.

[0501] Embodiment 242. The method according to Embodiment 241, wherein the disease, disorder, or condition is a disease, disorder, or condition of the heart.

[0502] Embodiment 243. The method according to any one of Embodiments 237 to 242, wherein the data corresponding to two or more biomarkers includes laboratory-derived data (e.g., derived from one or more assays) (e.g., such data).

[0503] Embodiment 244. The method according to any one of Embodiments 237 to 243, wherein two or more biomarkers include protein biomarkers, enzyme biomarkers, peptide biomarkers, or intermediate filament biomarkers (e.g., neurofilament biomarkers) [for example, each of the two or more biomarkers is a protein biomarker, enzyme biomarker, peptide biomarker, or intermediate filament biomarker (e.g., neurofilament biomarker)].

[0504] Embodiment 245. The method according to any one of Embodiments 237 to 244, wherein the input further includes data corresponding to one or more demographic factors (for example, data corresponding to two or more biomarkers, if used for training, for at least one subject).

[0505] Embodiment 246. The method according to any one of Embodiments 237 to 245, wherein the input further includes image-based data corresponding to one or more biomarkers (for example, for a subject, or for at least one subject, for which data corresponding to two or more biomarkers corresponds when used for training).

[0506] Embodiment 247. A non-temporary computer-readable medium comprising executable instructions causing a processor to perform an operation comprising the method described in any one of Embodiments 237 to 246 at runtime.

[0507] Embodiment 248. A system comprising a non-temporary computer-readable medium and a processor, wherein the non-temporary computer-readable medium has machine learning algorithms stored therein and executable instructions that, when executed by the processor, perform operations including the method described in any one of Embodiments 237 to 246. [Examples]

[0508] Identification of markers for ATTR amyloidosis This embodiment demonstrates a method for screening human plasma to identify biomarkers indicating ATTR amyloidosis and TTR-CM.

[0509] Mass spectrometry (MS) was used to identify biomarkers specific to ATTR amyloidosis. Normal human (N=4) and ATTRwt EDTA plasma (N=6) were added (50 μL) to pre-washed (1×TTBS) goat anti-mouse 96-well ELISA plates and incubated at room temperature for 1 hour. The plates were then washed (three times in 1×TTBS), residual capture material was extracted, and the plates were prepared for MS.

[0510] MS results were generated using the Exponentially Modified Protein Abundance Index (emPAI), and data were filtered from the highest to the lowest emPAI scores. The signal-to-noise (S / N) ratio was calculated for the mean emPAI score of ATTRwt / normal score. The inverse S / N was also calculated. Selected markers were chosen with an S / N ratio of 3 or higher (Figure 14). Criteria for marker selection included having multiple consistent score values ​​in the categories of pathological data, normal samples, or both. Missing or empty data corresponded to data below the detection limit. For the purpose of generating S / N values, empty data were artificially scored to a value of "1". Eight different markers were selected from the list in Table 1 to pursue as possible markers for ATTRwt (Table 6).

[0511] [Table 6] [Examples]

[0512] Patient cohort for biomarker testing This example provides information about the patient cohort used in an analysis of how various ATTR biomarkers functioned in the detection of TTR-CM. As shown in Figure 1, 273 heart failure patients, 46 clinical trial patients, and 20 independent estimated normal donors participated in this study. All individuals in this study were 60 years of age or older and were predominantly Caucasian. The number of males and females varied within each subset. The NYHA class of patients also differed between subsets. In particular, only a small number of positive samples with matched negatives were available in the cohort. [Examples]

[0513] Summary of test results This example summarizes the data obtained from an analysis of how various ATTR biomarkers worked in the detection of TTR-CM using the patient cohort described in Example 2, as shown in Figure 2. Of all the biomarkers tested, only one result was missing. This RBP result could not be obtained because the sample volume for the necessary retesting was insufficient. Overall, the results of the in vitro diagnostic assays were based on one test per sample; to account for the higher variability of studies using only the assay, the mean of two test repeats was used. [Examples]

[0514] Classification performance of a single biomarker This embodiment demonstrates the evaluation of the exemplary ATTR biomarkers described herein for their ability to be used as single biomarkers for the detection of TTR-CM. The results show that certain ATTR biomarkers described herein can work at a level of sensitivity and / or specificity sufficient to allow them to be used alone. However, this embodiment also shows that not all ATTR biomarkers met the criteria for both sensitivity and specificity when used as single biomarkers to evaluate TTR-CM. As shown in Figures 3-6, TnI met the criteria for both sensitivity and specificity, suggesting that TnI may be useful as a single biomarker for the detection of TTR-CM. The remaining ATTR biomarkers evaluated did not meet the criteria for both sensitivity and specificity set for use as single biomarkers. These biomarkers were then evaluated in combination to determine whether combinations of ATTR biomarkers could achieve improved sensitivity and specificity when detecting TTR-CM. [Examples]

[0515] Single biomarker screening for NYHA class response This example demonstrates the evaluation of the effects of biomarkers when considering each NYHA class. As shown in Figure 7, the effects of all biomarkers on NYHA classes were modeled, and p-values ​​for significance tests were reported. The data showed that the effects of the biomarkers were statistically significant for PKM, TnI, RBP4, RBP, and NT-proBNP. This indicates that these ATTR biomarkers could be useful for detecting TTR-CM despite the fact that none of these biomarkers met the sensitivity and specificity criteria, as described above in Example 4. [Examples]

[0516] Performance results This example demonstrates the screening of generalized regression fits for processing data obtained from biomarker combinations. Main effects and interactions were screened, and the best fit with four or fewer biomarkers was selected for further evaluation.

[0517] An adaptive double lasso generalized regression method using a complete response surface as input and missing validation was expected to select a near-optimal model using a minimum number of effects on a small dataset containing several correlated biomarkers. As shown in Figure 8, different subsets of biomarkers were found to be informative depending on the method used. However, based on practical considerations, a limitation of four biomarkers was decided upon. Several subsets of biomarkers and predictive models were generated. These biomarkers were further evaluated using additional machine learning, including PKM, TnI, DCN, TIMP2, and NT-proBNP.

[0518] Further model screening was performed using combinations of PKM, TnI, DCN, TIMP2, and NT-proBNP. As shown in Figure 9, the bootstrap Mori model was found to yield the best results compared to other machine learning methods. Neural network models also yielded good results, but with more false negatives.

[0519] These results demonstrate that combinations of three or four biomarkers, including TnI and PKM, and especially NT-proBNP and / or RBP4, can detect TTR-CM with high sensitivity and specificity. [Examples]

[0520] Identification of PKM and TIMP2 as elevated biomarkers in ATTR amyloidosis patients. This embodiment demonstrates two identified biomarkers (PKM and TIMP2) that show elevated plasma levels in ATTRwt patients, both individually and in combination, compared to a normal human control sample.

[0521] The immunoreactivity of the biomarkers selected in Table 2 to normal human plasma and ATTR amyloidosis plasma was tested by ELISA (Table 7).

[0522] [Table 7-1] [Table 7-2]

[0523] [Table 8-1] [Table 8-2]

[0524] The signal-to-noise (S / N) ratio (calculated for each biomarker by dividing the raw absorbance value of cardiomyopathy (CM) or normal (N) samples by the mean of the normal samples) was determined for each biomarker assay. The cutoff values ​​were set as the highest S / N values ​​of normal plasma samples for each biomarker assay, excluding assays for ILK and PKM, which were excluded because their highest values ​​for normal samples were greater than the mean + (4 × standard deviation).

[0525] Using the signal-to-noise ratio (S / N) values, each sample (i.e., CM or N) was scored as either positive (>cutoff) or negative (<cutoff) on a scale of 1 or 0, respectively, by comparing these values ​​to the cutoff values ​​for each biomarker (Table 8). The performance ratios for each biomarker, including sensitivity, specificity, and precision, were also determined by the following calculations: % sensitivity = number of CM samples scored positive / total CM samples; % specificity = (1 - number of normal samples scored positive) × 100; % precision = (number of reported positive CM samples + number of reported negative normal samples) / total samples tested (CM and N) × 100.

[0526] [Table 9-1] [Table 9-2] [Table 9-3]

[0527] The performance scores of the various biomarkers tested showed high sensitivity, specificity, and precision for two individual markers, TIMP2 (sensitivity 72%, specificity 100%, precision 87%) and PKM (sensitivity 83%, specificity 97%, precision 90%). When the two markers, TIMP2 and PKM, were combined across the same set of samples, sensitivity, specificity, and precision were significantly improved (sensitivity 93%, specificity 97%, precision 95%), suggesting that the combined TIMP2 and PKM can act as highly sensitive and specific markers for identifying ATTRwt plasma. [Examples]

[0528] Calculation of biomarker thresholds This example demonstrates the identification of thresholds for biomarkers of ATTR amyloidosis.

[0529] Plasma samples from a total of 30 diagnosed TTR-CM patients and 30 normal donor samples were evaluated for the expression of PKM, TIMP2, LIMS1, C3, and A11. Sensitivity, specificity, and precision were calculated as described above. Sensitivity and specificity calculations assumed no undiagnosed TTR-CM donors. Optimized detection cutoffs for each assay were determined by maximizing sensitivity while maintaining approximately 100% specificity (Table 9 and Figure 10).

[0530] The disclosed method can be performed manually or automated. For example, the disclosed method can be used with the ADVIA CENTAUR® immunoassay system or ATELLICA®. For example, the system can be automated to perform the following actions: 1. Dispense the sample into the cuvette. 2. Dispense a buffer containing a solid support bound to an anti-human IgM antibody and incubate it at, for example, 37°C for 18.25 minutes. 3. Desorb / separate the solid support, aspirate the cuvette, and wash it with the washing reagent. 4. Dispense a buffer containing the chemiluminescently tagged NS1 antigen and incubate it, for example, at 37°C for 18 minutes. 5. Desorb / separate the solid support, aspirate the cuvette, and wash it with the washing reagent. 6. Dispense the chemiluminescent reagent to induce the chemiluminescent reaction. 7. Report the results according to the options selected by the user.

[0531] In certain embodiments, the disclosed immunoassay may be suitable for use with the ADVIA CENTAUR immunoassay system (Siemens Healthcare, AG) and / or the ADVIA CENTAUR immunoassay system can be automated to perform the operations described above.

[0532] [Table 10] [Examples]

[0533] Patient evaluation For example, in Tables 2-5, patients at risk of TTR-CM are tested for the levels of each ATTR biomarker in combinations of ATTR biomarkers disclosed herein. Blood samples are taken from patients, and levels are measured using a Siemens Atellica® system or a Siemens Advia Centaur® system to obtain an ATTR biomarker profile for the patient. If necessary, the patient's demographic factors and image-based biomarkers are considered. Using the patient's ATTR biomarker profile, an ATTR biomarker score is calculated. Based on the ATTR biomarker score, patients are categorized as having TTR-CM. Administration of TTR stabilizers or other cardiac medications is recommended. [Examples]

[0534] Patient evaluation For example, in Tables 2-5, patients at risk of TTR-CM are tested for the levels of each ATTR biomarker in combinations of ATTR biomarkers disclosed herein. Blood samples are taken from the patient, and levels are measured using a Siemens Atellica® system or a Siemens Advia Centaur® system to obtain an ATTR biomarker profile for the patient. If necessary, the patient's demographic factors and image-based biomarkers are considered. The patient's ATTR biomarker profile is compared to a reference ATTR biomarker profile. Based on the comparison, the patient is categorized as having TTR-CM. Administration of TTR stabilizers or other cardiac medications is recommended. [Examples]

[0535] Patient evaluation For example, in Tables 2-5, patients at risk of TTR-CM are tested for the levels of each ATTR biomarker in combinations of ATTR biomarkers disclosed herein. Blood samples are taken from patients, and levels are measured using a Siemens Atellica® system or a Siemens Advia Centaur® system to obtain an ATTR biomarker profile for each patient. If necessary, the patient's demographic factors and image-based biomarkers are considered. Using the patient's ATTR biomarker profile, ATTR biomarker scores are calculated. Based on the ATTR biomarker scores, patients are categorized as being at risk of having TTR-CM. Further cardiomyopathy testing is recommended. [Examples]

[0536] Patient evaluation For example, in Tables 2-5, patients at risk of TTR-CM are tested for the levels of each ATTR biomarker in combinations of ATTR biomarkers disclosed herein. Blood samples are taken from patients, and levels are measured using a Siemens Atellica® system or a Siemens Advia Centaur® system to obtain an ATTR biomarker profile for each patient. If necessary, the patient's demographic factors and image-based biomarkers are considered. The patient's ATTR biomarker profile is compared to a reference ATTR biomarker profile. Based on the comparison, patients are categorized as being at risk of having TTR-CM. Further cardiomyopathy testing is recommended. [Examples]

[0537] Group evaluation For example, in Tables 2-5, test the levels of each ATTR biomarker in combinations of ATTR biomarkers disclosed herein in 1000 patients at risk of TTR-CM. Obtain ATTR biomarker profiles for patients by collecting blood samples from patients and measuring levels using a Siemens Atellica® system or a Siemens Advia Centaur® system. Consider patient demographic factors and image-based biomarkers as needed. Calculate scores for each ATTR biomarker using the patient's ATTR biomarker profile. Categorize a subset of patients as being at risk of having TTR-CM. Further cardiomyopathy testing is recommended. [Examples]

[0538] The clinical utility of non-invasive and accurate TTR-CM This embodiment demonstrates to the public the benefit of providing a non-invasive TTR-CM assay that is both sensitive and specific and easy to comply with. This embodiment shows that it is common for people to be unwilling to undergo evasive cardiomyopathy testing, and that if TTR-CM is not detected early, it may have serious health consequences.

[0539] The patient presented with symptoms of TTR-CM but refused cardiac biopsy. The patient's primary care physician requested an assessment of the patient's levels of each ATTR biomarker in combinations of ATTR biomarkers, such as those disclosed herein in Tables 2–5. The results indicated the patient was at high risk of TTR-CM. The patient consulted with their physician and was persuaded to schedule further cardiomyopathy tests to clarify the basis for TTR-CM. [Examples]

[0540] Performance results of machine learning-based algorithms We trained a machine learning-based algorithm and evaluated its performance in classifying subjects as having or not having TTR-CM. ATTR biomarker profiles, including measured levels of various ATTR biomarkers, were obtained for a population of subjects with diverse racial / ethnicity, sex, age, and geographical location. The population included subjects diagnosed with TTR-CM, subjects with preserved ejection fraction heart failure (non-TTR-CM), and normal subjects. Bootstrap Mori machine learning-based algorithms were trained and evaluated using K-fold cross-validation (k=5). Different ATTR biomarkers, and in some cases one or more demographic factors, were also considered in different algorithms. The results are shown in Figures 15–21, with the area under the receiver operating characteristic (ROC) curve (AUC) on the y-axis and specific combinations of ATTR biomarkers along the x-axis, and, where applicable, box plots of demographic factors (diamonds showing 95% confidence intervals). Misclassification rates are indicated by the coloring of individual plots for each combination (legend is in an inset). In these figures, TNIH is the form of TnI, and PBNP is used as an alternative abbreviated notation for NT-proBNP. Figure 22 provides information about the patient cohort used when analyzing how various ATTR biomarkers work in detecting TTR-CM using various machine learning models. Data about the patient cohort was also used to train the machine learning models. The patient cohort includes subjects with TTR-CM, subjects with non-TTR-CM heart failure with preserved ejection fraction (HF-PEF), and normal control patients.

[0541] Figure 15 illustrates that, on average across the entire training field, the three biomarker-based machine learning algorithms performed better than the two biomarker-based machine learning algorithms, with a significant performance difference between the worst-performing three biomarker algorithms and the best-performing two biomarker algorithms. All algorithms used TnI as the ATTR biomarker for classification. Algorithms using both TnI and RBP4 exhibited the best performance.

[0542] Figure 16 illustrates that, on average across the entire validation fold, most of the three biomarker-based machine learning algorithms performed better than the two biomarker-based machine learning algorithms. The exception was the average AUC of the algorithm using TnI and RBP4, which had a relatively high misclassification rate. In general, the misclassification rates were consistently better for the three biomarker-based algorithms than for the two biomarker-based algorithms.

[0543] Figure 17 illustrates the average performance across both the training and validation folds. As can be seen, the three biomarker-based algorithms generally performed better than the two biomarker-based algorithms.

[0544] Figure 18 illustrates that using a machine learning-prepared algorithm that considers age and sex as demographic factors along with the ATTR biomarker improves the performance of the algorithm. The performance improvement is observed for both two-biomarker-based and three-biomarker-based algorithms. Specific examples shown use TnI and RBP4, as well as TnI, RBP4, and NT-proBNP. Performance improvements are observed for both two-biomarker-based and three-biomarker-based algorithms, both measured by AUC and misclassification rate.

[0545] Figure 19 illustrates a surprising result: when machine learning-based algorithms consider one or more demographic factors, including age and sex in this embodiment, the performance of two biomarker-based algorithms can be comparable to that of three biomarker-based algorithms. A specific example of this phenomenon is that algorithms considering TnI, RBP4, age, and sex perform as well as those further considering TIMP2, DCN, and NT-proBNP. In particular, the misclassification rate of algorithms considering NT-proBNP but not RBP4 remains relatively high, even when age and sex are also considered, but the AUC is similar to that of those considering RBP4.

[0546] Figures 20 and 21 illustrate the results of further validation of the bootstrap Mori machine learning-prepared algorithms whose performance is outlined in Figures 15–19. Further evaluation of the algorithms generated across all five folds of cross-validation was performed using a stratified haulback test set (20% of the total) previously isolated from the analysis. The mean AUC was significantly higher for the TnI, RBP4, age, and sex combination algorithm. The algorithms performed very similarly when using an optimized detection cutoff (p≧0.060) that supported sensitivity performance. Sensitivity and specificity of >80% were achieved for both combinations: TnI, RBP4, age, and sex with and without NT-proBNP. [Examples]

[0547] Performance of comparative predictors As described in the previous example, a machine learning-based algorithm was trained and its ability to determine whether a subject has TTR-CM was evaluated. The performance was compared to other biomarkers for both its performance as a predictor of whether a subject has TTR-CM, its performance as a predictor of New York Heart Association (NYHA) class, and its ability to classify TTR-CM, non-TTR-CM with HF-PEF, and normal. NYHA class is related to (e.g., surrogate) overall cardiac function. A biomarker that is a good predictor of NYHA class is therefore a kind of surrogate biomarker of cardiac function. The obtained performance comparisons are plotted in Figures 23-27. As shown in Figure 23, the Bootstrap Mori (BF) model, which uses a combination of TnI (labeled as TNIH), NT-proBNP (labeled as PBNP), and RBP4 to determine the probability that a subject has TTR-CM (e.g., whether the subject is TTR-CM positive or negative), performs more than an order of magnitude better than other biomarkers, including image-based biomarkers (e.g., septal thickness, posterior wall thickness, and ejection fraction), individual ATTR biomarkers (e.g., TnI, NfL, NT-proBNP, TIMP2, DCN, and RBP4 individually), and individual demographic factors (e.g., age and sex individually). Figures 24-25 provide a more detailed comparison of how informative individual biomarkers are in classifying whether a subject is TTR-CM or not, with and without considering age and sex as biomarkers (Figure 24) and (Figure 25). As seen in Figure 26, the best-working BF model-based biomarker for determining the probability of whether a subject has TTR-CM or not also works well for classifying between TTR-CM, non-TTR-CM with HF-PEF, and normal, although other biomarkers work equally well for such classifications. In contrast, referring to Figure 27, this BF model using a combination of TnI, NT-proBNP, and RBP4 performs relatively poorly in predicting NYHA class compared to the same other biomarkers shown in Figure 23.Therefore, the predictive power of this BF model is specific to TTR-CM and does not simply predict cardiac function that is generally characterized by NYHA class (having TTR-CM is generally associated with general cardiac dysfunction).

[0548] References 1. Gertz, MA, Benson, MD, Dyck, PJ, Grogan, M. Coelho, T., Crus, M., Berk, JL, Plante-Bordeneuve, V., Schmidt, HH, Merlini, G. Diagnosis, Prognosis, and Therapy of Transthyretin Amyloidosis. JACC 66:2452-2466 (2015). 2. Ashley 2004 3. Krishnamurthy, R., Cheong, B., Muthypillai, R. Tools for cardiovascular magnetic resonance imaging. Current Cardiology Reviews, 9:185-190, 2013. 4. Doltra, A., Amundsen, BH, Gebker, R., Fleck, E., Kelle, S. Emerging concepts for myocardial late gadolinium enhancement MRI. Curr Cardiol Rev. 9(3):185-90, 2013. 5. Bokhari, S., Castano, A., Pozniakoff, T., Deslisle, S., Latif, F., Maurer, MS, (99m)Tc-pyrophosphate scintigraphy for differentiating light-chain cardiac amyloidosis from the transthyretin-related familial and senile cardiac amyloidoses. Circ Cardiovasc Imaging. 6(2):195-201, 2013. 6. Crotty, TB Li, CY, Edwards, WD, Suman, VJ Amyloidosis and endomyocardial biopsy: Correlation of extent and pattern of deposition with amyloid immunophenotype in 100 cases. Cardiovascular Pathology 4:39-42, 1995. 7. Arvanitis, M. et al. Identification of Transthyretin Cardiac Amyloidosis Using Serum Retinol-Binding Protein 4 and a Clinical Prediction Model. JAMA Cardiol., 2017.

[0549] All publications and patents cited in this disclosure (including those listed above) are incorporated herein by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference.

[0550] Equal portions Those skilled in the art can recognize or confirm many equivalents to the specific embodiments of the present disclosure described herein by means of routine experiments. The scope of the present disclosure is not intended to be limited to the above description, but rather as set forth in the following claims.

Claims

1. It is a method, and the method is A step of receiving an ATTR biomarker profile, which includes data corresponding to the levels of two or more (e.g., only two) ATTR biomarkers in a sample from a subject, using the processor of a computer; The process involves the processor providing input, including the ATTR biomarker profile, to a machine learning algorithm; and The process involves a processor determining the score of a target ATTR biomarker using a machine learning-based algorithm derived from the input. Includes, A method comprising two or more ATTR biomarkers, including (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), (v) retinol-binding protein 4 (RBP4), (vi) tissue metalloproteinase inhibitor 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viiii) a combination thereof.

2. It is a method, and the method is A step of receiving an ATTR biomarker profile, which includes data corresponding to the levels of two or more (e.g., only two) ATTR biomarkers in a sample from a subject, using the processor of a computer; The process involves the processor providing input, including the ATTR biomarker profile, to a machine learning algorithm; and The process involves a processor using a machine learning algorithm based on the input to determine whether a subject is at risk of or has (e.g., is suffering from) trans tiretin amyloid cardiomyopathy (TTR-CM). Includes, A method comprising two or more ATTR biomarkers, including (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), (v) retinol-binding protein 4 (RBP4), (vi) tissue metalloproteinase inhibitor 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viiii) a combination thereof.

3. The method according to claim 2, wherein the step of determining whether a subject is at risk of or has (e.g., suffers from) trans tiretin amyloid cardiomyopathy (TTR-CM) using a machine learning algorithm includes the step of determining the probability that the subject has TTR-CM by a processor.

4. The method according to claim 1 or 2, wherein two or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof.

5. The method according to any one of claims 1 to 4, wherein two or more ATTR biomarkers include TnI and RBP4.

6. The method according to claim 5, wherein the two or more ATTR biomarkers further include NT-proBNP, TIMP2, or both NT-proBNP and TIMP2.

7. The method according to any one of claims 1 to 6, wherein the two or more ATTR biomarkers are three or more ATTR biomarkers.

8. The method according to any one of claims 1 to 7, wherein the two or more ATTR biomarkers are two ATTR biomarkers or three ATTR biomarkers.

9. The method according to any one of claims 1 to 7, wherein the two or more ATTR biomarkers are four ATTR biomarkers.

10. The process of the processor receiving data corresponding to one or more demographic factors about the subject; and The process by which the processor provides an input containing data corresponding to one or more demographic factors to a machine learning algorithm. The method according to any one of claims 1 to 9, including the method described in any one of claims 1 to 9.

11. The method according to claim 10, wherein one or more demographic factors include sex, age, or both sex and age.

12. The process involves the processor receiving data corresponding to one or more image-based biomarkers about the subject; and The process by which the processor provides input, including data corresponding to image-based biomarkers, to a machine learning algorithm. The method according to any one of claims 1 to 11, including the method described in any one of claims 1 to 11.

13. The method according to claim 12, wherein one or more image-based biomarkers include septal thickness (e.g., left ventricular septal thickness), posterior wall thickness, or ejection fraction.

14. The method according to claim 12 or 13, further comprising the step of determining data corresponding to one or more image-based biomarkers from one or more images using a processor.

15. The method according to claim 14, wherein the step of determining data corresponding to one or more image-based biomarkers from one or more images includes the step of a processor determining measurements from one or more images [for example, by the processor performing pattern recognition on one or more images, and / or by the processor segmenting and classifying one or more features from one or more images using a machine learning algorithm].

16. The method according to any one of claims 1 to 15, wherein the machine learning algorithm is a decision tree, a neural boost, a bootstrap forest, a boost tree, or a support vector machine, or derived therefrom.

17. The method according to any one of claims 1 to 16, wherein the machine learning algorithm is a bootstrap Mori algorithm.

18. The method according to any one of claims 1 to 17, wherein the machine learning algorithm is a decision tree or derived from a decision tree.

19. The method according to claim 18, wherein the decision tree method is a pre-trained algorithm, which is a bagging, boosting, or additive tree method (for example, the pre-trained algorithm comprises at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees).

20. The method according to any one of claims 1 to 19, wherein the score of the ATTR biomarker is the output from a machine learning algorithm (for example, the step of a processor outputting the score of the ATTR biomarker from a machine learning algorithm).

21. The method according to any one of claims 1 to 20, further comprising the step of determining whether a subject from which a sample was obtained is at risk of or has trans tiretin amyloid cardiomyopathy (TTR-CM), using an ATTR biomarker score.

22. The method according to any one of claims 1 to 21, further comprising the step of using a machine learning algorithm by a processor to determine whether a subject is at risk of or has (e.g., is affected by) trans tiretin amyloid cardiomyopathy (TTR-CM) [for example, the output of the machine learning algorithm is a determination (e.g., probability) of whether the subject is at risk of or has (e.g., is affected by) TTR-CM].

23. The method according to any one of claims 1 to 22, further comprising the step of classifying objects by a processor, using a machine learning algorithm, whether or not they have TTR-CM.

24. The method according to any one of claims 1 to 23, wherein the machine learning algorithm is a classifier for TTR-CM.

25. The method according to claim 23 or 24, wherein each classifier or classification has a sensitivity and specificity of more than 80% (for example, at least one or both are more than 90%).

26. The method according to any one of claims 1 to 25, wherein the subject is a human subject.

27. The method according to any one of claims 1 to 26, wherein the sample comprises blood, serum, plasma, or cardiac tissue.

28. The method according to any one of claims 1 to 27, wherein two or more ATTR biomarkers do not include PKM1.

29. The method according to any one of claims 1 to 28, wherein two or more ATTR biomarkers do not include PKM2.

30. The method according to any one of claims 1 to 29, wherein the two or more ATTR biomarkers do not include PKM1 or PKM2.

31. The method according to any one of claims 1 to 30, wherein two or more ATTR biomarkers do not include SMOC-2.

32. The method according to any one of claims 1 to 31, wherein two or more ATTR biomarkers do not include DCN.

33. The method according to any one of claims 1 to 32, wherein the two or more ATTR biomarkers do not include SMOC-2 or DCN.

34. A non-temporary computer-readable medium comprising an executable instruction causing a processor to perform an operation comprising the method according to any one of claims 1 to 33 at runtime.

35. A system comprising a non-temporary computer-readable medium and a processor, wherein the non-temporary computer-readable medium has machine learning algorithms stored therein and executable instructions that, when executed by the processor, perform operations including the method according to any one of claims 1 to 33.

36. It is a method, and the method is A process of receiving, by a computer processor, ATTR biomarker profiles containing data corresponding to the levels of two or more (e.g., only two) ATTR biomarkers, and for each ATTR biomarker profile, corresponding data indicating the health status of the ATTR biomarker profile; and The process involves the processor training machine learning algorithms using ATTR biomarker profiles and corresponding data indicating health status. Includes, A method comprising two or more ATTR biomarkers, including (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal hormone precursor type B natriuretic peptide (NT-proBNP), (v) retinol-binding protein 4 (RBP4), (vi) tissue metalloproteinase inhibitor 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viiii) a combination thereof.

37. The method according to claim 36, wherein training includes the step of determining, by a processor, one or more features for corresponding ATTR biomarkers based on ATTR biomarker profiles and / or linear combinations thereof, via an algorithm.

38. The method according to claim 37, wherein training includes the step of determining one or more decision rules in one or more decision trees based on one or more features and / or linear combinations thereof, via an algorithm, by a processor.

39. The method according to claim 37 or 38, wherein each of one or more features corresponds to the level of a corresponding one of two or more ATTR biomarkers.

40. The method according to any one of claims 36 to 39, wherein training includes the step of a processor determining one or more decision rules based on an ATTR biomarker profile via an algorithm.

41. The method according to any one of claims 36 to 40, wherein at least two of the ATTR biomarker profiles are for different subjects.

42. The method according to any one of claims 36 to 41, wherein at least two of the ATTR biomarker profiles are for the same subject at different time points.

43. The method according to any one of claims 36 to 42, wherein the health status is the probability that the subject has TTR-CM, whether the subject is at risk of TTR-CM, or whether the subject has TTR-CM (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM).

44. The method according to any one of claims 36 to 43, wherein the corresponding data indicating health status is a probability or a score (e.g., a score for an ATTR biomarker).

45. The method according to any one of claims 36 to 44, wherein the ATTR biomarker profile is labeled with corresponding data.

46. The method according to any one of claims 36 to 45, wherein the machine learning algorithm is derived from a decision tree, neural boost, bootstrap forest, boost tree, or support vector machine.

47. The method according to any one of claims 36 to 46, wherein the machine learning algorithm is a bootstrap Mori algorithm.

48. The method according to any one of claims 36 to 47, wherein the machine learning algorithm is derived from the decision tree method.

49. The method according to claim 48, wherein the decision tree method is a bagging, boosting, or additive tree method (for example, a machine learning-prepared algorithm comprises at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees).

50. The method according to any one of claims 36 to 49, wherein the two or more ATTR biomarkers are three or more ATTR biomarkers.

51. The method according to any one of claims 36 to 50, wherein the two or more ATTR biomarkers are two ATTR biomarkers or three ATTR biomarkers.

52. The method according to any one of claims 36 to 51, wherein the two or more ATTR biomarkers are four ATTR biomarkers.

53. The process involves the processor receiving data corresponding to one or more demographic factors for each subject corresponding to the ATTR biomarker profile; and The process involves the processor training a machine learning algorithm based on data corresponding to one or more demographic factors, combined with an ATTR biomarker profile. The method according to any one of claims 36 to 52, including the method described in any one of claims 36 to 52.

54. The method according to claim 53, wherein one or more demographic factors include age, sex, or both age and sex.

55. The process involves the processor receiving data corresponding to one or more image-based biomarkers for each subject corresponding to the ATTR biomarker profile; and The process of training a machine learning algorithm by a processor based on image-based data corresponding to one or more biomarkers, combined with ATTR biomarker profiles (e.g., data corresponding to one or more demographic factors). The method according to any one of claims 36 to 54, including the method described in any one of claims 36 to 54.

56. The method according to claim 55, wherein one or more image-based biomarkers include septal thickness (e.g., left ventricular septal thickness), posterior wall thickness, or ejection fraction.

57. The method according to any one of claims 36 to 56, wherein two or more ATTR biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof.

58. The method according to any one of claims 36 to 57, wherein two or more ATTR biomarkers include TnI and RBP4.

59. The method according to claim 58, wherein the two or more ATTR biomarkers further include NT-proBNP, TIMP2, or both NT-proBNP and TIMP2.

60. The method according to any one of claims 36 to 59, wherein, after training, the machine learning algorithm is a trained algorithm that is a classifier for TTR-CM.

61. The method according to claim 60, wherein each classifier has a sensitivity and specificity of more than 80% (for example, at least one or both are more than 90%).

62. A non-temporary computer-readable medium comprising an executable instruction causing a processor to perform an operation comprising the method according to any one of claims 36 to 61 at runtime.

63. A system comprising a non-temporary computer-readable medium and a processor, wherein the non-temporary computer-readable medium has machine learning algorithms stored therein and executable instructions that, when executed by the processor, perform operations including the method according to any one of claims 36 to 62.

64. It is a method, and the method is A process of receiving data corresponding to two or more biomarkers about a subject using a computer processor; The process of providing input, including data, to a machine learning algorithm via a processor; and The process by which the processor determines the health status of the subject using a machine learning algorithm based on the input. Includes, Machine learning-based algorithms are derived from decision tree methods.

65. It is a method, A process of receiving training inputs, including data corresponding to two or more biomarkers and corresponding data indicating health status, by a computer processor; and The process by which the processor uses the input to train a machine learning algorithm based on the decision tree method. Methods that include...

66. The method according to claim 64 or 65, wherein the decision tree method is a bootstrap forest method.

67. The method according to any one of claims 64 to 66, wherein the data corresponding to two or more biomarkers are data corresponding to the levels of two or more biomarkers (for example, for subjects, or, if used for training, for at least one subject).

68. The method according to any one of claims 64 to 67, wherein the health status comprises (i) the status of the disease, disability or condition of the subject (e.g., the stage of the disease), (ii) whether the subject has the disease, disability or condition, and (iii) at least one (e.g., two or all three) probabilities that the subject has the disease, disability or condition.

69. The method according to claim 68, wherein the disease, disorder, or condition is a disease, disorder, or condition of the heart.

70. The method according to any one of claims 64 to 69, wherein the data corresponding to two or more biomarkers includes (e.g., is such data) laboratory-derived data (e.g., derived from one or more assays).

71. The method according to any one of claims 64 to 70, wherein two or more biomarkers include protein biomarkers, enzyme biomarkers, peptide biomarkers, or intermediate filament biomarkers (e.g., neurofilament biomarkers) [for example, each of the two or more biomarkers is a protein biomarker, enzyme biomarker, peptide biomarker, or intermediate filament biomarker (e.g., neurofilament biomarker)].

72. The method according to any one of claims 64 to 71, wherein the input further includes data corresponding to one or more demographic factors (for example, data corresponding to two or more biomarkers, if used for training, for at least one subject).

73. The method according to any one of claims 64 to 72, wherein the input further comprises image-based data corresponding to one or more biomarkers (for example, for subjects, or, if used for training, for at least one subject, for which data corresponding to two or more biomarkers corresponds).

74. A non-temporary computer-readable medium comprising an executable instruction causing a processor to perform an operation comprising the method according to any one of claims 58 to 73 at runtime.

75. A system comprising a non-temporary computer-readable medium and a processor, wherein the non-temporary computer-readable medium has machine-learned or machine-learning algorithms stored therein and executable instructions that, when executed by the processor, perform operations including the method according to any one of claims 58 to 73.

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