Biomarker compositions and methods of use thereof

Abstract

The present disclosure provides methods and kits for identifying and treating individuals at risk of or suffering from amyloid transthyretin cardiomyopathy. In general, detection or measurement of one or more biomarkers, such as TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof, assists in the identification of amyloid transthyretin cardiomyopathy. The present disclosure also provides methods for selecting patients for treatment of amyloid transthyretin cardiomyopathy, such as with transthyretin stabilizing agents.

Description

Docket No.: 2023P04092WO BIOMARKER COMPOSITIONS AND METHODS OF USE THEREOF CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 490,481, filed on March 15, 2023, the disclosure of which is hereby incorporated by reference herein in its entirety. CROSS-REFERENCE TO A SEQUENCE LISTING

[0002] This application contains a Sequence Listing in computer readable form entitled “ATTRSeqList.xml”, created on February 23, 2024, which is 12KB in size. The computer readable form is incorporated herein by reference. TECHNICAL FIELD

[0003] The present disclosure relates generally to the fields of molecular biology and cardiac health. BACKGROUND

[0004] Amyloid transthyretin cardiomyopathy (TTR-CM) is a rare condition that arises due to misfolding of the protein transthyretin (TTR), resulting in amyloid fibril deposition in the heart tissue and ultimately leading to heart failure. Early treatment of TTR- CM is therefore important for improved prognostic impact in affected individuals. However, treatment may be problematically delayed as screening for wild-type transthyretin amyloidosis (ATTRwt) involves performing multiple costly and invasive procedures that are not particularly specific to TTR-CM (including TTR-CM resulting from wild-type ATTR amyloidosis). Moreover, there is a lack of non-invasive, in-vitro screening tests for detecting and / or identifying amyloid transthyretin cardiomyopathy (TTR-CM) resulting from wild-type ATTR amyloidosis (ATTRwt).

[0005] Difficulty in identifying TTR-CM resulting from ATTRwt by current methods may contribute to this disease being largely underdiagnosed. Accordingly, there is a need in the art for compositions, kits, and methods for detecting TTR-CM, and particularly TTR-CM resulting from wild-type ATTR amyloidosis, with an acceptable level of specificity. Moreover, there is a continuous need for compositions, assays, devices, and methods for Page 1 of 135 11855652v1Docket No.: 2023P04092WO testing, identifying, or staging individuals or groups of individuals having misfolded protein TTR. SUMMARY

[0006] The present disclosure provides compositions, kits, and methods for detecting and / or identifying TTR-CM, and particularly TTR-CM resulting from wild-type ATTR amyloidosis. Further, the present disclosure provides methods of classifying TTR-CM patients (including TTR-CM resulting from wild-type ATTR amyloidosis) compared to normal individuals or individuals experiencing other heart conditions. Additionally, the present disclosure recognizes for the first time 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-pro hormone B-type natriuretic peptide (NT- proBNP), retinol binding protein 4 (RBP4), decorin (DCN), tissue inhibitor of metalloproteinase 2 (TIMP2), SPARC related modular calcium binding 2 (SMOC-2), neurofilament light chain (NfL), and combinations thereof) can assist in detecting and / or diagnosing TTR-CM, and / or assist in classification of TTR-CM patients. The present disclosure further recognizes that these biomarkers (e.g., ATTR Biomarkers, e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) in compositions, kits, and methods can be useful for classifying, detecting and / or diagnosing TTR-CM (including TTR-CM resulting from wild-type ATTR amyloidosis) without the need for performing invasive or costly tests. This represents a significant advancement in patient care, as TTR-CM resulting from ATTRwt can be detected and identified by methods that are more comfortable for the patient, inflict less harm to the patient, and / or decrease the amount of time a patient needs to recover following the detection and / or diagnostic method.

[0007] In addition, the present 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 in detecting TTR-CM with improved sensitivity. In particular, the present disclosure provides that TnI, RBP4, and NT-proBNP is an unexpectedly superior combination for detecting TTR-CM with improved sensitivity and / or specificity. The present disclosure further provides other ATTR Biomarker combinations that are especially 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. The present Page 2 of 135 11855652v1Docket No.: 2023P04092WO disclosure further provides that combinations of one or more demographic factors, especially age and / or sex, and two or more ATTR Biomarkers, especially TnI and RBP4, and optionally also NT-proBN, do unexpectedly well at detecting TTR-CM with improved sensitivity and / or specificity. In some embodiments, combinations of one or more demographic factors and two or more ATTR Biomarkers can be used to detect TTR-CM comparably as well as combinations of three or more ATTR Biomarkers. Combinations of one or more demographic factors and two or more ATTR Biomarkers may be used, for example, where data (e.g., a level) for a third ATTR Biomarker is unavailable or an assay for the third ATTR Biomarker lacks reproducibility.

[0008] The increased sensitivity achieved with ATTR Biomarkers described herein decreases the number of false negatives obtained when detecting and / or identifying TTR-CM. The decrease in false negatives will, in turn, help to ensure that more TTR-CM patients receive earlier treatments critical for reducing signs, symptoms, and conditions associated with TTR-CM, as well as promoting long-term survival of TTR-CM patients.

[0009] Among other things, the present disclosure provides a method including detecting a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample. In some embodiments, a sample is obtained from a subject.

[0010] The present disclosure also provides a method including: (a) detecting a level of each 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 compute a ATTR Biomarker score.

[0011] In some embodiments of 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-pro hormone B-type natriuretic peptide (NT-proBNP), (v) retinol binding protein 4 (RBP4), (vi) tissue inhibitor of metalloproteinase 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

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

[0013] In some embodiments, two or more ATTR Biomarkers are or include TnI, PKM2, NT-proBNP, and RBP4. In some embodiments, two or more ATTR Biomarkers are Page 3 of 135 11855652v1Docket No.: 2023P04092WO or include TnI, PKM2, and NT-proBNP. In some embodiments, two or more ATTR Biomarkers are or include TnI, PKM2, and RBP4. In some embodiments, two or more ATTR Biomarkers are or include TnI, PKM1, and NT-proBNP. In some embodiments, two or more ATTR Biomarkers are or include TnI, PKM1, and RBP4. In some embodiments, two or more ATTR Biomarkers are or include TnI, PKM1, PKM2, NT-proBNP, and RBP4. In some embodiments, two or more ATTR Biomarkers are or include TnI, PKM1, PKM2, and NT-proBNP. In some embodiments, two or more ATTR Biomarkers are or include TnI, PKM1, PKM2, and RBP4. In some embodiments, two or more ATTR Biomarkers are or include RBP4, SMOC-2, and TnI. In some embodiments, two or more ATTR Biomarkers are or include DCN, NT-proBNP, and TnI. In some embodiments, two or more ATTR Biomarkers are or include DCN, RBP4, and TnI. In some embodiments, two or more ATTR Biomarkers are or include NT-proBNP, SMOC-2, and TnI. In some embodiments, two or more ATTR Biomarkers are or include NT-proBNP, TIMP2, and TnI. In some embodiments, two or more ATTR Biomarkers are or include NT-proBNP and TnI. In some embodiments, two or more ATTR Biomarkers are or include DCN and TnI. In some embodiments, two or more ATTR Biomarkers are or include DCN, TIMP2, and TnI. In some embodiments, two or more ATTR Biomarkers are or include RBP4 and TnI. In some embodiments, two or more ATTR Biomarkers are or include RBP4, TIMP2, and TnI. In some embodiments, two or more ATTR Biomarkers are or include DCN, SMOC-2, and TnI. In some embodiments, two or more ATTR Biomarkers are or include TIMP2 and TnI. In some embodiments, two or more ATTR Biomarkers are or include SMOC-2, TIMP2, and TnI. In preferred embodiments, two or more ATTR Biomarkers are or include TnI and RBP4, and, more preferably, NT-proBNP, RBP4, and TnI.

[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 include SMOC- 2. In some embodiments, two or more ATTR Biomarkers do not include DCN. In some embodiments, two or more ATTR Biomarkers do not include either SMOC-2 or DCN.

[0016] In some embodiments, the step of using an ATTR Biomarker profile to compute an ATTR Biomarker score includes applying an algorithm to the ATTR Biomarker profile to compute an ATTR Biomarker score. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest Page 4 of 135 11855652v1Docket No.: 2023P04092WO methodology, a boosted tree methodology, a K nearest neighbors methodology, a generalized regression forward selection methodology, a generalized regression pruned forward selection methodology, a fit stepwise methodology, a generalized regression lasso methodology, a generalized regression elastic net methodology, a generalized regression ridge methodology, a nominal logistic methodology, a support vector machines methodology, a discriminant methodology, a naïve Bayes methodology, or a combination thereof. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a generalized regression lasso methodology, a generalized regression elastic net methodology, a generalized regression ridge methodology, a nominal logistic methodology, a support vector machines methodology, a discriminant methodology, or a combination thereof. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a support vector machines methodology, or a combination thereof.

[0017] In some embodiments, a method described herein includes the step of using the ATTR Biomarker score to determine if a subject from which the sample was obtained is at risk of or suffering from transthyretin amyloid cardiomyopathy (TTR-CM). In some embodiments, a method described herein includes the step of using the ATTR Biomarker score to diagnose a subject with TTR-CM. In some embodiments, a method described herein includes the step of using the ATTR Biomarker score to determine if a subject from which the sample was obtained is selected for one or more cardiomyopathy tests. In some embodiments, a method described herein includes the step of using the ATTR Biomarker score to determine if a subject from which the sample was obtained is selected to receive one or more doses of a TTR stabilizing agent.

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

[0019] In some embodiments, a sample includes blood, serum, plasma, or cardiac tissue.

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

[0021] In addition, the present disclosure provides compositions. In some embodiments, a composition includes one or more ATTR Biomarkers. In some Page 5 of 135 11855652v1Docket No.: 2023P04092WO embodiments, one or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof. In some embodiments, one or more ATTR Biomarkers are or include TnI, RBP4, and NT-proBNP.

[0022] In some embodiments, a composition includes one or more anti-ATTR Biomarker agents. In some embodiments, one or more anti-ATTR Biomarker agents are or include an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, an anti-NT-proBNP agent, an anti-RBP4 agent, an anti-TIMP2 agent, an anti-NfL agent, or a combination thereof. In some embodiments, one or more anti-ATTR Biomarker agents are or include an anti-TnI agent and an anti-RBP4 agent. In some embodiments, one or more anti-ATTR Biomarker agents are or include an anti-TnI agent, an anti-RBP4 agent, and an anti-NT-proBNP agent.

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

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

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

[0026] In some embodiments, a kit includes one or more anti-ATTR Biomarker agents, where the one or more anti-ATTR Biomarkers include one or more antibody agents. In some embodiments, one or more of the antibody agents are labeled with a detectable moiety.

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

[0028] The present disclosure provides uses of a kit described herein. In some embodiments, a kit can be used in an in vitro diagnostic assay to diagnose TTR-CM in a subject. Page 6 of 135 11855652v1Docket No.: 2023P04092WO BRIEF DESCRIPTION OF THE DRAWING

[0029] FIG.1 shows patient cohorts used in exemplary methods described herein.

[0030] FIG.2 shows a summary of data resulting from exemplary methods described herein.

[0031] FIG.3A includes a graph showing the individual sensitivity and specificity performance determined with TIMP2, and FIG.3B includes a graph showing the individual sensitivity and specificity performance determined with TnI.

[0032] FIG.4A includes a graph showing the individual sensitivity and specificity performance determined with PKM, and FIG.4B includes a graph showing the individual sensitivity and specificity performance determined with RBP4.

[0033] FIG.5A includes a graph showing the individual sensitivity and specificity performance determined with RBP, and FIG.5B includes a graph showing the individual sensitivity and specificity performance determined with DCN.

[0034] FIG.6A includes a graph showing the individual sensitivity and specificity performance determined with NT-proBNP, and FIG.6B includes a graph showing the individual sensitivity and specificity performance determined with SMOC-2.

[0035] FIG.7 includes a bar graph showing representative biomarker effects for each NYHA class.

[0036] FIG.8 includes a table providing the results of screens with exemplary biomarkers described herein, including an assessment of regression fits used.

[0037] FIG.9 includes tables summarizing data obtained when select subsets of biomarkers were used in various predictive models.

[0038] FIG.10 presents optimization of detection cutoff values for assays measuring PKM, TIMP2, LIMS-1, C3, and A11. FIG.10A depicts ROC plot data for optimization of detection cutoff values. FIG.10B depicts exemplary values for sensitivity, specificity, accuracy and detection cutoff values for the listed biomarkers.

[0039] FIG.11 depicts an exemplary block diagram of a computer system 1100.

[0040] FIG.12 depicts an exemplary flow chart of a method 1200.

[0041] FIG.13 depicts an exemplary flow chart of a method 1300.

[0042] FIG.14 includes a table showing the S / N ratios of selected markers. Page 7 of 135 11855652v1Docket No.: 2023P04092WO

[0043] FIGs.15-21 are plots demonstrating performance of machine-learned algorithms in classifying TTR-CM using various ATTR Biomarkers and, in some cases, also one or more demographic factors.

[0044] FIG.22 shows patient cohorts used in training and analyzing the machine- learned algorithms whose performance is demonstrated in FIGs.15-21.

[0045] FIG.23 is a plot showing performance of various biomarkers, including a bootstrap forest machine-learned model using a combination of ATTR Biomarkers (TnI, NT- proBNP, and RBP4), as predicting whether a subject has TTR-CM.

[0046] FIG.24-25 are plots showing relative contribution & portion of different individual biomarkers.

[0047] FIG.26 is a plot showing performance of various biomarkers, including a bootstrap forest machine-learned model using a combination of ATTR Biomarkers (TnI, NT- proBNP, and RBP4), as categorizing a subject as TTR-CM, non-TTR-CM with HF-PEF, or normal.

[0048] FIG.27 is a plot showing performance of various biomarkers, including a bootstrap forest machine-learned model using a combination of ATTR Biomarkers (TnI, NT- proBNP, and RBP4), as predicting New York Heart Association (NYHA) class.

[0049] FIG.28 is a block diagram of an example of a network environment for use in the methods and systems described herein.

[0050] FIG.29 is a block diagram of an example of a computing device and an example mobile computing device. DEFINITIONS

[0051] Antibody agent: As used herein, the term “antibody agent” refers to an agent that specifically binds to a particular antigen. In some embodiments, the term encompasses any polypeptide or polypeptide complex that includes immunoglobulin structural elements sufficient to confer specific binding. Such polypeptide may be naturally produced (e.g., generated by an organism reacting to an antigen), or produced by recombinant engineering, chemical synthesis, or other artificial system or methodology. Exemplary antibody agents include, but are not limited to, human antibodies, primatized antibodies, chimeric antibodies, bi-specific antibodies, humanized antibodies, conjugated antibodies (e.g., antibodies conjugated or fused to other proteins, radiolabels, cytotoxins), Small Modular ImmunoPharmaceuticals (“SMIPsTM”), single chain antibodies, cameloid antibodies, and Page 8 of 135 11855652v1Docket No.: 2023P04092WO antibody fragments. As used herein, the term “antibody agent” also includes intact monoclonal antibodies, polyclonal antibodies, single domain antibodies (e.g., shark single domain antibodies (e.g., IgNAR or fragments thereof)), multispecific antibodies (e.g. bi- specific antibodies) formed from at least two intact antibodies, and antibody fragments so long as they exhibit the desired biological activity. An antibody agent can have antibody constant region sequences that are characteristic of mouse, rabbit, primate, or human antibodies. In some embodiments, the term encompasses stapled peptides. In some embodiments, the term encompasses one or more antibody-like binding peptidomimetics. In some embodiments, the term encompasses one or more antibody-like binding scaffold proteins. In some embodiments, the term encompasses monobodies or adnectins. In many embodiments, an antibody agent is or includes a polypeptide whose amino acid sequence includes one or more structural elements recognized by those skilled in the art as a complementarity determining region (CDR); in some embodiments an antibody agent is or includes a polypeptide whose amino acid sequence includes at least one CDR (e.g., at least one heavy chain CDR and / or at least one light chain CDR) that is substantially identical to one found in a reference antibody. In some embodiments, an antibody agent is or includes a polypeptide whose amino acid sequence includes structural elements recognized by those skilled in the art as an immunoglobulin variable domain. In some embodiments, an antibody agent is a polypeptide protein having a binding domain which is homologous or largely homologous to an immunoglobulin-binding domain. In some embodiments, an antibody agent may contain a covalent modification (e.g., attachment of a glycan, a payload (e.g., a detectable moiety, a therapeutic moiety, a catalytic moiety, etc.), or other pendant group (e.g., poly-ethylene glycol, etc.).

[0052] Biomarker: The term “biomarker” or “biological marker” is used herein, consistent with its use in the art, to refer to an entity whose presence, level, or form, correlates with a particular biological event or state of interest, so that it is considered to be a “marker” of that event or state. To give but a few examples, in some embodiments, a biomarker may be or include a marker for a particular disease state, or for likelihood that a particular disease, disorder or condition may develop, occur, or reoccur. In some embodiments, a biomarker may be or include a marker for a particular disease or therapeutic outcome, or likelihood thereof. Thus, in some embodiments, a biomarker is predictive, in some embodiments, a biomarker is prognostic, in some embodiments, a biomarker is diagnostic, of the relevant biological event or state of interest. In some embodiments, a Page 9 of 135 11855652v1Docket No.: 2023P04092WO biomarker is a possible biomarker of the relevant biological event or state of interest. A biomarker may be an entity of any chemical class. For example, in some embodiments, a biomarker may be or include a nucleic acid, a polypeptide, a small molecule, or a combination thereof. In some embodiments, a biomarker is a cell surface marker. In some embodiments, a biomarker is intracellular. In some embodiments, a biomarker is found in a particular tissue (e.g., cardiac tissue). In some embodiments, a biomarker is found outside of cells (e.g., is secreted or is otherwise generated or present outside of cells, e.g., in a body fluid such as blood, urine, tears, saliva, cerebrospinal fluid, etc.

[0053] As described herein, in some embodiments, a biomarker is an ATTR Biomarker. An “ATTR Biomarker” as used herein 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. For avoidance of doubt, an ATTR Biomarker may be or include a gene product associated with the specific recited biomarker. For example, depending on context, “TnI” refers to a nucleotide encoding TnI or a characteristic fragment thereof, as well as a TnI protein or a characteristic fragment thereof.

[0054] Characteristic fragment: The term “characteristic fragment” refers to a fragment of a biomarker (e.g., ATTR Biomarker) that is sufficient to identify the biomarker from which the fragment was derived. For example, in some embodiments, a “characteristic fragment” of a biomarker is one that contains an amino acid sequence, or a collection of amino acid sequences, that together allow for the biomarker from which the fragment was derived to be distinguished from other possible biomarkers, proteins, or polypeptides. In some embodiments, a characteristic fragment includes at least 10, at least 20, at least 30, at least 40, or at least 50 amino acids.

[0055] Gene product or expression product: As used herein, the term “gene product” generally refers to an RNA transcribed from the gene (pre-and / or post-processing) or a polypeptide (pre- and / or post-modification) encoded by an RNA transcribed from the gene.

[0056] Hybridization: The term “hybridization” refers to the physical property of single-stranded nucleic acid molecules (e.g., DNA or RNA) to anneal to complementary nucleic acid molecules. Hybridization can typically be assessed in a variety of contexts– including where interacting nucleic acid molecules are studied in isolation or in the context of more complex systems (e.g., while covalently or otherwise associated with a carrier entity Page 10 of 135 11855652v1Docket No.: 2023P04092WO and / or in a biological system or cell). In some embodiments, hybridization can be detected by a hybridization technique, such as a technique selected from the group consisting of in situ hybridization (ISH), microarray, Northern blot, and Southern blot. In some embodiments, hybridization refers to 100% annealing between the single-stranded nucleic acid molecules 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 a single-stranded nucleic acid molecule anneals to a complementary nucleic acid molecule). Hybridization techniques, and methods for evaluating hybridization, are well known in the art. See, e.g., Sambrook, et al., 1989, Molecular Cloning: A Laboratory Manual, Second Edition, Cold Spring Harbor Press, Plainview, N.Y. Those skilled in the art understand how to estimate and adjust the stringency of hybridization conditions such that sequences having at least a desired level of complementary will stably hybridize, while those having lower complementary will not. For examples of hybridization conditions and parameters, see, e.g., Sambrook, et al., 1989, Molecular Cloning: A Laboratory Manual, Second Edition, Cold Spring Harbor Press, Plainview, N.Y.; Ausubel, F. M. et al.1994, Current Protocols in Molecular Biology. John Wiley & Sons, Secaucus, N.J.

[0057] Detection agent: The term “detection agent” as used herein refers to any element, molecule, functional group, compound, fragment or moiety that is detectable. In some embodiments, a detection agent is provided or utilized alone. In some embodiments, a detection agent is provided and / or utilized in association with (e.g., joined to) another agent. Examples of detection agents include, but are not limited to: various ligands, radionuclides (e.g.,3H,14C,18F,19F,32P,35S,135I,125I,123I,64Cu,187Re,111In,90Y,99mTc,177Lu,89Zr etc.), fluorescent dyes, chemiluminescent agents (such as, for example, acridinum esters, stabilized dioxetanes, and the like), bioluminescent agents, spectrally resolvable inorganic fluorescent semiconductors nanocrystals (i.e., quantum dots), metal nanoparticles (e.g., gold, silver, copper, platinum, etc.) nanoclusters, paramagnetic metal ions, enzymes, colorimetric labels (such as, for example, dyes, colloidal gold, and the like), biotin, dioxigenin, haptens, and proteins for which antisera or monoclonal antibodies are available.

[0058] Diagnostic test: As used herein, “diagnostic test” is a step or series of steps that is or has been performed to attain information that is useful in determining whether a patient has a disease, disorder or condition and / or in classifying a disease, disorder or condition into a phenotypic category or any category having significance with regard to prognosis of a disease, disorder or condition, or likely response to treatment (either treatment Page 11 of 135 11855652v1Docket No.: 2023P04092WO in general or any particular treatment) of a disease, disorder or condition. Similarly, “diagnosis” refers to providing any type of diagnostic information, including, but not limited to, whether a subject is likely to have or develop a disease, disorder or condition, state, staging or characteristic of a disease, disorder or condition as manifested in the subject, information related to the nature or classification of a tumor, information related to prognosis and / or information useful in selecting an appropriate treatment or additional diagnostic testing. Selection of treatment may include the choice of a particular therapeutic agent or other treatment modality such as surgery, radiation, etc., a choice about whether to withhold or deliver therapy, a choice relating to dosing regimen (e.g., frequency or level of one or more doses of a particular therapeutic agent or combination of therapeutic agents), etc. Selection of additional diagnostic testing may include more specific testing for a given disease, disorder, or condition.

[0059] Sample: As used herein, the term “sample” refers to a biological sample obtained or derived from a human subject, as described herein. In some embodiments, a biological sample includes biological tissue or fluid. In some embodiments, a biological sample may include blood; blood cells; tissue or fine needle biopsy samples; cell-containing body fluids; free floating nucleic acids; cerebrospinal fluid; lymph; tissue biopsy specimens; surgical specimens; other body fluids, secretions, and / or excretions; and / or cells therefrom. In some embodiments, a biological sample includes cells obtained from an individual, e.g., from a human or animal subject. In some embodiments, obtained cells are or include cells from an individual from whom the sample is obtained. In some embodiments, a sample is a “primary sample” obtained directly from a source of interest by any appropriate means. For example, in some embodiments, a primary biological sample is obtained by methods selected from the group consisting of biopsy (e.g., fine needle aspiration or tissue biopsy), surgery, collection of body fluid (e.g., blood). In some embodiments, a sample is cardiac tissue obtained from the subject. In some embodiments, as will be clear from context, the term “sample” refers to a preparation that is obtained by processing (e.g., by removing one or more components of and / or by adding one or more agents to) a primary sample. For example, filtering using a semi-permeable membrane. As another example of sample processing, the sample may be a plasma sample that is treated with an anticoagulant selected from the group consisting of EDTA, heparin, and citrate. As another example of sample processing, the sample may be processed to isolate one or more proteins (e.g., by capturing proteins with one or more antibodies). A “processed sample” may include, for example, nucleic acids or Page 12 of 135 11855652v1Docket No.: 2023P04092WO polypeptides extracted from a sample or obtained by subjecting a primary sample to techniques such as amplification or reverse transcription of mRNA, isolation and / or purification of certain components.

[0060] Subject: As used herein, the term “subject” refers to an organism, for example, a mammal (e.g., a human). In some embodiments a human subject is an adult, adolescent, or pediatric subject. In some embodiments, a subject is at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, or at least 80 years of age. In some embodiments, a subject is suffering from a disease, disorder or condition, e.g., a disease, disorder or condition 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 predisposed to and / or shows an increased risk (as compared to the average risk observed in a reference subject or population) of developing the disease, disorder or condition. In some embodiments, a subject displays one or more symptoms of a disease, disorder or condition. In some embodiments, a subject does not display a particular symptom (e.g., clinical manifestation of disease) or characteristic of a disease, disorder, or condition. In some embodiments, a subject does not display any symptom or characteristic of a disease, disorder, or condition. In some embodiments, a subject is a patient. In some embodiments, a subject is an individual to whom diagnosis and / or therapy is and / or has been administered.

[0061] Threshold value: As used herein, the term “threshold value” refers to a value (or values) that are used as a reference to attain information on and / or classify the results of a measurement, for example, the results of a measurement attained in an assay. A threshold value can be determined based on one or more control samples. A threshold value can be determined prior to, concurrently with, or after the measurement of interest is taken. In some embodiments, a threshold value can be a range of values. In some embodiments, a threshold value can be a value (or range of values) reported in the relevant field (e.g., a value found in a standard table). DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS Amyloid Transthyretin Cardiomyopathy

[0062] Transthyretin (TTR) is a transport protein in the serum and cerebrospinal fluid that carries the thyroid hormone thyroxin (T4) and retinol-binding protein. The liver secretes TTR into the blood and the choroid plexus secretes TTR into the cerebrospinal fluid. TTR is Page 13 of 135 11855652v1Docket No.: 2023P04092WO produced as a homotetrameric complex. However, TTR can undergo conformational transformation to aggregate into abnormal amyloid form leading to pathologic conditions.

[0063] The condition of ATTR amyloidosis is characterized by the deposition of amyloid fibrils, derived from transthyretin (TTR) protein, in various organs and tissues. For example, misfolding of TTR protein can cause amyloid fibril deposition in heart tissue and lead to cardiomyopathy (referred to here as “amyloid transthyretin cardiomyopathy” or “TTR-CM”). Clinical symptoms of TTR-CM include increasing ventricular wall thickness and heart failure.

[0064] There are three types of ATTR amyloidosis: (1) familial amyloid polyneuropathy (FAP), (2) familial amyloid cardiomyopathy (FAC), and (3) senile systemic amyloidosis, which is also called wild-type ATTR amyloidosis (ATTRwt). Familial amyloid polyneuropathy (FAP) affects the nervous system, in addition to 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 also manifests with carpal tunnel syndrome. FAP and FAC are hereditary conditions caused by mutations in the TTR gene that lead to production of abnormal (“variant”) TTR. Over 100 different mutations in the TTR gene have been observed, most of which cause production of variant TTR that can misfold into amyloid fibrils and cause aggregates of amyloid deposits in tissues. Most affected individuals are heterozygotes; thus, both mutant and wild-type TTR can be included in aggregates. As used herein, hereditary cardiomyopathy resulting from mutant or variant forms of TTR is referred to as familial amyloid cardiomyopathy (abbreviated ATTRm). Wild-type ATTR amyloidosis (ATTRwt) is a slowly-progressive, non-hereditary (sporadic) disease. Individuals with ATTRwt do not have a mutation in the TTR gene and the amyloid fibrils consist of wild-type TTR. Symptoms of ATTRwt include heart failure and, in some individuals, carpal tunnel syndrome. ATTRwt occurs more often in aged subjects 65 years or older.

[0065] While accurate prevalence is unknown, a rough estimate at 0.4 cases / million people / year in the United States has been given for ATTRm, and at least a two-fold higher incidence for ATTRwt. However, autopsy studies of patients over 80 years of age have shown a prevalence of about 25% for ATTRwt, suggesting that the disease is largely underdiagnosed and the actual prevalence, particularly in the elderly population is much higher. The challenge of testing numerous individuals with costly and invasive tests and procedures is a likely contributing factor to significant underdiagnoses of TTR-CM. Page 14 of 135 11855652v1Docket No.: 2023P04092WO Current Diagnosis of Amyloid Transthyretin Cardiomyopathy

[0066] TTR-CM is currently diagnosed using a series of different tests that begin with an echocardiogram (Gertz, M.A. et al. JACC 66:2452-2466, 2015). This technique is used for determining general heart function and looking for structural abnormalities (Ashley, E.A. and Niebauer, J., Cardiology Explained, London: Remedica, Chapter 4, 2004) and is used as a screen for cardiac amyloidosis as evidenced by thickening and hypertrophy of the heart ventricles. Although the test is not able to distinguish between hypertropic and hypertensive cardiomyopathy, it has shown relatively good specificity (e.g., 82%) in differentiating cardiac amyloidosis from cardiac hypertrophy in one study (Ashley 2004). 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.

[0067] Patients that test positive with an echocardiogram are next tested by cardiac magnetic resonance imaging (CMR) (Gertz, M.A. 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 the contrast agent gadolinium which is able to concentrate in areas of cardiac cellular damage (e.g., myocardial infarction) or in areas where the extracellular space has increased due to scarring or amyloid deposits (Krishnamurthy, R. et al. Current Cardiology Reviews, 9:185-190, 2013). The technique is better able to distinguish between hypertensive and hypertropic cardiomyopathy than an echocardiogram and in one study has shown greater specificity for detecting ATTR amyloidosis over AL (Ashley 2004). However, this test on its own is also not a definitive test for TTR-CM diagnosis.

[0068] Another imaging method for the detection of cardiac amyloidosis is scintigraphy, using radioisotope conjugates such as99mTc-Pyrophosphate (Bokhari et al. Circ Cardiovasc Imaging.6(2):195-201, 2013). This is performed using single photon emission computed tomography (SPECT) to gain a 3D image and while the radioactive tracer is not specific to heart tissue it is useful in detecting areas of poor blood flow, such as occurs in diseased heart tissue. This technique has been shown in one study to differentiate ATTR amyloidosis from AL amyloidosis with high specificity and sensitivity (100% and 97%, respectively).

[0069] Currently, the most definitive test for diagnosis of TTR-CM, which is typically conducted only after positive scores have been obtained using the above mentioned Page 15 of 135 11855652v1Docket No.: 2023P04092WO tests, is a cardiac biopsy followed by immunochemical staining. Polyclonal antibodies to kappa or light chain amyloid deposits in heart tissue are used for detection of AL, while polyclonal antibodies to transthyretin deposits are used for the detection of TTR-CM (Crotty, T.B., et al. Cardiovascular Pathology 4: 39-42, 1995).

[0070] Finally, antibodies for transthyretin (TTR) have previously been disclosed and applications of such antibodies in the diagnosis of amyloid diseases such as ATTRwt have been described (WO 2014 / 124334 A2 and WO 2016 / 120811). The disadvantages to using such anti-TTR antibodies for diagnosis of cardiomyopathies such as cardiomyopathy resulting from ATTRwt is that TTR may be unrecognizable in a misfolded or aggregate state in some patients, resulting in false negative test results and underdiagnosis. For example, both WO 2014 / 124334 A2 and WO 2016 / 120811 rely on exposure of 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-native form of TTR. Wild-type TTR, mutant TTR, or mixed TTR tetramers can dissociate, misfold, aggregate and / or form fibrils in ATTR amyloidosis disease. Such different forms may not be readily detected by anti-TTR antibodies. Additionally, the gene encoding TTR is reported to have many different mutations associated with ATTR amyloidosis. Therefore, anti-TTR antibodies may not be able to recognize disease in many patients. ATTR Biomarkers

[0071] Embodiments described herein provide a number of advantages over the prior techniques discussed herein. For example, technologies of the present disclosure are non- invasive, require minimal patient discomfort, are quick to perform, and are relatively cost- effective. Accordingly, technologies described herein offer advantages over these prior techniques including, but not limited to providing: a non-invasive in vitro diagnostic (IVD) test for TTR-CM resulting from ATTRwt, one or more specific in vitro biomarkers suitable for use in an IVD testing, alternatives to a single marker IVD test including more than one marker to effectively rule in or rule out candidates for the more costly and invasive procedures in the diagnosis of the disease.

[0072] The present disclosure relates particularly to biomarkers for ATTR amyloidosis and TTR-CM. Such biomarkers are referred to herein as “ATTR Biomarkers.” ATTR Biomarkers can include, for example, TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, characteristic fragments thereof, and / or variants thereof. In some Page 16 of 135 11855652v1Docket No.: 2023P04092WO embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, TIMP2, characteristic fragments thereof, and / or variants thereof. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, and TIMP2. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, DCN, characteristic fragments thereof, and / or variants thereof. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, and DCN. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, NT-proBNP, TIMP2, characteristic fragments thereof, and / or variants thereof. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, NT- proBNP, and TIMP2. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, NT-proBNP, DCN, characteristic fragments thereof, and / or variants thereof. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, NT-proBNP, and DCN. In preferred embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, NT-proBNP, characteristic fragments thereof, and / or variants thereof. In particularly preferred embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, and NT-proBNP. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, characteristic fragments thereof, and / or variants thereof.

[0073] As provided herein, an ATTR Biomarker includes gene products associated with the specific recited biomarker. For example, ATTR Biomarkers can include, for example, a protein or nucleotide (e.g., RNA, e.g., mRNA). ATTR Biomarkers also encompass full-length proteins, as well as fragments (e.g., characteristic fragments) of an ATTR Biomarker. For example, in some embodiments, an ATTR Biomarker can include a protein as listed in Table 1. In some embodiments, an ATTR Biomarker includes a fragment having an amino acid sequence identical to a contiguous span of at least 10 amino acids, at least 20 amino acids, at least 30 amino acids, at least 40 amino acids, at least 50 amino acids, at least 60 amino acids, at least 70 amino acids, at least 80 amino acids, at least 90 amino acids, or at least 100 amino acids of an amino acid sequence provided in Table 1. In some embodiments, an ATTR Biomarker includes a fragment having at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% identity to an amino acid sequence provided in Table 1. Variant or alternative forms of the biomarker include for example polypeptides encoded by any splice-variants of transcripts encoding the disclosed biomarkers. Page 17 of 135 11855652v1Docket No.: 2023P04092WO

[0074] Biomarkers contemplated herein also include truncated forms or polypeptide fragments of any of the proteins described herein. Truncated forms or polypeptide fragments of a protein can include N-terminally deleted or truncated forms and C-terminally deleted or truncated forms. Truncated forms or fragments of a protein can include fragments arising by any mechanism, such as, without limitation, by alternative translation, exo- and / or endo- proteolysis and / or degradation, for example, by physical, chemical and / or enzymatic proteolysis. Without limitation, a biomarker may include a truncated or fragment of a protein, polypeptide or peptide 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 an ATTR Biomarker protein.

[0075] In some instances, a fragment is N-terminally and / or C-terminally truncated by 1-20 amino acids, such as, for example, by 1-15 amino acids, by 1-10 amino acids, or by 1-5 amino acids, compared to the corresponding mature, full-length ATTR Biomarker protein.

[0076] Any ATTR Biomarker protein of the present disclosure such as a peptide, polypeptide or protein and fragments thereof may also encompass modified forms of said marker, peptide, polypeptide or protein and fragments such as bearing post-expression modifications including but not limited to, modifications such as phosphorylation, glycosylation, lipidation, methylation, selenocystine modification, cysteinylation, sulphonation, glutathionylation, acetylation, and / or oxidation of methionine to methionine sulphoxide or methionine sulphone.

[0077] In some embodiments, an ATTR Biomarker has different isoforms. Although only one or more isoforms may be disclosed herein, all isoforms of the ATTR Biomarkers are contemplated for use in the disclosed technologies.

[0078] In some embodiments, an ATTR Biomarker can be a nucleotide. In some embodiments, a nucleotide can be RNA or DNA. In some cases, corresponding RNA or DNA may exhibit better discriminatory power in diagnosis than the full-length protein.

[0079] Exemplary ATTR Biomakers for use with technologies provided herein are described briefly below. Troponin I (TnI) Page 18 of 135 11855652v1Docket No.: 2023P04092WO

[0080] Troponins are a group of proteins found in skeletal and cardiac muscle fibers. One function of troponins is to regulate muscular contraction. Three types of troponin proteins are known: troponin C, troponin I, and troponin T. Together, the three types of troponin form of a complex. Within the complex, troponin C binds to calcium ions. This binding initiates contraction by producing a conformational change in troponin I. Troponin I binds to actin in thin myofilaments to hold the actin-tropomyosin complex in place. Troponin T anchors the troponin complex to tropomyosin, a muscle fiber structure.

[0081] Prior analysis shows that there is little or no difference in troponin C between skeletal and cardiac muscle, while forms of troponin I and troponin T are understood to be different between skeletal and cardiac muscle. Normally, troponin is present in very small to undetectable quantities in the blood. However, when there is damage to heart muscle cells, troponin is released into the blood. A greater concentration of troponin I in the blood generally correlates with more damage to cardiac tissue.

[0082] In some embodiments of the present disclosure, TnI is an ATTR Biomarker. In some embodiments, detection of TnI, a characteristic fragment of TnI, and / or a variant of TnI is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, TnI is detected in a sample with anti-TnI agents (e.g., anti-TnI antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode TnI, nucleotides that encode characteristic fragments TnI, and / or nucleotides that encode variants of TnI is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode TnI are detected in a sample with anti-TnI nucleotide sequence agents (e.g., anti-TnI nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.). N-terminal-pro hormone B-type natriuretic peptide (NT-proBNP) by your heart. BNPa pressure and fluid balance. N-terminal (NT)-pro hormone BNP (NT-proBNP or NT-proBNP) is a non-active prohormone that is released from the same molecule that produces BNP. In particular, BNP is stored in and secreted predominantly from membrane granules in the heart ventricles in a pro form (proBNP). Once released from the heart in response to ventricle volume expansion Page 19 of 135 11855652v1Docket No.: 2023P04092WO or pressure overload, the N-terminal (NT) piece of 76 amino acids (NT-proBNP) is rapidly cleaved by the enzymes corin and furin to release the active 32-amino acid peptide (BNP).

[0084] In some embodiments of the present disclosure, NT-proBNP is an ATTR Biomarker. In some embodiments, detection of NT-proBNP, a characteristic fragment of NT-proBNP, and / or a variant of NT-proBNP is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, NT-proBNP is detected in a sample with anti-NT-proBNP agents (e.g., anti-NT-proBNP antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode NT-proBNP, nucleotides that encode a characteristic fragment of NT-proBNP, and / or nucleotides that encode variants of NT-proBNP is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode NT-proBNP are detected in a sample with anti-NT-proBNP nucleotide sequence agents (e.g., anti-NT- proBNP nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.). Retinol Binding Protein 4 (RBP4)

[0085] 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 by the RBP4 gene in humans. It is mainly, though not exclusively, synthesized in the liver. RBP4 delivers retinol from liver stores to the peripheral tissues. In plasma, RBP-retinol complexes interact with transthyretin, which prevents its loss by filtration through the kidney glomeruli. A deficiency of vitamin A blocks secretion of the binding protein posttranslationally and results in defective delivery and supply to the epidermal cells. Circulating RBP4 has previously been proposed as a way to discriminate ATTRm from non-amyloid heart failure (Arvanitis, M. et al., JAMA Cardiol., 2017). However, this prior study only examined ATTRm caused by a particular mutation in RBP4 (substitution of valine for isoleucine at codon 122 of the TTR gene (V122I)), and did not examine whether RBP4 could be used more generally as a biomarker for TTR-CM, whether assessed alone or with other possible biomarkers.

[0086] The present disclosure provides the recognition that RBP4 can be used as an ATTR Biomarker. Accordingly, in some embodiments of the present disclosure, RBP4 is an ATTR Biomarker. In some embodiments, detection of RBP4, a characteristic fragment of RBP4, and / or a variant of RBP4 is used in a method for assessing a subject’s risk of Page 20 of 135 11855652v1Docket No.: 2023P04092WO developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, RBP4 is detected in a sample with anti-RBP4 agents (e.g., anti-RBP4 antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode RBP4, nucleotides that encode a characteristic fragment of RBP4, and / or nucleotides that encode variants of RBP4 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode RBP4 are detected in a sample with anti-RBP4 nucleotide sequence agents (e.g., anti- RBP4 nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.). Pyruvate Kinase Muscle Isoform 1 and Isoform 2 (PKM1 and PKM2)

[0087] Pyruvate kinase is an enzyme that catalyzes the final step in 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 (erythrocytes), muscle isoform 1 (muscles, heart and brain) and muscle isoform 2 (early fetal tissue and most adult tissues). Pyruvate kinase protein can form dimers and tetramers.

[0088] The PKM gene encodes the muscle isoform 1 and muscle isoform 2 isozymes (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 1 noncoding variant. Among the splice variants of PKM are PKM1 and PKM2, which are produced by differential splicing and differ by 22 amino acids at their carboxy termini. Because the amino acid sequences of PKM1 and PKM2 share regions of identity, certain fragments of PKM1 and PKM2 will be characteristic fragments of both PKM1 and PKM2, and certain fragments (e.g., fragments from the 22 amino acids at the carboxy terminus) will be characteristic fragments of PKM1 or PKM2. Additionally, because the amino acid sequences of PKM1 and PKM2 share regions of identity, certain anti-PKM1 agents will also detect PKM2, and vice versa.

[0089] In some embodiments of the present disclosure, PKM (e.g., PKM1 and / or PKM2) is an ATTR Biomarker. In some embodiments, detection of PKM, a characteristic fragment of PKM, and / or a variant of PKM is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, PKM is detected in a sample with anti-PKM agents (e.g., anti-PKM antibody agents, probes, etc.). In some embodiments, Page 21 of 135 11855652v1Docket No.: 2023P04092WO detection of nucleotides that encode PKM, nucleotides that encode a characteristic fragment of PKM, and / or nucleotides that encode variants of PKM is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode PKM are detected in a sample with anti-PKM nucleotide sequence agents (e.g., anti- PKM nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.).

[0090] In some embodiments of the present disclosure, PKM1 is an ATTR Biomarker. In some embodiments, detection of PKM1, a characteristic fragment of PKM1, and / or a variant of PKM1 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, PKM1 is detected in a sample with anti- PKM1 agents (e.g., anti-PKM1 antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode PKM1, nucleotides that encode a characteristic fragment of PKM1, and / or nucleotides that encode variants of PKM1 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode PKM1 are detected in a sample with anti-PKM1 nucleotide sequence agents (e.g., anti-PKM1 nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.).

[0091] In some embodiments of the present disclosure, PKM2 is an ATTR Biomarker. In some embodiments, detection of PKM2, a characteristic fragment of PKM2, and / or a variant of PKM2 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, PKM2 is detected in a sample with anti- PKM2 agents (e.g., anti-PKM2 antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode PKM2, nucleotides that encode a characteristic fragment of PKM2, and / or nucleotides that encode variants of PKM2 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode PKM2 are detected in a sample with anti-PKM2 nucleotide sequence agents (e.g., anti-PKM2 nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.). Page 22 of 135 11855652v1Docket No.: 2023P04092WO Tissue Inhibitor of Metalloproteinases 2 (TIMP2)

[0092] Tissue inhibitor of metalloproteinases 2 (TIMP2) is a gene that encodes the TIMP2 protein. The TIMP2 gene is encoded by 5 exons spanning 83 kb of genomic DNA. The 5-prime end of the TIMP2 gene contains several regulatory elements, including Sp1-, AP2-, AP1-, and PEA3-binding sites.

[0093] The TIMP2 gene is a member of the TIMP gene family. Proteins encoded by genes of the TIMP gene family inhibit matrix metalloproteinases (MMP), a group of peptidases involved in degradation of the extracellular matrix. TIMP2 also has the ability to directly suppress the proliferation of endothelial cells. TIMP2 has been shown to suppress tumor metastasis.

[0094] In some embodiments of the present disclosure, TIMP2 is an ATTR Biomarker. In some embodiments, detection of TIMP2, a characteristic fragment of TIMP2, and / or a variant of TIMP2 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, TIMP2 is detected in a sample with anti- TIMP2 agents (e.g., anti-TIMP2 antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode TIMP2 nucleotides that encode a characteristic fragment of TIMP2, and / or nucleotides that encode variants of TIMP2 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode TIMP2 are detected in a sample with anti-TIMP2 nucleotide sequence agents (e.g., anti-TIMP2 nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.). Neurofilament Light Chain (NfL)

[0095] Neurofilaments are cytoskeletal components of neurons that are particularly abundant in axons. The functions of neurofilaments include provision of structural support and maintenance of the size, shape, and caliber of axons. Neurofilaments include three subunits: neurofilament light chain (NfL), neurofilament medium chain, and neurofilament heavy chain. NfL levels increase in cerebrospinal fluid (CSF) and blood proportionally to the degree of axonal damage in a variety of neurological disorders, including inflammatory, neurodegenerative, traumatic and cerebrovascular diseases. While NfL has been used as a biomarker for neurodegenerative disorders, its connection to other diseases and conditions, including cardiac conditions, has not been fully explored. Page 23 of 135 11855652v1Docket No.: 2023P04092WO

[0096] As described herein, in some embodiments, neurofilament light chain (NfL) can be useful for detecting and diagnosing TTR-CM. In some embodiments, NfL is an ATTR Biomarker. In some embodiments, detection of NfL, a characteristic fragment of NfL, and / or a variant of NfL is used in a method for assessing a subject’s risk of developing TTR- CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, NfL is detected in a sample with anti- NfL agents (e.g., anti- NfL antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode NfL nucleotides that encode a characteristic fragment of NfL, and / or nucleotides that encode variants of NfL is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode NfL are detected in a sample with anti-NfL nucleotide sequence agents (e.g., anti- NfL nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.). Decorin (DCN)

[0097] Decorin (DCN) is a proteoglycan that is on average 90 – 140 kilodaltons (kDa) in molecular weight. It belongs to the small leucine-rich proteoglycan (SLRP) family and includes a protein core containing leucine repeats with a glycosaminoglycan (GAG) chain of either chondroitin sulfate (CS) or dermatan sulfate (DS). DCN is a component of connective tissue, binds to type I collagen fibrils. DCN also acts as a ligand of various cytokines and growth factors by directly or indirectly interacting with corresponding signalling molecules involved in cell growth, differentiation, proliferation, adhesion and metastasis and that DCN especially plays vital roles in cancer cell proliferation, spread, pro- inflammatory processes and anti-fibrillogenesis.

[0098] In some embodiments of the present disclosure, DCN is an ATTR Biomarker. In some embodiments, detection of DCN, a characteristic fragment of DCN, and / or a variant of DCN is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, DCN is detected in a sample with anti-DCN agents (e.g., anti-DCN antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode DCN, nucleotides that encode a characteristic fragment of DCN, and / or nucleotides that encode variants of DCN is used in a method for assessing a subject’s risk of developing TTR- CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode DCN are detected in Page 24 of 135 11855652v1Docket No.: 2023P04092WO a sample with anti-DCN nucleotide sequence agents (e.g., anti-DCN nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.). SPARC-related modular calcium-binding 2 (SMOC-2)

[0099] SPARC-related, modular calcium-binding protein 2 (SMOC-2), previously called SMAP2 (smooth muscle-associated protein 2), is a 55 kDa glycoprotein that is a member of the SPARC family of matricellular proteins. SMOC-2 promotes cell cycle progression by signaling through the integrin-linked kinase (ILK) to upregulate cyclin-D1. When expressed in the endothelial extracellular matrix, it potentiates growth factor-induced angiogenesis. SMOC-2 expression is upregulated during neointima formation, promoting proliferation and migration of vascular smooth muscle. In the skin, it promotes keratinocyte attachment and migration. SMOC-2 may also inhibit proteases in the lung and artery. SMOC-2 has been proposed as a biomarker for a number of cancers.

[0100] In some embodiments, SPARC-related modular calcium-binding 2 (SMOC2) is an ATTR Biomarker. In some embodiments, detection of SMOC2, a characteristic fragment of SMOC2, and / or a variant of SMOC2 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, SMOC2 is detected in a sample with anti-SMOC2 agents (e.g., anti-SMOC2 antibody agents, probes, etc.). In some embodiments, detection of nucleotides that encode SMOC2 nucleotides that encode a characteristic fragment of SMOC2, and / or nucleotides that encode variants of SMOC2 is used in a method for assessing a subject’s risk of developing TTR-CM, diagnosing a subject with TTR-CM or recommending a subject for additional cardiomyopathy testing. In some embodiments, nucleotides that encode SMOC2 are detected in a sample with anti-SMOC2 nucleotide sequence agents (e.g., anti-SMOC2 nucleotide sequence antibody agents, probes, complementary nucleic acids, etc.).

[0101] Exemplary amino acid sequences for certain ATTR Biomarkers disclosed herein are included in Table 1 below. Table 1: Exemplary ATTR Biomarker Amino Acid Sequences ATTR UniProt ID Exemplary Amino Acid SequencePage 25 of 135 11855652v1Docket No.: 2023P04092WO VTKNITEIADLTQKIFDLRGKFKRPTLRRVRISADAMMQ ALLGARAKESLDLRAHLKQVKKEDTEKENREVGDWRKNIPage 26 of 135 11855652v1Docket No.: 2023P04092WO NT-proBNP P16860MDPQTAPSRALLLLLFLHLAFLGGRSHPLGSPGSASDLETSGLQEQRNHLQGKLSELQVEQTSLEPLQESPRPTGVWKPage 27 of 135 11855652v1Docket No.: 2023P04092WO VVIPECAHGGLYKPVQCHPSTGYCWCVLVDTGRPIPGTS TRYEQPKCDNTARAHPAKARDLYKGRQLQGCPGAKKHEF

[0102] In some embodiments, additional markers may be analyzed or assessed. In some such embodiments, the additional markers include misfolded or aggregate tranthyretin (TTR).

[0103] In some embodiments, additional factors are considered, including but not limited to demographic factors (e.g., one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographic location) and / or imaging- based biomarkers [e.g., posterior wall thickness, septal wall thickness (e.g., left ventricle septal wall thickness), or ejection fraction] of a subject from which a sample was obtained. Exemplary ATTR Biomarker Combinations

[0104] As provided herein, each ATTR Biomarker may be a full-length protein or a fragment thereof. In some embodiments, a fragment of an ATTR Biomarker is a Page 28 of 135 11855652v1Docket No.: 2023P04092WO characteristic fragment. In some embodiments, the ATTR Biomarkers are full length ATTR Biomarker proteins. In some embodiments, the ATTR Biomarkers are characteristic fragments of ATTR Biomarkers. In some embodiments, a subset of the ATTR Biomarkers are full length ATTR Biomarker proteins and a subset of the ATTR Biomarkers are characteristic fragments of ATTR Biomarkers.

[0105] In some embodiments, an ATTR Biomarker has a wild-type amino acid sequence. In some embodiments, an ATTR Biomarker has a variant amino acid sequence, e.g., an amino acid sequence including one or more mutations. In some embodiments, the ATTR Biomarkers each have a wild-type amino acid sequence. In some embodiments, the ATTR Biomarkers each have a variant amino acid sequence. In some embodiments, a subset of the ATTR Biomarkers each have a wild-type amino acid sequence and a subset of the ATTR Biomarkers each have a variant amino acid sequence.

[0106] In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes a combination of ATTR Biomarkers listed below in Tables 2-5. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes a combination of ATTR Biomarkers listed below in Table 2. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes a combination of ATTR Biomarkers listed below in Table 3. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes a combination of ATTR Biomarkers listed below in Table 4. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes a combination of ATTR Biomarkers listed below in Table 5.

[0107] In some embodiments, a combination of ATTR Biomarkers includes one or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a combination of ATTR Biomarkers includes two or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a combination of ATTR Biomarkers includes three or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a combination of ATTR Biomarkers includes four or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a combination of ATTR Biomarkers includes Page 29 of 135 11855652v1Docket No.: 2023P04092WO five or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a combination of ATTR Biomarkers includes six or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a combination of ATTR Biomarkers includes seven or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a combination of ATTR Biomarkers includes eight or more of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a combination of ATTR Biomarkers includes TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, or NfL.

[0108] In some embodiments, a combination of ATTR Biomarkers includes TnI. In some embodiments, a combination of ATTR Biomarkers is or includes TnI and PKM1. In some embodiments, a combination of ATTR Biomarkers is or includes TnI and PKM2. In some embodiments, a combination of ATTR Biomarkers is or includes TnI, PKM1, and PKM2. In some embodiments, a combination of ATTR Biomarkers is or includes TnI and RBP4. In some embodiments, a combination of ATTR Biomarkers further includes NT- proBNP, RBP4, or both. In some embodiments, a combination of ATTR Biomarkers is or includes TnI, RBP4, and NT-proBNP. In some embodiments, a combination of ATTR Biomarkers further includes TIMP2, NfL, or both. In some embodiments, a combination of ATTR Biomarkers further includes NT-proBNP, TIMP2, or both.

[0109] In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, and TIMP2. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, and DCN. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, NT-proBNP, and TIMP2. In some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, NT- proBNP, and DCN. In particularly some embodiments, one or more ATTR Biomarkers as described herein are or include TnI and RBP4. In particularly some embodiments, one or more ATTR Biomarkers as described herein are or include TnI, RBP4, and NT-proBNP.

[0110] In some embodiments, a combination of ATTR Biomarkers includes NT- proBNP. In some embodiments, a combination of ATTR Biomarkers further includes TnI, PKM1, PKM2, RBP4, or a combination thereof. In some embodiments, a combination of ATTR Biomarkers further includes TIMP2, NfL, or both. In some embodiments, a combination of ATTR Biomarkers includes or is TnI, RBP4, and NT-proBNP. In some Page 30 of 135 11855652v1Docket No.: 2023P04092WO embodiments, a combination of ATTR Biomarkers includes or is TnI, TIMP2, and NT- proBNP.

[0111] In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TnI, PKM2, NT-proBNP, and RBP4. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, PKM2, and NT-proBNP. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, PKM2, and RBP4. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, PKM1, and NT-proBNP. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, PKM1, and RBP4. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, PKM1, PKM2, NT-proBNP, and RBP4. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, PKM1, PKM2, and NT-proBNP. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, PKM1, PKM2, and RBP4. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes RBP4, SMOC-2, and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes DCN, NT- proBNP, and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes DCN, RBP4, and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes NT-proBNP, SMOC-2, and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes NT-proBNP, TIMP2, and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes NT-proBNP and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes DCN and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes DCN, TIMP2, and TnI. In some embodiments, a combination (e.g., one or more, two or Page 31 of 135 11855652v1Docket No.: 2023P04092WO more, three or more, four or more, etc.) of ATTR Biomarkers is or includes RBP4 and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes RBP4, TIMP2, and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes DCN, SMOC-2, and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TIMP2 and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes SMOC-2, TIMP2, and TnI. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, TIMP2, and RBP4. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, DCN, and RBP4. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, DCN, and NT-proBNP. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, TIMP2, and NT-proBNP. In preferred embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI and RBP4. In preferred embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers is or includes TnI, NT-proBNP, and RBP4.

[0112] Exemplary combinations of ATTR Biomarkers consistent with the present disclosure are included in Table 2 below. Also disclosed herein are combinations of ATTR Biomarkers including the combinations listed Table 2 below. Table 2: Exemplary Combinations of ATTR Biomarkers Exemplary ATTR Biomarker Combinationsage o 11855652v1Docket No.: 2023P04092WO TnI, PKM1, PKM2, NT-proBNP TnI, PKM1, PKM2, RBP4

[0113] E consistent with the present disclosure are ncudedn abe 3 beow. so dscosed eren are combinations of ATTR Biomarkers including the combinations listed Table 3 below. Table 3: Exemplary Combinations Including Two ATTR Biomarkers Exemplary Two ATTR Biomarker CombinationsPage 33 of 135 11855652v1Docket No.: 2023P04092WO PKM1, TIMP2 PKM1, SMOC-2

[0114] E s consistent withthe present disclosure are included in Table 4 below. Also disclosed herein are combinations of ATTR Biomarkers including the combinations listed Table 4 below. Table 4: Exemplary Combinations Including Three ATTR Biomarkers Exemplary Three ATTR Biomarker CombinationsPage 34 of 135 11855652v1Docket No.: 2023P04092WO TnI, PKM1, DCN TnI, PKM1, TIMP2Page 35 of 135 11855652v1Docket No.: 2023P04092WO PKM1, NT-proBNP, DCN PKM1, NT-proBNP, TIMP2Page 36 of 135 11855652v1Docket No.: 2023P04092WO NT-proBNP, RBP4, NfL NT-proBNP, DCN, TIMP2

[0115] E consistent with the present disclosure are included in Table 5 below. Also disclosed herein are combinations of ATTR Biomarkers including the combinations listed Table 5 below. Table 5: Exemplary Combinations Including Four ATTR Biomarkers Exemplary Four ATTR Biomarker CombinationsPage 37 of 135 11855652v1Docket No.: 2023P04092WO TnI, PKM1, NT-proBNP, NfL TnI, PKM1, RBP4, DCNPage 38 of 135 11855652v1Docket No.: 2023P04092WO TnI, NT-proBNP, DCN, NfL TnI, NT-proBNP, TIMP2, SMOC-2Page 39 of 135 11855652v1Docket No.: 2023P04092WO PKM1, NT-proBNP, RBP4, NfL PKM1, NT-proBNP, DCN, TIMP2Page 40 of 135 11855652v1Docket No.: 2023P04092WO PKM2, RBP4, SMOC-2, NfL PKM2, DCN, TIMP2, SMOC-2

[0116] I o or more, three or more, four or more, etc.) of ATTR Biomarkers does not include PKM1. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers does not include PKM2. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers does not include either PKM1 or PKM2.

[0117] In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers does not include SMOC-2. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers does not include DCN. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers does not include either SMOC-2 or DCN. Page 41 of 135 11855652v1Docket No.: 2023P04092WO Methods

[0118] Methods of the present disclosure further allow for earlier identification of more patients who are at risk of TTR-CM, while minimizing the number of false negatives. In some embodiments, methods disclosed herein provide the advantage of an early screen for the presence of ATTRwt. In some embodiments, methods disclosed herein can assist in the detection or diagnosis of ATTRwt after genetic testing rules out ATTRm. In some embodiments, methods disclosed herein reduces or eliminates the need for initiating screening for TTR-CM with costly and complex process of an echocardiogram, followed by CMR and scintigraphy. In some embodiments, if methods disclosed herein result in detection of possible TTR-CM in a subject, the subject can undergo subsequent confirmatory testing, such as echocardiogram, cardiac magnetic resonance imaging (CMR), scintigraphy, and / or cardiac biopsy.

[0119] In some embodiments, a method disclosed herein is a method of determining a subject’s risk of developing amyloid transthyretin cardiomyopathy (TTR-CM). In some embodiments, a method disclosed herein is a method of diagnosing a subject with TTR-CM, and the sample was obtained from the subject. In some embodiments, a method disclosed herein is a method of treating TTR-CM in a subject at risk of or suffering from TTR-CM. In some embodiments, a method disclosed herein is a method of determining a patient does not have or is not at risk of developing TTR-CM.

[0120] In some embodiments, TTR-CM results from wild-type transthyretin amyloidosis (ATTRwt). In some embodiments, a subject tests negative for familial amyloid cardiomyopathy (ATTRm) by genetic testing.

[0121] In some embodiments, a method disclosed herein is a method of selecting a subject to receive one or more doses of a TTR stabilizing agent, and the sample was obtained from the subject. In some embodiments, a method disclosed herein includes administering to the subject one or more doses a TTR stabilizing agent.

[0122] In some embodiments, a method disclosed herein is a method of selecting a subject for one or more cardiomyopathy tests, and the sample was obtained from the subject. In some embodiments, one or more cardiomyopathy tests include an echocardiogram, an advanced imaging method, or both. In some embodiments, an advanced imaging method includes cardiac magnetic resonance imaging (CMR), scintigraphy, or both. In some embodiments, scintigraphy includes use of a radioisotope conjugate such as 99m Tc- Page 42 of 135 11855652v1Docket No.: 2023P04092WO Pyrophosphate. In some embodiments, scintigraphy is performed using single photon emission computed tomography (SPECT).

[0123] The present disclosure provides diagnostic tests for TTR-CM (including TTR- CM resulting from wild-type ATTR amyloidosis) characterized by detection of biomarkers according to the methods above.

[0124] In some embodiments, methods of detecting, diagnosing, or identifying a risk of TTR-CM as taught by the present disclosure are improved methods as compared to standard techniques in that the methods of the present 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 for TTR-CM, and / or reduced cost of screening patients for TTR-CM.

[0125] In some embodiments, the biomarkers disclosed herein can be used for an in- vitro diagnostic (IVD) or screening test for the condition of amyloid transthyretin cardiomyopathy. In some embodiments, a diagnostic test as taught by the present disclosure detects whether one or more biomarkers are present in a sample obtained from a subject. In some embodiments, a diagnostic test as taught by the present disclosure can assist in the detection or diagnosis of TTR-CM (including TTR-CM resulting from wild-type ATTR amyloidosis) in a subject.

[0126] In some embodiments, a diagnostic test as taught by the present disclosure is adapted to an immunoassay platform. In some embodiments, such an immunoassay platform includes a semi-automated or automated immunoassay platform. In some embodiments, a diagnostic test as taught by the present disclosure is adapted for semi-automated testing of one or more biomarkers.

[0127] In some embodiments, a diagnostic test as taught by the present disclosure is an improved diagnostic for TTR-CM as compared to standard techniques in that the diagnostic test of the present disclosure includes 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 for TTR-CM, and / or reduced cost of screening patients for TTR-CM.

[0128] In some embodiments, a diagnostic test as disclosed herein can be a plasma- based screening assay. In some embodiments, a diagnostic test is adapted for, e.g., the Siemens Atellica® system or the Siemens Advia Centaur® system. Page 43 of 135 11855652v1Docket No.: 2023P04092WO

[0129] As set out in greater detail below, in some embodiments, methods provided herein include detecting a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers are present in a sample.

[0130] In some embodiments, methods provided herein include detecting a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample to obtain an ATTR Biomarker profile, and using the ATTR Biomarker profile to compute an ATTR Biomarker score. In some embodiments, methods provided herein include detecting a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample to obtain an ATTR Biomarker profile, and using the ATTR Biomarker profile and demographic factors to compute an ATTR Biomarker score. In some embodiments, methods provided herein include detecting a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample to obtain an ATTR Biomarker profile, and using the ATTR Biomarker profile and imaging-based biomarkers to compute an ATTR Biomarker score. In some embodiments, methods provided herein include detecting a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample to obtain an ATTR Biomarker profile, and using the ATTR Biomarker profile, demographic factors, and imaging-based biomarkers to compute an ATTR Biomarker score.

[0131] In some embodiments, methods provided herein including receiving a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample. In some embodiments, receiving includes electronically receiving.

[0132] In some embodiments, methods provided herein include using a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample to obtain an ATTR Biomarker profile, and using the ATTR Biomarker profile to compute an ATTR Biomarker score. In some embodiments, methods provided herein include using a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample to obtain an ATTR Biomarker profile, and using the ATTR Biomarker profile and demographic factors to compute an ATTR Biomarker score. In some embodiments, methods provided herein include using a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample to obtain an ATTR Biomarker profile, and using the ATTR Biomarker profile and imaging-based biomarkers to compute an ATTR Biomarker score. In some embodiments, methods provided herein include using a level of each of two or more transthyretin amyloidosis (ATTR) Biomarkers in a sample to obtain an ATTR Biomarker profile, and Page 44 of 135 11855652v1Docket No.: 2023P04092WO using the ATTR Biomarker profile, demographic factors, and imaging-based biomarkers to compute an ATTR Biomarker score.

[0133] In some embodiments, demographic factors include one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographic location. In some embodiments, imaging-based biomarkers include one or more of: posterior wall thickness, septal wall thickness (e.g., left ventricle septal wall thickness), and ejection fraction.

[0134] In some embodiments, methods described herein include using an ATTR Biomarker score to select a subject for further cardiomyopathy tests. In some embodiments, methods described herein include using an ATTR Biomarker score to select a subject to received one or more doses of a TTR stabilizing agent. In some embodiments, methods described herein include using an ATTR Biomarker score to identify a subject as having or being at risk of having TTR-CM.

[0135] In some embodiments, methods described herein include comparing an ATTR Biomarker score to a reference ATTR Biomarker score. In some embodiments, methods described herein include administering one or more doses of a TTR stabilizing agent to a subject. In some embodiments, methods described herein include performing one or more cardiomyopathy tests on a subject.

[0136] Additional description of exemplary methods disclosed herein are provided below. Detecting ATTR Biomarkers and Obtaining an ATTR Biomarker Profile

[0137] Among other things, methods provided herein include assessment of a level of one or more ATTR Biomarkers in a sample. A level of an ATTR Biomarker can be detected in a sample. Exemplary methods for detecting a level of one or more ATTR Biomarkers are described herein. However, a level of an ATTR Biomarker can also be provided, for example, in electronic form from, e.g., a laboratory that has detected a level of one or more ATTR Biomarkers in sample.

[0138] In some embodiments, the present disclosure provides technologies according to which one or more ATTR Biomarkers are detected, analyzed and / or assessed in a sample. In some embodiments, one or more ATTR Biomarkers are in a sample obtained from a subject; in some embodiments, a diagnosis or therapeutic decision is made based on such detection, analysis and / or assessment. In some embodiments, the biomarkers that are Page 45 of 135 11855652v1Docket No.: 2023P04092WO detected, analyzed or assessed are one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof).

[0139] In some embodiments, the present disclosure provides a method of detecting a level of one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) in a sample. A level of an ATTR Biomarker, as described herein, encompasses the presence of an ATTR Biomarker, the absence of an ATTR Biomarker, an amount of an ATTR Biomarker, an absolute amount of an ATTR Biomarker, a relative amount of an ATTR Biomarker, or a concentration of an ATTR Biomarker.

[0140] In some embodiments, a method provided herein includes detecting a level of each ATTR Biomarker of a combination listed in Tables 2-5 in a sample. In some embodiments, a method provided herein includes detecting a level of each ATTR Biomarker of a combination listed in Table 2 in a sample. In some embodiments, a method provided herein includes detecting a level of each ATTR Biomarker of a combination listed in Table 3 in a sample. In some embodiments, a method provided herein includes detecting a level of each ATTR Biomarker of a combination listed in Table 4 in a sample. In some embodiments, a method provided herein includes detecting a level of each ATTR Biomarker of a combination listed in Table 5 in a sample.

[0141] In some embodiments, a method provided herein includes detecting a level of each of one or more ATTR Biomarkers in a sample, wherein the one or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a method provided herein includes detecting a level of each of two or more ATTR Biomarkers in a sample, wherein the two or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a method provided herein includes detecting a level of each of three or more ATTR Biomarkers in a sample, wherein the three or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a method provided herein includes detecting a 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 combinations thereof. In some embodiments, a method provided herein includes detecting a 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, Page 46 of 135 11855652v1Docket No.: 2023P04092WO DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, a method provided herein includes detecting a 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 combinations thereof. In some embodiments, a method provided herein includes detecting a level of each of seven or more ATTR Biomarkers in a sample, wherein the seven or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, and NfL. In some embodiments, a method provided herein includes detecting a level of each of eight or more ATTR Biomarkers in a sample, wherein the eight or more ATTR Biomarkers include TnI, PKM1, PKM2, NT- proBNP, RBP4, DCN, TIMP2, SMOC-2, and NfL. In some embodiments, a method provided herein includes detecting a level of each of TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, and NfL in a sample.

[0142] In some embodiments, a method provided herein includes detecting a level of TnI in a sample. In some embodiments, a method provided herein includes detecting a level of TnI and PKM1 in a sample. In some embodiments, a method provided herein includes detecting a level of TnI and PKM2 in a sample. In some embodiments, a method provided herein includes detecting a level of TnI, PKM1, and PKM2 in a sample. In some embodiments, a method provided herein further includes detecting a level of NT-proBNP, RBP4, or both in a sample. In some embodiments, a method provided herein further includes detecting a level of TIMP2, NfL, or both in a sample.

[0143] In some embodiments, a method provided herein includes detecting a level of NT-proBNP in a sample. In some embodiments, a method provided herein further includes detecting a level of TnI, PKM1, PKM2, RBP4, or a combination thereof in a sample. In some embodiments, a method provided herein further includes detecting a level of TIMP2, NfL, or both in a sample.

[0144] In some embodiments, a method provided herein includes detecting a level of each of TnI, PKM2, NT-proBNP, and RBP4 in a sample. In some embodiments, a method provided herein includes detecting a level of each of TnI, PKM2, and NT-proBNP in a sample. In some embodiments, a method provided herein includes detecting a level of each of TnI, PKM2, and RBP4 in a sample. In some embodiments, a method provided herein includes detecting a level of each of TnI, PKM1, and NT-proBNP in a sample. In some embodiments, a method provided herein includes detecting a level of each of TnI, PKM1, and RBP4 in a sample. In some embodiments, a method provided herein includes detecting a Page 47 of 135 11855652v1Docket No.: 2023P04092WO level of each of TnI, PKM1, PKM2, NT-proBNP, and RBP4 in a sample. In some embodiments, a method provided herein includes detecting a level of each of TnI, PKM1, PKM2, and NT-proBNP in a sample. In some embodiments, a method provided herein includes detecting a level of each of TnI, PKM1, PKM2, and RBP4 in a sample. In some embodiments, a method provided herein includes detecting a level of each of NT-proBNP, RBP4, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of RBP4, SMOC-2, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of DCN, NT-proBNP, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of DCN, RBP4, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of NT-proBNP, SMOC-2, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of NT-proBNP, TIMP2, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of NT-proBNP and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of DCN and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of DCN, TIMP2, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of RBP4 and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of RBP4, TIMP2, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of DCN, SMOC-2, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of TIMP2 and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of SMOC-2, TIMP2, and TnI in a sample.

[0145] In some embodiments, a method provided herein includes detecting a level of each of NT-proBNP, RBP4, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of DCN, NT-proBNP, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of DCN, RBP4, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of NT-proBNP, TIMP2, and TnI in a sample. In some embodiments, a method provided herein includes detecting a level of each of RBP4, TIMP2, and TnI in a sample.

[0146] Methods of detecting biomarkers (e.g., ATTR Biomarkers) include methods for detecting biomarkers as proteins. Protein-based methods of detecting biomarkers include, for example, mass spectrometry (MS), immunoassays (e.g., immunoprecipitation), Western Page 48 of 135 11855652v1Docket No.: 2023P04092WO blots, ELISAs, immunohistochemistry, immunocytochemistry, flow cytometry, and / or immuno-PCR.

[0147] In some embodiments, mass spectrometry includes MS, MS / MS, MALDI- TOF, electrospray ionization mass spectrometry (ESIMS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF- MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI- TOF-MS), tandem liquid chromatography-mass spectrometry (LC-MS / MS) mass spectrometry, desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS), atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS)n, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry. Often, the MS approach quantifies a fragment of a biomarker rather than the full-length protein. MS approaches, however, can be sufficient to determine the protein level of the biomarker to an accuracy sufficient for methods and / or assessments as disclosed.

[0148] In some embodiments, an immunoassay can be a chemiluminescent immunoassay. In some embodiments, an immunoassay can 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.

[0149] In some embodiments, methods of detecting biomarkers as proteins in a sample include contacting a sample with one or more antibody agents directed to the biomarkers of interest. In some embodiments, such methods also include contacting the sample with a first set of one or more detection agents. In some embodiments, the antibody agents are labeled with the first set of one or more detection agents. In some embodiments, the first set of one or more detection agents include one or more acridinium ester molecules.

[0150] Acridinium ester (AE) molecules can be used to label proteins and nucleic acids. Acridinium-labeled proteins can be used for detection in immunoassays. Exposing AE to an alkaline H2O2(hydrogen peroxide) produces chemiluminscence. Light is emitted at a wavelength maximum in the range of 430 to 480 nm, depending on the specific AE variant. Such light can be detected, for example, by high-efficiency photomultiplier tubes. The light emission is rapid and completes within 1 to 5 seconds. Diversity in AE forms contributes to Page 49 of 135 11855652v1Docket No.: 2023P04092WO better assay performance, including improved sensitivity and robustness. AE molecules can be used to label small molecules, large analytes, and antibodies.

[0151] Additional methods of detecting biomarkers include methods for detecting biomarkers as nucleic acids. Nucleic acid-based methods of detecting biomarkers include performing nucleic acid amplification methods, such as polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), transcription-mediated amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence based amplification (NASBA). In some embodiments, a nucleic acid-based method of detecting biomarkers includes detecting hybridization between one or more nucleic acid probes and one or more nucleotides that encode the biomarker of interest. In some embodiments, the nucleic acid probes are each complementary to at least a portion of one of the one or more nucleotides that encode the biomarker of interest. In some embodiments, the nucleotides that encode the biomarker of interest include DNA (e.g., cDNA). In some embodiments, the nucleotides that encode the biomarker of interest include RNA (e.g., mRNA).

[0152] In some embodiments, a method provided herein detects a level of one or more ATTR Biomarkers (e.g., a level of two or more, three or more, four or more, or five or more ATTR Biomarkers) to obtain an ATTR Biomarker profile. In some embodiments, an ATTR Biomarker profile includes a level of each of the ATTR Biomarkers being assessed. In some embodiments, an ATTR Biomarker profile includes a level of each of the ATTR Biomarkers detected, e.g., as described herein. Sample

[0153] In some embodiments, a sample as disclosed herein is a biological sample. In some embodiments, a biological sample is a blood sample, e.g., drawn from an artery or vein of a subject. A blood sample can be a whole blood sample, a plasma sample, or a serum sample. In some embodiments, a biological sample includes cardiac tissue.

[0154] In some embodiments, a sample is obtained from a subject. In some embodiments, a subject from which a sample was obtained is being assessed for TTR-CM. In some embodiments, a subject from which a sample was obtained is suffering from or is at risk of developing amyloid transthyretin cardiomyopathy (TTR-CM).

[0155] In some embodiments, a method disclosed herein includes obtaining a biological sample from a subject. In some embodiments, obtaining a biological sample from Page 50 of 135 11855652v1Docket No.: 2023P04092WO a subject includes drawing blood. In some embodiments, obtaining a biological sample from a subject includes performing a biopsy.

[0156] In some embodiments of methods disclosed herein, a sample is provided by, e.g., medical personnel. Subject

[0157] In some embodiments, a subject as disclosed herein is a mammal. In some embodiments, a mammal is a human.

[0158] In some embodiments, a subject as disclosed herein is a biological male. In some embodiments, a subject as disclosed herein is a biological female.

[0159] In some embodiments, a subject as disclosed herein is overweight. In some embodiments, a subject has a body mass index (BMI) of 25 or more. In some embodiments, a subject has a body mass index (BMI) of 30 or more.

[0160] In some embodiments, a subject is at least 50 years old. In some embodiments, a subject is at least 55 years old. In some embodiments, a subject is at least 60 years old. In some embodiments, a subject is at least 65 years old. Methods Utilizing Thresholds

[0161] In some methods disclosed herein, a level of one or more ATTR Biomarkers can be compared to a threshold. In some embodiments, methods disclosed herein include a comparison of a level of one or more ATTR Biomarkers to respective thresholds. In some embodiments, methods disclosed herein include a comparison of a level of one or more ATTR Biomarkers to reference thresholds.

[0162] A reference threshold may be a threshold from a subject known or independently verified to have good cardiac health, or from a subject known or independently verified to have poor cardiac health, such as is the case for a subject having TTR-CM. Alternately or in combination a subject’s ATTR Biomarker profile is compared to a reference threshold determined from a plurality of subject of common known status (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM). In some embodiments, a reference threshold is an average of known level of an ATTR Biomarker from a plurality of subjects, or alternately is a range defined by the range of levels of an ATTR Biomarker observed in reference subjects.

[0163] In more complex assessment approaches, a subject’s ATTR Biomarker level is compared to a reference ATTR Biomarker level constructed from a larger number of subjects of a common status (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR- Page 51 of 135 11855652v1Docket No.: 2023P04092WO CM), such as at least 10, at least 50, at least 100, at least 500, at least 1000 or more subjects. Often, reference subjects are evenly distributed in status between (1) healthy / not diagnosed with TTR-CM and (2) diagnosed with TTR-CM. Assessment includes in some cases iterative or simultaneous comparison of a subject’s ATTR Biomarker level to a plurality of profiles of known status.

[0164] A plurality of known reference ATTR Biomarker profiles (e.g., detected levels of one or more ATTR Biomarkers in a reference sample) can also be used to train a computational assessment algorithm, e.g., a machine learning model, such that a single comparison between a subject’s ATTR Biomarker profile and a reference ATTR Biomarker profile provides an outcome that integrates or aggregates information from a large number of subjects of common known health status (e.g., healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM), such as at least 10, at least 50, at least 100, at least 500, at least 1000 or more individuals. Generation of such a reference ATTR Biomarker profile can facilitate much faster assessment of a subject’s risk of TTR-CM, or assessment using much less computational power.

[0165] A reference ATTR Biomarker profile can be generated from a plurality of reference ATTR Biomarker profiles through any of a number of computational approaches known to one of skill in the art. Machine learning models are readily constructed, for example, using any number of statistical programming languages such as R, scripting languages such as Python and associated machine learning packages, data mining software such as Weka or Java, Mathematica, Matlab or SAS.

[0166] A subject’s ATTR Biomarker profile can be compared to a reference ATTR Biomarker profile as generated above or otherwise by one of skill in the art, and an output assessment is generated. A number of output assessments are consistent with the disclosure herein. Output assessments include a single assessment, often narrowed by a sensitivity, specificity or sensitivity and specificity parameter, indicating a health status assessment (e.g., probability subject has TTR-CM, subject is not at risk of TTR-CM, subject is at risk of TTR- CM, subject has TTR-CM). Alternately or in combination, additional parameters are provided, such as the subject’s demographic factors (e.g., one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographic location) and / or the subject’s imaging-based biomarkers (e.g., left ventricle septal wall thickness and / or ejection fraction). Page 52 of 135 11855652v1Docket No.: 2023P04092WO

[0167] More specifically, in some embodiments, methods disclosed herein further include diagnosing a subject with amyloid transthyretin cardiomyopathy (TTR-CM) if the level of at least one of the one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT- proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) that is detected is above a threshold value. In some embodiments, methods disclosed herein further include diagnosing a subject with amyloid transthyretin cardiomyopathy (TTR-CM) if the level of at least one of the one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value. In some embodiments, methods disclosed herein further include diagnosing a subject with amyloid transthyretin cardiomyopathy (TTR-CM) if the level of each of the one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) that is detected is above a threshold value. In some embodiments, methods disclosed herein further include diagnosing a subject with amyloid transthyretin cardiomyopathy (TTR-CM) if the level of each of the one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value.

[0168] In some embodiments, the methods disclosed herein further include recommending a subject for one or more cardiomyopathy tests if the level of at least one or the one or more ATTR Biomarkers is above threshold value. In some such methods, the subject is recommended for one or more cardiomyopathy tests if the level of at least one of the one or more ATTR Biomarkers that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value. In some embodiments, the methods disclosed herein further include recommending a subject for one or more cardiomyopathy tests if the level of each of the one or more ATTR Biomarkers is above threshold value. In some such methods, the subject is recommended for one or more cardiomyopathy tests if the level of each of the one or more ATTR Biomarkers that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value.

[0169] As discussed above, in some embodiments, methods of detecting one or more ATTR Biomarkers (e.g., ATTR Biomarker proteins) in a sample include contacting a sample with one or more antibody agents directed to the ATTR Biomarker. In some embodiments, Page 53 of 135 11855652v1Docket No.: 2023P04092WO such methods also include contacting the sample with a first set of one or more detection agents. In some embodiments, the antibody agents are labeled with the first set of one or more detection agents. In some embodiments, the first set of one or more detection agents include one or more acridinium ester molecules.

[0170] Acridinium ester (AE) molecules can be used to label proteins and nucleic acids. Acridinium-labeled proteins can be used for detection in immunoassays. Exposing AE to an alkaline H2O2 (hydrogen peroxide) produces chemiluminscence. Light is emitted at a wavelength maximum in the range of 430 to 480 nm, depending on the specific AE variant. Such light can be detected, for example, by high-efficiency photomultiplier tubes.

[0171] In some embodiments, detecting binding between an ATTR Biomarker and one or more antibody agents directed against the ATTR Biomarker includes determining absorbance values or emission values for the first set of one or more detection agents. For example, the absorbance values are indicative of the level of binding (e.g., higher absorbance is indicative of more binding). In some embodiments, the absorbance values or emission values for the first set of one or more detection agents are above a threshold value. In some embodiments, the absorbance values or emission values for the first set of one or more detection agents is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value. In some embodiments, the threshold value is an average of absorbance values or emission values determined for a second set of one or more detection agents that label two or more control samples. In some such embodiments, the second set of one or more detection agents is similar to or the same as the first set of one or more detection agents.

[0172] In one aspect, the present disclosure provides a method of detecting one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) in a subject, said method including detecting whether one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) are present in a sample obtained from the subject according to the methods of detecting biological markers described above. In some embodiments, methods of detecting one or more ATTR Biomarkers include detecting binding between a ATTR Biomarker and one or more anti-ATTR Biomarker antibody agents. In some embodiments, detecting whether one or more ATTR Biomarkers are present in a sample obtained from the subject includes detecting a level of one or more ATTR Biomarkers present in a sample obtained from the subject. In some embodiments, the level of an ATTR Page 54 of 135 11855652v1Docket No.: 2023P04092WO Biomarkers that is detected is above a threshold value for the respective ATTR Biomarker(s). In some embodiments, the level of an ATTR Biomarkers that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value.

[0173] In some embodiments, the present disclosure provides methods of detecting one or more one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) in a sample obtained from a subject. Biological markers for amyloid transthyretin cardiomyopathy can include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof. In some embodiments, control samples used in the methods described above include samples obtained from one or more subjects who do not have ATTR amyloidosis and / or TTR-CM.

[0174] In some embodiments, the methods disclosed herein further include diagnosing the subject with amyloid transthyretin cardiomyopathy (TTR-CM) if the level of at least one or the one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) that is detected is above a threshold value. In some embodiments, the method includes diagnosing a subject with TTR- CM if the level of at least one or the one or more ATTR Biomarkers that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value.

[0175] In some embodiments, the methods disclosed herein further include diagnosing the subject with amyloid transthyretin cardiomyopathy (TTR-CM) if the absorbance values or emission values for the first set of one or more detection agents is above a threshold value. In some embodiments, the method includes diagnosing a subject with TTR-CM if the absorbance values or emission values for the first set of one or more detection agents is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value. In some embodiments, the threshold value is an average of absorbance values or emission values determined for a second set of one or more detection agents that label two or more control samples. In some such embodiments, the second set of one or more detection agents is similar to or the same as the first set of one or more detection agents.

[0176] In some embodiments, the methods disclosed herein further include recommending a subject for one or more cardiomyopathy tests if the level of at least one or the one or more ATTR Biomarkers is above threshold value. In some such methods, the Page 55 of 135 11855652v1Docket No.: 2023P04092WO subject is recommended for one or more cardiomyopathy tests if the level of at least one or the one or more ATTR Biomarkers that is detected is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value.

[0177] In some embodiments, the methods disclosed herein further include recommending the subject for one or more cardiomyopathy tests if the absorbance values or emission values for the first set of one or more detection agents is above a threshold value. In some such methods, the subject is recommended for one or more cardiomyopathy tests if the absorbance values or emission values for the first set of one or more detection agents is at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, at least 1.8, or at least 1.9 fold greater than a threshold value.

[0178] Cardiomyopathy tests that may be used according to the methods of the present disclosure include echocardiogram or advanced imaging methods. In some embodiments, the advanced imaging methods include cardiac magnetic resonance imaging (CMR) or scintigraphy (e.g. using a radioisotope conjugate such as99mTc-Pyrophosphate and / or using single photon emission computed tomography (SPECT). In some embodiments the methods disclosed herein further include recommending the subject for cardiac biopsy if the level of at least one or the one or more ATTR Biomarkers that is detected is above a threshold value and the subject tests positive for cardiomyopathy in one or more of the cardiomyopathy tests. In some such embodiments, the biopsy tissue is tested for cardiomyopathy by immunochemical staining. For example, immunochemical staining includes use of one or more antibody agents to kappa or lambda light chain amyloid deposits in heart tissue and / or one or more antibody agents to transthyretin deposits. A subject can be diagnosed with amyloid transthyretin cardiomyopathy (TTR-CM) if immunochemical staining indicates presence of transthyretin deposits in the cardiac tissue.

[0179] In any of the above embodiments, a threshold value may be an average of values detected for two or more control samples. In some such embodiments, the values detected for the two or more control samples represent control levels for one or more 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 combinations thereof). In some embodiments, the two or more control samples are each a sample obtained from a subject who does not have TTR-CM. In some embodiments, the threshold value is a value reported in a standard table. Methods Utilizing ATTR Biomarker Profiles Page 56 of 135 11855652v1Docket No.: 2023P04092WO

[0180] An algorithm-based assay and associated information provided by the practice of any of the methods described herein can facilitate optimal treatment and decision making for subjects. For example, methods described herein can enable a physician or caretaker to identify patients who have a low likelihood of having TTR-CM and therefore would not need treatment, would not need additional cardiac tests, or would not need increased monitoring for TTR-CM, or who have a high likelihood of having TTR-CM, would need treatment, would need additional cardiac tests, or would need increased monitoring for TTR-CM.

[0181] An ATTR Biomarker score can be determined by the application of a specific algorithm in some cases. In some embodiments, an ATTR Biomarker score is quantitative. The algorithm used to calculate the ATTR Biomarker score in the methods disclosed herein may group the expression level values of an ATTR Biomarker or groups of ATTR Biomarkers. The formation of a particular group of ATTR Biomarkers, in addition, can facilitate the mathematical weighting of the contribution of various expression levels of ATTR Biomarker or ATTR Biomarker subsets (for example classifier) to the quantitative score.

[0182] Exemplary ATTR Biomarkers and corresponding amino acid sequences are listed in Table 1. Exemplary combinations of ATTR Biomarkers are listed in Tables 2-5.

[0183] Methods described herein, as well as kits and systems provided herein, can utilize an algorithm-based diagnostic assay for predicting if a subject from which the sample was obtained is at risk of or suffering from TTR-CM, selecting a subject from which the sample was obtained for one or more cardiomyopathy tests, and / or selecting a subject from which the sample was obtained to receive one or more doses of a TTR stabilizing agent.

[0184] Levels of one or more ATTR Biomarkers, and optionally one or more demographic factors (e.g., one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographic location) and / or imaging-based biomarkers (e.g., left ventricle septal wall thickness and / or ejection fraction) can be used alone or arranged into functional subsets to calculate an ATTR Biomarker score that is used to predict if a subject from which the sample was obtained is at risk of or suffering from TTR-CM, select a subject from which the sample was obtained for one or more cardiomyopathy tests, and / or select a subject from which the sample was obtained to receive one or more doses of a TTR stabilizing agent.

[0185] Methods disclosed herein include using an ATTR Biomarker profile to compute an ATTR Biomarker score. In some embodiments, using an ATTR Biomarker Page 57 of 135 11855652v1Docket No.: 2023P04092WO profile to compute an ATTR Biomarker score includes applying an algorithm to the ATTR Biomarker profile to compute an ATTR Biomarker score. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a K nearest neighbors methodology, a generalized regression forward selection methodology, a generalized regression pruned forward selection methodology, a fit stepwise methodology, a generalized regression lasso methodology, a generalized regression elastic net methodology, a generalized regression ridge methodology, a nominal logistic methodology, a support vector machines methodology, a discriminant methodology, a naïve Bayes methodology, or a combination thereof. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a generalized regression lasso methodology, a generalized regression elastic net methodology, a generalized regression ridge methodology, a nominal logistic methodology, a support vector machines methodology, a discriminant methodology, or a combination thereof. In some embodiments, an algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, a support vector machines methodology, or a combination thereof.

[0186] A machine-learned algorithm may use data corresponding to one or more (e.g., two or more) demographic factors and an ATTR Biomarker profile that includes data corresponding to levels of one or more ATTR Biomarkers in a sample from a subject. 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 is three or more ATTR Biomarkers. In some embodiments, the two or more ATTR Biomarkers is two ATTR Biomarkers or three ATTR Biomarkers. In some embodiments, the two or more ATTR Biomarkers is four ATTR Biomarkers. In some embodiments, the two or more ATTR Biomarkers is only two ATTR Biomarkers. In some embodiments, the one or more demographic factors include one or more of age, weight, biological sex, ethnicity, BMI, medical history, risk factors, family history, and geographic location. In some embodiments, the one or more demographic factors include age, biological sex, or both age and biological sex. In some embodiments, the Page 58 of 135 11855652v1Docket No.: 2023P04092WO one or more demographic factors is two demographic factors. In some embodiments, the two demographic factors are age and biological sex.

[0187] A machine-learned algorithm may use an ATTR Biomarker profile that includes data corresponding to levels of one or more ATTR Biomarkers in a sample from a subject and data corresponding to one or more imaging-based biomarkers for the subject, for example in further combination with data corresponding to one or more demographic factors for the subject. For example, septal wall thickness and TnI may be used in combination with each other (even without using a machine-learned algorithm). In some embodiments, the ATTR Biomarker includes data that corresponds to 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 is three or more ATTR Biomarkers. In some embodiments, the two or more ATTR Biomarkers is two ATTR Biomarkers or three ATTR Biomarkers. In some embodiments, the two or more ATTR Biomarkers is four ATTR Biomarkers. In some embodiments, the two or more ATTR Biomarkers is only two ATTR Biomarkers. In some embodiments, the one or more imaging-based biomarkers are derived from one or more images (e.g., a video) of a subject. Images of a subject may be obtained from any suitable source, including, for example, ultrasound, such as echocardiography, tomography, such as optical coherence tomography (OCT), angiography, magnetic resonance imaging (MRI), ventriculography, and nuclear medicine. In some embodiments, an imaging- based biomarker is a measurement derived from one or more images. In some embodiments, an imaging-based biomarker includes septal wall thickness (e.g., left ventricle septal wall thickness), posterior wall thickness, ejection fraction, or a combination thereof. Pattern recognition and / or segmentation and classification may be used to determine a state of an imaging-based biomarker from(e.g., a measurement corresponding to one or more imaging- based biomarkers) from one or more images (e.g., for one or more subjects). One or more images may be processed or pre-processed before determining a measurement or as part of determining a measurement for one or more imaging-based biomarkers. In some embodiments, data corresponding to an imaging-based biomarker (e.g., a measurement) is manually derived from one or more images, for example by a physician. A machine-learned algorithm may be used to determine a measurement for an imaging-based biomarker, for Page 59 of 135 11855652v1Docket No.: 2023P04092WO example by performing automated segmentation and classification. Data corresponding to one or more imaging-based biomarkers may be used to train a machine-learning algorithm, for example in combination with ATTR Biomarker profiles and / or demographic factor(s). Data corresponding to one or more imaging-based biomarkers may include one or more images and / or one or more measurements.

[0188] In some embodiments, a machine-learned algorithm processes only data corresponding to one or more (e.g., two or more) demographic factors and an ATTR Biomarker profile that includes data corresponding to levels of two or more (e.g., only two) ATTR Biomarkers to determine one or more outputs. In some embodiments, a machine- learned algorithm receives no other input than data corresponding to one or more (e.g., two or more) demographic factors and an ATTR Biomarker profile that includes data corresponding to levels of two or more (e.g., only two) ATTR Biomarkers to determine one or more outputs.

[0189] In some embodiments, a method includes providing data corresponding to one or more demographic factors of a subject and a corresponding ATTR Biomarker profile as input to a machine-learned algorithm. In some embodiments, the one or more demographic factors include biological sex, age, or both biological sex and age. The corresponding ATTR Biomarker profile includes data for levels of two or more ATTR Biomarkers in a sample of a subject characterized by the demographic factor(s). The two or more ATTR Biomarkers may be two ATTR Biomarkers (e.g., TnI and RBP4) or three ATTR Biomarkers (e.g., TnI, RBP4, and NT-proBNP), for example. Performance (e.g., predictive power, sensitivity and / or selectivity, and / or classification accuracy) of a machine-learned algorithm may be improved by using a machine-learned algorithm that considers a combination of two or more ATTR Biomarkers with one or more (e.g., two or more) demographic factors, as compared to a machine-learned algorithm that does not consider any demographic factor.

[0190] Additional algorithms can be used in methods provided herein, and the algorithms provided above are merely exemplary of the types of algorithms that can be used to generate an ATTR Biomarker score. Exemplary algorithms have been described, e.g., by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp.396-408 and pp. 411-412; and Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, each of which is hereby incorporated by reference. Moreover, as indicated above, combinations of algorithms can be used in methods provided herein. For example, a boosted tree methodology can be a combination of a decision tree methodology and a boosting methodology. Further combinations are possible and are contemplated for use in Page 60 of 135 11855652v1Docket No.: 2023P04092WO methods provided herein. Exemplary algorithms that can be used in the methods provided herein are described below. Decision Tree

[0191] One methodology that can be used to calculate an ATTR Biomarker score from an ATTR Biomarker profile is a decision tree. A decision tree can be constructed using a training population and specific data analysis algorithms. Decision trees are described generally by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 395-396, which is hereby incorporated by reference. Tree-based methods partition the feature space into a set of rectangles, and then fit a model (like a constant) in each one.

[0192] A training population data can include ATTR Biomarker profiles (e.g., including a level of one or more ATTR Biomarkers in a sample) across a training set population. One specific algorithm that can be used to construct a decision tree is a classification and regression tree (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and Random Forests. CART, ID3, and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 396-408 and pp.411-412, which is hereby incorporated by reference. CART, MART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer- Verlag, New York, Chapter 9, which is hereby incorporated by reference in its entirety. Random Forests are described in Breiman, 1999, “Random Forests—Random Features,” Technical Report 567, Statistics Department, U.C. Berkeley, September 1999, which is hereby incorporated by reference in its entirety.

[0193] An aim of a decision tree is to induce a classifier (a tree) from real-world example data. This tree can be used to classify unseen examples that have not been used to derive the decision tree. As such, a decision tree can be derived from training data. Exemplary training data contains data for a plurality of subjects (e.g., a training population). An ATTR Biomarker profile can be provided and / or used for each respective subject. In some embodiments, training data includes ATTR Biomarker profiles for the training population.

[0194] The following algorithm describes an exemplary decision tree derivation: Tree (Examples,Class,Features) Create a root node If all Examples have the same Class value, give the root this label Else if Features is empty label the root according to the most common value Page 61 of 135 11855652v1Docket No.: 2023P04092WO Else begin Calculate the information gain for each Feature Select the Feature A with highest information gain and make this the root Feature For each possible value, v, of this Feature Add a new branch below the root, corresponding to A = v Let Examples(v) be those examples with A = v If Examples(v) is empty, make the new branch a leaf node labeled with the most common value among Examples Else let the new branch be the tree created by Tree(Examples(v),Class,Features - {A}) end

[0195] In univariate decision trees, each split is based on a feature value (e.g., a level) for a corresponding biomarker. Further, multivariate decision trees can be implemented in a method described herein. In some embodiments, a split is based on a feature value corresponding to a demographic factor, either alone or in combination with a feature value for a corresponding biomarker. For example, a split may be based on a combination of feature values corresponding to sex and level(s) of one or more biomarkers, to age and level(s) of one or more biomarkers, or to both age and sex and level(s) of one or more biomarkers. Multivariate decision trees are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp.408-409, which is hereby incorporated by reference. In such multivariate decision trees, some or all of the decisions include a linear combination of feature values (e.g., levels) for a plurality of ATTR Biomarkers of an ATTR profile. Such a linear combination can be trained using known techniques such as gradient descent on a classification or by the use of a sum-squared-error criterion.

[0196] As an illustrative example, the following expression is used: 0.05(X1) + 0.2(X2) < 500

[0197] In this example, X1 and X2 refer to two different features (e.g., levels) for two different ATTR Biomarkers. To apply the methodology, the values of features X1 and X2 (e.g., as part of an ATTR Biomarker profile) are obtained from the measurements obtained from an unclassified subject. These values are then inserted into the equation. If a value of less than 500 is computed, then a first branch in the decision tree is taken. Otherwise, a second branch in the decision tree is taken. Page 62 of 135 11855652v1Docket No.: 2023P04092WO

[0198] ATTR Biomarker profiles may be used to train a machine-learning algorithm. ATTR Biomarker profiles may be from different subjects, a same subject at different times, or a combination thereof. ATTR Biomarker profiles may be associated with (e.g., labelled with) corresponding data indicative of health status. For example, an ATTR Biomarker profile for a subject used as training data may be labelled with data indicative of health status for the subject. Such corresponding data may be a probability or a score (e.g., an ATTR Biomarker score). The health status may be a probability that a subject has TTR-CM, whether or not a subject is or is not at risk of TTR-CM, or whether or not a subject does or does not have TTR-CM (e.g., is healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM). Health status may be determined manually, for example by a physician. Labelling ATTR Biomarker profiles with corresponding data indicative of health status may be performed manually (e.g., by the physician who determines health status).

[0199] Demographic factor data and / or imaging-based biomarkers may be used in combination with ATTR Biomarker profiles to train a machine-learning algorithm. For example, an ATTR Biomarker profile and data for one or more demographic factors and / or one or more imaging-based biomarkers for each of a set of subjects may be used to train a machine-learning algorithm. The set may be, for example, at least 10 subjects, at least 20 subjects, at least 50 subjects, at least 100 subjects, at least 200 subjects, at least 300 subjects, at least 500 subjects, at least 1,000 subjects, at least 1,500 subjects, at least 2,000 subjects, at least 5,000 subjects, or at least 10,000 subjects. In some embodiments, a combination of one or more demographic factors of age, sex, or both age and sex and an ATTR Biomarker profile for each of a set of subjects is used for training a machine-learning algorithm. The ATTR Biomarker profile may include data corresponding to levels of two or more ATTR Biomarkers. The ATTR Biomarkers may be or include TnI and RBP4, for example in combination with NT-proBNP, TIMP2, or both NT-proBNP and TIMP2.

[0200] In some embodiments, training a machine-learning algorithm includes determining one or more feature values. Each feature value may correspond to an ATTR Biomarker. One or more feature values may be determined based on ATTR Biomarker profiles and / or a linear combination thereof. One or more feature values may correspond to a level of an ATTR Biomarker. Training a machine-learning algorithm may include determining one or more decision rules based on ATTR Biomarker profiles used as training data. The one or more decision rules may be further based on one or more demographic factors corresponding to subject(s) that the ATTR Biomarker profiles are for. The one or Page 63 of 135 11855652v1Docket No.: 2023P04092WO more decision rules may be further based on one or more imaging-based biomarkers corresponding to subject(s) that the ATTR Biomarker profiles are for. The one or more decision rules may be based one or more feature values (e.g., determined during training). The one or more decision rules may be used in one or more decision trees.

[0201] Bagging, boosting, and additive trees can be combined with a decision methodology to improve weak decision rules. These techniques are designed for, and usually applied to, decision trees, such as the decision trees described above. In some embodiments, a machine-learned algorithm may include at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees. In addition, such techniques can also be useful in decision rules developed using other types of data analysis algorithms such as linear discriminant analysis.

[0202] In bagging, a training set is sampled, generating random independent bootstrap replicates, constructing a decision rule on each of these, and aggregating them by a simple majority vote in the final decision rule. See, for example, Breiman, 1996, Machine Learning 24, 123-140; and Efron & Tibshirani, An Introduction to Boostrap, Chapman & Hall, New York, 1993, which is hereby incorporated by reference in its entirety.

[0203] In boosting, decision rules are constructed on weighted versions of the training set, which are dependent on previous classification results. Initially, all features under consideration have equal weights, and the first decision rule is constructed on this data set. Then, weights are changed according to the performance of the decision rule. Erroneously classified features get larger weights, and the next decision rule is boosted on the reweighted training set. In this way, a sequence of training sets and decision rules is obtained, which is then combined by simple majority voting or by weighted majority voting in the final decision rule. See, for example, Freund & Schapire, “Experiments with a new boosting algorithm,” Proceedings 13th International Conference on Machine Learning, 1996, 148-156, which is hereby incorporated by reference in its entirety.

[0204] It has been found that machine-learned algorithms that are or are derived from decision tree methodologies perform unexpectedly well as compared to other types of algorithms (e.g., neural networks) when used to make determinations of health status using combinations of biomarkers, especially laboratory-based biomarkers (e.g., that are ATTR Biomarkers). Health status may include at least one of (e.g., two of, or all three of) (i) a state (e.g., stage) of a disease, disorder, or condition for the subject, (ii) whether the subject has or does not have a disease, disorder, or condition, and (iii) a probability that the subject has a disease, disorder or condition. A range of biomarkers could be used in this context, including Page 64 of 135 11855652v1Docket No.: 2023P04092WO protein biomarkers, enzyme biomarkers, peptide biomarkers, or intermediate filament biomarkers (e.g., neurofilament biomarkers). Bootstrapped forest algorithms perform particularly well within this class of methodologies, for example having high specificity and sensitivity. The nature of the decision rules learned and then employed as part of decision tree methodologies may contribute to the performance of such methodologies, for example when using biomarkers that are quantitative. Demographic factors may also be particularly well-suited to be used in decision-tree methodologies. These observations are not limited to the specific case of TTR-CM, or even necessarily to heart disease, disorders, or conditions generally.

[0205] Measurement data used in the methods, systems, kits and compositions disclosed herein are optionally normalized. Normalization refers to a process to correct for example, differences in the amount of genes or protein levels assayed and variability in the quality of the template used, to remove unwanted sources of systematic variation measurements involved in the processing and detection of genes or protein expression. Other sources of systematic variation are attributable to laboratory processing conditions.

[0206] In some instances, normalization methods are used for the normalization of laboratory processing conditions. Non-limiting examples of normalization of laboratory processing that may be used with methods of the disclosure include but are not limited to: accounting for systematic differences between the instruments, reagents, and equipment used during the data generation process, and / or the date and time or lapse of time in the data collection.

[0207] Assays can provide for normalization by incorporating the expression of certain normalizing standard genes or proteins, which do not significantly differ in expression levels under the relevant conditions, that is to say they are known to have a stabilized and consistent expression level in that particular sample type. Suitable normalization genes and proteins that can be used with the present disclosure include housekeeping genes. (See, for example, E. Eisenberg, et al., Trends in Genetics 19(7):362-365 (2003). In some applications, the normalizing biomarkers (genes and proteins), also referred to as reference genes, known not to exhibit meaningfully different expression levels in subjects with TTR-CM as compared to control subjects without TTR-CM. In some applications, it may be useful to add a stable isotope labeled standards which can be used and represent an entity with known properties for use in data normalization. Page 65 of 135 11855652v1Docket No.: 2023P04092WO

[0208] In other applications, a standard, fixed sample can be measured with each analytical batch to account for instrument and day-to-day measurement variability.

[0209] Machine learning algorithms for sub-selecting discriminating biomarkers and optionally subject characteristics, and for building classification models, are used in some methods and systems herein to determine clinical outcome scores. Examples of such algorithms are described above. These algorithms can aid in selection of important biomarker features and transform the underlying measurements into a score or probability relating to, for example, clinical outcome, disease risk, disease likelihood, presence or absence of disease, treatment response, and / or classification of disease status.

[0210] A machine-learned algorithm may output an ATTR Biomarker score. A machine-learned algorithm may determine if a subject is at risk of or is suffering from TTR- CM. In some embodiments, an output of a machine-learned algorithm is a determination (e.g., a probability) of whether a subject is at risk of or is suffering from TTR-CM. In some embodiments, a machine-learned algorithm determines whether a subject is at risk of or has TTR-CM, for example by determining a probability that the subject has TTR-CM or by determining a probability that the subject is at risk for developing TTR-CM. A machine- learned algorithm may be a classifier for TTR-CM. In some embodiments, a machine- learned algorithm can be used to classify whether or not a subject has TTR-CM, for example based on an ATTR Biomarker profile for the subject. In some embodiments, the classifier or the classifying has a sensitivity and a specificity each of more than 80% (e.g., at least one or both of more than 90%).

[0211] A machine-learned model may be used to make a determination of whether a subject has or does not have TTR-CM. For example, in some embodiments, a machine- learned algorithm outputs a probability that a subject has TTR-CM. In general, and according to some embodiments, a determination that a subject has TTR-CM may be made where it is determined that it is more likely that the subject has TTR-CM than that the subject does not have TTR-CM (i.e., a 0.5 or 50% probability cutoff), and vice versa (to determine when a subject does not have TTR-CM). However, other probability cutoffs may be used instead. Sensitivity and specificity of a machine-learned algorithm may change when the probability cutoff is altered.

[0212] An ATTR Biomarker Score can be determined by comparing a subject- specific ATTR Biomarker profile to a reference ATTR Biomarker profile. A reference ATTR Biomarker profile can be representative a known diagnosis. For example, a ATTR Page 66 of 135 11855652v1Docket No.: 2023P04092WO Biomarker profile can represent a positive diagnosis of TTR-CM. As another example, a reference ATTR Biomarker profile can represent a negative diagnosis of TTR-CM. In some cases, an increase in a score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some cases, a decrease in the quantitative score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non- response, and recommended treatments for disease management.

[0213] A similar ATTR Biomarker profile from a subject to a reference ATTR Biomarker profile often indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a dissimilar ATTR Biomarker profile between a subject and a reference indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management.

[0214] Results can be provided to a subject, a health care professional or other professional. Results are optionally accompanied by a heath recommendation, such as a recommendation to confirm or independently assess TTR-CM risk, for example using one or more cardiomyopathy tests.

[0215] A recommendation optionally includes information relevant to a treatment regimen, such as information indicating that a treatment regimen. Efficacy of a regimen can be assessed in some cases by comparison of a subject’s ATTR Biomarker profile at a first time point, optionally prior to a treatment and a later second time point, optionally subsequent to a treatment instance. ATTR Biomarker profiles can be compared to one another, each to a reference, or otherwise assessed so as to determine whether a treatment regimen demonstrates efficacy such that it should be continued, increased, replaced with an alternate regimen or discontinued because of its success in addressing TTR-CM or associated signs and symptoms. Some assessments rely upon comparison of a subject’s ATTR Biomarker profile at multiple time points, such as at least one time point prior to a treatment and at least one time point following a treatment. ATTR Biomarker profiles can be compared one to another Page 67 of 135 11855652v1Docket No.: 2023P04092WO or to at least one reference biomarker panel level or both to one another and to at least one reference biomarker panel level. Treatment for Amyloid Transthyretin Cardiomyopathy

[0216] Therapeutic approaches for ATTR amyloidosis include reducing production of TTR, inhibiting or reducing aggregation of TTR, inhibiting or reducing TTR fibril or amyloid formation, reducing or clearing TTR deposits, and stabilizing non-toxic conformations of TTR (e.g., tetrameric forms). In some embodiments, the methods disclosed herein further include detecting a level of each of one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) in a sample obtained from a subject and administering an effective amount of a transthyretin (TTR) stabilizing agent to the diagnosed subject. In some embodiments, the TTR stabilizing agent is Tafamidis.

[0217] In some embodiments, the present disclosure includes a method for selecting a patient for treatment with a TTR stabilizing agent including the step of detecting a level of each of one or more ATTR Biomarkers (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) in a sample obtained from the subject.

[0218] In some embodiments, the present disclosure includes a method of treating TTR-CM in subject at risk of or suffering from TTR-CM, the method including administering to the subject a therapeutically effective amount of a TTR stabilizing agent, wherein the subject expresses a level of an ATTR Biomarker or ATTR Biomarker gene product above a threshold value. In some embodiments, a method of treating TTR-CM in subject at risk of or suffering from TTR-CM includes administering to the subject a therapeutically effective amount of a TTR stabilizing agent, wherein the subject expresses a level of more than one ATTR Biomarker or ATTR Biomarker gene product above a threshold value. In some embodiments, the method further includes determining that the subject expresses a level of an ATTR Biomarker or ATTR Biomarker gene product above a threshold value. In some embodiments, prior to administration, the subject has been determined to express a level of an ATTR Biomarker or ATTR Biomarker gene product above a threshold value.

[0219] In some embodiments, the biomarkers disclosed herein can be used for screening patients for effective therapies for ATTR amyloidosis and TTR-CM. In some embodiments, a therapy for TTR-CM is a stabilizer of the TTR tetramer. Examples of TTR stabilizers include tafamidis and diflunisal. Page 68 of 135 11855652v1Docket No.: 2023P04092WO

[0220] Another approach to therapy is reducing total TTR production. For example, antisense oligonucleotide (ASO)-based therapy and RNA interference (RNAi) are therapeutic approaches to lowering total TTR. ISIS-TTRRx is an ASO-based therapy that causes destruction of both wild-type and mutant forms of the TTR transcript. Other possible therapeutic agents include ALN-TTR02 (Patisiran), ALN-TTRsc (Revurisan), doxycycline, tauroursodeoxycholic acid (TUDCA), the combination of doxycycline and TUDCA, epigallocatechin gallate (EGCG), curcumin, or resveratrol. Antibodies targeting TTR can also be used as a therapy; for example by antibody-mediated inhibition of TTR aggregation and fibril formation; antibody-mediated stabilization of non-toxic conformations of TTR (e.g., tetrameric forms); or antibody-mediated clearance of aggregated TTR, oligomeric TTR, or monomeric TTR. Additionally, antibodies to TTR can be coupled or conjugated to therapeutic agents, for example, to target TTR. Kits

[0221] Also provided by the present disclosure are kits including one or more anti- ATTR Biomarker agents and instructions for use (e.g., treatment, 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 kits of the disclosure further include a TTR stabilizing agent (e.g., Tafamidis).

[0222] In some embodiments, the one or more anti-ATTR Biomarker agents include antibody agents. In some embodiments, one or more of the antibody agents are labeled with a detectable moiety. In some embodiments, the kit further includes a detection agent (e.g., one or more acridinium ester molecules). In some embodiments, one or more of the antibody agents are labeled with one or more of the acridinium ester molecules. In some embodiments, the kit further includes one or more secondary antibody agents that specifically bind to one or more of the anti-ATTR Biomarker antibody agents.

[0223] In some embodiments, the one or more anti-ATTR Biomarker agents include nucleic acid probes. In some embodiments, at least a portion of each nucleic acid probe hybridizes to one or more portions of a nucleotide that encodes an ATTR Biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof). Nucleotides that encode an ATTR Biomarker can be DNA (e.g., cDNA) or RNA (e.g. mRNA). In some embodiments, the nucleic acid probes are labeled with one or more Page 69 of 135 11855652v1Docket No.: 2023P04092WO detection agents (e.g., wherein the detection agents indicate presence of nucleotides that encode an ATTR Biomarker).

[0224] 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, an ATTR Biomarker standard includes recombinant an ATTR Biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof). In some embodiments, an ATTR Biomarker standard includes synthetic ATTR Biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) nucleic acids.

[0225] In addition to the above, a kit can include other ingredients, such as a solvent or buffer, a stabilizer or a preservative, and / or an agent for treating a condition or disorder described herein. Alternatively, other ingredients can be included in a kit, but in different compositions or containers than the anti-ATTR Biomarker agents. In such embodiments, a kit can include instructions for admixing the anti-ATTR Biomarker agents and the other ingredients, or for using the anti-ATTR Biomarker together with the other ingredients.

[0226] In certain embodiments, kits for use in accordance with the present disclosure may include, a reference or control sample(s), instructions for processing samples, performing tests on samples, instructions for interpreting the results, buffers and / or other reagents necessary for performing tests.

[0227] The present disclosure also provides that recognition that certain single ATTR Biomarkers can be helpful for detecting and / or diagnosing ATTR amyloidosis or TTR-CM. The present disclosure further provides the insight that particular combinations of ATTR Biomarkers are especially useful for detecting and / or diagnosing ATTR amyloidosis or TTR- CM. Thus, methods, compositions, and kits described herein can be used for assays to assess the risk of TTR-CM, assess whether a subject should undergo further cardiac tests, and / or diagnose TTR-CM based on detection or measurement of ATTR Biomarkers in a sample, e.g., a biological sample obtained from a subject.

[0228] Methods and kits provided herein are able to detect TTR-CM in a sample with a sensitivity and a specificity that renders the outcome of the test reliable enough to be medically actionable. Methods and kits described herein for detection and / or diagnosis of TTR-CM in a subject detects TTR-CM with a sensitivity greater than 75%, greater than 80%, greater than 85%, greater than 90%, greater than 95%, greater than 96%, greater than 97%, greater than 98%, greater than 99%, or about 100%. In some embodiments, methods and kits Page 70 of 135 11855652v1Docket No.: 2023P04092WO provided herein can detect TTR-CM with a sensitivity that is between about 70%-100%, between about 80%-100%, or between about 90-100%. In some embodiments, methods and kits provided herein can detect TTR-CM with a specificity greater than 70%, greater than 75%, greater than 80%, greater than 85%, greater than 90%, greater than 95%, greater than 96%, greater than 97%, greater than 98%, greater than 99%, or about 100%. In some embodiments, methods and kits provided herein can detect TTR-CM with a specificity that is between about 50%-100%, between about 60%-100%, between about 70%-100%, between about 80%-100%, or between about 90-100%. In some embodiments, methods and kits provided herein can detect TTR-CM with a sensitivity and a specificity that is 50% or greater, 60% or greater, 70% or greater, 75% or greater, 80% or greater, 85% or greater, 90% or greater. In some embodiments, methods and kits provided herein can detect TTR-CM with a sensitivity and a specificity that is between about 50%-100%, between about 60%-100%, between about 70%-100%, between about 80%-100%, or between about 90-100%. Compositions

[0229] Also provided herein are compositions. In some embodiments, a composition includes one or more ATTR Biomarkers and one or more anti-ATTR Biomarker agents. In some embodiments, one or more ATTR Biomarkers include TnI, PKM1, PKM2, NT- proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combinations thereof, and one or more anti-ATTR Biomarker agents include an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, an anti-NT-proBNP agent, an anti-RBP4 agent, an anti-TIMP2 agent, an anti-NfL agent, or a combination thereof.

[0230] In some embodiments, a composition includes a combination (e.g., one or more, two or more, three or more, four or more, five or more, etc.) of ATTR Biomarkers and a corresponding combination of anti-ATTR Biomarker agents.

[0231] In some embodiments, a composition includes two or more ATTR Biomarkers and two or more anti-ATTR Biomarker agents. In some embodiments, two or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combinations thereof, and two or more anti-ATTR Biomarker agents include an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, an anti-NT-proBNP agent, an anti-RBP4 agent, an anti-TIMP2 agent, an anti-NfL agent, or a combination thereof.

[0232] In some embodiments, a composition includes three or more ATTR Biomarkers and three or more anti-ATTR Biomarker agents. In some embodiments, three or Page 71 of 135 11855652v1Docket No.: 2023P04092WO more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combinations thereof. In some embodiments, three or more anti-ATTR Biomarker agents include an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, an anti-NT-proBNP agent, an anti-RBP4 agent, an anti-TIMP2 agent, an anti-NfL agent, or a combination thereof.

[0233] In some embodiments, a composition includes four or more ATTR Biomarkers and four or more anti-ATTR Biomarker agents. In some embodiments, four or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combinations thereof. In some embodiments, four or more anti-ATTR Biomarker agents include an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, an anti-NT-proBNP agent, an anti-RBP4 agent, an anti-TIMP2 agent, an anti-NfL agent, or a combination thereof.

[0234] In some embodiments, a composition includes five or more ATTR Biomarkers and five or more anti-ATTR Biomarker agents. In some embodiments, five or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or a combinations thereof. In some embodiments, five or more anti-ATTR Biomarker agents include an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, an anti-NT-proBNP agent, an anti-RBP4 agent, an anti-TIMP2 agent, an anti-NfL agent, or a combination thereof.

[0235] In some embodiments, a composition includes a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers and a corresponding combination of anti-ATTR Biomarker agents. In some embodiments, a composition includes a combination of ATTR Biomarkers from Tables 2-5 and a corresponding combination of anti-ATTR Biomarker agents. In some embodiments, a composition includes a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers from Table 2 and a corresponding combination of anti- ATTR Biomarker agents. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers from Table 3 and a corresponding combination of anti-ATTR Biomarker agents. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers from Table 4 and a corresponding combination of anti-ATTR Biomarker agents. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers from Table 5 and a corresponding combination of anti- ATTR Biomarker agents. Page 72 of 135 11855652v1Docket No.: 2023P04092WO

[0236] In some embodiments, a composition includes TnI and an anti-TnI agent. In some embodiments, a composition includes TnI, PKM1, an anti-TnI agent, and an anti-PKM1 agent. In some embodiments, a composition includes TnI, PKM2, an anti-TnI agent, and an anti-PKM2 agent. In some embodiments, a composition includes TnI, PKM1, PKM2, an anti-TnI agent, an anti-PKM1 agent, and an anti-PKM2 agent. In some embodiments, a composition includes NT-proBNP, RBP4, or both, and an anti-NT-proBNP agent, an anti- RBP4 agent, or both. In some embodiments, a composition includes TIMP2, NfL, or both, and an anti-TIMP2 agent, an anti-NfL agent, or both.

[0237] In some embodiments, a composition includes NT-proBNP and an anti-NT- proBNP agent. In some embodiments, a composition includes 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, a composition includes TIMP2, NfL, or both, and an anti-TIMP2 agent, an anti-NfL agent, or both.

[0238] In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TnI, PKM2, NT-proBNP, RBP4, an anti-TnI agent, an anti-PKM2 agent, an anti-NT-proBNP agent, and an anti-RBP4 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TnI, PKM2, NT-proBNP, an anti-TnI agent, an anti-NT-proBNP agent, and an anti-RBP4 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TnI, PKM2, RBP4, an anti-TnI agent, an anti-PKM2 agent, and an anti-RBP4 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TnI, PKM1, NT-proBNP, an anti-TnI agent, an anti- PKM1 agent, and an anti-NT-proBNP agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TnI, PKM1, RBP4, an anti-TnI agent, an anti-PKM1 agent, and an anti-RBP4 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TnI, PKM1, PKM2, NT-proBNP, RBP4, an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, an anti-NT-proBNP agent, and an anti- RBP4 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TnI, PKM1, PKM2, NT-proBNP, an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, and an anti-NT-proBNP agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or Page 73 of 135 11855652v1Docket No.: 2023P04092WO more, etc.) of ATTR Biomarkers includes TnI, PKM1, PKM2, RBP4, an anti-TnI agent, an anti-PKM1 agent, an anti-PKM2 agent, and an anti-RBP4 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes NT-proBNP, RBP4, TnI, an anti-TnI agent, an anti-NT-proBNP agent, and an anti-RBP4 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes RBP4, SMOC-2, TnI, an anti-TnI agent, an anti-RBP4 agent, and an anti-SMOC-2 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes DCN, NT-proBNP, TnI, an anti-TnI agent, an anti-NT-proBNP agent, and an anti-DCN agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes DCN, RBP4, TnI, an anti-TnI agent, an anti-RBP4 agent, and an anti-DCN agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes NT-proBNP, SMOC-2, TnI, an anti-TnI agent, an anti-NT-proBNP agent, and an anti-SMOC-2 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes NT-proBNP, TIMP2, TnI, an anti-TnI agent, an anti-NT-proBNP agent, and an anti-TIMP2 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes NT-proBNP, TnI, an anti-TnI agent, and an anti-NT- proBNP agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes DCN, TnI, an anti-TnI agent, and an anti-DCN agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes DCN, TIMP2, TnI, an anti- TnI agent, an anti-TIMP2 agent, and an anti-DCN agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes RBP4, TnI, an anti-TnI agent, and an anti-RBP4 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes RBP4, TIMP2, TnI, an anti-TnI agent, an anti-RBP4 agent, and an anti-TIMP2 agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes DCN, SMOC- 2, TnI, an anti-TnI agent, an anti-SMOC-2 agent, and an anti-DCN agent. In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes TIMP2, TnI, an anti-TnI agent, and an anti-TIMP2 agent. Page 74 of 135 11855652v1Docket No.: 2023P04092WO In some embodiments, a combination (e.g., one or more, two or more, three or more, four or more, etc.) of ATTR Biomarkers includes SMOC-2, TIMP2, TnI, an anti-TnI agent, an anti- SMOC-2 agent, and an anti-TIMP2 agent.

[0239] In some embodiments, one or more anti-ATTR Biomarker agents in a composition provided herein include antibody agents. In some embodiments, one or more of the antibody agents are labeled with a detectable moiety. In some embodiments, the kit further includes a detection agent (e.g., one or more acridinium ester molecules). In some embodiments, one or more of the antibody agents are labeled with one or more of the acridinium ester molecules. In some embodiments, the kit further includes one or more secondary antibody agents that specifically bind to one or more of the anti-ATTR Biomarker antibody agents.

[0240] In some embodiments, one or more anti-ATTR Biomarker agents in a composition provided herein include nucleic acid probes. In some embodiments, at least a portion of each nucleic acid probe hybridizes to one or more portions of a nucleotide that encodes an ATTR Biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof). Nucleotides that encode an ATTR Biomarker can be DNA (e.g., cDNA) or RNA (e.g. mRNA). In some embodiments, the nucleic acid probes are labeled with one or more detection agents (e.g., wherein the detection agents indicate presence of nucleotides that encode an ATTR Biomarker).

[0241] In some embodiments, a composition includes one or more control samples. In some embodiments, the control samples include one or more ATTR Biomarker standards. In some embodiments, an ATTR Biomarker standard includes recombinant an ATTR Biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof). In some embodiments, an ATTR Biomarker standard includes synthetic ATTR Biomarker (e.g., TnI, PKM1, PKM2, NT-proBNP, RBP4, DCN, TIMP2, SMOC-2, NfL, or combinations thereof) nucleic acids.

[0242] In addition to the above, a composition can include other ingredients, such as a solvent or buffer, a stabilizer or a preservative, and / or an agent for treating a condition or disorder described herein. Computer Systems

[0243] Methods described herein can be implemented in a computer system having a processor that executes specific instructions in a computer program. In some embodiments, a Page 75 of 135 11855652v1Docket No.: 2023P04092WO computer system may be arranged to output an ATTR Biomarker score based on receiving an ATTR Biomarker profile and / or a level of two or more ATTR Biomarkers. Particularly, a computer program may include instructions for the system to select appropriate next steps, including additional medication (e.g., a TTR stabilizer), a treatment, and / or additional testing (e.g., cardiomyopathy tests) for a subject.

[0244] In some embodiments, the computer program may be configured such that the computer system can identify a subject for further testing (e.g., cardiomyopathy tests), identify a subject as being at risk or having TTR-CM, and / or identify a subject to receive medication (e.g., a TTR stabilizer) based on received data (e.g., an ATTR Biomarker profile) and use the data to calculate an ATTR Biomarker score. A system may be able to rank-order identified next steps based on an ATTR Biomarker profile with demographic factors and / or imaging-based biomarkers. A system may be able to adjust the rank ordering based on, e.g., a clinical response of a subject or of a family member of the subject who has or is suspected of having TTR-CM.

[0245] FIG.11 is a block diagram of a computer system 1100 that can be used in the operations described above, according to one embodiment. The system 1100 includes a processor 1110, a memory 1120, a storage device 1130 and an input / output device 1140. Each of the components 1110, 1120, 1130 and 1140 are interconnected using a system bus 1150. The system may include analyzing equipment 1160 for determining a level of one or more ATTR Biomarkers in a sample.

[0246] The processor 1110 is capable of processing instructions for execution within the system 1100. In one embodiment, the processor 1110 is a single-threaded processor. In another embodiment, the processor 1110 is a multi-threaded processor. The processor 1110 is capable of processing instructions stored in the memory 1120 or on the storage device 1130, including for receiving or sending information through the input / output device 1140.

[0247] The memory 1120 stores information within the system 1100. In one embodiment, the memory 1120 is a computer-readable medium. In one embodiment, the memory 1120 is a volatile memory unit. In another embodiment, the memory 1120 is a non- volatile memory unit.

[0248] The storage device 1130 is capable of providing mass storage for the system 1100. In one embodiment, the storage device 1130 is a computer-readable medium.

[0249] The input / output device 1140 provides input / output operations for the system 1100. In one embodiment, the input / output device 1140 includes a keyboard and / or pointing Page 76 of 135 11855652v1Docket No.: 2023P04092WO device. In one embodiment, the input / output device 1140 includes a display unit for displaying graphical user interfaces.

[0250] The system 1100 can be used to build a database. FIG.12 shows a flow chart of a method 1200 for building a database for use in identifying a subject for further testing (e.g., cardiomyopathy tests), identifying a subject as being at risk or having TTR-CM, and / or identifying a subject to receive medication (e.g., a TTR stabilizer). Preferably, a method 1200 is performed in a system 1100. For example, a computer program product can include instructions that cause a processor 1110 to perform the steps of a method 1200 or a method 1300.

[0251] Method 1200 includes the following steps. Receiving, in step 1210, an subject’s ATTR Biomarker Profile (e.g., levels of one or more ATTR Biomarkers in a sample). A computer program in the system 600 may include instructions for presenting a suitable graphical user interface on input / output device 640, and the graphical user interface may prompt the user to enter the levels 670 using the input / output device 640, such as a keyboard. Calculating, in step 1220, an ATTR Biomarker score from an ATTR Biomarker profile. As described herein, calculating, in step 1220, an ATTR Biomarker score from (i) an ATTR Biomarker profile and (ii) demographic factors and / or imaging-based biomarkers. Storing, in step 1230, an ATTR Biomarker score. The system 600 may store an ATTR Biomarker score in the storage device 630. Additionally or alternatively, a system 600 may provide a readout including an ATTR Biomarker score. A readout may also include proposed next steps for a subject and / or a confidence level associated with an ATTR Biomarker score.

[0252] Method 1300 includes the following steps. Detecting, in step 1310, levels of one or more ATTR Biomarkers in a sample, e.g., from a subject. Using, in step 1320, levels of one or more ATTR Biomarkers to obtain an ATTR Biomarker Profile. Calculating, in step 1330, an ATTR Biomarker score from an ATTR Biomarker profile. As described herein, calculating, in step 1330, an ATTR Biomarker score from (i) an ATTR Biomarker profile and (ii) demographic factors and / or imaging-based biomarkers. Storing, in step 1340, an ATTR Biomarker score. The system 600 may store an ATTR Biomarker score in the storage device 630. Additionally or alternatively, a system 600 may provide a readout including an ATTR Biomarker score. A readout may also include proposed next steps for a subject and / or a confidence level associated with an ATTR Biomarker score. Page 77 of 135 11855652v1Docket No.: 2023P04092WO

[0253] Additionally, non-transitory computer readable media containing executable instructions that when executed cause a processor to perform operations including a method as provided herein are provided. For example, a non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations including a method of 1200 or 1300 described above.

[0254] Illustrative embodiments of systems and methods disclosed herein were described above with reference to computations performed locally by a computing device. However, computations performed over a network are also contemplated. FIG.28 shows an illustrative network environment 2800 for use in the methods and systems described herein. In brief overview, referring now to FIG.28, a block diagram of an illustrative cloud computing environment 2800 is shown and described. The cloud computing environment 2800 may include one or more resource providers 2802a, 2802b, 2802c (collectively, 2802). Each resource provider 2802 may include computing resources. In some implementations, computing resources may include any hardware and / or software used to process data. For example, computing resources may include hardware and / or software capable of executing algorithms, computer programs, and / or computer applications. In some implementations, illustrative computing resources may include application servers and / or databases with storage and retrieval capabilities. Each resource provider 2802 may be connected to any other resource provider 2802 in the cloud computing environment 2800. In some implementations, the resource providers 2802 may be connected over a computer network 2808. Each resource provider 2802 may be connected to one or more computing device 2804a, 2804b, 2804c (collectively, 2804), over the computer network 2808.

[0255] The cloud computing environment 2800 may include a resource manager 2806. The resource manager 2806 may be connected to the resource providers 2802 and the computing devices 2804 over the computer network 2808. In some implementations, the resource manager 2806 may facilitate the provision of computing resources by one or more resource providers 2802 to one or more computing devices 2804. The resource manager 2806 may receive a request for a computing resource from a particular computing device 2804. The resource manager 2806 may identify one or more resource providers 2802 capable of providing the computing resource requested by the computing device 2804. The resource manager 2806 may select a resource provider 2802 to provide the computing resource. The resource manager 2806 may facilitate a connection between the resource provider 2802 and a particular computing device 2804. In some implementations, the resource manager 2806 may Page 78 of 135 11855652v1Docket No.: 2023P04092WO establish a connection between a particular resource provider 2802 and a particular computing device 2804. In some implementations, the resource manager 2806 may redirect a particular computing device 2804 to a particular resource provider 2802 with the requested computing resource.

[0256] FIG.29 shows an example of a computing device 2900 and a mobile computing device 2950 that can be used in the methods and systems described in this disclosure. The computing device 2900 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device 2950 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.

[0257] The computing device 2900 includes a processor 2902, a memory 2904, a storage device 2906, a high-speed interface 2908 connecting to the memory 2904 and multiple high-speed expansion ports 2910, and a low-speed interface 2912 connecting to a low-speed expansion port 2914 and the storage device 2906. Each of the processor 2902, the memory 2904, the storage device 2906, the high-speed interface 2908, the high-speed expansion ports 2910, and the low-speed interface 2912, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 2902 can process instructions for execution within the computing device 2900, including instructions stored in the memory 2904 or on the storage device 2906 to display graphical information for a GUI on an external input / output device, such as a display 2916 coupled to the high-speed interface 2908. In other implementations, multiple processors and / or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). Thus, as the term is used herein, where a plurality of functions are described as being performed by “a processor”, this encompasses embodiments wherein the plurality of functions are performed by any number of processors (e.g., one or more processors) of any number of computing devices (e.g., one or more computing devices). Page 79 of 135 11855652v1Docket No.: 2023P04092WO Furthermore, where a function is described as being performed by “a processor”, this encompasses embodiments wherein the function is performed by any number of processors (e.g., one or more processors) of any number of computing devices (e.g., one or more computing devices) (e.g., in a distributed computing system).

[0258] The memory 2904 stores information within the computing device 2900. In some implementations, the memory 2904 is a volatile memory unit or units. In some implementations, the memory 2904 is a non-volatile memory unit or units. The memory 2904 may also be another form of computer-readable medium, such as a magnetic or optical disk.

[0259] The storage device 2906 is capable of providing mass storage for the computing device 2900. In some implementations, the storage device 2906 may be or contain a computer-readable medium, such as a hard disk device, an optical disk device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 2902), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices such as computer- or machine- readable mediums (for example, the memory 2904, the storage device 2906, or memory on the processor 2902).

[0260] The high-speed interface 2908 manages bandwidth-intensive operations for the computing device 2900, while the low-speed interface 2912 manages lower bandwidth- intensive operations. Such allocation of functions is an example only. In some implementations, the high-speed interface 2908 is coupled to the memory 2904, the display 2916 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 2910, which may accept various expansion cards (not shown). In the implementation, the low-speed interface 2912 is coupled to the storage device 2906 and the low-speed expansion port 2914. The low-speed expansion port 2914, which may include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or more input / output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

[0261] The computing device 2900 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 2920, or multiple times in a group of such servers. In addition, it may be implemented in a Page 80 of 135 11855652v1Docket No.: 2023P04092WO personal computer such as a laptop computer 2922. It may also be implemented as part of a rack server system 2924. Alternatively, components from the computing device 2900 may be combined with other components in a mobile device (not shown), such as a mobile computing device 2950. Each of such devices may contain one or more of the computing device 2900 and the mobile computing device 2950, and an entire system may be made up of multiple computing devices communicating with each other.

[0262] The mobile computing device 2950 includes a processor 2952, a memory 2964, an input / output device such as a display 2954, a communication interface 2966, and a transceiver 2968, among other components. The mobile computing device 2950 may also be provided with a storage device, such as a micro-drive or other device, to provide additional storage. Each of the processor 2952, the memory 2964, the display 2954, the communication interface 2966, and the transceiver 2968, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.

[0263] The processor 2952 can execute instructions within the mobile computing device 2950, including instructions stored in the memory 2964. The processor 2952 may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor 2952 may provide, for example, for coordination of the other components of the mobile computing device 2950, such as control of user interfaces, applications run by the mobile computing device 2950, and wireless communication by the mobile computing device 2950.

[0264] The processor 2952 may communicate with a user through a control interface 2958 and a display interface 2956 coupled to the display 2954. The display 2954 may be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 2956 may comprise appropriate circuitry for driving the display 2954 to present graphical and other information to a user. The control interface 2958 may receive commands from a user and convert them for submission to the processor 2952. In addition, an external interface 2962 may provide communication with the processor 2952, so as to enable near area communication of the mobile computing device 2950 with other devices. The external interface 2962 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used. Page 81 of 135 11855652v1Docket No.: 2023P04092WO

[0265] The memory 2964 stores information within the mobile computing device 2950. The memory 2964 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory 2974 may also be provided and connected to the mobile computing device 2950 through an expansion interface 2972, which may include, for example, a SIMM (Single In Line Memory Module) card interface. The expansion memory 2974 may provide extra storage space for the mobile computing device 2950, or may also store applications or other information for the mobile computing device 2950. Specifically, the expansion memory 2974 may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, the expansion memory 2974 may be provided as a security module for the mobile computing device 2950, and may be programmed with instructions that permit secure use of the mobile computing device 2950. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non- hackable manner.

[0266] The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as discussed below. In some implementations, instructions are stored in an information carrier and, when executed by one or more processing devices (for example, processor 2952), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 2964, the expansion memory 2974, or memory on the processor 2952). In some implementations, the instructions can be received in a propagated signal, for example, over the transceiver 2968 or the external interface 2962.

[0267] The mobile computing device 2950 may communicate wirelessly through the communication interface 2966, which may include digital signal processing circuitry where necessary. The communication interface 2966 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others. Such communication may occur, for example, through the Page 82 of 135 11855652v1Docket No.: 2023P04092WO transceiver 2968 using a radio-frequency. In addition, short-range communication may occur, such as using a Bluetooth®, Wi-Fi™, or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 2970 may provide additional navigation- and location-related wireless data to the mobile computing device 2950, which may be used as appropriate by applications running on the mobile computing device 2950.

[0268] The mobile computing device 2950 may also communicate audibly using an audio codec 2960, which may receive spoken information from a user and convert it to usable digital information. The audio codec 2960 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 2950. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile computing device 2950.

[0269] The mobile computing device 2950 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 2980. It may also be implemented as part of a smart-phone 2982, personal digital assistant, or other similar mobile device.

[0270] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0271] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine- readable medium that receives machine instructions as a machine-readable signal. The term Page 83 of 135 11855652v1Docket No.: 2023P04092WO machine-readable signal refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0272] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0273] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0274] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. EXEMPLARY NUMBERED EMBODIMENTS

[0275] Embodiment 1. A method including detecting a level of each of two or more transthyretin 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-pro hormone B-type natriuretic peptide (NT-proBNP), (v) retinol binding protein 4 (RBP4), (vi) tissue inhibitor of metalloproteinase 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof. Page 84 of 135 11855652v1Docket No.: 2023P04092WO

[0276] Embodiment 2. The method of embodiment 1, detecting a level of each of three or more ATTR Biomarkers in a sample, wherein 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.

[0277] Embodiment 3. The method of embodiment 1 or 2, detecting a level of four or more ATTR Biomarkers in a sample, wherein 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.

[0278] Embodiment 4. A method including detecting a level of two or more ATTR Biomarkers in a sample, wherein the two or more ATTR Biomarkers include TnI.

[0279] Embodiment 5. The method of embodiment 4, wherein the two or more ATTR Biomarkers include TnI and PKM1.

[0280] Embodiment 6. The method of embodiment 4, wherein the two or more ATTR Biomarkers include TnI and PKM2.

[0281] Embodiment 7. The method of embodiment 4, wherein the two or more ATTR Biomarkers include TnI, PKM1, and PKM2.

[0282] Embodiment 8. The method of any one of embodiments 4-7, wherein the two or more ATTR Biomarkers further include NT-proBNP, RBP4, or both.

[0283] Embodiment 9. The method of any one of embodiments 4-8, wherein the two or more ATTR Biomarkers further include TIMP2, NfL, or both.

[0284] Embodiment 10. A method including detecting a level of two or more ATTR Biomarkers in a sample, wherein the two or more ATTR Biomarkers include NT-proBNP.

[0285] Embodiment 11. The method of any one of embodiments 4-7, wherein the two or more ATTR Biomarkers further include TnI, PKM1, PKM2, RBP4, or a combination thereof.

[0286] Embodiment 12. The method of any one of embodiments 4-8, wherein the two or more ATTR Biomarkers further include TIMP2, NfL, or both.

[0287] Embodiment 13. A method including: (a) detecting a level of two or more ATTR Biomarkers in a sample to obtain an ATTR Biomarker profile; and (b) using the ATTR Biomarker profile to compute a ATTR Biomarker score. Page 85 of 135 11855652v1Docket No.: 2023P04092WO

[0288] Embodiment 14. The method of embodiment 13, wherein the 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.

[0289] Embodiment 15. The method of embodiment 13 or 14, detecting a level of three or more ATTR Biomarkers in a sample, wherein 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.

[0290] Embodiment 16. The method of any one of embodiments 13-15, detecting a level of four or more ATTR Biomarkers in a sample, wherein 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.

[0291] Embodiment 17. The method of any one of embodiments 13-16, wherein the two or more ATTR Biomarkers include TnI.

[0292] Embodiment 18. The method of any one of embodiments 13-17, wherein the two or more ATTR Biomarkers include TnI and PKM1.

[0293] Embodiment 19. The method of any one of embodiments 13-18, wherein the two or more ATTR Biomarkers include TnI and PKM2.

[0294] Embodiment 20. The method of any one of embodiments 13-19, wherein the two or more ATTR Biomarkers include TnI, PKM1, and PKM2.

[0295] Embodiment 21. The method of any one of embodiments 17-20, wherein the two or more ATTR Biomarkers further include NT-proBNP, RBP4, or both.

[0296] Embodiment 22. The method of any one of embodiments 17-21, wherein the two or more ATTR Biomarkers further include TIMP2, NfL, or both.

[0297] Embodiment 23. The method of any one of embodiments 13-16, wherein the two or more ATTR Biomarkers include NT-proBNP.

[0298] Embodiment 24. The method of embodiment 23, wherein the two or more ATTR Biomarkers further include TnI, PKM1, PKM2, RBP4, or a combination thereof.

[0299] Embodiment 25. The method of embodiment 23 or 24, wherein the two or more ATTR Biomarkers further include TIMP2, NfL, or both.

[0300] Embodiment 26. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, PKM1, NT-proBNP, and RBP4.

[0301] Embodiment 27. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, PKM1, and RBP4. Page 86 of 135 11855652v1Docket No.: 2023P04092WO

[0302] Embodiment 28. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, PKM1, and NT-proBNP.

[0303] Embodiment 29. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, PKM2, NT-proBNP, and RBP4.

[0304] Embodiment 30. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, PKM2, and RBP4.

[0305] Embodiment 31. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, PKM2, and NT-proBNP.

[0306] Embodiment 32. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, PKM1, PKM2, NT-proBNP, and RBP4.

[0307] Embodiment 33. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, PKM1, PKM2, and RBP4.

[0308] Embodiment 34. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, NT-proBNP, and RBP4.

[0309] Embodiment 35. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, NT-proBNP, and TIMP2.

[0310] Embodiment 36. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI and NT-proBNP.

[0311] Embodiment 37. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI and RBP4.

[0312] Embodiment 38. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, RBP4 and TIMP2.

[0313] Embodiment 39. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI and TIMP2.

[0314] Embodiment 40. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, NT-proBNP, and DCN.

[0315] Embodiment 41. The method of embodiment 1, wherein the two or more ATTR Biomarkers include or consist of TnI, RBP4, and DCN.

[0316] Embodiment 42. The method of any one of embodiments 1-3, 4, 6, 8, 9, 10- 12, 13-17, 19, and 21-25, wherein the two or more ATTR Biomarkers do not include PKM1.

[0317] Embodiment 43. The method of any one of embodiments 1-3, 4, 5, 8, 9, 10- 12, 13-18, and 21-25, wherein the two or more ATTR Biomarkers do not include PKM2. Page 87 of 135 11855652v1Docket No.: 2023P04092WO

[0318] Embodiment 44. The method of 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 either PKM1 or PKM2.

[0319] Embodiment 45. The method of any one of embodiments 1-44, wherein the two or more ATTR Biomarkers do not include SMOC-2.

[0320] Embodiment 46. The method of any one of embodiments 1-39 and 42-44, wherein the two or more ATTR Biomarkers do not include DCN.

[0321] Embodiment 47. The method of any one of embodiments 1-39 and 42-44, wherein the two or more ATTR Biomarkers do not include either SMOC-2 or DCN.

[0322] Embodiment 48. The method of any one of embodiments 1-47, wherein the method is a method of determining a subject’s risk of developing amyloid transthyretin cardiomyopathy (TTR-CM), and the sample was obtained from the subject.

[0323] Embodiment 49. The method of any one of embodiments 1-47, wherein the method is a method of diagnosing a subject with TTR-CM, and the sample was obtained from the subject.

[0324] Embodiment 50. The method of any one of embodiments 1-47, wherein the method is a method of treating TTR-CM in a subject at risk of or suffering from TTR-CM.

[0325] Embodiment 51. The method of any one of embodiments 48-50, wherein TTR-CM results from wild-type transthyretin amyloidosis (ATTRwt).

[0326] Embodiment 52. The method of any one of embodiments 48-51, wherein the subject tests negative for familial amyloid cardiomyopathy (ATTRm) by genetic testing.

[0327] Embodiment 53. The method of any one of embodiments 1-47, wherein the method is a method of selecting a subject to receive one or more doses of a TTR stabilizing agent, and the sample was obtained from the subject.

[0328] Embodiment 54. The method of embodiment 53, wherein the method further includes administering to the subject one or more doses a TTR stabilizing agent.

[0329] Embodiment 55. The method of any one of embodiments 1-47, wherein the method is a method of selecting a subject for one or more cardiomyopathy tests, and the sample was obtained from the subject.

[0330] Embodiment 56. The method of embodiment 55, wherein the one or more cardiomyopathy tests include an echocardiogram, an advanced imaging method, or both.

[0331] Embodiment 57. The method of embodiment 56, wherein the advanced imaging method includes cardiac magnetic resonance imaging (CMR), scintigraphy, or both. Page 88 of 135 11855652v1Docket No.: 2023P04092WO

[0332] Embodiment 58. The method of embodiment 57, wherein scintigraphy includes use of a radioisotope conjugate such as977Tc-Pyrophosphate.

[0333] Embodiment 59. The method of embodiment 57 or 58, wherein scintigraphy is performed using single photon emission computed tomography (SPECT).

[0334] Embodiment 60. The method of any one of embodiments 1-47, wherein the method is a method of determining a patient does not have or is not at risk of developing TTR-CM.

[0335] Embodiment 61. The method of any one of embodiments 1-59, wherein the sample is or includes a biological sample.

[0336] Embodiment 62. The method of embodiment 61, wherein the biological sample includes blood, serum, plasma, or cardiac tissue.

[0337] Embodiment 63. The method of any one of embodiments 1-62, wherein the sample was obtained from a subject.

[0338] Embodiment 64. The method of embodiment 63, wherein the subject is a human.

[0339] Embodiment 65. The method of embodiment 63 or 64, wherein the subject is suffering from or is at risk of developing amyloid transthyretin cardiomyopathy (TTR-CM).

[0340] Embodiment 66. The method of any one of embodiments 48-65, wherein the subject is at least 65 years old.

[0341] Embodiment 67. The method of any one of embodiments 1-66, wherein the step of detecting a level of two or more ATTR Biomarkers in the sample includes detecting the presence, absence, level or genotype of each of the two or more ATTR Biomarkers in the sample.

[0342] Embodiment 68. The method of any one of embodiments 1-67, wherein the step of detecting a level of two or more ATTR Biomarkers in the sample includes performing mass spectrometry.

[0343] Embodiment 69. The method of any one of embodiments 1-68, wherein the step of detecting a level of two or more ATTR Biomarkers in the sample includes measuring chemiluminescence.

[0344] Embodiment 70. The method of any one of embodiments 1-69, wherein the step of detecting a level of two or more ATTR Biomarkers in the sample includes detecting two or more nucleotides that encode the two or more ATTR Biomarkers in the sample. Page 89 of 135 11855652v1Docket No.: 2023P04092WO

[0345] Embodiment 71. The method of embodiment 70, wherein detecting two or more nucleotides that encode the two or more ATTR Biomarkers includes performing a nucleic acid amplification method.

[0346] Embodiment 72. The method of 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-mediated amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence based amplification (NASBA).

[0347] Embodiment 73. The method of any one of embodiments 70-72, wherein detecting two or more nucleotides that encode the two or more ATTR Biomarkers includes detecting hybridization between two or more nucleic acid probes and the two or more nucleotides that encode the two or more ATTR Biomarkers.

[0348] Embodiment 74. The method of embodiment 73, wherein the two or more nucleic acid probes are each complementary to at least a portion of one of the two or more nucleotides that encode the two or more ATTR Biomarkers.

[0349] Embodiment 75. The method of any one of embodiments 70-74, wherein the nucleotides that encode the two or more ATTR Biomarkers are DNA.

[0350] Embodiment 76. The method of embodiment 75, wherein the DNA is cDNA.

[0351] Embodiment 77. The method of any one of embodiments 70-74, wherein the nucleotides that encode the two or more ATTR Biomarkers are RNA.

[0352] Embodiment 78. The method of any one of embodiments 1-77, wherein the step of detecting a level of the two or more ATTR Biomarkers in the sample includes performing an immunoassay.

[0353] Embodiment 79. The method of embodiment 78, wherein the immunoassay is a chemiluminescent immunoassay.

[0354] Embodiment 80. The method of embodiment 78 or 79, wherein the immunoassay is selected from the group consisting of immunoprecipitation; Western blot; ELISA; immunohistochemistry; immunocytochemistry; flow cytometry; and, immuno-PCR.

[0355] Embodiment 81. The method of any one of embodiments 78-80, wherein the immunoassay is an ELISA.

[0356] Embodiment 82. The method of any one of embodiments 78-81, wherein the immunoassay is a high-throughput and / or automated immunoassay platform. Page 90 of 135 11855652v1Docket No.: 2023P04092WO

[0357] Embodiment 83. The method of any one of embodiments 1-81, wherein detecting a level of the two or more ATTR Biomarkers in the sample includes contacting the sample with two or more anti-ATTR Biomarker antibody agents.

[0358] Embodiment 84. The method of any one of embodiments 1-83, wherein detecting a level of the two or more ATTR Biomarkers in a sample obtained from the subject includes contacting the sample with two or more detection agents.

[0359] Embodiment 85. The method of embodiment 84, wherein the two or more anti-ATTR Biomarker antibody agents are labeled with the two or more detection agents.

[0360] Embodiment 86. The method of embodiment 84 or 85, wherein the two or more detection agents are two or more acridinium ester molecules.

[0361] Embodiment 87. The method of any one of embodiments 1-86, wherein detecting a level of the two or more ATTR Biomarkers in the sample includes detecting binding between the two or more ATTR Biomarkers and the two or more anti-ATTR Biomarker antibody agents.

[0362] Embodiment 88. The method of embodiment 87, wherein detecting binding between the two or more ATTR Biomarkers and the two or more anti-ATTR Biomarker antibody agents includes performing immunocytochemistry (ICC).

[0363] Embodiment 89. The method of embodiment 87 or 88, wherein detecting binding between the two or more ATTR Biomarkers and the two or more anti-ATTR Biomarker antibody agents includes determining at least one ATTR Biomarker absorbance value or emission value.

[0364] Embodiment 90. The method of embodiment 1-89, wherein detecting a level of two or more ATTR Biomarkers in the sample includes detecting a level of each of the two or more ATTR Biomarkers, wherein the method further includes comparing a level of each of the two or more ATTR Biomarkers to a threshold value for the respective ATTR Biomarkers, and wherein the level of each of the one or more ATTR Biomarkers that is detected is above the threshold value for the respective ATTR Biomarkers.

[0365] Embodiment 91. The method of embodiment 90, further including determining the subject is suffering from or is at risk of developing TTR-CM if the level of each of the one or more ATTR Biomarkers is above a threshold value for the respective ATTR Biomarkers. Page 91 of 135 11855652v1Docket No.: 2023P04092WO

[0366] Embodiment 92. The method of embodiment 90 or 91, further including diagnosing the subject with TTR-CM if the level of each of the one or more ATTR Biomarkers is above a threshold value for the respective ATTR Biomarkers.

[0367] Embodiment 93. The method of any one of embodiments 90-92, further including recommending the subject for one or more cardiomyopathy tests if the level of each of the one or more ATTR Biomarkers is above a threshold value for the respective ATTR Biomarkers.

[0368] Embodiment 94. The method of any one of embodiments 90-93, wherein the level of each of the 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 fold greater than a threshold value for the respective ATTR Biomarkers.

[0369] Embodiment 95. The method of any one of embodiments 90-94, wherein the threshold value for an ATTR Biomarker is an average of values for the ATTR Biomarker detected for two or more control samples.

[0370] Embodiment 96. The method of embodiment 95, wherein the two or more control samples are each a sample obtained from a subject who does not have ATTR amyloidosis.

[0371] Embodiment 97. The method of any one of embodiments 90-94, wherein the threshold value for an ATTR Biomarker is a value reported in a standard table.

[0372] Embodiment 98. The method of any one of embodiments 90-94, further including recommending the subject for cardiac biopsy if the level of each of the one or more ATTR Biomarkers is above a threshold value and the subject tests positive for cardiomyopathy in one or more of the cardiomyopathy tests.

[0373] Embodiment 99. The method of any one of embodiments 13-89, wherein using the ATTR Biomarker profile to compute an ATTR Biomarker score includes applying an algorithm to the ATTR Biomarker profile to compute an ATTR Biomarker score, wherein the algorithm is a decision tree algorithm, a neural boosted algorithm, a bootstrap forest algorithm, a boosted tree algorithm, a K nearest neighbors algorithm, a generalized regression forward selection algorithm, a generalized regression pruned forward selection 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 machines algorithm, a discriminant algorithm, or a naïve Bayes algorithm. Page 92 of 135 11855652v1Docket No.: 2023P04092WO

[0374] Embodiment 100. The method of any one of embodiments 13-89 and 99, wherein using the ATTR Biomarker profile to compute an ATTR Biomarker score includes applying an algorithm to the ATTR Biomarker profile to compute an ATTR Biomarker score, wherein the algorithm is a decision tree algorithm, a neural boosted algorithm, a bootstrap forest algorithm, a boosted 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 machines algorithm, or a discriminant algorithm.

[0375] Embodiment 101. The method of any one of embodiments 13-89, 99, and 100, wherein using the ATTR Biomarker profile to compute an ATTR Biomarker score includes applying an algorithm to the ATTR Biomarker profile to compute an ATTR Biomarker score, wherein the algorithm is a decision tree algorithm, a neural boosted algorithm, a bootstrap forest algorithm, a boosted tree algorithm, or a support vector machines algorithm.

[0376] Embodiment 102. The method of any one of embodiments 13-89 and 99-101, further including using the ATTR Biomarker score to determine if a subject from which the sample was obtained is at risk of or suffering from TTR-CM.

[0377] Embodiment 103. The method of any one of embodiments 13-89 and 99-102, further including using the ATTR Biomarker score to determine if a subject from which the sample was obtained is selected for one or more cardiomyopathy tests.

[0378] Embodiment 104. The method of any one of embodiments 13-89 and 99-103, further including using the ATTR Biomarker score to determine if a subject from which the sample was obtained is selected to receive one or more doses of a TTR stabilizing agent.

[0379] Embodiment 105. The method of any one of embodiments 1-104, further including immunochemical staining of biopsy tissue from the subject.

[0380] Embodiment 106. The method of embodiment 90, wherein the biopsy tissue includes cardiac biopsy tissue.

[0381] Embodiment 107. The method of embodiment 90 or 91, wherein immunochemical staining includes use of two or more antibody agents to kappa or lambda light chain amyloid deposits in heart tissue and / or two or more antibody agents to transthyretin deposits.

[0382] Embodiment 108. The method of any one of embodiments 90-92, further including diagnosing the subject with TTR-CM if immunochemical staining indicates presence of transthyretin deposits in the cardiac tissue. Page 93 of 135 11855652v1Docket No.: 2023P04092WO

[0383] Embodiment 109. A method including: (a) detecting a level 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 compute a ATTR Biomarker score, wherein the 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, and wherein using the ATTR Biomarker profile to compute an ATTR Biomarker score includes applying an algorithm to the ATTR Biomarker profile to compute an ATTR Biomarker score, wherein the algorithm is a decision tree algorithm, a neural boosted algorithm, a bootstrap forest algorithm, a boosted tree algorithm, or a support vector machines algorithm.

[0384] Embodiment 110. The method of embodiment 109, wherein the subject is a human subject.

[0385] Embodiment 111. The method of embodiment 109 or 110, wherein the subject is at least 65 years old.

[0386] Embodiment 112. The method of any one of embodiments 109-111, wherein the sample includes blood, serum, plasma, or cardiac tissue.

[0387] Embodiment 113. The method of any one of embodiments 109-112, further including using the ATTR Biomarker score to determine if a subject from which the sample was obtained is at risk of or suffering from TTR-CM.

[0388] Embodiment 114. The method of any one of embodiments 109-113, further including using the ATTR Biomarker score to determine a subject’s risk of developing amyloid transthyretin cardiomyopathy (TTR-CM).

[0389] Embodiment 115. The method of any one of embodiments 109-114, further including using the ATTR Biomarker score to diagnose a subject with TTR-CM.

[0390] Embodiment 116. The method of embodiment 115, wherein TTR-CM results from wild-type transthyretin amyloidosis (ATTRwt).

[0391] Embodiment 117. The method of any one of embodiments 109-116, further including using the ATTR Biomarker score to determine if a subject from which the sample was obtained is selected for one or more cardiomyopathy tests. Page 94 of 135 11855652v1Docket No.: 2023P04092WO

[0392] Embodiment 118. The method of embodiment 117, wherein the one or more cardiomyopathy tests include an echocardiogram, an advanced imaging method, or both.

[0393] Embodiment 119. The method of embodiment 118, wherein the advanced imaging method includes cardiac magnetic resonance imaging (CMR), scintigraphy, or both.

[0394] Embodiment 120. The method of embodiment 119, wherein scintigraphy includes use of a radioisotope conjugate such as99mTc-Pyrophosphate.

[0395] Embodiment 121. The method of embodiment 119 or 120, wherein scintigraphy is performed using single photon emission computed tomography (SPECT).

[0396] Embodiment 122. The method of any one of embodiments 109-121, further including using the ATTR Biomarker score to determine if a subject from which the sample was obtained is selected to receive one or more doses of a TTR stabilizing agent.

[0397] Embodiment 123. The method of embodiment 122, wherein the method further includes administering to the subject one or more doses a TTR stabilizing agent.

[0398] Embodiment 124. The method of any one of embodiments 13-123, including using one or more demographic factors to compute the ATTR Biomarker score (e.g., wherein applying the algorithm includes using the one or more demographic factors in the algorithm).

[0399] Embodiments 125. The method of embodiment 124, wherein the one or more demographic factors include or consist of age and / or sex.

[0400] Embodiment 126. A non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations including the method of any one of embodiments 1-125.

[0401] Embodiment 127. A composition including: (a) one or more ATTR Biomarkers, wherein the one 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; and (b) one or more anti-ATTR Biomarker agents, wherein the one or more anti- ATTR Biomarker agents include: (i) an anti-TnI agent, (ii) an anti-PKM1 agent, (iii) an anti-PKM2 agent, (iv) an anti-NT-proBNP agent, (v) an anti-RBP4 agent, (vi) an anti-TIMP2 agent, (vii) an anti-NfL agent, or (viii) a combination thereof.

[0402] Embodiment 128. A composition including: (a) one or more ATTR Biomarkers, wherein the one or more ATTR Biomarkers include TnI, and Page 95 of 135 11855652v1Docket No.: 2023P04092WO (b) one or more anti-ATTR Biomarker agents, wherein the one or more anti- ATTR Biomarker agents include an anti-TnI agent.

[0403] Embodiment 129. The composition of embodiment 128, wherein: (a) the one or more ATTR Biomarkers include TnI and PKM1, and (b) the one or more anti-ATTR Biomarker agents include an anti-TnI agent and an anti-PKM1 agent.

[0404] Embodiment 130. The composition of embodiment 128, wherein: (a) the one or more ATTR Biomarkers include TnI and PKM2, and (b) the one or more anti-ATTR Biomarker agents include an anti-TnI agent and an anti-PKM2 agent.

[0405] Embodiment 131. The composition of embodiment 128, wherein: (a) the one or more ATTR Biomarkers include TnI, PKM1, and PKM2, and (b) the one or more anti-ATTR Biomarker agents include an anti-TnI agent, an anti-PKM1 agent, and an anti-PKM2 agent.

[0406] Embodiment 132. The composition of any one of embodiments 127-131, wherein: (a) the one or more ATTR Biomarkers further include NT-proBNP, RBP4, or both, and (b) the one or more anti-ATTR Biomarker agents further include an anti-NT- proBNP agent, an anti-RBP4 agent, or both.

[0407] Embodiment 133. The composition of any one of embodiments 127-132, wherein: (a) the one or more ATTR Biomarkers further include TIMP2, NfL, or both, and (b) the one or more anti-ATTR Biomarker agents further include an anti- TIMP2 agent, an anti-NfL agent, or both.

[0408] Embodiment 134. A composition including: (a) one or more ATTR Biomarkers, wherein the one or more ATTR Biomarkers include NT-proBNP, and (b) one or more anti-ATTR Biomarker agents, wherein the one or more anti- ATTR Biomarker agents include an anti-NT-proBNP agent.

[0409] Embodiment 135. The composition of embodiment 134, wherein: Page 96 of 135 11855652v1Docket No.: 2023P04092WO (a) the one or more ATTR Biomarkers further include: (i) TnI, (ii) PKM1, (iii) PKM2, (iv) RBP4, (v) TIMP2, (vi) NfL, or (vii) a combination thereof; and (b) the one or more anti-ATTR Biomarker agents further include: (i) an anti- TnI agent, (ii) an anti-PKM1 agent, (iii) an anti-PKM2 agent, (iv) an anti-RBP4 agent, (v) an anti-TIMP2 agent, (vi) an anti-NfL agent, or (vii) a combination thereof.

[0410] Embodiment 136. The composition of embodiment 134 or 135, wherein: (a) the one or more ATTR Biomarkers include NT proBNP, TnI, and RBP, and (b) the one or more anti-ATTR Biomarker agents include an anti-NT proBNP agent, an anti-TnI agent, and an anti-RBP agent.

[0411] Embodiment 137. The composition of any one of embodiments 127, 128, 130, and 132-136, wherein the one or more ATTR Biomarkers do not include PKM1 and the one or more anti-ATTR Biomarker agents do not include an anti-PKM1 agent.

[0412] Embodiment 138. The composition of any one of embodiments 127-129, and 132-136, wherein the one or more ATTR Biomarkers do not include PKM2 and the one or more anti-ATTR Biomarker agents do not include an anti-PKM2 agent.

[0413] Embodiment 139. The composition of any one of embodiments 127, 128, and 132-136, wherein the one or more ATTR Biomarkers do not include either PKM1 or PKM2, and the one or more anti-ATTR Biomarker agents do not include either an anti-PKM1 agent or an anti-PKM2 agent.

[0414] Embodiment 140. A kit for detecting TTR-CM, said kit including: (a) one or more anti-ATTR Biomarker agents, wherein the one or more anti- ATTR Biomarker agents include: (i) an anti-TnI agent, (ii) an anti-PKM1 agent, (iii) an anti-PKM2 agent, (iv) an anti-NT-proBNP agent, (v) an anti-RBP4 agent, (vi) an anti-TIMP2 agent, (vii) an anti-NfL agent, or (viii) a combination thereof; and (b) instructions for use.

[0415] Embodiment 141. A kit for detecting TTR-CM, said kit including: (a) one or more anti-ATTR Biomarker agents, wherein the one or more anti- ATTR Biomarker agents include an anti-TnI agent; and (b) instructions for use.

[0416] Embodiment 142. The kit of embodiment 141, wherein the one or more anti- ATTR Biomarker agents include an anti-TnI agent and an anti-PKM1 agent. Page 97 of 135 11855652v1Docket No.: 2023P04092WO

[0417] Embodiment 143. The kit of embodiment 141, wherein the one or more anti- ATTR Biomarker agents include an anti-TnI agent and an anti-PKM2 agent.

[0418] Embodiment 144. The kit of embodiment 141, wherein the one or more anti- ATTR Biomarker agents include an anti-TnI agent, an anti-PKM1 agent, and an anti-PKM2 agent.

[0419] Embodiment 145. The kit of any one of embodiments 141-144, wherein the one or more anti-ATTR Biomarker agents further include an anti-NT-proBNP agent, an anti- RBP4 agent, or both.

[0420] Embodiment 146. The kit of any one of embodiments 141-145, wherein the one or more anti-ATTR Biomarker agents further include an anti-TIMP2 agent, an anti-NfL agent, or both.

[0421] Embodiment 147. A kit including: (a) one or more anti-ATTR Biomarker agents, wherein the one or more anti- ATTR Biomarker agents include an anti-NT-proBNP agent; and (b) instructions for use.

[0422] Embodiment 148. The kit of embodiment 147, wherein the one or more anti- ATTR Biomarker agents further include: (i) an anti-TnI agent, (ii) an anti-PKM1 agent, (iii) an anti-PKM2 agent, (iv) an anti-RBP4 agent, (v) an anti-TIMP2 agent, (vi) an anti-NfL agent, or (vii) a combination thereof.

[0423] Embodiment 149. The kit of embodiment 147 or 148, wherein the one or more anti-ATTR Biomarker agents include an anti-NT-proBNP agent, an anti-TnI agent, and an anti-RBP agent.

[0424] Embodiment 150. The method of any one of embodiments 140, 141, 143, and 145-149, wherein the one or more ATTR Biomarkers do not include PKM1 and the one or more anti-ATTR Biomarker agents do not include an anti-PKM1 agent.

[0425] Embodiment 151. The method of any one of embodiments 140-142 and 141- 145, wherein the one or more ATTR Biomarkers do not include PKM2 and the one or more anti-ATTR Biomarker agents do not include an anti-PKM2 agent.

[0426] Embodiment 152. The method of any one of embodiments 140, 141, and 145- 149, wherein the one or more ATTR Biomarkers do not include either PKM1 or PKM2, and the one or more anti-ATTR Biomarker agents do not include either an anti-PKM1 agent or an anti-PKM2 agent. Page 98 of 135 11855652v1Docket No.: 2023P04092WO

[0427] Embodiment 153. The kit of any one of embodiments 140-152, wherein the kit further includes a TTR stabilizing agent.

[0428] Embodiment 154. The kit of any one of embodiments 140-153, wherein the one or more anti-ATTR Biomarker agents include one or more antibody agents.

[0429] Embodiment 155. The kit of any one of embodiments 140-154 wherein the one or more anti-ATTR Biomarker agents include one or more nucleic acid probes.

[0430] Embodiment 156. The kit of embodiment 154 or 155, wherein one or more of the antibody agents are labeled with a detectable moiety.

[0431] Embodiment 157. The kit of embodiments 154-156, further including one or more secondary antibody agents that specifically bind to the one or more anti-ATTR Biomarker antibody agents.

[0432] Embodiment 158. The kit of embodiment 157, wherein the one or more anti- ATTR Biomarker antibody agents and / or the one or more of the secondary antibody agents are linked to an enzyme.

[0433] Embodiment 159. The kit of embodiments 140-158, further including a detection agent.

[0434] Embodiment 160. The kit of embodiment 159, wherein the detection agent is or includes a substrate for the enzyme.

[0435] Embodiment 161. The kit of embodiment 159, wherein the detection agent is or includes one or more acridinium ester molecules.

[0436] Embodiment 162. The kit of embodiment 157, wherein the one or more anti- ATTR Biomarker antibody agents and / or the one or more of the secondary antibody agents are labeled with one or more of the acridinium ester molecules.

[0437] Embodiment 163. The kit of any one of embodiments 155-162, wherein the one or more of anti-ATTR Biomarker nucleic acid probes are complementary to one or more nucleotides that encode an ATTR Biomarker.

[0438] Embodiment 164. The kit of any one of embodiments 155-163, wherein at least a portion of each anti-ATTR Biomarker nucleic acid probe hybridizes to one or more nucleotides that encode an ATTR Biomarker.

[0439] Embodiment 165. The kit of any one of embodiments 163 and 164, wherein the nucleotides that encode one or more ATTR Biomarkers are DNA.

[0440] Embodiment 166. The kit of embodiment 165, wherein the DNA is cDNA. Page 99 of 135 11855652v1Docket No.: 2023P04092WO

[0441] Embodiment 167. The kit of any one of embodiments 163 and 164, wherein the nucleotides that encode one or more ATTR Biomarkers are RNA.

[0442] Embodiment 168. The kit of embodiments 155-167, wherein the one or more of anti-ATTR Biomarker nucleic acid probes are labeled with one or more of the detection agents.

[0443] Embodiment 169. The kit of embodiment 168, wherein the detection agents indicate presence of nucleotides that encode one or more ATTR Biomarkers.

[0444] Embodiment 170. The kit of embodiments 140-169, further including one or more control samples.

[0445] Embodiment 171. The kit of embodiment 170, wherein the control samples include one or more ATTR Biomarker standards.

[0446] Embodiment 172. The kit of embodiment 171, wherein the one or more ATTR Biomarker standards include a recombinant ATTR Biomarker.

[0447] Embodiment 173. The kit of embodiment 171, wherein the one or more ATTR Biomarker standards include synthetic ATTR Biomarker nucleic acids.

[0448] Embodiment 174. Use of a kit according to embodiments 140-173 in an in vitro diagnostic assay to diagnose TTR-CM in a subject.

[0449] Embodiment 175. A method including: a) receiving, by a processor of a computing device, an ATTR Biomarker profile including data corresponding to levels of two or more (e.g., only two) ATTR Biomarkers in a sample from a subject; b) providing, by the processor, input to a machine-learned algorithm, the input including the ATTR Biomarker profile; and c) determining, by the processor, an ATTR Biomarker score for the subject with the machine-learned algorithm based on the input, 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-pro hormone B-type natriuretic peptide (NT-proBNP), (v) retinol binding protein 4 (RBP4), (vi) tissue inhibitor of metalloproteinase 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

[0450] Embodiment 176. A method including: Page 100 of 135 11855652v1Docket No.: 2023P04092WO a) receiving, by a processor of a computing device, an ATTR Biomarker profile including data corresponding to levels of two or more (e.g., only two) ATTR Biomarkers in a sample from a subject; b) providing, by the processor, input to a machine-learned algorithm, the input including the ATTR Biomarker profile; and c) determining, by the processor, whether the subject is at risk of or has (e.g., is suffering from) transthyretin amyloid cardiomyopathy (TTR-CM) with the machine-learned algorithm based on the input, 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-pro hormone B-type natriuretic peptide (NT-proBNP), (v) retinol binding protein 4 (RBP4), (vi) tissue inhibitor of metalloproteinase 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

[0451] Embodiment 177. The method of embodiment 176, wherein determining whether the subject is at risk of or has TTR-CM with the machine-learned algorithm includes determining, by the processor, a probability that the subject has TTR-CM.

[0452] Embodiment 178. The method of any one of embodiments 175-177, wherein the two or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof.

[0453] Embodiment 179. The method of any one of embodiments 175-178, wherein the two or more ATTR Biomarkers includes TnI and RBP4.

[0454] Embodiment 180. The method of embodiment 179, wherein the two or more ATTR Biomarkers further includes NT-proBNP, TIMP2, or both NT-proBNP and TIMP2.

[0455] Embodiment 181. The method of any one of embodiments 175-180, wherein the two or more ATTR Biomarkers is three or more ATTR Biomarkers.

[0456] Embodiment 182. The method of any one of embodiments 175-181, wherein the two or more ATTR Biomarkers is two ATTR Biomarkers or three ATTR Biomarkers.

[0457] Embodiment 183. The method of any one of embodiments 175-182, wherein the two or more ATTR Biomarkers is four ATTR Biomarkers.

[0458] Embodiment 184. The method of any one of embodiments 175-183, including: a) receiving, by the processor, data corresponding to one or more demographic factors for the subject; and Page 101 of 135 11855652v1Docket No.: 2023P04092WO b) providing, by the processor, the input to the machine-learned algorithm, wherein the input includes the data corresponding to the one or more demographic factors.

[0459] Embodiment 184. The method of embodiment 183, wherein the one or more demographic factors include sex, age, or both sex and age.

[0460] Embodiment 185. The method of any one of embodiments 175-184, including: a) receiving, by the processor, data corresponding to one or more imaging-based biomarkers for the subject; and b) providing, by the processor, the input to the machine-learned algorithm, wherein the input includes the data corresponding to the imaging-based biomarkers.

[0461] Embodiment 186. The method of embodiment 185, wherein the one or more imaging-based biomarkers includes septal wall thickness (e.g., left ventricle septal wall thickness), posterior wall thickness, or ejection fraction.

[0462] Embodiment 187. The method of embodiment 185 or embodiment 186, including determining, by the processor, the data corresponding to the one or more imaging- based biomarkers from one or more images.

[0463] Embodiment 188. The method of embodiment 187, wherein determining the data corresponding to the one or more imaging-based biomarkers from one or more images includes determining, by the processor, a measurement from the one or more images [e.g., by performing, by the processor, a pattern recognition on the one or more images and / or segmenting and classifying, by the processor, one or more features from the one or more images with a (e.g., the) machine-learned algorithm].

[0464] Embodiment 189. The method of any one of embodiments 175-188, wherein the machine-learned algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, or a support vector machines methodology.

[0465] Embodiment 190. The method of any one of embodiments 175-189, wherein the machine-learned algorithm is a bootstrap forest algorithm.

[0466] Embodiment 191. The method of any one of embodiments 175-190, wherein the machine-learned algorithm is or is derived from a decision tree methodology.

[0467] Embodiment 192. The method of embodiment 191, wherein the machine- learned algorithm, wherein the decision tree methodology is a bagged, boosted, or additive Page 102 of 135 11855652v1Docket No.: 2023P04092WO tree methodology (e.g., wherein the machine-learned algorithm includes at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees).

[0468] Embodiment 193. The method of any one of embodiments 175-192, wherein the ATTR Biomarker score is an output from the machine-learned algorithm (e.g., including outputting, by the processor, the ATTR Biomarker score from the machine-learned algorithm).

[0469] Embodiment 194. The method of any one of embodiments 175-193, further including using the ATTR Biomarker score to determine if a subject from which the sample was obtained is at risk of or suffering from transthyretin amyloid cardiomyopathy (TTR- CM).

[0470] Embodiment 195. The method of any one of embodiments 175-194, including determining, by the processor, if the subject is at risk of or is suffering from transthyretin amyloid cardiomyopathy (TTR-CM) with the machine-learned algorithm (e.g., wherein an output of the machine-learned algorithm is a determination (e.g., a probability) of whether the subject is at risk of or is suffering from TTR-CM).

[0471] Embodiment 196. The method of any one of embodiments 175-195, including classifying, by the processor, the subject as having or not having TTR-CM with the machine- learned algorithm.

[0472] Embodiment 197. The method of any one of embodiments 175-196, wherein the machine-learned algorithm is a classifier for TTR-CM.

[0473] Embodiment 198. The method of embodiment 196 or embodiment 197, wherein the classifier or the classifying has a sensitivity and a specificity each of more than 80% (e.g., at least one or both of more than 90%).

[0474] Embodiment 199. The method of any one of embodiments 175-198, wherein the subject is a human subject.

[0475] Embodiment 200. The method of any one of embodiments 175-199, wherein the sample includes blood, serum, plasma, or cardiac tissue.

[0476] Embodiment 201. The method of any one of embodiments 175-200, wherein the two or more ATTR Biomarkers do not include PKM1.

[0477] Embodiment 202. The method of any one of embodiments 175-201, wherein the two or more ATTR Biomarkers do not include PKM2.

[0478] Embodiment 203. The method of any one of embodiments 175-202, wherein the two or more ATTR Biomarkers do not include either PKM1 or PKM2. Page 103 of 135 11855652v1Docket No.: 2023P04092WO

[0479] Embodiment 204. The method of any one of embodiments 175-203, wherein the two or more ATTR Biomarkers do not include SMOC-2.

[0480] Embodiment 205. The method of any one of embodiments 175-204, wherein the two or more ATTR Biomarkers do not include DCN.

[0481] Embodiment 206. The method of any one of embodiments 175-205, wherein the two or more ATTR Biomarkers do not include either SMOC-2 or DCN.

[0482] Embodiment 207. A non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations including the method of any one of embodiments 175-206.

[0483] Embodiment 208. A system including a non-transitory computer readable medium and a processor, wherein the non-transitory computer readable medium has the machine-learned algorithm stored thereon and executable instructions that, when executed by the processor, perform operations including the method of any one of embodiments 175-206.

[0484] Embodiment 209. A method including: receiving, by a processor of a computing device, ATTR Biomarker profiles each including data corresponding to levels of two or more (e.g., only two) ATTR Biomarkers and, for each of the ATTR Biomarker profiles, corresponding data indicative of health status for the ATTR Biomarker profile; and training, by the processor, a machine-learning algorithm using the ATTR Biomarker profiles and the corresponding data indicative of 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-pro hormone B-type natriuretic peptide (NT-proBNP), (v) retinol binding protein 4 (RBP4), (vi) tissue inhibitor of metalloproteinase 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

[0485] Embodiments 210. The method of embodiment 209, wherein the training includes determining, by the processor, via the algorithm, one or more feature values for a corresponding one of the two or more ATTR Biomarkers based on the ATTR Biomarker profiles and / or a linear combination thereof.

[0486] Embodiment 211. The method of embodiment 210, wherein the training includes determining, by the processor, via the algorithm, one or more decision rules in one or more decision trees based on the one or more feature values and / or the linear combination thereof. Page 104 of 135 11855652v1Docket No.: 2023P04092WO

[0487] Embodiment 212. The method of embodiment 210 or embodiment 211, wherein each of the one or more feature values corresponds to a level of the corresponding one of the two or more ATTR Biomarkers.

[0488] Embodiment 213. The method of any one of embodiments 209-212, wherein the training includes determining, by the processor, via the algorithm, one or more decision rules based on the ATTR Biomarker profiles.

[0489] Embodiment 214. The method of any one of embodiments 209-213, wherein the ATTR Biomarker profiles are labelled with the corresponding data.

[0490] Embodiment 215. The method of any one of embodiments 209-214, wherein at least two of the ATTR Biomarker profiles are for different subjects (e.g., wherein each of the ATTR Biomarker profiles includes data corresponding to levels of two or more ATTR Biomarkers for a unique subject).

[0491] Embodiment 216. The method of any one of embodiments 209-215, wherein at least two of the ATTR Biomarker profiles are for a same subject at different times.

[0492] Embodiment 217. The method of any one of embodiments 209-216, wherein the health status is a probability that a subject has TTR-CM, whether or not a subject is or is not at risk of TTR-CM, or whether or not a subject does or does not have TTR-CM (e.g., is healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM).

[0493] Embodiment 218. The method of any one of embodiments 209-217, wherein the corresponding data indicative of health status is a probability or a score (e.g., an ATTR Biomarker score).

[0494] Embodiment 219. The method of any one of embodiments 209-218, wherein the machine-learning algorithm is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, or a support vector machines methodology.

[0495] Embodiment 220. The method of any one of embodiments 209-219, wherein the machine-learned algorithm is a bootstrap forest algorithm.

[0496] Embodiment 221. The method of any one of embodiments 209-220, wherein the machine-learned algorithm is derived from a decision tree methodology.

[0497] Embodiment 222. The method of embodiment 221, wherein the decision tree methodology is a bagged, boosted, or additive tree methodology (e.g., wherein the machine- learned algorithm includes at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees). Page 105 of 135 11855652v1Docket No.: 2023P04092WO

[0498] Embodiment 223. The method of any one of embodiments 209-222, wherein the two or more ATTR Biomarkers is three or more ATTR Biomarkers.

[0499] Embodiment 224. The method of any one of embodiments 209-223, wherein the two or more ATTR Biomarkers is two ATTR Biomarkers or three ATTR Biomarkers.

[0500] Embodiment 225. The method of any one of embodiments 209-224, wherein the two or more ATTR Biomarkers is four ATTR Biomarkers.

[0501] Embodiment 226. The method of any one of embodiments 209-225, including: receiving, by the processor, data corresponding to one or more demographic factors for each subject corresponding to the ATTR Biomarker profiles (e.g., for each subject that the ATTR Biomarker profiles correspond to); and training, by the processor, the machine-learning algorithm based on the data corresponding to the one or more demographic factors in combination with the ATTR Biomarker profiles.

[0502] Embodiment 227. The method of embodiment 226, wherein the one or more demographic factors includes age, sex, or both age and sex.

[0503] Embodiment 228. The method of any one of embodiments 209-227, including: a) receiving, by the processor, data corresponding to one or more imaging-based biomarkers for each subject corresponding to the ATTR Biomarker profiles; and b) training, by the processor, the machine-learning algorithm based on the data corresponding to the one or more imaging-based biomarkers in combination with the ATTR Biomarker profiles (e.g., and the data corresponding to the one or more demographic factors).

[0504] Embodiment 229. The method of embodiment 228, wherein the one or more imaging-based biomarkers includes septal wall thickness (e.g., left ventricle septal wall thickness), posterior wall thickness, or ejection fraction.

[0505] Embodiment 230. The method of any one of embodiments 209-229, wherein the two or more ATTR Biomarkers include TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL or a combination thereof.

[0506] Embodiment 231. The method of any one of embodiments 209-230, wherein the two or more ATTR Biomarkers includes TnI and RBP4.

[0507] Embodiment 232. The method of embodiment 231, wherein the two or more ATTR Biomarkers further includes NT-proBNP, TIMP2, or both NT-proBNP and TIMP2. Page 106 of 135 11855652v1Docket No.: 2023P04092WO

[0508] Embodiment 233. The method of any one of embodiments 209-232, wherein, after the training, the machine-learning algorithm is a machine-learned algorithm that is a classifier for TTR-CM.

[0509] Embodiment 234. The method of embodiment 233, wherein the classifier has a sensitivity and a specificity each of more than 80% (e.g., at least one or both of more than 90%).

[0510] Embodiment 235. A non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations including the method of any one of embodiments 208-234.

[0511] Embodiment 236. A system including a non-transitory computer readable medium and a processor, wherein the non-transitory computer readable medium has the machine-learning algorithm stored thereon and executable instructions that, when executed by the processor, perform operations including the method of any one of embodiments 209- 234.

[0512] Embodiment 237. A method including: a) receiving, by a processor of a computing device, data corresponding to two or more biomarkers for a subject; b) providing, by the processor, input to a machine-learned algorithm, the input including the data; and c) determining, by the processor, a health status for the subject with the machine-learned algorithm based on the input, wherein the machine-learned algorithm has been derived from a decision tree methodology.

[0513] Embodiment 238. A method including: a) receiving, by a processor of a computing device, training input including data corresponding to two or more biomarkers and corresponding data indicative of health status; and b) training, by the processor, a machine-learning algorithm using the input, wherein the machine-learning algorithm is based on a decision tree methodology.

[0514] Embodiment 239. The method of embodiment 237 or embodiment 238, wherein the decision tree methodology is a bootstrap forest methodology.

[0515] Embodiment 240. The method of any one of embodiments 237-239, wherein the data corresponding to two or more biomarkers is data corresponding to levels of the two or more biomarkers (e.g., for the subject, or, if used for training, for at least one subject). Page 107 of 135 11855652v1Docket No.: 2023P04092WO

[0516] Embodiment 241. The method of any one of embodiments 237-240, wherein the health status includes at least one of (e.g., two of, or all three of) (i) a state (e.g., stage) of a disease, disorder, or condition for the subject, (ii) whether the subject has or does not have a disease, disorder, or condition, and (iii) a probability that the subject has a disease, disorder or condition.

[0517] Embodiment 242. The method of embodiment 241, wherein the disease, disorder, or condition is a heart disease, disorder, or condition.

[0518] Embodiment 243. The method of any one of embodiments 237-242, wherein the data corresponding to the two or more biomarkers includes (e.g., is) laboratory-derived data (e.g., has been derived from one or more assays).

[0519] Embodiment 244. The method of any one of embodiments 237-243, wherein the two or more biomarkers include a protein biomarker, an enzyme biomarker, a peptide biomarker, or an intermediate filament biomarker (e.g., a neurofilament biomarker) [e.g., wherein each of the two or more biomarkers is a protein biomarker, an enzyme biomarker, a peptide biomarker, or an intermediate filament biomarker (e.g., a neurofilament biomarker)].

[0520] Embodiment 245. The method of any one of embodiments 237-244, wherein the input further includes data corresponding to one or more demographic factors (e.g., for the subject, or, if used for training, for at least one subject to which the data corresponding to two or more biomarkers corresponds).

[0521] Embodiment 246. The method of any one of embodiments 237-245, wherein the input further includes data corresponding to one or more imaging-based biomarkers (e.g., for the subject, or, if used for training, for at least one subject to which the data corresponding to two or more biomarkers corresponds).

[0522] Embodiment 247. A non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations including the method of any one of embodiments 237-246.

[0523] Embodiment 248. A system including a non-transitory computer readable medium and a processor, wherein the non-transitory computer readable medium has the machine-learned algorithm stored thereon and executable instructions that, when executed by the processor, perform operations including the method of any one of embodiments 237-246. Page 108 of 135 11855652v1Docket No.: 2023P04092WO EXEMPLIFICATION Example 1: Identification of markers for ATTR amyloidosis

[0524] This example demonstrates a method for screening human plasma to identify biomarkers indicative of ATTR amyloidosis and TTR-CM.

[0525] Mass spectrometry (MS) was employed to identify biomarkers specific for ATTR amyloidosis. Human normal (N=4) and ATTRwt EDTA plasma (N=6) were added (50uL) to prewashed (1 x TTBS) goat-anti-mouse 96-well ELISA plates and incubated for 1 hour at room temperature. Plates were subsequently washed (three times in 1 x TTBS) and residual captured material was extracted and prepared for MS.

[0526] Results of MS, expressed as Exponentially Modified Protein Abundance Index (emPAI), were generated and data was sorted from highest to lowest emPAI scores. Signal to noise (S / N) ratios were calculated for average emPAI scores for ATTRwt / normal scores. The inverse S / N was also calculated. Selected markers were chosen with S / N ratios of 3 or greater (FIG.14). Criteria for marker selection included having multiple and consistent score values in diseased, normal samples, or both categories. Missing or empty data corresponds to lower than the detectable limit. For the purpose of generating S / N values, empty data was artificially scored with a value of “1.” Eight different markers were selected from the list in Table 1 to pursue as possible markers for ATTRwt (Table 6). Table 6: Selected Mass Spectrometry Hits Signal to Noise emPAI Scores D ri ti n G n N m ATTR / Cnt Cnt / ATTRExample 2: Patient Cohorts for Biomarker Testing

[0527] This example provides information regarding patient cohorts used in the analysis of how various ATTR Biomarkers performed in the detection of TTR-CM. As Page 109 of 135 11855652v1Docket No.: 2023P04092WO shown in FIG.1, 273 heart failure patients, 46 clinical trial patients, and 20 independent presumed normal donors participated in this study. All individuals in this study were 60 years of age or older, and were predominantly white. The number of males and females in each subset varied. The NYHA class of the patients also varied among the subsets. Notably, only a small number of positive samples with matching negatives were available in the cohorts. Example 3: Summary of Test Results

[0528] This example summarizes the data obtained from an analysis of how various ATTR Biomarkers performed in the detection of TTR-CM using the patient cohorts described in Example 2. As shown in FIG.2, only one result was missing across all biomarkers tested. This RBP result could not be obtained due to insufficient quantity of sample for a necessary retest. In total, the in vitro diagnostic assay results were based on one test per sample; to account for higher variability of research only use assays, the mean of two test replicates were utilized. Example 4: Single Biomarker Classification Performance

[0529] This example demonstrates an assessment of exemplary ATTR Biomarkers described herein for the ability to be used as a single biomarker for the detection of TTR-CM. The results show that certain ATTR Biomarkers described herein can perform at a sensitivity and / or specificity level sufficient that would allow them to be used alone. However, this example also demonstrates that not all ATTR Biomarkers met the criteria for both sensitivity and specificity when used as a single biomarker for assessing TTR-CM. As shown in FIGs. 3-6, TnI met both the sensitivity and specificity criteria, suggesting TnI may be useful as a single biomarker for the detection of TTR-CM. The remaining ATTR Biomarkers assessed did not meet both the sensitivity and specificity criteria set out for use a single biomarker. These biomarkers were then assessed in combination to determine whether combinations of the ATTR Biomarkers could achieve improved sensitivity and specificity when detecting for TTR-CM. Example 5: Single Biomarker NYHA Class Response Screening

[0530] This example demonstrates an assessment of biomarker effects when considered for each NYHA class. As shown in FIG.7, all biomarker effects for NYHA class Page 110 of 135 11855652v1Docket No.: 2023P04092WO were modeled and the p-value for the significance test was reported. The data showed that biomarker effects were statistically significant for PKM, TnI, RBP4, RBP, and NT-proBNP indicating that these ATTR Biomarkers could be useful for the detection of TTR-CM, despite the fact that not of these biomarkers met the sensitivity and specificity criteria, as discussed above in Example 4. Example 6: Performance Results

[0531] This example demonstrates the screening of generalized regression fits for processing the data obtained from biomarker combinations. Main effects and interactions were screened, and best fits with 4 or less biomarkers were selected for further evaluation.

[0532] The adaptive double lasso generalized regression method with leave-one-out validation using the complete response surface as input was expected to select a near optimal model using the minimum number of effects with a small data set including some correlated biomarkers. As shown in FIG.8, depending on the method used, different subsets of biomarkers were found informative. However, based on practical considerations, a limit of 4 biomarkers was determined. Several subsets of biomarkers and predictive models were generated. These biomarkers were further assessed using additional machine learning methods including: PKM, TnI, DCN, TIMP2, and NT-proBNP.

[0533] Additional model screening using combinations of PKM, TnI, DCN, TIMP2, and NT-proBNP were performed. As shown in FIG.9, the bootstrap forest model was found to produce best results compared to other machine learning methods. The neural networks models also produced good results, but experience more false negatives.

[0534] These results indicate that three biomarker or four biomarker combinations, including TnI and PKM, in particular with NT-proBNP and / or RBP4, can detect TTR-CM with high sensitivity and specificity. Example 7: Identification of PKM and TIMP2 as biomarkers elevated in ATTR amyloidosis patients

[0535] This example demonstrates two identified biomarkers (PKM and TIMP2) that individually and in combination show elevated plasma levels in ATTRwt patients compared with normal human control samples.

[0536] Biomarkers selected in Table 2 were tested for immunoreactivity to normal human plasma and ATTR amyloidosis plasma by ELISA (Table 7). Page 111 of 135 11855652v1Docket No.: 2023P04092WO Table 7: Biomarker Raw absorbance 450nm values for ATTRwt and Normal human EDTA Plasma (Part 1) Raw Absorbance at 450nm FHL1 ILK TGFB1 LIMS-1 0 0 0 1 1 0 3 3 5 4 2 7 0 8 0 1 7 3 0 3 5 0 5 7 3 8 6 2 1age o 11855652v1Docket No.: 2023P04092WO 0.40 0.22 Avg 0.04 0.05 0.06 0.08 2.11 1.94 0.41 0.28 N = Normal human EDTA plasma CM = ATTRwt human EDTA plasma Table 7: Biomarker Raw absorbance 450nm values for ATTRwt and Normal human EDTA Plasma (Part 2) Raw Absorbance at 450nm C3 TIMP-2 PKM2 SAA 2 3 9 2 6 5 7 5 0 0 3 0 2 6 5 5 2 9 9 6 3 8 8 7 9Page 113 of 135 11855652v1Docket No.: 2023P04092WO 0.04 0.02 0.91 0.75 0.93 0.59 1.77 2.41 0.12 0.04 1.31 0.57 1.16 0.39 0.55 0.31 3 8 3 4

[0537] Signal-to-noise (S / N) ratios (calculated as raw absorbance value of the cardiomyopathy (CM) or normal (N) samples divided by the average of the normal samples for each biomarker) were determined for each biomarker assay. A cutoff value was set as the highest S / N value of the normal plasma samples for each biomarker assay, with the exception of assays for ILK and PKM, where the highest value normal sample from each was excluded for being greater than the average + (4 x standard deviation).

[0538] Using S / N values and comparing these with a cutoff value for each biomarker, each sample (i.e. CM or N) was scored as positive (> cutoff) or negative (< cutoff) with 1 or 0, respectively (Table 8). Performance ratios for each biomarker, including sensitivity, specificity and accuracy, were also determined by the following calculations: % sensitivity = # of positively scored CM samples / total CM samples; % specificity = (1 - # of positively scored Normal samples) x 100; % accuracy = (# of reported positive CM samples + # reported negative Normal samples) / total samples (CM and N) tested x 100. Table 8: Scored values for CM and Normal samples and performance summary Scored CM samplesPage 114 of 135 11855652v1Docket No.: 2023P04092WO 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 1 0 1age o 11855652v1Docket No.: 2023P04092WO 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Reported PositiveReported NegativeSensitivitySpecificityAccuracy

[0539] Performance scores for various biomarkers tested showed high sensitivity, specificity, and accuracy for two individual markers, TIMP2 (72% sensitivity, 100% specificity, 87% accuracy) and PKM (83% sensitivity, 97% specificity, 90% accuracy). When the two markers, TIMP2 and PKM are combined across the same set of samples, sensitivity, specificity, and accuracy significantly improves (93% sensitivity, 97% specificity, 95% accuracy), suggesting that TIMP2 and PKM combined can serve as sensitive and specific markers for the identification of ATTRwt plasma. Page 116 of 135 11855652v1Docket No.: 2023P04092WO Example 8: Calculation of biomarker threshold values

[0540] This example demonstrates identification of threshold values for biomarkers of ATTR amyloidosis.

[0541] A total of N=30 diagnosed TTR-CM patient plasma samples and N=30 normal donor samples were assessed for expression of PKM, TIMP2, LIMS1, C3 and A11. Sensitivity, specificity, and accuracy 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 retaining approximately 100% specificity (Table 9 and FIG.10).

[0542] The disclosed methods may be performed manually or may be automated. For example, the disclosed methods may use ADVIA CENTAUR ® immunoassay system or ATELLICA @TM. For example, a system may be automated to perform the following actions: 1. the samples were dispensed into cuvettes. 2. The buffer containing the solid support with anti-human IgM antibody bound thereto is dispensed and incubated, for example, at 37 ℃ for 18.25 minutes. 3. The solid support is detached / separated, the cuvette is aspirated and washed with washing reagent. 4. The buffer containing the chemiluminescent-tagged NS1 antigen is dispensed and incubated, for example, at 37 ℃ for 18 minutes. 5. The solid support is detached / separated, the cuvette is aspirated and washed with washing reagent. 6. Dispensing a chemiluminescent reagent to initiate a chemiluminescent reaction. 7. Reporting the result according to the option selected by the user.

[0543] In a particular embodiment, the disclosed immunoassays may be suitable for use on the ADVIA CENTAUR immunoassay system (Siemens Healthcare, AG), and / or the ADVIA CENTAUR immunoassay system may be automated to perform the actions described above. Table 9: ROC curve data for optimized detection cutoffs of biomarkers LIMS C3 TIMP2 A11Page 117 of 135 11855652v1Docket No.: 2023P04092WO yti yti yti yti yti yti yti ytfcfo e if vuliic tifcsfo e if vuliic tifcsfo e if vuliic tcifsfo e i ifvuliic tisExample 9: Patient Assessment

[0544] A patient at risk of TTR-CM is tested for a level of each ATTR Biomarker in a combinations of ATTR Biomarkers, as disclosed herein, e.g., in Tables 2-5. A blood sample is taken from the patient and levels are measured using a Siemens Atellica® system or the Siemens Advia Centaur® system to obtain an ATTR Biomarker profile for the patient. As needed, the patient's demographic factors and imaging-based biomarkers are considered. The patient’s ATTR Biomarker profile is used to calculate an ATTR Biomarker score. Based on the ATTR Biomarker score, patient is categorized as having TTR-CM. Administration of a TTR stabilizer or other cardiac medication is recommended. Page 118 of 135 11855652v1Docket No.: 2023P04092WO Example 10: Patient Assessment

[0545] A patient at risk of TTR-CM is tested for a level of each ATTR Biomarker in a combinations of ATTR Biomarkers, as disclosed herein, e.g., in Tables 2-5. A blood sample is taken from the patient and levels are measured using a Siemens Atellica® system or the Siemens Advia Centaur® system to obtain an ATTR Biomarker profile for the patient. As needed, the patient's demographic factors and imaging-based biomarkers are considered. The patient’s ATTR Biomarker profile is compared to a reference ATTR Biomarker profile. Based on the comparison, patient is categorized as having TTR-CM. Administration of a TTR stabilizer or other cardiac medication is recommended. Example 11: Patient Assessment

[0546] A patient at risk of TTR-CM is tested for a level of each ATTR Biomarker in a combinations of ATTR Biomarkers, as disclosed herein, e.g., in Tables 2-5. A blood sample is taken from the patient and levels are measured using a Siemens Atellica® system or the Siemens Advia Centaur® system to obtain an ATTR Biomarker profile for the patient. As needed, the patient's demographic factors and imaging-based biomarkers are considered. The patient’s ATTR Biomarker profile is used to calculate an ATTR Biomarker score. Based on the ATTR Biomarker score, patient is categorized as being at risk of having TTR-CM. Further cardiomyopathy testing is recommended. Example 12: Patient Assessment

[0547] A patient at risk of TTR-CM is tested for a level of each ATTR Biomarker in a combinations of ATTR Biomarkers, as disclosed herein, e.g., in Tables 2-5. A blood sample is taken from the patient and levels are measured using a Siemens Atellica® system or the Siemens Advia Centaur® system to obtain an ATTR Biomarker profile for the patient. As needed, the patient's demographic factors and imaging-based biomarkers are considered. The patient’s ATTR Biomarker profile is compared to a reference ATTR Biomarker profile. Based on the comparison, patient is categorized as being at risk of having TTR-CM. Further cardiomyopathy testing is recommended. Example 13: Population Assessment

[0548] 1000 patients at risk of TTR-CM are tested for a level of each ATTR Biomarker in a combinations of ATTR Biomarkers, as disclosed herein, e.g., in Tables 2-5. A Page 119 of 135 11855652v1Docket No.: 2023P04092WO blood sample is taken from the patient and levels are measured using a Siemens Atellica® system or the Siemens Advia Centaur® system to obtain an ATTR Biomarker profile for the patient. As needed, the patient's demographic factors and imaging-based biomarkers are considered. The ATTR Biomarker profiles of the patients are used to calculate respective ATTR Biomarker scores. A subset of patients is categorized as being at risk of having TTR- CM. Further cardiomyopathy testing is recommended. Example 14: Clinical Utility of Noninvasive, Accurate TTR-CM

[0549] This Example demonstrates the benefit to the public of offering a noninvasive TTR-CM assay that is both sensitive and specific, and is easily complied with. This example demonstrates that the reluctance to undergo evasive cardiomyopathy testing is common, and that it can have severe health consequences if TTR-CM is not detected early.

[0550] A patient demonstrated symptoms of TTR-CM but refused a heart biopsy. The patient's primary care physician ordered an assessment of the patient’s level of each ATTR Biomarker in a combinations of ATTR Biomarkers, as disclosed herein, e.g., in Tables 2-5. The results indicated that the patient was at a high risk for TTR-CM. The patient consulted with their physician and was convinced to schedule further cardiomyopathy testing, which reveal evidence of TTR-CM. Example 15: Machine-Learned Algorithm Performance Results

[0551] Machine-learned algorithms were trained and assessed for performance in classifying subjects as having TTR-CM or not. ATTR Biomarker profiles that included measured levels of various ATTR Biomarkers were obtained for a population of subjects of varying race / ethnicity, sex, age, and geographic location. The population included subjects diagnosed as having TTR-CM, subjects having heart failure with preserved ejection fraction non-TTR-CM, and normal subjects. Bootstrap forest machine-learned algorithms were trained and evaluated utilizing k-fold cross-validation (k = 5). Different ATTR Biomarkers, and in some cases also one or more demographic factors, were considered in different algorithms. Results are shown in FIGs.15-21 as box-and-whisker plots (with 95% confidence interval diamonds shown) of area under curve (AUC) of a receiver operating characteristic (ROC) curve on the y-axis and the particular combination of ATTR Biomarkers, and, in some cases, demographic factor(s) arranged along the x-axis. Misclassification rate is shown by coloration of the individual plot for each combination Page 120 of 135 11855652v1Docket No.: 2023P04092WO (legend is inset). In these figures, TNIH is a form of TnI and PBNP is used as an alternative shorthand for NT-proBNP. FIG.22 provides information regarding the patient cohorts used in the analysis of how various ATTR Biomarkers performed in the detection of TTR-CM using various machine-learned models. Data for the patient cohorts were also used for training of the machine-learned models. The patient cohorts include subjects with TTR-CM, subjects with non-TTR-CM heart failure with preserved ejection fraction (HF-PEF), and normal control patients.

[0552] FIG.15 illustrates that, averaged across the training folds, three-biomarker- based machine-learned algorithms performed better than two-biomarker-based machine- learned algorithms with an appreciable difference in performance between the worst three biomarker algorithm and the best two biomarker algorithm. All of the algorithms used TnI as an ATTR Biomarker for classification. Algorithms that used both TnI and RBP4 had the highest performance.

[0553] FIG.16 illustrates that, averaged across the validation folds, most of the three- biomarker-based machine-learned algorithms performed better than the two-biomarker-based machine-learned algorithms. An exception was the average AUC for an algorithm using TnI and RBP4, though this algorithm had a relatively high misclassification rate. In general, misclassification rates were consistently better for the three-biomarker-based algorithms than the two-biomarker-based algorithms.

[0554] FIG.17 illustrates performance averaged across both training and validation folds. As can be seen, three-biomarker-based algorithms generally performed better than two-biomarker-based algorithms.

[0555] FIG.18 illustrates that using machine-learned algorithms that consider age and sex as demographic factors along with ATTR Biomarkers improves performance of the algorithm. Algorithm performance is improved for both two-biomarker-based algorithms and three-biomarker-based algorithms. The particular examples shown use TnI and RBP4 and TnI, RBP4, and NT-proBNP. Performance is improved both when measured by AUC and misclassification rate, for both two-biomarker-based and three-biomarker-based algorithms.

[0556] FIG.19 illustrates the surprising result that when a machine-learned algorithm also considers one or more demographic factors, in this example age and sex, then performance of two-biomarker-based algorithms can be comparable to performance of three- biomarker-based algorithms. The particular example of this phenomenon shown is that an algorithm that considers TnI, RBP4, age, and sex performs comparably to ones that further Page 121 of 135 11855652v1Docket No.: 2023P04092WO consider TIMP2, DCN, and NT-proBNP. Notably, misclassification rate of an algorithm that considers NT-proBNP and not RBP4 remains relatively high, even when also considering age and sex, though AUC is similar to those that consider RBP4.

[0557] FIGs.20 and 21 illustrate results of further validation of ones of the bootstrap forest machine-learned algorithms whose performance is summarized in FIGs.15-19. Further evaluation was performed on the algorithms generated across 5 folds of cross- validation utilizing the stratified holdback test set (20% of total) that was previously sequestered from analysis. Average AUC was significantly higher for the TnI, RBP4, age, and sex combination algorithm. With an optimized detection cutoff favoring sensitivity performance (p≥0.060), the algorithms performed very similarly. Sensitivity and specificity of >80% were achieved for both combinations: TnI, RBP4, age, and sex, with NT-proBNP and without NT-proBNP. Example 16: Comparative Predictor Performance

[0558] Machine-learned algorithms were trained and assessed for performance in determining whether subjects have TTR-CM or not, as discussed in the prior example. Performance was compared to other biomarkers both for performance as a predictor for whether a subject has TTR-CM or not, classification between categorical TTR-CM, non- TTR-CM with HF-PEF, and normal, and for performance as a predictor for New York Heart Association (NYHA) class. NYHA class is related to (e.g., a proxy for) overall heart function. A biomarker that is a good predictor for NYHA class is thus a sort of surrogate biomarker for heart function. The resulting performance comparisons are plotted in FIGs. 23-27. Looking at FIG.23, a bootstrap forest (BF) model that uses a combination of TnI (written as TNIH), NT-proBNP (written as PBNP), and RBP4 to determine a probability that a subject has TTR-CM (e.g., whether a subject is positive or negative for TTR-CM) has a performance that is over an order of magnitude better than other biomarkers, including imaging-based biomarkers (e.g., septal wall 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). FIGs.24-25 provide a more detail comparison of how informative the related individual biomarkers are in classifying for TTR-CM or not when considering age and sex as biomarkers (FIG.24) and when not (FIG.25). Looking at FIG.26, the best-performing BF- model-based biomarker for determining a probability that a subject has TTR-CM or not also Page 122 of 135 11855652v1Docket No.: 2023P04092WO performs well in classifying between TTR-CM, non-TTR-CM with HF-PEF, and normal, though, for such classification, other biomarkers perform comparably. In contrast, and with reference to FIG.27, this BF model using the combination of TnI, NT-proBNP, and RBP4 shows relatively poor performance at predicting NYHA class as compared to the same other biomarkers shown in FIG.23. Therefore, the predictive power of this BF model is specific to TTR-CM and is not simply predictive of heart function generally, as characterized by NYHA class (where having TTR-CM is associated with poorer general heart function generally). REFERENCES

[0559] 1. Gertz, M.A., Benson, M.D., Dyck, P.J., Grogan, M. Coelho, T., Crus, M., Berk, J.L., Plante-Bordeneuve, V., Schmidt, H.H., Merlini, G. Diagnosis, Prognosis, and Therapy of Transthyretin Amyloidosis. JACC 66:2452-2466 (2015).

[0560] 2. Ashley 2004

[0561] 3. Krishnamurthy, R., Cheong, B., Muthypillai, R. Tools for cardiovascular magnetic resonance imaging. Current Cardiology Reviews, 9:185-190, 2013.

[0562] 4. Doltra, A., Amundsen, B.H., Gebker, R., Fleck, E., Kelle, S. Emerging concepts for myocardial late gadolinium enhancement MRI. Curr Cardiol Rev.9(3):185-90, 2013.

[0563] 5. Bokhari, S., Castano, A., Pozniakoff, T., Deslisle, S., Latif, F., Maurer, M.S., (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.

[0564] 6. Crotty, T.B. Li, C.Y., Edwards, W.D., Suman, V.J. Amyloidosis and endomyocardial biopsy: Correlation of extent and pattern of deposition with amyloid immunophenotype in 100 cases. Cardiovascular Pathology 4:39-42, 1995.

[0565] 7. Arvanitis, M. et al. Identification of Transthyretin Cardiac Amyloidosis Using Serum Retinol-Binding Protein 4 and a Clinical Prediction Model. JAMA Cardiol., 2017.

[0566] All publications and patents cited in this disclosure (including those listed above) are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference. EQUIVALENTS Page 123 of 135 11855652v1Docket No.: 2023P04092WO

[0567] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the disclosure described herein. The scope of the present disclosure is not intended to be limited to the above Description, but rather is as set forth in the following claims: Page 124 of 135 11855652v1

Claims

Docket No.: 2023P04092WO CLAIMS What is claimed is:

1. A method comprising: receiving, by a processor of a computing device, an ATTR Biomarker profile comprising data corresponding to levels of two or more (e.g., only two) ATTR Biomarkers in a sample from a subject; providing, by the processor, input to a machine-learned algorithm, the input comprising the ATTR Biomarker profile; and determining, by the processor, an ATTR Biomarker score for the subject with the machine-learned algorithm based on the input, wherein the two or more ATTR Biomarkers comprise (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal-pro hormone B-type natriuretic peptide (NT-proBNP), (v) retinol binding protein 4 (RBP4), (vi) tissue inhibitor of metalloproteinase 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

2. A method comprising: receiving, by a processor of a computing device, an ATTR Biomarker profile comprising data corresponding to levels of two or more (e.g., only two) ATTR Biomarkers in a sample from a subject; providing, by the processor, input to a machine-learned algorithm, the input comprising the ATTR Biomarker profile; and determining, by the processor, whether the subject is at risk of or has (e.g., is suffering from) transthyretin amyloid cardiomyopathy (TTR-CM) with the machine-learned algorithm based on the input, wherein the two or more ATTR Biomarkers comprise (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal-pro hormone B-type natriuretic peptide (NT-proBNP), (v) retinol binding protein 4 (RBP4), (vi) tissue inhibitor of metalloproteinase 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

3. The method of claim 2, wherein determining whether the subject is at risk of or has (e.g., is suffering from) transthyretin amyloid cardiomyopathy (TTR-CM) with the machine- Page 125 of 135 11855652v1Docket No.: 2023P04092WO learned algorithm comprises determining, by the processor, a probability that the subject has TTR-CM.

3. The method of claim 1 or claim 2, wherein the two or more ATTR Biomarkers comprise TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof.

4. The method of any one of claims 1-3, wherein the two or more ATTR Biomarkers comprises TnI and RBP4.

5. The method of claim 4, wherein the two or more ATTR Biomarkers further comprises NT-proBNP, TIMP2, or both NT-proBNP and TIMP2.

6. The method of any one of claims 1-5, wherein the two or more ATTR Biomarkers is three or more ATTR Biomarkers.

7. The method of any one of claims 1-6, wherein the two or more ATTR Biomarkers is two ATTR Biomarkers or three ATTR Biomarkers.

8. The method of any one of claims 1-6, wherein the two or more ATTR Biomarkers is four ATTR Biomarkers.

9. The method of any one of claims 1-8, comprising: receiving, by the processor, data corresponding to one or more demographic factors for the subject; and providing, by the processor, the input to the machine-learned algorithm, wherein the input comprises the data corresponding to the one or more demographic factors.

10. The method of claim 9, wherein the one or more demographic factors comprise sex, age, or both sex and age.

11. The method of any one of claims 1-10, comprising: receiving, by the processor, data corresponding to one or more imaging-based biomarkers for the subject; and Page 126 of 135 11855652v1Docket No.: 2023P04092WO providing, by the processor, the input to the machine-learned algorithm, wherein the input comprises the data corresponding to the imaging-based biomarkers.

12. The method of claim 11, wherein the one or more imaging-based biomarkers comprises septal wall thickness (e.g., left ventricle septal wall thickness), posterior wall thickness, or ejection fraction.

13. The method of claim 11 or claim 12, comprising determining, by the processor, the data corresponding to the one or more imaging-based biomarkers from one or more images.

14. The method of claim 13, wherein determining the data corresponding to the one or more imaging-based biomarkers from one or more images comprises determining, by the processor, a measurement from the one or more images [e.g., by performing, by the processor, a pattern recognition on the one or more images and / or segmenting and classifying, by the processor, one or more features from the one or more images with a (e.g., the) machine-learned algorithm].

15. The method of any one of claims 1-14, wherein the machine-learned algorithm is or is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, or a support vector machines methodology.

16. The method of any one of claims 1-15, wherein the machine-learned algorithm is a bootstrap forest algorithm.

17. The method of any one of claims 1-16, wherein the machine-learned algorithm is or is derived from a decision tree methodology.

18. The method of claim 17, wherein the machine-learned algorithm, wherein the decision tree methodology is a bagged, boosted, or additive tree methodology (e.g., wherein the machine-learned algorithm comprises at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees). Page 127 of 135 11855652v1Docket No.: 2023P04092WO 19. The method of any one of claims 1-18, wherein the ATTR Biomarker score is an output from the machine-learned algorithm (e.g., comprising outputting, by the processor, the ATTR Biomarker score from the machine-learned algorithm).

20. The method of any one of claims 1-19, further comprising using the ATTR Biomarker score to determine if a subject from which the sample was obtained is at risk of or suffering from transthyretin amyloid cardiomyopathy (TTR-CM).

21. The method of any one of claims 1-20, comprising determining, by the processor, if the subject is at risk of or has (e.g., is suffering from) transthyretin amyloid cardiomyopathy (TTR-CM) with the machine-learned algorithm [e.g., wherein an output of the machine- learned algorithm is a determination (e.g., a probability) of whether the subject is at risk of or has (e.g., is suffering from) TTR-CM].

22. The method of any one of claims 1-21, comprising classifying, by the processor, the subject as having or not having TTR-CM with the machine-learned algorithm.

23. The method of any one of claims 1-22, wherein the machine-learned algorithm is a classifier for TTR-CM.

24. The method of claim 22 or claim 23, wherein the classifier or the classifying has a sensitivity and a specificity each of more than 80% (e.g., at least one or both of more than 90%).

25. The method of any one of claims 1-24, wherein the subject is a human subject.

26. The method of any one of claims 1-25, wherein the sample comprises blood, serum, plasma, or cardiac tissue.

27. The method of any one of claims 1-26, wherein the two or more ATTR Biomarkers do not comprise PKM1. Page 128 of 135 11855652v1Docket No.: 2023P04092WO 28. The method of any one of claims 1-27, wherein the two or more ATTR Biomarkers do not comprise PKM2.

29. The method of any one of claims 1-28, wherein the two or more ATTR Biomarkers do not comprise either PKM1 or PKM2.

30. The method of any one of claims 1-29, wherein the two or more ATTR Biomarkers do not comprise SMOC-2.

31. The method of any one of claims 1-30, wherein the two or more ATTR Biomarkers do not comprise DCN.

32. The method of any one of claims 1-31, wherein the two or more ATTR Biomarkers do not comprise either SMOC-2 or DCN.

33. A non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations comprising the method of any one of claims 1-32.

34. A system comprising a non-transitory computer readable medium and a processor, wherein the non-transitory computer readable medium has the machine-learned algorithm stored thereon and executable instructions that, when executed by the processor, perform operations comprising the method of any one of claims 1-32.

35. A method comprising: receiving, by a processor of a computing device, ATTR Biomarker profiles each comprising data corresponding to levels of two or more (e.g., only two) ATTR Biomarkers and, for each of the ATTR Biomarker profiles, corresponding data indicative of health status for the ATTR Biomarker profile; and training, by the processor, a machine-learning algorithm using the ATTR Biomarker profiles and the corresponding data indicative of health status, wherein the two or more ATTR Biomarkers comprise (i) troponin I (TnI), (ii) pyruvate kinase muscle isoform 1 (PKM1), (iii) pyruvate kinase muscle isoform 2 (PKM2), (iv) N-terminal-pro hormone B-type natriuretic peptide (NT-proBNP), (v) retinol binding Page 129 of 135 11855652v1Docket No.: 2023P04092WO protein 4 (RBP4), (vi) tissue inhibitor of metalloproteinase 2 (TIMP2), (vii) neurofilament light chain (NfL), or (viii) a combination thereof.

36. The method of claim 35, wherein the training comprises determining, by the processor, via the algorithm, one or more feature values for a corresponding one of the two or more ATTR Biomarkers based on the ATTR Biomarker profiles and / or a linear combination thereof.

37. The method of claim 36, wherein the training comprises determining, by the processor, via the algorithm, one or more decision rules in one or more decision trees based on the one or more feature values and / or the linear combination thereof.

38. The method of claim 36 or claim 37, wherein each of the one or more feature values corresponds to a level of the corresponding one of the two or more ATTR Biomarkers.

39. The method of any one of claims 35-38, wherein the training comprises determining, by the processor, via the algorithm, one or more decision rules based on the ATTR Biomarker profiles.

40. The method of any one of claims 35-39, wherein at least two of the ATTR Biomarker profiles are for different subjects.

41. The method of any one of claims 35-40, wherein at least two of the ATTR Biomarker profiles are for a same subject at different times.

42. The method of any one of claims 35-41, wherein the health status is a probability that a subject has TTR-CM, whether or not a subject is or is not at risk of TTR-CM, or whether or not a subject does or does not have TTR-CM (e.g., is healthy, not diagnosed with TTR-CM, or diagnosed with TTR-CM).

43. The method of any one of claims 35-42, wherein the corresponding data indicative of health status is a probability or a score (e.g., an ATTR Biomarker score). Page 130 of 135 11855652v1Docket No.: 2023P04092WO 44. The method of any one of claims 35-43, wherein the ATTR Biomarker profiles are labelled with the corresponding data.

45. The method of any one of claims 35-44, wherein the machine-learning algorithm is derived from a decision tree methodology, a neural boosted methodology, a bootstrap forest methodology, a boosted tree methodology, or a support vector machines methodology.

46. The method of any one of claims 35-45, wherein the machine-learned algorithm is a bootstrap forest algorithm.

47. The method of any one of claims 35-46, wherein the machine-learned algorithm is derived from a decision tree methodology.

48. The method of claim 47, wherein the decision tree methodology is a bagged, boosted, or additive tree methodology (e.g., wherein the machine-learned algorithm comprises at least 25 decision trees, at least 50 decision trees, or at least 100 decision trees).

49. The method of any one of claims 35-48, wherein the two or more ATTR Biomarkers is three or more ATTR Biomarkers.

50. The method of any one of claims 35-49, wherein the two or more ATTR Biomarkers is two ATTR Biomarkers or three ATTR Biomarkers.

51. The method of any one of claims 35-50, wherein the two or more ATTR Biomarkers is four ATTR Biomarkers.

52. The method of any one of claims 35-51, comprising: receiving, by the processor, data corresponding to one or more demographic factors for each subject corresponding to the ATTR Biomarker profiles; and training, by the processor, the machine-learning algorithm based on the data corresponding to the one or more demographic factors in combination with the ATTR Biomarker profiles. Page 131 of 135 11855652v1Docket No.: 2023P04092WO 53. The method of claim 52, wherein the one or more demographic factors comprises age, sex, or both age and sex.

54. The method of any one of claims 35-53, comprising: receiving, by the processor, data corresponding to one or more imaging-based biomarkers for each subject corresponding to the ATTR Biomarker profiles; and training, by the processor, the machine-learning algorithm based on the data corresponding to the one or more imaging-based biomarkers in combination with the ATTR Biomarker profiles (e.g., and the data corresponding to the one or more demographic factors).

55. The method of claim 54, wherein the one or more imaging-based biomarkers comprises septal wall thickness (e.g., left ventricle septal wall thickness), posterior wall thickness, or ejection fraction.

56. The method of any one of claims 35-55, wherein the two or more ATTR Biomarkers comprise TnI, PKM1, PKM2, NT-proBNP, RBP4, TIMP2, NfL, or a combination thereof.

57. The method of any one of claims 35-56, wherein the two or more ATTR Biomarkers comprises TnI and RBP4.

58. The method of claim 57, wherein the two or more ATTR Biomarkers further comprises NT-proBNP, TIMP2, or both NT-proBNP and TIMP2.

59. The method of any one of claims 35-58, wherein, after the training, the machine- learning algorithm is a machine-learned algorithm that is a classifier for TTR-CM.

60. The method of claim 59, wherein the classifier has a sensitivity and a specificity each of more than 80% (e.g., at least one or both of more than 90%).

61. A non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations comprising the method of any one of claims 35-60. Page 132 of 135 11855652v1Docket No.: 2023P04092WO 62. A system comprising a non-transitory computer readable medium and a processor, wherein the non-transitory computer readable medium has the machine-learning algorithm stored thereon and executable instructions that, when executed by the processor, perform operations comprising the method of any one of claims 35-61.

63. A method comprising: receiving, by a processor of a computing device, data corresponding to two or more biomarkers for a subject; providing, by the processor, input to a machine-learned algorithm, the input comprising the data; and determining, by the processor, a health status for the subject with the machine-learned algorithm based on the input, wherein the machine-learned algorithm has been derived from a decision tree methodology.

64. A method comprising: receiving, by a processor of a computing device, training input comprising data corresponding to two or more biomarkers and corresponding data indicative of health status; and training, by the processor, a machine-learning algorithm using the input, wherein the machine-learning algorithm is based on a decision tree methodology.

65. The method of claim 63 or claim 64, wherein the decision tree methodology is a bootstrap forest methodology.

66. The method of any one of claims 63-65, wherein the data corresponding to two or more biomarkers is data corresponding to levels of the two or more biomarkers (e.g., for the subject, or, if used for training, for at least one subject).

67. The method of any one of claims 63-66, wherein the health status comprises at least one of (e.g., two of, or all three of) (i) a state (e.g., stage) of a disease, disorder, or condition for the subject, (ii) whether the subject has or does not have a disease, disorder, or condition, and (iii) a probability that the subject has a disease, disorder or condition. Page 133 of 135 11855652v1Docket No.: 2023P04092WO 68. The method of claim 67, wherein the disease, disorder, or condition is a heart disease, disorder, or condition.

69. The method of any one of claims 63-68, wherein the data corresponding to the two or more biomarkers comprises (e.g., is) laboratory-derived data (e.g., has been derived from one or more assays).

70. The method of any one of claims 63-69, wherein the two or more biomarkers comprise a protein biomarker, an enzyme biomarker, a peptide biomarker, or an intermediate filament biomarker (e.g., a neurofilament biomarker) [e.g., wherein each of the two or more biomarkers is a protein biomarker, an enzyme biomarker, a peptide biomarker, or an intermediate filament biomarker (e.g., a neurofilament biomarker)].

71. The method of any one of claims 63-70, wherein the input further comprises data corresponding to one or more demographic factors (e.g., for the subject, or, if used for training, for at least one subject to which the data corresponding to two or more biomarkers corresponds).

72. The method of any one of claims 63-71, wherein the input further comprises data corresponding to one or more imaging-based biomarkers (e.g., for the subject, or, if used for training, for at least one subject to which the data corresponding to two or more biomarkers corresponds).

73. A non-transitory computer readable medium containing executable instructions that when executed cause a processor to perform operations comprising the method of any one of claims 57-72.

74. A system comprising a non-transitory computer readable medium and a processor, wherein the non-transitory computer readable medium has the machine-learned or machine- learning algorithm stored thereon and executable instructions that, when executed by the processor, perform operations comprising the method of any one of claims 57-72. Page 134 of 135 11855652v1

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