Multi-parameter metabolic vulnerability index evaluation

Abstract

To provide risk calculators comprising factors that can better predict or assess a person's risk of premature death from any cause.SOLUTION: Disclosed are methods and systems for determining a subject's metabolic vulnerability index (MVX) score using at least one defined mathematical model of risk. The methods comprise evaluating various biomarkers to distinguish various health risks. In one embodiment, the method comprises evaluating biomarkers to determine a relative risk of premature all-cause mortality. The model may include NMR-derived measurements of GlycA, S-HDLP, branched chain amino acids (BCAAs), ketone bodies, total serum protein and citrate in at least one biosample of the subject.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This patent application claims the benefit of U.S. Provisional Patent Application No. 62 / 555,421, filed Sep. 7, 2017, entitled METHODS AND SYSTEMS FOR MEASURING PVX, and U.S. Provisional Patent Application No. 62 / 619,497, filed Jan. 19, 2018, entitled MULTI-PARAMETER METABOLIC VULNERABILITY INDEX EVALUATIONS, the entire contents of which are incorporated herein by reference, including all text, tables, and figures.

[0002] The present disclosure relates generally to the analysis of in vitro biological samples. The present disclosure is believed to be particularly suited to NMR analysis of in vitro biological samples. [Background technology]

[0003] Cardiovascular disease (CVD)-related death is considered the most serious outcome of long-term exposure to "traditional" CVD risk factors. These risk factors, which have been extensively studied and documented in the Framingham Offspring Study, include age, sex, blood pressure, smoking habits, and cholesterol levels. See, et al., 2003, 111:1311-1320 (2003). These factors have long been used to differentiate normative populations and populations at risk of suffering from CVD-related events.

[0004] Typically, a patient's overall risk of coronary heart disease (CHD) and / or CVD is initially assessed based on measurements of the cholesterol content of the patient's LDL and HDL particles, designated as LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C), rather than their number of these particles. Treatment decisions are often made with the goal of reducing "bad" cholesterol (LDL-C) and / or increasing "good" cholesterol (HDL-C), with a secondary focus on optimizing modifiable Framingham risk factors.

[0005] These risk calculators based on the Framingham factors may generally predict the incidence of CVD-related events. However, these risk calculators were developed to predict a composite CVD outcome consisting of both non-fatal and fatal CVD events. Furthermore, clinical trials usually use a composite CVD outcome as the study endpoint. Recent analyses of clinical trials have shown heterogeneous drug effects on the fatal and non-fatal components of a composite CVD endpoint. For example, LDL-lowering drugs in some trials reduced the incidence of a composite CVD endpoint and the non-fatal event component of that endpoint, but the same drugs failed to reduce the fatal CVD event component of the combined endpoint or all-cause mortality. Conversely, some diabetes drugs reduced the composite CVD endpoint by only reducing CVD death and all-cause mortality, but not non-fatal CVD events. The results of these studies indicate that determinants of fatal and non-fatal CVD events may differ depending on the potentially problematic composite CVD outcome.

[0006] Conventional CVD risk calculators fail to take into account the complex pathophysiology of various disease states, and some conventional CVD risk factors are often paradoxically associated with survival in malnourished, chronically ill patients. For example, in patients with chronic heart and kidney disease, those with higher body mass index, serum cholesterol levels, and blood pressure have increased survival rates. See, e.g., "Clinical Risk Assessment of CVD in Patients with Chronic Heart and Kidney Disease," Journal of Clinical Nutrition, 1999, 144:1311-1323 (2012). This calls into question the clinical reliability of the Framingham Risk Factors to determine a patient's risk of morbidity and mortality, independent of disease state.

[0007] Given the fact that clinicians currently rely on traditional CVD risk factors derived from composite outcome-based risk calculators to predict patient prognosis, there remains a need for risk calculators composed of factors that can better predict or assess a person's risk of premature death from any cause, as opposed to the risk of suffering a non-fatal event. [Prior art documents] [Non-patent literature]

[0008] [Non-Patent Document 1] Wilson et al., "Impact of National Guidelines for Cholesterol Risk Factor Screening, The Framingham Offspring Study," Journal of the American Medical Association (JAMA), 1989, 262, pp. 41-44. [Non-Patent Document 2] Kalanttar Zadeh et al., "Reverse Epidemiology of Conventional Cardiovascular Risk Factors in Patients with Chronic Heart Failure," Journal of the American College of Cardiology, 2004, 42, pp. 1439-44 Summary of the Invention [Means for solving the problem]

[0009] Embodiments of the present disclosure include methods and systems for assessing a person's relative risk of early all-cause mortality and / or providing mortality risk stratification by evaluating NMR spectra of in vitro plasma or serum patient samples using a defined multi-factor risk assessment model to determine a patient's Metabolic Vulnerability Index (MVX) score.

[0010] The MVX score may be calculated using multiple NMR-derived measurements, including a subclass of high density lipoprotein (HDL), such as small HDL particles (S-HDLP), an inflammatory indicator GlycA, one or more branched chain amino acids (valine, leucine, and / or isoleucine), one or more ketone bodies (beta-hydroxybutyrate, acetoacetate, and / or acetone), and optionally the amount of citrate and / or serum protein. In one embodiment, the HDLP subclass is small HDL particles (S-HDLP).

[0011] Embodiments of the present disclosure include a method of determining a level of an indicator associated with a risk of premature death in a person. The method may include obtaining a sample from the person and measuring GlycA, at least one subclass of high density lipoprotein particles (HDLP), at least one branched chain amino acid (BCAA), and at least one ketone body. In one embodiment, the high density lipoprotein particles are small HDL particles (S-HDLP). In some embodiments, these measurements are used to generate an MVX score. In some embodiments, the MVX score is determined by using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the MVX value is determined by using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody

[0012] It should be noted that throughout this disclosure, empirical values ​​for A and β1-βn may vary depending on the model used. For example, β1 in the first formula for MVX (i.e., the formula without the term β3*(lnGlycA*lnS-HDLP)) is generally a different value than β1 in the second formula (i.e., the formula with the product term).

[0013] In some embodiments, the method includes measuring at least one of citrate and protein, at least one HDLP subclass, at least one BCAA, and at least one ketone body in addition to GlycA. In some embodiments, the measurements including at least one of citrate and serum protein are performed on subjects believed to be at high risk for CVD-related mortality. In some embodiments, the MVX value is determined using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein)

[0014] In some embodiments, the MVX value is defined as including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. In some embodiments, the measurements of at least GlycA and the at least one HDLP subclass are used to generate an Inflammation Index (INFX) value. In some embodiments, the INFX value is determined using the following model: INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)

[0015] In some embodiments, the measurements of at least one BCAA and at least one ketone body are used to generate a metabolic malnutrition index (MMX) value. In some embodiments, the MMX value is determined using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody This model can be expressed as MMX1 and may be calculated to be used for populations / subjects believed to be at low risk (e.g., have no known cardiovascular (CV) events), as discussed in detail herein.

[0016] In other embodiments, measurements of at least one BCAA, at least one ketone body, citrate, and protein are used to generate an alternative MMX value. For example, in some embodiments, the MMX value may be determined using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein Alternatively, the MMX value may be determined using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In such embodiments, MMX may be written as follows: MMX=β9*MMX1+β10*MMX2, where MMX1 is as above and MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein).

[0017] The measurements used to generate the MMX value, including citrate and protein measurements, are generally performed in subjects and / or populations considered to be at high risk for CVD-related mortality (e.g., subjects with a cardiovascular event or symptoms suggestive of underlying CVD).

[0018] Thus, in some embodiments, the metabolic vulnerability index may be written as: MVX = βi*INFX + βm*MMX To apply to a normal (i.e., low) risk patient population (e.g., those who have not had a known CV event), the metabolic vulnerability index can be written as MVX1=βi*INFX+βm*MMX1, where βi and βm may have unique values ​​depending on the model used. Conversely, to apply to a high risk patient population (e.g., those who have had a known CV event), the metabolic vulnerability index can be written as MVX=βi*INFX+βm*MMX, where βi and βm may have unique values ​​depending on the model used and MMX=β9*MMX1+β10*MMX2. In some cases, βm is the same for both the low risk and high risk models (see, e.g., FIG. 16).

[0019] For example, in some cases, MVX scores may be observed in high-risk subjects or populations as a way to monitor the subjects' overall health and risk for fatal CV events or death from other causes. Or, MVX scores may be observed in clinical trials of new drugs being conducted in high-risk populations as a way to monitor the efficacy of a drug. For low-risk subjects, clinicians may choose to monitor MVX1 values ​​as a means to assess general health and well-being. In certain embodiments, MVX1 values ​​may be used as a guide for lifestyle changes that promote heart health. Yet other embodiments are directed to a system. The system includes an NMR spectrometer for acquiring at least one NMR spectrum of an in vitro biological sample, and at least one processor in communication with the NMR spectrometer. At least one processor may be configured to determine a metabolic vulnerability index score based on at least one defined mathematical model of risk of premature death that may consider at least one HDL subclass element measure, at least one branched chain amino acid measure, at least one ketone body measure, GlycA measure, and optionally citrate and / or protein measures obtained from at least one of the subject's in vitro biological samples using at least one NMR spectrum. In one embodiment, the HDLP subclass is small HDLP (S-HDLP). In some embodiments, the processor may be configured to calculate an MVX score based on the measures of GlycA, at least one ketone body, at least one branched chain amino acid, and the at least one HDLP subclass using the following formula: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the processor may be configured to calculate the MVX score using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the processor may be configured to calculate the MVX score using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In some embodiments, the processor may be configured to calculate an MVX score that includes an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. Thus, in some embodiments, the processor may be configured to calculate an MVX score based on the following model: MVX = βi*INFX + βm*MMX In these embodiments, the processor may be configured to calculate the INFX value using the following model: INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) In some embodiments, the processor may be configured to calculate the MMX value using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody This model may be denoted as MMX1. In some embodiments, the processor may be configured to calculate MMX using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein In another embodiment, the processor may be configured to calculate MMX using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In such an embodiment, MMX may be written as follows: MMX=β9*MMX1+β10*MMX2, where MMX1 is as above, and MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein).

[0020] Yet another embodiment is directed to an NMR system. The system includes an NMR spectrometer, a flow probe in communication with the spectrometer, and at least one processor in communication with the spectrometer. The at least one processor may be configured to acquire (i) at least one NMR signal in a defined GlycA compatible region of an NMR spectrum associated with GlycA of a blood plasma or serum sample in the flow probe, (ii) at least one NMR signal in a defined ketone body compatible region of an NMR spectrum associated with a sample in the flow probe, (iii) at least one NMR signal in a defined BCAA compatible region of an NMR spectrum associated with a sample in the flow probe, and (iv) at least one NMR signal of an HDLP subclass parameter. The processor may be further configured to calculate measurements of GlycA, at least one ketone body, at least one branched chain amino acid, and the HDLP subclass parameter. The system may be further configured to calculate an MVX score using a defined mathematical model of risk of all-cause mortality using calculated measurements of GlycA, at least one ketone body, at least one branched chain amino acid, at least one of the HDLP subclass parameters, and optionally serum protein and / or citrate. In one embodiment, the HDLP subclass is small HDLP (S-HDLP). In some embodiments, the system may be further configured to calculate an MVX score based on at least one measurement of GlycA, at least one ketone body, at least one branched chain amino acid, and the HDLP subclass using the following formula: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the system may be further configured to calculate the MVX score using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the system may be further configured to calculate the MVX score using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In some embodiments, the system may be further configured to calculate an MVX score that includes an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. Thus, in some embodiments, the system may be further configured to calculate an MVX score based on the following model: MVX = βi*INFX + βm*MMX In these embodiments, the system may be further configured to calculate the INFX value using the following model: INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) In some embodiments, the system may be further configured to calculate the MMX value using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody This model may be denoted as MMX1. In some embodiments, the system may be further configured to calculate MMX using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein In another embodiment, the system may be further configured to calculate MMX using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In such an embodiment, MMX may be written as follows: MMX=β9*MMX1+β10*MMX2, where MMX1 is as above, and MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein).

[0021] A further aspect of the present disclosure is a method for monitoring a patient to evaluate a treatment or determine whether the patient is at risk for premature death. The method may include programmatically providing at least one defined MVX mathematical model as disclosed herein, comprising a plurality of elements including at least one selected HDLP subclass, at least one branched chain amino acid, at least one ketone body, and GlycA, and any at least one protein or citrate derived NMR-derived measurement. The method may further include programmatically analyzing a spectrum comprising the NMR-derived measurement. The method may also include programmatically calculating an MVX score for each patient using the at least one defined model and the corresponding patient sample measurement, and evaluating at least one of (i) whether the MVX score is above a defined level of a population norm associated with an increased risk of all-cause mortality, and / or (ii) whether the metabolic vulnerability index increases or decreases over time in response to the treatment. In some embodiments, the MVX score may be calculated based on measurements of GlycA, at least one ketone body, at least one branched chain amino acid, and at least one HDLP subclass using the following formula: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the MVX score may be calculated by using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the MVX score may be calculated by using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In some embodiments, the calculation of the MVX score includes an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. Thus, in some embodiments, the calculation of the MVX score may be based on the following model: MVX = βi*INFX + βm*MMX In these embodiments, the calculation of the INFX value may use the following model: INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) In some embodiments, the calculation of the MMX value may use the following model: MMX=β4*lnBCAA+β5*lnKetoneBody This model may be denoted as MMX1. In some embodiments, the calculation of the MMX score may be the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein In another embodiment, the calculation of the MMX score may use the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In such an embodiment, MMX may be written as follows: MMX=β9*MMX1+β10*MMX2, where MMX1 is as above, and MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein).

[0022] Further features, advantages and details of the present disclosure will be understood by those skilled in the art upon reading the drawings and detailed description of the preferred embodiments below. Moreover, such description is merely exemplary of the present disclosure. Features described with respect to one embodiment may be incorporated with other embodiments, but are not limited thereto. That is, it will be understood that aspects of the present disclosure described with respect to one embodiment may be incorporated in another embodiment, even if not specifically described. That is, all embodiments and / or features of any embodiment may be combined in any manner and / or combination. These and other objects and / or aspects of the present disclosure are described in detail in the specification set forth below.

[0023] As will be appreciated by one of skill in the art upon consideration of this disclosure, embodiments of the present disclosure may include methods, systems, apparatus and / or computer program products, or combinations thereof. [Brief description of the drawings]

[0024] [Figure 1] 1 shows an overview of a method for measuring MVX according to one embodiment of the present disclosure. [Diagram 2] FIG. 1 shows a schematic diagram of a method for using MVX measurements according to an embodiment of the present disclosure. [Diagram 3] 1 is a graph showing cumulative mortality over 5 years for subgroups of CATHGEN participants stratified by MVX score according to an embodiment of the present disclosure. In total, 1263 participants died during the 5-year follow-up. [Figure 4]1 is a table showing the predictive power (X2) and statistical significance (p-value) of parameters, including those used in calculating INFX, MMX, and MVX, in a Cox proportional hazards prediction model for mortality in the CATHGEN study population, according to one embodiment of the present disclosure. During the 5-year follow-up, 1259 deaths occurred among 6936 CATHGEN participants. [Diagram 5] 2 shows two tables showing the predictive power and statistical significance of parameters in Cox prediction models for mortality during 5-year follow-up in the CATHGEN study. The left model includes the Inflammation Index (INFX) and Metabolic Malnutrition Index (MMX) parameters, and the right model includes the MVX parameter, each according to an embodiment of the present disclosure. [Figure 6] FIG. 1 shows two tables illustrating the predictive power and statistical significance of parameters, including MVX according to one embodiment of the present disclosure, in the Cox predictive model, showing mortality during a short-term follow-up of 1 year (left table) and a longer average follow-up of 7 years (right table) in the CATHGEN study population. [Figure 7] FIG. 1 is a table showing the predictive power provided by the likelihood ratio chi-squared statistic of Cox predictive models including MVX alone (top row) or MVX plus nine additional covariate parameters (bottom row) for mortality occurring during three follow-up periods in the CATHGEN study population, according to an embodiment of the present disclosure. [Figure 8] The left table is a graph showing cumulative mortality over 12 years of follow-up for female participants in the MESA study stratified by quintiles of MVX score, according to an embodiment of the present disclosure. Of the 3581 female MESA participants, 412 died during the 12-year follow-up period. The right table is a graph showing cumulative mortality over 12 years of follow-up for male participants in the MESA study stratified by quintiles of MVX score, according to an embodiment of the present disclosure. Of the 3198 male MESA participants, 554 died during the 12-year follow-up period. [Figure 9]1 is a table showing the predictive power and statistical significance of MVX determined according to one embodiment of the present disclosure and other parameters for non-CVD mortality and fatal and non-fatal components of the composite CVD outcome as assessed in a multinomial logistic regression model for MESA participants. [Figure 10] 1 is a table showing the predictive power and statistical significance of MVX determined according to one embodiment of the present disclosure and other parameters for the dual outcomes of congestive heart failure (CHF) and mortality as assessed in a multinomial logistic regression model for MESA participants. [Figure 11] 1 is a table showing the predictive power and statistical significance of MVX determined according to one embodiment of the present disclosure and other parameters for the dual outcomes of cancer and mortality as assessed in a multinomial logistic regression model for MESA participants. [Figure 12] 1 is a table showing the predictive power and statistical significance of MVX determined according to one embodiment of the present disclosure and other parameters for the dual outcomes of chronic kidney disease (CKD) and mortality as assessed in a multinomial logistic regression model for MESA participants. [Figure 13] 1 shows a schematic diagram of a system for an MVX evaluation module and / or circuit according to an embodiment of the present disclosure. [Figure 14] 1 shows a schematic diagram of an NMR spectrometer according to an embodiment of the present disclosure. [Figure 15] 1 shows a schematic diagram of a data processing system according to an embodiment of the present disclosure. [Figure 16] 1 shows examples of analyses used to develop INFX, MMX1, MMX2, and MMX scores broadly and in different patient populations according to embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] These and other objects and aspects of the present disclosure will be explained in detail in the specification set forth below.

[0026] The present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, which show embodiments of the present disclosure. However, the present disclosure may be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein, but rather these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0027] Like reference numbers refer to like elements throughout. In the drawings, some line thickness, layers, components, elements or features may be exaggerated for clarity. Dashed lines indicate optional features or operations unless otherwise specified.

[0028] A. Definitions and Terminology The terms used herein are used for the purpose of describing particular embodiments only and are not intended to limit the disclosure. In this specification, the singular as well as the plural are intended to be included unless the context clearly indicates otherwise. Furthermore, it will be understood that the terms "comprise" and / or "comprising" as used herein describe the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, phrases such as "between X and Y" and "between about X and Y" are understood to include X and Y. As used herein, phrases such as "between about X and Y" mean "between about X and about Y". As used herein, phrases such as "from about X to Y" mean "from about X to about Y".

[0029] Unless otherwise defined, all terms (including technical or scientific terms) used herein have the same meaning as generally understood by those skilled in the art to which this disclosure belongs. Terms as defined in commonly used dictionaries should be understood to have a meaning consistent with the meaning in the context of the specification and the relevant art, and it will be further understood that they should not be interpreted in an idealized or overly formal sense unless expressly defined herein. Well-known functions or structures may not be described for brevity and / or clarity.

[0030] Although various elements, components, regions, layers, and / or sections may be described herein with reference to "first," "second," and the like, it will be understood that these elements, components, regions, layers, and / or sections are not limited by these terms. These terms are only used to distinguish one element, component, region, layer, or section from another region, layer, or section. Thus, a first element, component, region, layer, or section discussed below can be referred to as a second element, component, region, layer, or section without departing from the teachings of the present disclosure. The order of operations (or steps) is not limited to the order shown in the claims or figures, unless otherwise specified.

[0031] The term "programmatically" means performed using operations directed to a computer program and / or software, processor, or ASIC. The term "electronic" and its derivatives refer to automated or semi-automated operations performed using devices having electrical circuits and / or modules, rather than through mental steps, and typically refer to operations that are programmatically performed. The terms "automated" and "automated" mean that an operation can be performed with minimal effort or without manual effort or input. The term "semi-automated" refers to allowing an operator to perform some inputs or activations, but the calculations and signal acquisition as well as the calculation of the concentration of the ionized components are performed electronically, typically programmatically, without the need for manual input.

[0032] The term "about" refers to + / - 10% (median or mean) of a specified value or number.

[0033] The term "patient" is used broadly to refer to an individual who provides a biological sample for testing or analysis.

[0034] The term "GlycA" refers to a biomarker derived from the measurement of a composite NMR signal from the carbohydrate moiety of an acute phase reactant glycoprotein containing an N-acetylglucosamine and / or N-acetylgalactosamine moiety, more specifically from the protons of two NAcGlc and two NAcGal methyl groups. The GlycA signal is centered at about 2.00 ppm in the plasma NMR spectrum at about 47°C (+ / -0.5°C). The peak position is independent of the spectrometer field, but can vary depending on the analysis temperature of the biological sample, and is not found in urine biological samples. Thus, if the temperature of the test sample changes, the GlycA peak area may also change. The GlycA NMR signal may include a subset of the NMR signal at a defined peak area to include only clinically relevant signal contributions, and may exclude protein contributions to the signal in this area, as discussed further below. See U.S. Patent Nos. 9,361,429, 9,470,771, and 9,792,410, the contents of which are incorporated by reference herein in their entireties as if fully recited.

[0035] As used herein, chemical shift positions (ppm) refer to NMR spectra that are internally or externally referenced. In one embodiment, the positions may be internally referenced to the CaEDTA signal at 2.519 ppm. Thus, the prominent peak positions discussed and / or claimed herein may vary depending on how the chemical shifts are generated or referenced, as is well known to those skilled in the art. Thus, for clarity, some of the peak positions described and / or claimed have equivalent different peak positions in other corresponding chemical shifts, as is well known to those skilled in the art.

[0036] The term "biological sample" refers to a human or animal in vitro blood, plasma, serum, CSF, saliva, lavage, sputum, or tissue sample. Embodiments of the present disclosure are particularly suited for evaluating human plasma or serum biological samples, particularly for GlycA (e.g., not found in urine). Plasma or serum samples may be fasting or non-fasting.

[0037] The terms "population norm" and "standard" refer to values ​​defined by large study(s) of average-risk patients, such as those enrolled in the Framingham Offspring Study or the Multi-Ethnic Study of Atherosclerosis (MESA), or high-risk patients, such as those enrolled in the Catheter Genetics (CATHGEN) Cardiac Catheterization Biorepository, or other studies with sufficient samples representative of the general population or target patient population. As used herein, the term low risk (or normal risk) refers to individuals who have not had a known cardiovascular event. Such populations are also known as primary prevention populations. For example, MESA is a low-risk population. As used herein, the term "high risk" refers to individuals who have had a known cardiovascular event. Such populations are also known as secondary prevention populations. For example, CATHGEN is a high-risk population. However, the present disclosure is not limited to population values ​​in MESA or CATHGEN, as currently defined normal or low-risk and high-risk population values ​​or levels may change over time. Thus, reference ranges associated with values ​​from defined populations in risk segments (e.g., quartiles or quintiles) may be provided and used to assess elevated or decreased levels and / or risk of having a clinical disease state.

[0038] The term "clinical disease state" is used broadly to include dangerous medical conditions indicating medical intervention, treatment, treatment adjustment, or the elimination and / or monitoring of a particular treatment (e.g., pharmaceutical agent). Identifying the likelihood of clinical disease allows the clinician to treat, delay the onset of symptoms, or prevent the onset of the disease accordingly.

[0039] As used herein, the term "NMR spectroscopy" refers to the analysis of protons ( 1 H) means to obtain data that allows measuring the respective parameters present in a biological sample, e.g., plasma or serum, using nuclear magnetic resonance spectroscopy. "Measure" and its derivatives refer to determining the level or concentration and / or for a particular lipoprotein subclass, measuring its average particle size. The term "NMR-derived" means that the associated measurements are calculated using NMR signals / spectra derived from one or more scans of the in vitro biological sample in an NMR spectrometer.

[0040] The term "downfield" refers to the area / location on the NMR spectrum to the left of a particular peak / location / point (higher on the ppm scale compared to the reference). Conversely, the term "upfield" refers to the area / location on the NMR spectrum to the right of a particular peak / location / point.

[0041] The terms "mathematical model" and "model" are used interchangeably and when used with "MVX", "metabolic vulnerability index", or "risk", refer to a statistical model of risk used to assess a subject's future risk of premature death, usually within 1-12 years. The risk model may be or may include any suitable model, including, but not limited to, one or more of a logistic regression model, a Cox proportional hazards regression model, a mixed model, or a hierarchical linear model. The risk model can provide a measure of risk based on the probability of premature death within a defined time frame, usually within 1-12 years. The risk model is particularly suitable for risk stratification for patients with "intermediate risk" associated with a slight to moderate chance of having a clinical event based on traditional risk factors. The MVX risk model can stratify the relative risk of premature death as measured by standard chi-square and / or p-value (the latter with a sufficiently representative study population).

[0042] The term "interaction parameter" refers to at least two different defined parameters combined as a (multiplied) product and / or ratio. Examples of interaction parameters include, but are not limited to, (S-HDLP)(GlycA) and (protein)(citrate).

[0043] The term "multi-index" refers to a multi-component biomarker.

[0044] The term "lipoprotein component" refers to a component in a mathematical risk model associated with a lipoprotein particle, including the size and / or concentration of one or more subclasses (subtypes) of lipoproteins. Lipoprotein components may include any of the subclasses of lipoprotein particles, concentrations, sizes, ratios and / or (integrated) mathematical results of lipoprotein parameters, and / or defined lipoprotein parameters, or lipoprotein subclass measurements in combination with other parameters, e.g., GlycA.

[0045] The term "HDLP" refers to a high density lipoprotein particle number measurement (e.g., HDLP count) that sums the particle concentrations of defined HDL subclasses. Total HDLP may be generated using a total high density lipoprotein particle measurement that sums the concentrations (μmol / L) of all HDL subclasses (which can be categorized into different size categories based on size, such as large, medium, and small) in a size range of about 7 nm (mean) to about 14 nm (mean), usually between 7.4 and 13.5 nm. In some embodiments, HDL may be identified as a number of discrete size components, such as seven subpopulations of different sizes (H1-H7) that range from the smallest HDLP size associated with H1 to the largest HDLP size associated with H7. In some embodiments, the defined subclass of HDL particles includes small HDL particles (S-HDLP). In some embodiments, S-HDLP may include an HDL particle subclass having a diameter of about 7.3 nm (mean) to about 9.0 nm (mean).

[0046] As used herein, an "unprocessed biological sample" refers to a biological sample that is not subjected to a process that physically or chemically changes the biological sample after it is obtained, unlike sample preparation for mass spectrometry (although buffers and diluents may be used). Thus, once the biological sample is obtained, components from the biological sample are not altered or removed. For example, once a serum biological sample is obtained, the serum is not subjected to a process that removes components from the serum. In some embodiments, an unprocessed biological sample is not subjected to a filtration and / or ultrafiltration process.

[0047] B. Methods for Determining a Subject's MVX Disclosed herein are methods and systems for determining a metabolic vulnerability index (MVX) of a subject. These methods include utilizing a multivariate model of defined biomarkers to predict the patient's chance of premature death. The methods include obtaining a sample from the subject, measuring GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), and at least one ketone body, and calculating a metabolic vulnerability index value for the subject based on the measurements. In one embodiment, the HDLP is small HDLP (S-HDLP). In some embodiments, the MVX score may be calculated based on the measurements of GlycA, at least one ketone body, at least one branched chain amino acid, and at least one HDLP subclass using the following formula: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the MVX score may be calculated by using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody In these embodiments, MVX values ​​may be determined for subjects considered to be at low risk for a cardiovascular event.

[0048] In some embodiments, the MVX value may include measurements of serum Protein and / or Citrate. In some embodiments, the MVX value may be determined using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In these embodiments, MVX values ​​may be determined for subjects believed to be at high risk for a cardiovascular event.

[0049] In another embodiment, the MVX value may include an Inflammation Index (INFX) and / or a Metabolic Malnutrition Index (MMX), as disclosed in more detail herein. Thus, in some embodiments, the calculation of the MVX score may be based on the following model: MVX = βi*INFX + βm*MMX In these embodiments, the calculation of the INFX value may use the following model: INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) In some embodiments, the calculation of the MMX value may use the following model: MMX=β4*lnBCAA+β5*lnKetoneBody This model may be denoted MMX1 and may be used for subjects deemed to be at low risk for cardiovascular disease related events. In some embodiments, the calculation of the MMX score may be the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein In another embodiment, the calculation of the MMX score may use the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In such an embodiment, MMX may be written as follows: MMX = β9 * MMX1 + β10 * MMX2, where MMX1 is as above and MMX2 = β6 * lnCitrate + β7 * lnProtein + β8 * (lnCitrate * lnProtein) In these embodiments, an MVX score may be determined for subjects considered to be at high risk for a cardiovascular disease related event.

[0050] In some embodiments, the BCAA can be at least one of leucine, isoleucine, or valine. In some embodiments, the ketone body can be at least one of acetone, acetoacetate, or beta-hydroxybutyrate. In some embodiments, the measurement is performed by NMR.

[0051] The metabolic vulnerability index can provide short-term (1 year) to long-term (12 years) premature death risk assessments. These risk assessments can generate MVX values ​​that are separate from traditional risk factors.

[0052] FIG. 1 shows a schematic diagram of one embodiment of a method for determining a subject's MVX value. The method may include an initial step of obtaining a nuclear magnetic resonance (NMR) spectrum of a plasma or serum sample obtained from the subject. A calculated line shape may then be generated for the sample, which is based on derived concentrations of lipoprotein and metabolite components potentially present in the sample (each derived concentration is a function of a reference spectrum and a calculated standard coefficient for that component). The method may conclude by determining the subject's MVX value based on the NMR spectrum of the sample. This method allows practitioners to identify the risk of premature death in a subject during routine and simple screening, and to initiate diagnosis and treatment of conditions associated with premature death, or to prevent the subject from receiving potentially harmful medications.

[0053] 2 is a schematic diagram showing one embodiment of the use of MVX to determine a patient's response to treatment. Multivariate models can be used to evaluate patients at the start of or during clinical trials, during treatment(s), to identify or monitor drug development and / or anti-inflammatory, anti-obesity, or other drug or nutritional therapeutic candidates.

[0054] In some embodiments, measurements are obtained by obtaining NMR signals of in vitro plasma or serum patient samples and determining NMR-derived concentration measurements of HDL particle subclasses, GlycA and / or multiple metabolic malnutrition biomarkers such as, for example, ketone bodies and BCAAs.

[0055] Embodiments of the present disclosure provide a patient's risk assessment of early all-cause mortality using a multi-parameter (multivariate) model of defined predictive biomarkers.

[0056] The multivariate risk assessment model can include at least one defined HDLP component, such as, but not limited to, S-HDLP, at least one ketone body, at least one defined branched chain amino acid, and GlycA. Additionally, the risk assessment model can include one or more of citrate and serum proteins.

[0057] The multivariate model may include at least one of NMR measurements of GlycA, branched chain amino acids, citrate, ketone bodies, total protein, and HDLP components (eg, subclasses) from the same NMR spectrum.

[0058] At least one HDLP component of the defined mathematical model of risk may include a first interaction parameter of a measure of GlycA multiplied by a concentration of a defined HDLP subpopulation. The defined subpopulation of HDL particles may include small HDL particles (S-HDLP). In some embodiments, S-HDLP may include a HDL particle subclass having a diameter of about 7.3 nm (mean) to about 9.0 nm (mean).

[0059] Embodiments of the present disclosure provide novel biomarkers that can stratify the risk of premature death for patients in both low-risk and high-risk categories.

[0060] The embodiments of the present disclosure may be particularly suitable for stratifying the risk of patients with similar traditional risk factors. In general, it is believed that the MVX score can be used to stratify the relative risk for premature death. The MVX score can stratify the risk of premature death of patients with the same age, sex, blood pressure and BMI, independent of these clinical factors.

[0061] Embodiments of the present disclosure can use multiple risk model parameters to assess a patient's risk of premature death within a 1-12 year time frame.

[0062] As discussed above, embodiments of the present disclosure may include bioindicators associated with inflammation (e.g., INFX) and metabolic malnutrition (e.g., MMX), as shown in Table 1. In some embodiments, an MVX score may be defined as including an inflammation index (INFX) value and a metabolic malnutrition index (MMX) value. In some embodiments of the present disclosure, a method for determining a level of an indicator associated with a relative risk of premature death in a subject may include obtaining a sample from the subject, measuring at least one of GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), at least one ketone body, and optionally citrate and serum protein, generating an inflammation index (INFX) value using the measurements of GlycA and the at least one HDLP subclass, generating at least one metabolic malnutrition index (MMX) value using the measurements of at least one BCAA, at least one ketone body, and optionally protein and citrate, and determining a metabolic vulnerability index (MVX) value based on the INFX value and the MMX value. [Table 1]

[0063] Inflammation can be associated with many different disease states, including but not limited to CVD. It is also believed that inflammation can regulate HDL functionality. See, e.g., Fogelman, When Good Cholesterol Go Bad, Nature Medicine, 2004. The carbohydrate moieties of glycoproteins can serve biological functions in protein sorting, immune and receptor recognition, inflammation, and other cellular processes.

[0064] As disclosed herein, the MVX model can include at least two inflammatory indicators, such as GlycA and S-HDLP, and at least two metabolic malnutrition biomarkers, such as at least one ketone body and at least one BCAA. In some embodiments, the MVX model includes at least one interaction parameter.

[0065] The MVX mathematical model can consider other clinical parameters such as gender, age, BMI, and in the case of hypertension medications, the MVX mathematical model can generate an MVX value independent of any clinical parameter.

[0066] As described above, embodiments of the present disclosure can be used to generate at least one MVX score using one or more defined risk mathematical models. These models may utilize measurements of different defined biomarkers or parameters obtained from in vitro biological samples of patients at risk of premature death who may benefit from pharmaceutical, medical, nutritional, exercise or other interventions.

[0067] The MVX assessment can be separated from traditional Framingham or other risk assessments and may be used relatively easily as a screening tool, allowing at-risk individuals to be identified earlier than with traditional tests.

[0068] For example, Figure 3 is a graph showing the 5-year cumulative mortality for nine subgroups of high-risk cardiac catheterization patients enrolled in CATHGEN stratified by MVX score according to an embodiment of the present disclosure. In total, 1263 of the 6971 participants died during the 5-year follow-up. As shown in Figure 3, the higher the MVX score, the higher the risk of premature death. For example, individuals with a high MVX score >70 (red line) were more than 10 times more likely to die prematurely than those with a low MVX score <35 (dark green line). As shown by the mean age and sex composition of each of the nine subgroups in Figure 3, the large differences in mortality associated with MVX score are largely independent of age and sex.

[0069] In some embodiments, MVX could be used to stratify risk of death in high risk populations, such as in the CATHGEN study, as seen in FIG.

[0070] As disclosed herein, MVX values ​​are calculated using multiple parameters including at least measurements of GlycA, S-HDLP, at least one branched chain amino acid, and at least one ketone body. The parameters may also include measurements of citrate and protein, as well as various interaction parameters. Figure 4 is a table showing the predictive power (χ2) and statistical significance (p-value) of traditional risk factors and MVX-related parameters in a Cox proportional hazards prediction model for mortality in the CATHGEN study population. In this table, "BCAA" refers to the sum of the concentrations of three branched chain amino acids (valine, leucine, isoleucine), and "ketone bodies" refers to the sum of the concentrations of three ketone bodies (β-hydroxybutyrate, acetoacetate, acetone). The MVX score used for the mortality risk stratification in Figure 3 was calculated using the general formula: MVX = A + β1 * lnGlycA + β2 * lnS-HDLP + β3 * (lnGlycA * lnS-HDLP) + β4 * lnBCAA + β5 * ln KetoneBody + β6 * lnCitrate + β7 * lnProtein + β8 * (lnCitrate * lnProtein) using the six MVX parameters shown in Figure 4 (including the interaction parameters for lnGlycA * lnS-HDLP and lnCitrate * lnProtein). The MVX value may also be decomposed into two component parts: the estimated inflammation site, which can be estimated by the inflammation index (INFX) parameter, and the estimated malnutrition site, which can be estimated by the metabolic malnutrition index (MMX) parameter. Figure 5 shows two tables depicting the predictive power and statistical significance of these parameters and the MVX of the Cox prediction model for mortality during the 5-year follow-up period in the CATHGEN study. The model on the left contains INFX and MMX parameters, and the model on the right contains MVX parameters.

[0071] In some embodiments, MVX can be used to predict short-term and long-term early all-cause mortality risk. Figure 6 shows two tables showing the predictive power and statistical significance of parameters, including MVX, in the Cox prediction model, showing mortality during a short-term follow-up of 1 year (left table) and a longer average follow-up of 7 years (right table) in the CATHGEN study population.

[0072] In some embodiments, MVX is calculated independently of traditional risk assessment parameters. Figure 7 is a table showing the predictive power provided by the likelihood ratio chi-square statistic of Cox prediction models including MVX alone (top row) or MVX plus nine additional covariate parameters (age, race, sex, smoking, hypertension, diabetes, BMI, triglyceride-rich lipoprotein particles, and low-density lipoprotein particles) for mortality during three follow-up periods in the CATHGEN study population.

[0073] In some embodiments, it is believed that MVX can be used to stratify risk of death among low-risk populations, as seen in FIG. 8. The left table in FIG. 8 is a graph showing cumulative mortality over 12 years of follow-up for female participants in the MESA study stratified by quintiles of low-risk MVX scores (e.g., MVX1), according to an embodiment of the present disclosure. 412 of the 3581 female MESA participants died during the 12-year follow-up period. The right table in FIG. 8 is a graph showing cumulative mortality over 12 years of follow-up for male participants in the MESA study stratified by quintiles of MVX1 scores, according to an embodiment of the present disclosure. 554 of the 3198 male MESA participants died during the 12-year follow-up period.

[0074] In some embodiments, MVX may be used to predict the relative risk of premature death, regardless of the disease condition(s) believed to be the "cause" of death. FIG. 9 is a table showing the predictive power and statistical significance of MVX (e.g., MVX1) and other parameters for four different CVD and mortality outcomes in MESA (low risk) participants evaluated in a multinomial logistic regression model. Three of the four outcomes are commonly referred to as the composite outcome of "CVD": left column: non-fatal CVD events without death during follow-up (n=432); second column from the left: non-fatal CVD events but later death during follow-up (n=128); third column from the left: fatal CVD events (n=225). The outcome evaluated in the right column is mortality without premature or concurrent CVD (n=610). Results show that MVX is highly predictive of three outcomes including death (p<0.0001), but not CVD without or without subsequent death (p=0.50). Thus, MVX predictions of the fatal and nonfatal components of the composite CVD outcome differ substantially, suggesting that the etiology of nonfatal and fatal CVD is more heterogeneous than believed and calling into question the theory of combining fatal and nonfatal CVD into a single outcome.

[0075] As shown in the results of Figures 10 to 12, MVX also predicts mortality caused or attributed to diseases other than CVD. Figure 10 is a table showing the predictive power and statistical significance of MVX (e.g., MVX1) and other parameters for combinations of outcomes of congestive heart failure (CHF) and mortality of participants in MESA (low risk) evaluated with a multinomial logistic regression model. Figure 11 is a table showing the predictive power and statistical significance of MVX and other parameters for combinations of outcomes of cancer and mortality of participants in MESA evaluated with a multinomial logistic regression model. Figure 12 is a table showing the predictive power and statistical significance of MVX and other parameters for combinations of outcomes of chronic kidney disease (CKD) and mortality of participants in MESA evaluated with a multinomial logistic regression model.

[0076] In some embodiments, gender may be included as a factor in the MVX model. In some embodiments, age may be included as a factor in the MVX model. In other embodiments, the MVX model may exclude consideration of gender or age, for example, to avoid generating false negatives or false positives based on corruption of such ancillary data not directly linked to the biological sample.

[0077] In some embodiments, the MVX score is provided to the clinician based on data electronically correlated with the sample, or based on input at the clinician or intake lab, e.g., patient characterization such as fasting "F" or non-fasting "NF" or statin "S" or non-statin "NS", which data can be written on a label associated with the biological sample at the NMR analyzer. Alternatively, the patient characterization data can be held in a computer database (remote or via a server or other defined path) and entered into an electronic correlation file by the clinician or intake lab, and can include patient identifiers, sample type, test type, etc. accessible or manageable by the intake lab in communication with the NMR analyzer. The patient characterization data can also enable a suitable MVX model to be used for a particular patient.

[0078] The Metabolic Vulnerability Index could be used to monitor subjects in clinical trials and / or drug therapy, to identify drug conflicts, and / or to monitor changes in risk status (positive or negative) associated with specific drugs, patient lifestyle, etc., which may be patient-specific.

[0079] Lipoproteins Lipoproteins include a wide variety of particles found in plasma, serum, whole blood and lymph, containing various types and amounts of triglycerides, cholesterol, phospholipids, sphingolipids and proteins. These various particles solubilize hydrophobic lipid molecules in the blood and perform various functions associated with lipolysis, lipogenesis and lipid transport between the gastrointestinal tract, liver, muscle tissue and adipose tissue. In blood and / or plasma, lipoproteins have been classified in many ways, mostly based on physical properties such as density or electrophoresis, or apolipoprotein content, e.g., apoB or apoA-1, the major proteins in LDL and HDL, respectively.

[0080] Classification based on particle size as determined by nuclear magnetic resonance distinguishes distinct lipoprotein particles based on size or size range. For example, NMR measurements can identify at least 15 distinct lipoprotein particle subtypes, including at least seven subtypes of high density lipoprotein (HDL), at least three subtypes of low density lipoprotein (LDL), and at least five subtypes of very low density lipoprotein (VLDL), also known as TRL (triglyceride rich lipoprotein).

[0081] Current analytical methodologies allow NMR measurements that reveal the concentrations of VLDL, LDL, and HDL subpopulations to produce group measurements of large and small subpopulations of each group. For example, different size groupings of HDL subpopulations, as discussed further below, may be utilized to optimize risk associations with early all-cause mortality.

[0082] The NMR-derived estimated lipoprotein sizes given herein typically refer to average measurements, although other size boundaries may be used.

[0083] In a preferred embodiment, the MVX risk assessment model parameters may include NMR-derived measurements of common NMR spectra of lipoproteins and analytic signals associated with HDL in particular, using defined analytical models that characterize analytic components of proteins and lipoproteins, including HDL, LDL, VLDL / TRL. This type of analysis allows for rapid acquisition times of less than 2 minutes, typically about 20 to 90 seconds, and corresponding rapid programmatic calculations that can generate measurements of model components, as well as programmatic calculation of one or more MVX risk scores using one or more defined risk models.

[0084] Moreover, while NMR measurements of lipoprotein particles are believed to be particularly suitable for the analyses described herein, other techniques are now and may be used in the future to measure these parameters, and it is believed that embodiments of the present disclosure are not limited to this measurement methodology. It is also believed that different protocols using NMR may be used in place of (e.g., including different analytical protocols) the analytical protocols described herein. See, e.g., Kaess et al., "The Lipoprotein subfraction profile: heritability and identification of quantitative trait loci," J Lipid Res. 49:715-723 (2008), and Suna et al., "1H NMR metabolomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self-organizing maps," NMR Biomed. 20:658-672 (2007). Flotation and ultracentrifugation using density-based separation techniques to assess lipoprotein particles and ion mobility analysis are alternative techniques for measuring lipoprotein subclass particle concentrations.

[0085] The lipoprotein subclass groupings may be summed, for example, to determine HDL or LDL particle numbers according to certain embodiments of the present disclosure. Note that the prominent "small, large, medium" size ranges may be modified or redefined to broaden or narrow the upper or lower limits, or to exclude certain ranges within the prominent ranges. The particle sizes listed above typically refer to average measurements, although other divisions may be used.

[0086] The embodiments of the present disclosure classify lipoprotein particles into subclasses grouped by size range based on their functional / metabolic relevance as assessed by their correlation with lipid and metabolic variables. Thus, as described above, the assessment can measure 15 distinct subpopulations (sizes) of lipoprotein particles. These distinct subpopulations can be grouped into subclasses defined for VLDL / TRL, HDL, and LDL. Intermediate density lipoproteins (IDL) can be combined with VLDL / TRL or LDL, or combined as a separate category with a size range between large LDL and small VLDL. For example, HDL subclass particles usually range (on average) from about 7 nm to about 15 nm, more commonly from about 7.3 nm to about 14 nm (e.g., 7.4 nm to 13.5 nm). The total HDL concentration is the sum of the particle concentrations of each subpopulation of that HDL subclass. The different subpopulations of HDLPs can be identified by numbers 1 to 7, with "H1" representing the smallest size HDL subpopulation and "H7" being the largest size HDL subpopulation. In some embodiments, the defined subclasses of HDL particles include small HDL particles (S-HDLPs). In some embodiments, S-HDLPs can include a subclass of HDL particles having a diameter of about 7.3 nm (mean) to about 9.0 nm (mean). BCAA

[0087] In some embodiments, the MVX model includes measurements of at least one BCAA, as described in U.S. Patent No. 9,361,429 and U.S. Patent Application No. 20150149095, which are incorporated herein by reference. The MVX model may include one or more BCAAs, including one or more of isoleucine, leucine, and valine (as described herein). In some embodiments, one or more of the set of three BCAAs (valine, leucine, and isoleucine) may be quantified by NMR.

[0088] Ketone bodies In some embodiments, the MVX model includes measurements of at least one ketone body (β-hydroxybutyrate, acetoacetate, acetone) that can be obtained via NMR analysis of the biological sample NMR spectrum. NMR quantification of each of the three ketone bodies is based on their NMR signal amplitudes derived from separate analytical models specific to the three spectral regions in which the ketone body NMR signals appear. Analytical analysis, rather than simple integration of the ketone body signals, is required due to the extensive overlap with signals from multiple lipoprotein subspecies and identified and unidentified small molecule metabolites. The derived amplitudes of the β-hydroxybutyrate, acetoacetate, and acetone signals may be converted to concentration units in μmol / L using conversion factors determined by spiking serum with stock ketone body solutions of known concentrations.

[0089] In one embodiment, the doublet β-hydroxybutyrate methyl signal appearing at approximately 1.16 and 1.15 ppm is quantified using a linear analytical model covering the spectral region from 1.07 ppm to 1.33 ppm. This region includes overlapping interfering NMR signals from lipid fatty acid methylene protons of multiple TRL, LDL, and HDL lipoprotein subtypes, serum protein signals, triplet signals from ethanol (1.13, 1.15, and 1.17 ppm), doublet signals from lactate (1.29 and 1.31 ppm), and doublet signals from unidentified metabolites that appear rarely in human serum samples (1.10 and 1.11 ppm). In one embodiment, the analytical model includes a library of 83 spectral components to accurately capture the amplitudes of NMR signals from β-hydroxybutyrate and various interfering substances in serum.

[0090] In one embodiment, the singlet methyl acetoacetate signal appearing at about 2.24 ppm is quantified using a linear analytical model encompassing the spectral region from 2.22 ppm to 2.39 ppm. This region includes overlapping interfering NMR signals from lipid fatty acid methylene protons of multiple TRL, LDL and HDL lipoprotein subtypes, serum protein signals, an octet signal from β-hydroxybutyrate (2.25-2.39 ppm), and signals from three uncharacterized metabolites appearing at 2.22, 2.30 and 2.35-2.41 ppm. In one embodiment, the analytical model includes a library of 82 spectral components to accurately capture the amplitudes of the NMR signals from acetoacetate and various interfering substances in serum.

[0091] In one embodiment, the singlet acetone methyl signal appearing at approximately 2.19 ppm is quantified using a linear analytical model encompassing the spectral region from 2.14 ppm to 2.22 ppm, which includes overlapping interfering NMR signals from lipid fatty acid methylene protons of numerous TRL, LDL and HDL lipoprotein subtypes, serum protein signals, and a singlet signal from an unidentified metabolite at 2.22 ppm. In one embodiment, the analytical model includes a library of 70 spectral components to accurately capture the amplitudes of the NMR signals from acetone and various interfering substances in serum.

[0092] GlycA A defined linear GlycA mathematical analysis model can be used to measure GlycA as described in U.S. Patent No. 9,470,771, which is incorporated herein by reference in its entirety. The GlycA measurement may be a unitless parameter as assessed by NMR by calculating the area under the peak region at defined peak locations in the NMR spectrum. In either case, measurements of GlycA for a known population (e.g., MESA) can be used to define levels or risks for particular subgroups, e.g., those with values ​​within the upper half of a defined range, including values ​​such as the third and fourth quartiles, or the top three to five quintiles.

[0093] Citrate In some embodiments, the MVX model includes measurements of citrate that can be obtained through NMR analysis of biological sample NMR spectra. The NMR quantification of citrate is based on the NMR signal amplitudes of three of the four members of a methylene proton quartet that appear at approximately 2.64, 2.60, and 2.48 ppm derived from an analytical model that assumes a linear baseline and variable offset. The fourth member of the citrate signal quartet appears at approximately 2.52 ppm and overlaps with a singlet signal derived from CaEDTA, which serves as an internal chemical shift reference. The derived citrate signal amplitudes can be converted to concentrations in μmol / L using a conversion factor determined by spiking serum with a stock citrate solution of known concentration.

[0094] Serum proteins In some embodiments, the MVX model includes serum protein measurements obtained via NMR analysis of biological sample NMR spectra. Alternatively, serum protein or serum albumin measurements, if used, can be obtained by conventional methods. NMR quantification of serum proteins is based on broad NMR signal amplitudes from non-lipoprotein proteins derived from a linear analytical model encompassing the spectral region from 0.71 ppm to 1.03 ppm. This region includes overlapping interfering NMR signals from lipid fatty acid methyl protons of multiple TRL, LDL and HDL lipoprotein subtypes, as well as those from the branched chain amino acids valine, leucine and isoleucine. In one embodiment, the analytical model includes a library of 66 spectral components to accurately capture the amplitudes of NMR signals from serum proteins and various interfering substances in serum. The derived serum protein signal amplitudes can be reported in any signal amplitude units or converted to molar concentration units using a conversion factor determined by spiking serum with a stock serum albumin solution of known concentration.

[0095] C. System for measuring MVX With reference to some embodiments of the present disclosure, a system capable of performing each method described herein may be provided. In some embodiments, the system may include: an NMR spectrometer configured to acquire an NMR spectrum(s) comprising at least one signal for GlycA, at least one signal for at least one high density lipoprotein particle (HDLP) subclass, at least one signal for at least one branched chain amino acid (BCAA), and at least one signal for at least one ketone body; and a processor for determining a metabolic vulnerability index (MVX) value based on the at least one signal measured for the GlycA, the at least one high density lipoprotein particle (HDLP) subclass, the at least one branched chain amino acid (BCAA), and the at least one ketone body, the processor including a memory or in communication with a memory. The system may further include an NMR spectrometer configured to acquire an NMR spectrum(s) including at least one signal for serum protein and / or citrate, and a processor for determining a metabolic vulnerability index (MVX) value based on the at least one signal measured for serum protein and citrate.

[0096] With reference to some embodiments of the present disclosure, the NMR system may include an NMR spectrometer, a flow probe in communication with the spectrometer, and a processor in communication with the spectrometer configured to obtain (i) at least one NMR signal in a defined GlycA compatible region of the NMR spectrum associated with GlycA of a blood plasma or serum sample in the flow probe, (ii) at least one NMR signal in a defined ketone body compatible region of the NMR spectrum associated with a sample in the flow probe, (iii) at least one NMR signal in a defined BCAA compatible region of the NMR spectrum associated with a sample in the flow probe, and (iv) at least one HDLP subclass, and optionally at least one NMR signal for serum protein and / or citrate. In some embodiments, the processor is further configured to calculate an MVX score based on measurements obtained by the spectrometer according to any of the embodiments of the present invention disclosed herein.

[0097] The flowcharts and block diagrams in the several figures herein illustrate the architecture, functionality, and operation of possible implementations of the analytical model and evaluation system and / or program according to the present invention. In this regard, each block in the flowcharts or block diagrams represents a module, segment, operation, or portion of code that includes one or more executable instructions for implementing the specified logical function. It should be noted that in some alternative implementations, the functions noted in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved.

[0098] 13, it is believed that most, if not all, measurements may be performed in or using system 10 in communication with or at least partially contained within an NMR clinical analyzer 22, for example as described for U.S. Patent No. 8,013,602, the contents of which are incorporated by reference as if fully set forth herein. Analyzer 22 includes a spectrometer and a sample handler system.

[0099] The system 10 may include a processor (e.g., a metabolic vulnerability index module) 350 for collecting data suitable for determining an MVX value, which may include, for example, HDLP subclasses such as, but not limited to, GlycA, BCAA, ketone bodies, and S-HDLP and / or citrate and / or protein. In some embodiments, the processor may be configured to calculate an MVX score based on the measurements of GlycA, at least one ketone body, at least one branched chain amino acid, and the at least one HDLP subclass using the following formula: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the processor may be configured to calculate the MVX score using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody In some embodiments, the processor may be configured to calculate the MVX score using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In some embodiments, the processor may be configured to calculate an MVX score that includes an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. Thus, in some embodiments, the processor may be configured to calculate an MVX score based on the following model: MVX = βi*INFX + βm*MMX In these embodiments, the processor may be configured to calculate the INFX value using the following model: INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) In some embodiments, the processor may be configured to calculate the MMX value using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody This model may be denoted as MMX1. In some embodiments, the processor may be configured to calculate MMX using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein In another embodiment, the processor may be configured to calculate MMX using the following model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) In such an embodiment, MMX may be written as follows: MMX=β9*MMX1+β10*MMX2, where MMX1 is as above, and MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein).

[0100] The system may include an analysis circuit 20 including at least one processor that may be on-board the analyzer 22 or at least partially remote from the analyzer 22. In the latter case, the processor / analysis circuit 20 may be located in whole or in part on a server. The server may be provided using cloud computing, which involves the provision of computationally capable resources on demand over a computer network. The resources may be embodied as various infrastructure services (e.g., computers, storage, etc.), as well as applications, databases, file services, email, etc. In traditional models of computing, both data and software are typically contained entirely on the user's computer; in cloud computing, the user's computer may have little software or data (perhaps an operating system and / or a web browser) and little more than a display terminal for processes taking place on a network of external computers. A cloud computing service (or a collection of multiple cloud resources) may be generally referred to as a "cloud." Cloud storage may include a model of networked computer data storage in which data is stored on multiple virtual servers, rather than hosted on one or more dedicated servers. Data transmission may be encrypted over the Internet using any suitable firewall to comply with industry or regulatory standards such as HIPAA. The term "HIPAA" refers to the United States law defined by the Health Insurance Portability and Accountability Act. Patient data may include access numbers or identifiers, gender, age, and exam data.

[0101] The results of the MVX determination may be transmitted to the patient, clinician site 50, health insurance agency or pharmacy 51, via a computer network such as the Internet 227, by e-mail or the like. The results may be transmitted directly or indirectly from the analysis site. The results may be printed and sent via traditional mail. This information may also be transmitted to pharmacies and / or health insurance companies, or even to patients, to distribute medical alerts to monitor prescriptions or drug use that may increase the risk of adverse events, or to prevent prescription of conflicting medications. The results may be e-mailed to the patient's "home" computer, or to a computing device commonly used in the environment, such as a smartphone or notepad. The results may be, for example, an e-mail attachment of a comprehensive report, a text message alert, or the like.

[0102] One or more electronic devices associated with different users, e.g., clinician sites, patients, and / or test or lab sites, may be configured to access electronic analysis circuitry that communicates with the display of the respective electronic device. The analysis circuitry may be hosted on a server and may provide an internet portal or a downloadable APP or other computer program to the various devices. The circuitry may be configured to allow a user, e.g., a clinician, to enter one or more of: (i) a patient's traditional risk factor values, (ii) a patient's traditional risk factor values ​​and an individual vulnerability index score, or (iii) an individual vulnerability index score. The circuitry may automatically populate different data fields based on a patient identifier or other password upon sign-in, or may allow a user to enter both an MVX score and traditional factor measurements for each patient. The analysis circuitry may be configured to track changes in MVX scores over time and generate electronic reports that can be sent to clinicians, patients, or other users. The analysis circuitry may also send notifications regarding recommendations for retesting, follow-up testing, etc. For example, if the MVX risk score is elevated, e.g., in the intermediate risk category, or exceeds the low risk value, the circuitry can notify the clinician that further testing is appropriate, or can send a notification to the patient to consult with a physician as to which testing is appropriate or whether an increased observation interval for follow-up MVX testing is advisable. The analysis circuitry can generate a risk assessment pathway or analysis to provide a graphical information stratifying future early all-cause mortality risk for patients with the same traditional risk factor values. The electronic analysis circuitry can be mounted on a server in the cloud, or otherwise accessible via the internet, or associated with different client architectures, as will be appreciated by those skilled in the art. Thus, a clinician, patient, or other user can generate a customized report regarding risk assessment, or obtain risk stratification information.

[0103] FIG. 14 shows an example of measuring MVX using NMR. Some embodiments of the present disclosure include an NMR system capable of performing each method described herein. In some embodiments, the NMR system may include an NMR spectrometer, a flow probe in communication with the spectrometer, and a processor in communication with the spectrometer configured to obtain (i) at least one NMR signal in a defined GlycA compatible region of the NMR spectrum associated with GlycA of a blood plasma or serum sample in the flow probe, (ii) at least one NMR signal in a defined ketone body compatible region of the NMR spectrum associated with a sample in the flow probe, (iii) at least one NMR signal in a defined BCAA compatible region of the NMR spectrum associated with a sample in the flow probe, and (iv) at least one HDLP subclass, and optionally at least one NMR signal for serum protein and / or citrate. In some embodiments, the processor is further configured to calculate an MVX score based on the measurements obtained by the spectrometer according to any of the embodiments of the present invention disclosed herein.

[0104] Referring to FIG. 14, a system 207 for obtaining and calculating the line shape of a selected sample is shown. The system 207 includes an NMR spectrometer 22 for performing NMR measurements on the sample. In one embodiment, the spectrometer 22 is configured to perform NMR measurements on the proton signal at 400 MHz, while in other embodiments, the measurements may be performed at between 200 MHz and about 900 MHz, or other suitable frequencies. Other frequencies corresponding to the desired operable magnetic field strength may be used. Typically, a proton flow probe is provided, and a temperature controller is provided to maintain the sample temperature at 47+ / -0.5°C. The spectrometer 22 is controlled by a digital computer 211 or other signal processing unit. The computer 211 should be capable of performing fast Fourier transforms. It may also include a data link 212 to another processor or computer 213, and a direct memory access channel 214 that can be connected to a hard memory storage unit 215.

[0105] The digital computer 211 may also include a set of analog-to-digital converters, digital-to-analog converters, and low speed device I / O ports that connect to the operating elements of the spectrometer 22 via pulse control and interface circuitry 216. These elements include an RF transmitter 217 that generates RF excitation pulses of duration, frequency, and magnitude dictated by at least one digital signal processor on board or in communication with the digital computer 211, and an RF power amplifier 218 that amplifies the pulse and connects it to an RE transmit coil 219 that surrounds the sample cell 220 and / or flow probe 220. NMR signals generated by the sample excited in the presence of a 9.4 Tesla polarizing magnetic field generated by a superconducting magnet 221 are received by coil 222 and applied to an RF receiver 223. The amplified and filtered NMR signals are demodulated at 224, and the resulting quadrature signals are applied to the interface circuitry 216 where they are digitized and input via the digital computer 211. The processor and / or analyzer circuit 20 of Figures 13 and 14 and / or the multi-parameter MVX risk module 350 of Figures 13 and 15 may be located in one or more processors associated with the digital computer 211 and / or secondary computer 213 or other computers which may be within the facility or remote and accessible via a worldwide network such as the Internet 227.

[0106] After acquiring NMR data from the sample in the measurement cell 220, processing by the computer 211 generates another file that can be stored in the storage device 215 as needed. This second file is a digital representation of the chemical shift spectrum, which is then read by the computer 213 and stored in its storage device 225 or in a database associated with one or more servers. The computer 213, which may be a laptop computer, desktop computer, workstation computer, electronic notepad, electronic tablet, smartphone or other device with at least one processor or other computer, under the direction of a program stored in its memory or accessible by the computer 213, processes the chemical shift spectrum according to the teachings of the present invention to generate a report that can be output to a printer 226 or electronically stored or relayed to a desired email address or URI. Those skilled in the art will recognize that other output devices, such as a computer display screen, electronic notepad, smartphone, etc., can also be employed to display the results.

[0107] It will be apparent to those skilled in the art that the functions performed by computer 213 and its separate storage device 225 may be incorporated into the functions performed by the spectrometer's digital computer 211. In such a case, printer 226 may be connected directly to digital computer 211. Other interfaces and output devices may also be used, as will be familiar to those skilled in the art.

[0108] Certain embodiments of the present invention aim to provide methods, systems and / or computer program products using MVX assessments that may be particularly useful in automated screening tests for clinical disease states and / or risk assessment evaluations for screening in vitro biological samples.

[0109] Embodiments of the present disclosure may take the form of entirely software embodiments or embodiments combining software and hardware aspects, generally referred to herein as "circuits" or "modules."

[0110] As will be appreciated by those skilled in the art, the present disclosure may be embodied as an apparatus, a method, a data or signal processing system, or a computer program product. Thus, the present disclosure may take the form of an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, some embodiments of the present disclosure may take the form of a computer program product on a computer usable storage medium having computer usable program code means embodied in the medium. Any suitable computer readable medium may be utilized, including hard disks, CD-ROMs, optical storage devices, or magnetic storage devices.

[0111] The computer usable or computer readable medium may be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples of computer readable media (non-exhaustive list) include an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, and a portable compact disk read-only memory (CD-ROM). Note that the computer usable or computer readable medium may be paper or another suitable medium on which the program is printed, such that the program can be electronically captured, for example, via optical scanning of the paper or other medium, and then compiled, interpreted, or processed in a suitable manner, if necessary, and further stored in the computer memory.

[0112] Computer program code for carrying out the operations of the present disclosure may be written in an object-oriented programming language such as Java7, Smalltalk, Python, Labview, C++, or Visual Basic. However, computer program code for carrying out the operations of the present disclosure may also be written in conventional procedural programming languages ​​such as the "C" programming language or even assembly language. The program code may run entirely on the user's computer, partially on the user's computer, or as a standalone software package and partially on the user's computer, partially on a remote computer, or entirely on a remote computer. In the latter scenario, the remote computer may be connected to the user's computer via a Local Area Network (LAN) or a Wide Area Network (WAN) or a Secure Area Network (SAN), or the connection may be made to an external computer (e.g., via the Internet using an Internet Service Provider).

[0113] 15 is a block diagram of an exemplary embodiment of a data processing system 305 illustrating systems, methods and computer program products according to embodiments of the present invention. Processor 310 may include or communicate with memory 314 via address / data bus 348. Processor 310 may be any commercially available or custom microprocessor. Memory 314 represents an overall hierarchy of memory devices that contain software and data used to implement the functionality of data processing system 305. Memory 314 may include types of devices such as, but not limited to, cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

[0114] 15, memory 314 may include several categories of software and data used by data processing system 305: operating system 352, application programs 354, input / output (I / O) device drivers 358, MVX evaluation module 350, and data 356. MVX evaluation module 350 can analyze NMR signals to reveal defined NMR signal peak regions in the proton NMR spectrum of each biological sample and calculate MVX values ​​using various approaches disclosed herein.

[0115] Data 356 may include signal (component and / or composite spectral line shape) data 362 that may be obtained from a data or signal acquisition system 225 (e.g., NMR spectrometer 22 and / or analyzer 22). As will be appreciated by those skilled in the art, operating system 352 may be any operation system suitable for use with a data processing system, such as OS / 2, AIX, or OS / 390 from International Business Machines Corporation of Armonk, NY, Windows CE, Windows NT, Windows 95, Windows 98, Windows 2000, Windows XP, Windows 10 from Microsoft Corporation of Redmond, WA, Palm OS from Palm, Inc., MacOS from Apple Inc., UNIX, FreeBSD, or Linux, a proprietary operating system, or a dedicated operating system for, for example, an embedded data processing system.

[0116] I / O device drivers 358 typically include software routines that application programs 354 access via operating system 352 to communicate with devices such as I / O data port(s), data storage device 356, and certain memory 314 components and / or image acquisition system 225. Application programs 354 are examples of programs that implement various features of data processing system 305 and may include at least one application that supports operation in accordance with embodiments of the present invention. Finally, data 356 represents static and dynamic data used by application programs 354, operating system 352, I / O device drivers 358, and other software programs that may be resident in memory 314.

[0117] Although the present invention has been illustrated with reference to, for example, application program module 350 of Figure 15, those skilled in the art will appreciate that other configurations may be utilized while still benefiting from the teachings of the present invention. Thus, the present invention should not be construed as limited to the configuration of Figure 15, which is intended to include any configuration capable of performing the operations described herein.

[0118] In certain embodiments, module 350 includes computer program code for providing MVX measurements that can be used as indicators to assess clinical disease state or risk and / or whether therapeutic intervention is desirable and / or to indicate the efficacy of treatment or even unintended consequences of treatment.

[0119] Further embodiments of the present disclosure are illustrated by the following non-limiting examples.

[0120] Working Example Example 1 The Metabolic Vulnerability Index (MVX) mathematical model was developed using data collected from the CATHGEN study population of 6936 participants. Traditional clinical parameters including age, race, sex, smoking status, hypertension status, diabetes status, BMI, triglyceride-rich lipoprotein particles (TRLP), and LDL particles (LDLP) were recorded for each patient. In addition, measurements of GlycA, S-HDLP, BCAA, ketone bodies, citrate, and protein were derived from single nuclear magnetic resonance (NMR) spectra of plasma samples from each study participant. A Cox proportional hazards prediction model for mortality was used to generate the predictive power (χ2) and statistical significance (p-value) of each parameter, as seen in Figures 4 and 5. In this case, all listed parameters were used in the prediction model to generate the predictive power of each parameter, but the MVX of the model is by itself a very strong and statistically significant predictor of mortality risk, as seen in Figure 7.

[0121] An MVX mathematical model, generated from the Cox proportional hazards prediction model for mortality, was used to generate an MVX score (1-100) value for each participant in the CATHGEN study. These MVX scores were then used to subdivide the population into nine subgroups, and as can be seen in Figure 3, the cumulative incidence of death in each subgroup over the 5-year follow-up period increased in direct proportion to the increase in MVX score.

[0122] Figure 16 shows an example of how various models of INFX, MMX1, MMX2 and MMX can be used to develop a numerical score as shown in Figure 3. For application to normal (i.e., not high-risk) populations, MVX1 = (INFX * 0.84310) + (MMX1 * 1.0), where MMX1 = 10 + (lnBCAA * -1.10056) + (lnKetoneBody * 0.2373). On the other hand, for high-risk populations, the contribution of MMX includes MMX1 and MMX2 (Figure 16). The actual coefficients used (e.g., for MMX1, β4 = -1.10056 and β5 = 0.2373) may vary depending on the population used and / or the analysis performed.

[0123] Embodiment A method for determining the level of an indicator associated with a subject's relative risk of premature death is Obtaining a sample from a subject; The method includes measuring GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), and at least one ketone body. A2. The method of A1, wherein the measurements of GlycA, at least one HDLP subclass, at least one BCAA, and at least one ketone body are used to generate a Metabolic Vulnerability Index (MVX) value. A3. The method of A1-A2, wherein said HDLP subclass is small HDLP (S-HDLP). A4. The MVX value is MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody The methods of A2 to A3 are determined using the model. A5. The MVX value is MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody The methods of A2 to A3 are determined using the model. A6. The method of A5, wherein said MVX value is determined for subjects considered to be at low risk for a cardiovascular event. A7. The method of A1-A2, further comprising measuring at least one of citrate and serum protein. A8. The method of A7, wherein at least one of the measurements of citrate and serum protein is performed in subjects believed to be at high risk for CVD-related death. A9. The method of A7 to A8, wherein the MVX value is determined using the model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). A10. The method of any one of the preceding embodiments, wherein the MVX value is defined as including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. A11. The method of A10, wherein said measures of GlycA and said at least one HDLP subclass are used to generate an Inflammation Index (INFX) value. A12.INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) The method of A10 to A11, wherein the INFX value is determined using the model. A13. The method of A10, wherein the at least one BCAA, the at least one ketone body, and optionally protein and citrate measurements are used to generate a Metabolic Malnutrition Index (MMX) value. A14. The metabolic malnutrition index (MMX) value is MMX = β4 * lnBCAA + β5 * lnKetoneBody + β6 * lnCitrate + β7 * lnProtein + β8 * (lnCitrate * lnProtein). The methods of A10 and A13 are defined as follows. A15. The method of any one of A10, A13 and A14, wherein said metabolic malnutrition index (MMX) comprises a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. A16.The method of A15 where MMX=β9*MMX1+β10*MMX2. A17. The method of A15 to A16, wherein MMX1=β4*lnBCAA+β5*lnKetoneBody. A18. The method of A15 to A16, wherein MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). A19. The method of any one of A15 to A17, wherein the MVX value is determined using a model where MVX1=βi*INFX+βm*MMX1. A20.MVX1 is a method of A19 assessed for subjects believed to be at low risk for a CVD-related event. A21. The method of A15, wherein the MVX value is determined using a model where MVX=βi*INFX+βm*MMX, where MMX=β9*MMX1+β10*MMX2. A22.MVX is a method of A21 in which MVX is assessed for subjects believed to be at high risk for a CVD-related event. A23. The method of any one of the preceding embodiments, wherein the BCAA is at least one of leucine, isoleucine, or valine. A24. The method of any one of the preceding embodiments, wherein the ketone body is at least one of acetone, acetoacetate, or beta-hydroxybutyrate. A25. The method of any one of the preceding embodiments, wherein the measuring is performed by NMR. B1. A method for determining the level of an indicator associated with a subject's relative risk of premature death comprises: obtaining a sample from the subject; Measuring at least one of GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), at least one ketone body, and optionally at least one of citrate and serum protein; generating an inflammatory index (INFX) value using the measurements of GlycA and the at least one HDLP subclass; generating at least one metabolic malnutrition index (MMX) value using said at least one BCAA, said at least one ketone body, and optionally said measures of protein and citrate; and determining a metabolic vulnerability index (MVX) value based on the INFX value and the MMX value. B2. The method of B1, wherein said HDLP subclass is small HDLP (S-HDLP). B3. The method of B1 to B2, wherein the INFX value is determined using the model: INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP). B4. The method of B1 to B2, wherein said Metabolic Malnutrition Index (MMX) value is defined as MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). B5. The method of any one of embodiments B1-B4, wherein the metabolic malnutrition index (MMX) comprises a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. The method of B5 where B6.MMX=β9*MMX1+β10*MMX2. B7. Methods B5 to B6, where MMX1=β4*lnBCAA+β5*lnKetoneBody. B8. The method of B5 to B6, wherein MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). B9. The method of any one of B5 to B7, wherein said MVX value is determined using a model where MVX1=βi*INFX+βm*MMX1. The method of B9, wherein MVX1 is determined for subjects believed to be at low risk for a CVD-related event. B11. Any of the methods of B5-B8, wherein the MVX value is determined using a model where MVX=βi*INFX+βm*MMX, where MMX=β9*MMX1+β10*MMX2. The method of B11, wherein MVX is determined for a subject believed to be at high risk for a CVD-related event. B13. Any one of the methods of B1-B12, wherein said BCAA is at least one of leucine, isoleucine, or valine. B14. Any one of the methods of B1-B13, wherein said ketone bodies are at least one of acetone, acetoacetate, or beta-hydroxybutyrate. B15. The method of any one of B1 to B14, wherein said measuring is performed by NMR. C1. A system implementing any one of the preceding embodiments. D1. An NMR spectrometer configured to acquire an NMR spectrum(s) comprising at least one signal for GlycA, at least one signal for at least one high density lipoprotein particle (HDLP) subclass, at least one signal for at least one branched chain amino acid (BCAA), and at least one signal for at least one ketone body; The system includes a processor for determining a metabolic vulnerability index (MVX) value based on at least one signal measured for the GlycA, the at least one high density lipoprotein particle (HDLP) subclass, the at least one branched chain amino acid (BCAA), and the at least one ketone body, wherein the processor includes a memory or is in communication with a memory. D2. The system of D1, further comprising an NMR spectrometer configured to acquire an NMR spectrum(s) including at least one signal for serum protein (Protein) and / or citrate (Citrate), and a processor to determine a Metabolic Vulnerability Index (MVX) value based on the at least one signal measured for serum protein (Protein) and citrate (Citrate). D3. The system of D1 to D2, wherein the HDLP subclass is a small HDLP (S-HDLP). D4. The processor further calculates MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody. Any one of the D1 or D3 systems configured to calculate an MVX score based on measurements of GlycA, at least one ketone body, at least one branched chain amino acid, and at least one HDLP subclass using the formula: D5. The system of any one of D1 or D3, wherein the processor is further configured to calculate the MVX score using the model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody. D6. Any one of the systems of D1 to D3, wherein the processor is further configured to calculate the MVX value using the model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). D7. The system of any one of D1-D6, wherein the processor is further configured to calculate an MVX value including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. D8. The system of D7, wherein the processor is further configured to calculate the INFX value using a model of INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP). D9. The system of D7, wherein the processor is further configured to calculate the MMX value using the model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). D10. The system of any one of D7-D9, wherein the processor is further configured to calculate a metabolic malnutrition index (MMX) comprising a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. D11. The system of D10, wherein the processor is further configured to calculate MMX values ​​using a model of MMX=β9*MMX1+β10*MMX2. D12. The system of D10 to D11, wherein the processor is further configured to calculate the MMX1 value using a model of MMX1=β4*lnBCAA+β5*lnKetoneBody. D13. The processor further calculates MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) The system of D10 to D11 configured to calculate MMX2 values ​​using the model of D14. The processor further: MVX1=βi*INFX+βm*MMX1 Any one of the systems D10 to D12 configured to calculate the MVX value using the model. D15. The processor further comprises: MVX=βi*INFX+βm*MMX, where MMX=β9*MMX1+β10*MMX2. The system of D10 or any one of D11 to D13, configured to calculate the MVX value using a model where: D16. Any one of the systems D1 to D15, wherein the BCAA is at least one of leucine, isoleucine or valine. D17. The system of any one of D1 to D16, wherein the ketone body is at least one of acetone, acetoacetate, or beta-hydroxybutyrate. E1. An NMR spectrometer; a flow probe in communication with the spectrometer; a processor in communication with said spectrometer configured to obtain (i) at least one NMR signal in a defined GlycA compatibility region of an NMR spectrum associated with GlycA of a blood plasma or serum sample in the flow probe, (ii) at least one NMR signal in a defined Ketone Body compatibility region of an NMR spectrum associated with the sample in the flow probe, (iii) at least one NMR signal in a defined BCAA compatibility region of an NMR spectrum associated with the sample in the flow probe, and (iv) at least one NMR signal for at least one HDLP subclass compatibility region of an NMR spectrum associated with the sample in the flow probe, and optionally at least one NMR signal in a serum Protein and / or Citrate compatibility region(s) of an NMR spectrum associated with the sample in the flow probe; An NMR system equipped with E2. The system of E1, wherein said HDLP subclass is a small HDLP (S-HDLP). E3. The system of E1-E2, wherein the processor is further configured to calculate an MVX score based on the measures of GlycA, at least one ketone body, at least one branched chain amino acid, and the at least one HDLP subclass using the formula: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody. E4. The system of E1 to E2, wherein the processor is further configured to calculate the MVX score using the model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody. E5. The processor further calculates the following: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein) Any one of the systems E1 to E4 configured to calculate the MVX value using the model. E6. The system of any one of E1 to E5, wherein the processor is further configured to calculate an MVX value including an inflammation index (INFX) value and a metabolic malnutrition index (MMX value). E7. The system of E6, wherein the processor is further configured to calculate the INFX value using a model of INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP). E8. The system of E6, wherein the processor is further configured to calculate the MMX value using the model: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). E9. The system of any one of E6-E7, wherein the processor is further configured to calculate a metabolic malnutrition index (MMX) comprising a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. E10. The processor further: MMX=β9*MMX1+β10*MMX2 The E9 system is configured to calculate the MMX value using the model. E11. The system of E9 to E10, wherein the processor is further configured to calculate the MMX1 value using a model of MMX1=β4*lnBCAA+β5*lnKetoneBody. E12. The system of E9-E10, wherein the processor is further configured to calculate the MMX2 value using a model of MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). E13. The system of any one of E9 to E11, wherein the processor is further configured to calculate the MVX value using a model of MVX1=βi*INFX+βm*MMX1. E14. The system of any one of E10 to E13, wherein the processor is further configured to calculate the MVX value using a model where MVX=βi*INFX+βm*MMX, where MMX=β9*MMX1+β10*MMX2. E15. Any one of the systems E1-E14, wherein the BCAA is at least one of leucine, isoleucine, or valine. E16. Any one of the systems E1-E15, wherein the ketone bodies are at least one of acetone, acetoacetate, or beta-hydroxybutyrate. F1. A method of observing a patient, comprising: obtaining a sample from the subject; Measuring at least one of GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), at least one ketone body, and optionally citrate and / or serum protein in the sample; determining a metabolic fragility index (MVX) value based on the measurements; and assessing whether at least the MVX value is above a defined level of a population norm associated with an increased risk of all-cause mortality. F2. The method of F1, wherein said HDLP subclass is small HDLPs (S-HDLPs). F3. The method of F1 to F2, wherein the MVX value is determined using a model of MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody. F4. The method of F1-F2, wherein the MVX value is determined using a model of MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody. F5. The method of F1 to F4, wherein said MVX value is determined in subjects considered to be at low risk for a cardiovascular event. F6. The method of F1, wherein at least one of the measurements of citrate and serum protein is performed in subjects believed to be at high risk for CVD-related death. F7. The methods of F1 to F2, wherein the MVX value is determined using the model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). F8. The method of any one of F1 to F7, wherein said MVX value is defined as comprising an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. F9. The method of F8, wherein said measures of GlycA and said at least one HDLP subclass are used to generate an inflammatory index (INFX) value. F10. The method of F8 to F9, wherein the INFX value is determined using the model: INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP). F11. The method of F8, wherein said at least one BCAA, said at least one ketone body, and optionally protein and citrate measurements are used to generate a Metabolic Malnutrition Index (MMX) value. F12. Any one of the methods F8 or F11, wherein the Metabolic Malnutrition Index (MMX) value is defined as: MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). F13. The method of any one of F8 or F11, wherein said metabolic malnutrition index (MMX) comprises a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. F14.The method of F13 where MMX = β9*MMX1 + β10*MMX2. F15. Either method F13 or F14, where MMX1=β4*lnBCAA+β5*lnKetoneBody. F16. Any one of the methods F14, F13, where MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). F17. The method of any one of F13 to F15, wherein the MVX value is determined using a model where MVX1=βi*INFX+βm*MMX1. F18.MVX1 is a method of F17 assessed for subjects considered to be at low risk for CVD-related events. F19. The method of F13, wherein the MVX value is determined using a model where MVX=βi*INFX+βm*MMX, where MMX=β9*MMX1+β10*MMX2. F20.MVX is a method of F19 that is adjudicated for subjects considered to be at high risk for CVD-related events. F21. Any one of the methods of F1 to F20, wherein said BCAA is at least one of leucine, isoleucine, or valine. F22. The method of any one of F1 to F21, wherein said ketone body is at least one of acetone, acetoacetate, or beta-hydroxybutyrate. F23. Any one of the methods F1 to F21, wherein the measurement is performed by NMR. G1. A method of observing a patient, comprising: (a) obtaining a sample from a subject; (b) measuring GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), at least one ketone body, and optionally at least one of citrate and serum protein in the sample; (c) determining a metabolic fragility index (MVX) value based on the measurements; and (d) repeating steps (a) through (c) at a later time; and (e) assessing whether at least the MVX value has increased or decreased over time. G2. The method of G1, wherein said HDLP subclass is small HDLP (S-HDLP). G3. The method of G1 to G2, wherein the MVX value is determined using a model of MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody. G4. The method of G1-G2, wherein the MVX value is determined using a model of MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody. G5. Any one of the methods of G1 to G4, wherein the MVX value is determined in a subject considered to be at low risk for a cardiovascular event. G6. The method of G1 to G2, wherein at least one of the measurements of citrate and serum protein is performed in subjects believed to be at high risk for CVD-related death. G7. Any one of the methods of G1, G2, or G6, wherein the MVX value is determined using the model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). G8. Any one of the methods of G1 to G7, wherein the MVX value is defined as including an inflammation index (INFX) value and a metabolic malnutrition index (MMX) value. G9. The method of G8, wherein said measures of GlycA and said at least one HDLP subclass are used to generate an inflammatory index (INFX) value. G10.INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) The method of G8 to G9, wherein the INFX value is determined using the model. G11. The method of G8, wherein the at least one BCAA, the at least one ketone body, and optionally protein and citrate measurements are used to generate a Metabolic Malnutrition Index (MMX) value. G12. Metabolic Malnutrition Index (MMX) value is: MMX = β4*lnBCAA + β5*lnKetoneBody + β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein) Either one of the methods G8 or G11 defined as: G13. Any one of the methods of G8, G11, or G12, wherein the metabolic malnutrition index (MMX) comprises a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. The method of G13 where G14.MMX=β9*MMX1+β10*MMX2. G15.The method of G13 where MMX1=β4*lnBCAA+β5*lnKetoneBody. The method of G13 where G16.MMX2=β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein). G17. The method of G15, wherein the MVX value is determined using a model where MVX1=βi*INFX+βm*MMX1. G18.MVX1 is a method of G17 assessed for subjects considered to be at low risk for CVD-related events. G19. The method of G13, wherein the MVX value is determined using the model MVX=βi*INFX+βm*MMX, where MMX=β9*MMX1+β10*MMX2. G20.MVX is a method of G19 that is assessed for subjects considered to be at high risk for CVD-related events. G21. Any one of the methods of G1 to G20, wherein the BCAA is at least one of leucine, isoleucine, or valine. G22. Any one of the methods of G1 to G21, wherein the ketone body is at least one of acetone, acetoacetate, or beta-hydroxybutyrate. G23. The method of G1, wherein said measuring is performed by NMR.

[0124] The above is illustrative of the disclosure and should not be construed as limiting the present invention. Although several exemplary embodiments of the disclosure have been described, those skilled in the art will readily appreciate that many modifications can be made in the exemplary embodiments without substantially departing from the novel teachings and advantages of the disclosure. Accordingly, all such modifications are intended to be included within the scope of the disclosure as defined in the claims. When means-plus-function clauses are used in the claims, they are intended to encompass the structures described herein as performing the recited function over equivalent structures as well as structural equivalents. Thus, the foregoing is illustrative of the disclosure and should not be construed as limited to the specific embodiments disclosed, and modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The disclosure is defined by the appended claims and the equivalents of the claims contained therein. [Explanation of symbols]

[0125] 20 Processor / Analysis Circuit 22 NMR analyzer 211 Digital Computers / Processors 213 Computers / Processors 215 Memory storage device 216 Pulse control & interface circuit 217 RF transmitter 218 RF Power Amplifier 221 Magnet 223 RF Receiver 224 Demodulator 225 Memory Storage Device 225 NMR data or signal acquisition system 226 Printer 227 Internet 310 Processor 314 Memory 350 MVX Evaluation Module 352 Operating Systems 354 Application Programs 356 Data 358 I / O device drivers 362 Signal Data 370 Clinical Disease State Module 375 Risk Prediction Module 378 All-cause Mortality Risk Assessment Module

Claims

1. 1. A method for determining a level of an index associated with a relative risk of premature death in a subject, comprising: Obtaining a sample from a subject; The method includes measuring GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), and at least one ketone body.

2. 2. The method of claim 1, wherein the measurements of GlycA, the at least one HDLP subclass, the at least one BCAA, and the at least one ketone body are used to generate a Metabolic Vulnerability Index (MVX) value.

3. 3. The method according to claim 1 or 2, wherein the HDLP subclass is small HDLP (S-HDLP).

4. 4. The method of claim 2 or 3, wherein the MVX value is determined using the model MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody.

5. 4. The method of claim 2 or 3, wherein the MVX value is determined using the model MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody.

6. The method of claim 5, wherein the MVX value is determined in subjects considered to be at low risk for a cardiovascular event.

7. 4. The method of claim 1, 2, or 3, further comprising measuring at least one of citrate and serum protein.

8. 8. The method of claim 7, wherein said measurements of at least one of citrate and serum protein are performed in subjects believed to be at high risk for CVD-related death.

9. 9. The method of claim 7 or 8, wherein the MVX value is determined using the model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein).

10. 10. The method of claim 1, wherein the MVX value is defined as including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value.

11. The method of claim 10, wherein the measurements of GlycA and the at least one HDLP subclass are used to generate an inflammatory index (INFX) value.

12. INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) 12. A method according to claim 10 or 11, wherein the INFX value is determined using a model of:

13. 11. The method of claim 10, wherein the measurements of at least one BCAA, at least one ketone body, and optionally protein and citrate are used to generate a Metabolic Malnutrition Index (MMX) value.

14. The metabolic malnutrition index (MMX) value is MMX=β4*lnBCAA+β5*lnKetoneBody+β6*lnCitrate+β7*lnProtein+β8*(lnCitrate*lnProtein).

14. The method according to claim 10 or 13, wherein said method is defined as:

15. 15. The method of any one of claims 10, 13, or 14, wherein the metabolic malnutrition index (MMX) comprises a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value.

16. 16. The method of claim 15, wherein MMX = β9 * MMX1 + β10 * MMX2.

17. The method according to any one of claims 15 or 16, wherein MMX1 = β4 * ln BCAA + β5 * ln KetoneBody.

18. 17. The method of claim 15 or 16, wherein MMX2 = β6 * lnCitrate + β7 * lnProtein + β8 * (lnCitrate * lnProtein).

19. 18. A method according to any one of claims 15 to 17, wherein the MVX value is determined using a model where MVX1 = βi * INFX + βm * MMX1.

20. 20. The method of claim 19, wherein MVX1 is determined for subjects believed to be at low risk for a CVD-related event.

21. 16. The method of claim 15, wherein the MVX value is determined using a model where MVX=βi*INFX+βm*MMX, where MMX=β9*MMX1+β10*MMX2.

22. 22. The method of claim 21, wherein MVX is determined for subjects believed to be at high risk for a CVD-related event.

23. 23. The method of any one of claims 1 to 22, wherein the BCAA is at least one of leucine, isoleucine, or valine.

24. 24. The method of any one of claims 1 to 23, wherein the ketone body is at least one of acetone, acetoacetate, or beta-hydroxybutyrate.

25. 25. The method of any one of claims 1 to 24, wherein the measuring is performed by NMR.

26. The method for determining a level of an indicator associated with a relative risk of premature death in a subject comprises: obtaining a sample from the subject; Measuring GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), at least one ketone body, and optionally at least one of citrate and serum protein; generating an inflammatory index (INFX) value using the measurements of GlycA and the at least one HDLP subclass; generating at least one metabolic malnutrition index (MMX) value using said measures of said at least one BCAA, said at least one ketone body, and optionally protein and citrate; determining a metabolic vulnerability index (MVX) value based on the INFX value and the MMX value.

27. 27. The method of claim 26, wherein the HDLP subclass is small HDLP (S-HDLP).

28. 28. The method of claim 26 or 27, wherein the measuring is performed by NMR.

29. 29. A system for carrying out any one of claims 1 to 28.

30. an NMR spectrometer configured to acquire NMR spectrum(s) comprising at least one signal for GlycA, at least one signal for at least one high density lipoprotein particle (HDLP) subclass, at least one signal for at least one branched chain amino acid (BCAA), and at least one signal for at least one ketone body; and a processor for determining a metabolic vulnerability index (MVX) value based on at least one signal measured for the GlycA, the at least one high density lipoprotein particle (HDLP) subclass, the at least one branched chain amino acid (BCAA), and the at least one ketone body, The processor is a system that includes or is in communication with a memory.

31. an NMR spectrometer configured to obtain an NMR spectrum and / or a plurality of spectra including at least one signal for a serum protein and / or citrate; 31. The system of claim 30, further comprising: a processor for determining a metabolic vulnerability index (MVX) value based on at least one signal measured for said serum protein (Protein) and said citrate (Citrate).

32. 32. The system of claim 30 or 31, wherein the HDLP subclass is small HDLP (S-HDLP).

33. 33. The system of any one of claims 30 to 32, wherein the processor is further configured to calculate an MVX score based on the measurements of GlycA, at least one ketone body, at least one branched chain amino acid, and the at least one HDLP subclass using the formula: MVX = A + β1 * lnGlycA + β2 * lnS-HDLP + β4 * lnBCAA + β5 * lnKetoneBody.

34. 33. The system of any one of claims 30 to 32, wherein the MVX value is determined using the model: MVX = A + β1 * lnGlycA + β2 * lnS-HDLP + β3 * (lnGlycA * lnS-HDLP) + β4 * lnBCAA + β5 * lnKetoneBody + β6 * lnCitrate + β7 * lnProtein + β8 * (lnCitrate * lnProtein).

35. 33. A method according to any one of claims 30 to 32, wherein the MVX value is defined as including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value.

36. an NMR spectrometer; a flow probe in communication with the spectrometer; a processor in communication with the spectrometer configured to acquire (i) at least one NMR signal in a defined GlycA matching region of an NMR spectrum associated with GlycA of a blood plasma or serum sample in the flow probe, (ii) at least one NMR signal in a defined Ketone body matching region of an NMR spectrum associated with a sample in the flow probe, (iii) at least one NMR signal in a defined BCAA matching region of an NMR spectrum associated with a sample in the flow probe, and (iv) at least one NMR signal for at least one HDLP subclass, and optionally for serum Protein and / or Citrate; 1. An NMR system comprising:

37. 37. The method of claim 36, wherein the processor is further configured to calculate the amount of each of GlycA, the at least one ketone body, the at least one branched chain amino acid, and the HDLP subclass.

38. 38. The system of claim 36 or 37, wherein the HDLP subclass is small HDLP (S-HDLP).

39. The processor further calculates MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody.

39. The system of any of claims 36 to 38, configured to calculate an MVX score based on at least one of the measurements of GlycA, at least one ketone body, at least one branched chain amino acid, and the HDLP subclass using the formula:

40. 39. The system of any of claims 36 to 38, wherein the MVX value is determined using the model: MVX = A + β1 * lnGlycA + β2 * lnS-HDLP + β3 * (lnGlycA * lnS-HDLP) + β4 * lnBCAA + β5 * lnKetoneBody + β6 * lnCitrate + β7 * lnProtein + β8 * (lnCitrate * lnProtein).

41. 41. The method of claim 40, wherein the MVX value is defined as including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value.

42. 1. A method of observing a patient, comprising: obtaining a sample from the subject; Measuring GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), at least one ketone body, and optionally at least one of citrate and serum protein in the sample; determining a metabolic vulnerability index (MVX) value based on said measurements; and assessing whether at least said MVX value is above a defined level in a population norm associated with an increased risk of all-cause mortality.

43. 43. The method of claim 42, wherein said HDLP subclass is small HDLP (S-HDLP).

44. 44. A method according to any one of claims 42 to 43, wherein the measuring is carried out by NMR.

45. 1. A method of observing a patient, comprising: (a) obtaining a sample from a subject; (b) measuring GlycA, at least one high density lipoprotein particle (HDLP) subclass, at least one branched chain amino acid (BCAA), at least one ketone body, and optionally at least one of citrate and serum protein in the sample; (c) determining a metabolic vulnerability index (MVX) value based on the measurements; and (d) repeating steps (a) through (c) at a later time; and (e) assessing at least whether said MVX value has increased or decreased over time.

46. 46. ​​The method of claim 45, wherein the HDLP subclass is small HDLP (S-HDLP).

47. 47. The method of claim 45 or 46, wherein the measuring is performed by NMR.

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