Multi-parameter metabolic vulnerability index assessment

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 on September 7, 2017, entitled "METHODS AND SYSTEMS FOR MEASURING PVX", and U.S. Provisional Patent Application No. 62 / 619,497, filed on January 19, 2018, entitled "MULTI - PARAMETER METABOLIC VULNERABILITY INDEX EVALUATIONS". The entire contents of the above applications are incorporated herein by reference, including all text, tables, and drawings.

[0002] The present disclosure generally relates to the analysis of in vitro biological samples. The present disclosure is considered to be particularly suitable for NMR analysis of in vitro biological samples.

Background Art

[0003] Cardiovascular disease (CVD) - related death is considered to be the most severe outcome of long - term exposure to "conventional" CVD risk factors. These risk factors, which have been extensively studied and documented in the Framingham Offspring Study, include age, gender, blood pressure, smoking habits, and cholesterol levels. See Non - Patent Document 1. These factors have long been used to differentiate standard populations and groups at risk of suffering 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 LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C), expressed as LDL-C and HDL-C, rather than the number of LDL and HDL particles in the patient. Treatment decisions are often aimed at reducing "bad" cholesterol (LDL-C) and / or increasing "good" cholesterol (HDL-C), with a secondary focus on optimizing modifiable Framingham risk factors.

[0005] Risk calculations based on these Framingham factors may generally predict the incidence of CVD-related events. However, these risk calculators were developed to predict composite CVD outcomes, which consist of both non-fatal and fatal CVD events. Furthermore, clinical trials typically use composite CVD outcomes as the study's endpoint. Recent analyses of clinical trials have shown heterogeneous drug effects on the fatal and non-fatal components of composite CVD endpoints. For example, in several trials, LDL-lowering drugs reduced the incidence of composite CVD endpoints and their non-fatal event components, but the same drugs failed to reduce the fatal CVD event component or all-cause mortality of the combined endpoints. Conversely, some diabetes medications reduced composite CVD endpoints, but only by reducing CVD-related death and all-cause mortality, rather than non-fatal CVD events. The results of these studies suggest that potentially problematic composite CVD outcomes may have different determinants for fatal and non-fatal CVD events.

[0006] Conventional CVD risk calculators fail to account for the complex pathophysiology of various disease states, and some conventional CVD risk factors are often paradoxically associated with survival in malnourished and 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 Non-Patent Literature 2. This raises questions about the clinical reliability of Framingham risk factors in determining the risk of morbidity and mortality in patients, regardless of disease state.

[0007] Given that clinicians currently rely on conventional CVD risk factors derived from composite outcome-based risk calculators to predict patient outcomes, there is still a need for risk calculators composed of factors that can better predict or assess the risk of premature death from any cause, as opposed to the risk of suffering non-fatal events. [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] Kalantar 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. [Overview of the Initiative] [Means for solving the problem]

[0009] Embodiments of the present disclosure include methods and systems for determining a patient's metabolic vulnerability index (MVX) score by evaluating the NMR spectrum of an in vitro plasma or serum patient sample using a defined multi-factor risk assessment model, thereby assessing the relative risk of early all-cause mortality in a person and / or providing mortality risk stratification.

[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), the inflammation index GlycA, one or more branched-chain amino acids (valine, leucine, and / or isoleucine), one or more ketone bodies (beta-hydroxybutyrate, acetacetate, and / or acetone), and optionally citrate and / or serum protein levels. In one embodiment, the HDLP subclass is small HDL particles (S-HDLP).

[0011] Embodiments of this disclosure include a method for determining the level of an indicator associated with the risk of premature death in a person. The method may include taking a sample from a 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] Furthermore, throughout this disclosure, the empirical values ​​for A and β1-βn may vary depending on the model used. For example, β1 in the first equation above for MVX (i.e., the equation that does not include the term β3*(lnGlycA*lnS-HDLP)) is generally different from β1 in the second equation above (i.e., the equation that includes the product term).

[0013] In some embodiments, the method includes measuring, in addition to GlycA, at least one citrate and protein, at least one HDLP subclass, at least one BCAA, and at least one ketone body. In some embodiments, the measurement including citrate and at least one serum protein is performed on subjects suspected to be at high risk of CVD-related death. 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 Inflammation Index (INFX) value is generated using at least GlycA and the measured values ​​of at least one HDLP subclass. 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 aforementioned 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 used for calculations of a population / subjects believed to be low-risk (e.g., without known cardiovascular (CV) events), as will be discussed in detail herein.

[0016] In other embodiments, alternative MMX values ​​are generated using measurements of at least one BCAA, at least one ketone body, citrate, and protein. For example, in some embodiments, the MMX values ​​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 described above and MMX2 = β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein).

[0017] Measurements used to generate MMX values, including citrate and protein levels, are generally taken in subjects and / or populations considered to be at high risk for CVD-related death (e.g., subjects with cardiovascular events or symptoms suggestive of endogenous CVD).

[0018] Therefore, in some embodiments, the metabolic vulnerability index may be described as follows: MVX = βi * INFX + βm * MMX For application to a normal (i.e., low-risk) patient population (e.g., individuals without known cardiovascular events), the metabolic vulnerability index can be written as MVX1 = βi*INFX + βm*MMX1 (whereas βi and βm may have eigenvalues ​​depending on the model used). Conversely, for application to a high-risk patient population (e.g., individuals with known cardiovascular events), the metabolic vulnerability index can be written as MVX = βi*INFX + βm*MMX, where βi and βm may have eigenvalues ​​depending on the model used and MMX = β9*MMX1 + β10*MMX2. In some cases, βm is the same in both the low-risk and high-risk models (see, for example, Figure 16).

[0019] For example, in some cases, the MVX score may be observed in high-risk subjects or populations as a way to observe the overall health and risk of subjects for fatal CV events or death due to other causes. Or, the MVX score may be observed in clinical trials of new drugs being conducted in high-risk populations as a way to observe the effectiveness of the drug. In the case of low-risk subjects, a clinician may choose to observe the MVX1 value as a means of assessing general health and well-being. In certain embodiments, the MVX1 value may be used as a guide regarding changes to a lifestyle that promotes heart health. Still other embodiments are directed to a system. The system includes an NMR spectrometer for obtaining at least one NMR spectrum of an in vitro biological sample, and at least one processor in communication with the NMR spectrometer. The at least one processor is configured to determine a score of a metabolic vulnerability index based on at least one defined mathematical model of the risk of premature death that can consider, for each biological sample, at least one HDL subclass component measurement, at least one branched-chain amino acid measurement, at least one ketone body measurement, a GlycA measurement, and optionally, a citrate measurement and / or a protein measurement obtained from at least one of the subject's in vitro biological samples, using the 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 the MVX score using the following formula, based on the GlycA, at least one ketone body, at least one branched-chain amino acid, and the measurement of the at least one HDLP subclass. 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 the MVX score including an inflammation index (INFX) value and a metabolic dysnutrition index (MMX) value. Accordingly, in some embodiments, the processor may be configured to calculate the 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 can 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) [[ID=I]] In such an embodiment, MMX may be described as follows. MMX = β9 * MMX1 + β10 * MMX2, where MMX1 is as described above, and MMX2 = β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein).

[0020] Another embodiment relates to an NMR system. This system includes an NMR spectrometer, a flow probe communicating with the spectrometer, and at least one processor communicating with the spectrometer. The at least one processor may be configured to acquire (i) at least one NMR signal of a defined GlycA-matched 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 of a defined ketone body-matched region of the NMR spectrum associated with the sample in the flow probe, (iii) at least one NMR signal of a defined BCAA-matched region of the NMR spectrum associated with the sample in the flow probe, and (iv) at least one NMR signal of an HDLP subclass parameter. The processor may further be configured to calculate the measurements of GlycA, at least one ketone body, at least one branched-chain amino acid, and the HDLP subclass parameter. The system may further be configured to calculate the MVX score using a defined mathematical model of the risk of all-cause mortality, which uses calculated values ​​of GlycA, at least one ketone body, at least one branched-chain amino acid, at least one HDLP subclass parameter, and optionally serum protein and / or citrate. In one embodiment, the HDLP subclass is small HDLP (S-HDLP). In some embodiments, the system may further be configured to calculate the MVX score based on GlycA, at least one ketone body, at least one branched-chain amino acid, and at least one value of 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 further 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 system may further be configured to calculate an MVX score based on the following model: MVX = βi * INFX + βm * MMX In these embodiments, the system may further 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 system may be further configured to calculate the MMX value using the following model. MMX=β4*lnBCAA+β5*lnKetoneBody This model can 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 embodiments, MMX may be written as follows: MMX = β9 * MMX1 + β10 * MMX2, where MMX1 is as described above, and MMX2 = β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein).

[0021] A further aspect of this disclosure relates to a method for observing a patient to evaluate treatment or to determine whether the patient is at risk of premature death. The method may include programmatically providing at least one defined MVX mathematical model disclosed herein, comprising several elements including at least one selected HDLP subclass, at least one branched-chain amino acid, at least one ketone body, and NMR-derived measurements of GlycA and at least one arbitrary protein or citrate. The method may further include programmatically analyzing a spectrum including the NMR-derived measurements. The method may also include programmatically calculating an MVX score for each patient using at least one defined model and corresponding patient sample measurements, and evaluating at least one of the following: (i) whether the MVX score is above a defined level of the 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 treatment. In some embodiments, the MVX score may be calculated using the following formula based on measurements of GlycA, at least one ketone body, at least one branched-chain amino acid, and at least one HDLP subclass. 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. Therefore, 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 can be denoted as MMX1. In some embodiments, the calculation of the MMX score may follow 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 embodiments, MMX may be written as follows: MMX = β9 * MMX1 + β10 * MMX2, where MMX1 is as described above, and MMX2 = β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein).

[0022] Further features, advantages, and details of this disclosure will be understood by those skilled in the art by reading the following drawings and detailed descriptions of preferred embodiments. Such descriptions are merely illustrative of this disclosure. Features described in relation to one embodiment may be incorporated with other embodiments, but are not limited thereto. That is, aspects of this disclosure described with respect to one embodiment may be incorporated into other embodiments, although this is not specifically stated. In other words, features of all embodiments and / or any embodiments may be combined in any way and / or combination. The above and other purposes and / or aspects of this disclosure are described in detail in the following specification.

[0023] As will be understood by those skilled in the art in light of this disclosure, embodiments of the disclosure may include methods, systems, apparatus and / or computer program products or combinations thereof. [Brief explanation of the drawing]

[0024] [Figure 1] A schematic diagram of a method for measuring MVX according to one embodiment of this disclosure is provided. [Figure 2] A schematic diagram of a method using MVX measurements according to one embodiment of this disclosure is shown. [Figure 3] This graph shows the cumulative mortality rate over 5 years for subgroups of CATHGEN participants stratified by MVX scores according to embodiments of this disclosure. In total, 1263 participants died during the 5-year follow-up period. [Figure 4]This table shows the predictive power (X²) and statistical significance (p-value) of parameters, including the parameters used to calculate INFX, MMX, and MVX in a Cox proportional hazards predictive model for mortality in the CATHGEN trial population according to one embodiment of the present disclosure. During the 5-year follow-up period, 1259 out of 6936 CATHGEN participants died. [Figure 5] Two tables are shown illustrating the predictive power and statistical significance of parameters in the Cox predictive model for mortality during the 5-year follow-up period in the CATHGEN study. The model on the left includes the Inflammation Index (INFX) and Metabolic Malnutrition Index (MMX) parameters, and the model on the right includes the MVX parameter, each representing one embodiment of the present disclosure. [Figure 6] In the Cox predictive model, two tables are shown illustrating the predictive power and statistical significance of parameters including MVX according to one embodiment of the disclosure, showing mortality rates in the CATHGEN trial population during a short-term follow-up of one year (left table) and during a longer-term follow-up of an average of seven years (right table). [Figure 7] This table shows the predictive power provided by the likelihood ratio chi-squared statistic of the Cox predictive model, which includes MVX alone (top row) or MVX plus nine additional covariate parameters (bottom row), for mortality occurring during three follow-up periods in the CATHGEN trial population according to embodiments of the present disclosure. [Figure 8] The left table is a graph showing the cumulative mortality rate over 12 years for female participants in an MESA study stratified by quintiles of MVX scores, according to an embodiment of the present disclosure. Of the 3,581 female MESA participants, 412 died during the 12-year follow-up period. The right table is a graph showing the cumulative mortality rate over 12 years for male participants in an MESA study stratified by quintiles of MVX scores, according to an embodiment of the present disclosure. Of the 3,198 male MESA participants, 554 died during the 12-year follow-up period. [Figure 9]This table shows the predictive power and statistical significance of MVX as determined according to one embodiment of the present disclosure, evaluated by a multinomial logistic regression model for MESA participants, and other parameters relating to fatal and non-fatal components of non-CVD mortality and composite CVD outcomes. [Figure 10] This table shows the predictive power and statistical significance of MVX as determined according to one embodiment of the present disclosure, evaluated by a multinomial logistic regression model for MESA participants, along with other parameters related to the dual outcomes of congestive heart failure (CHF) and mortality. [Figure 11] This table shows the predictive power and statistical significance of MVX as determined according to one embodiment of the present disclosure, evaluated by a multinomial logistic regression model for MESA participants, along with other parameters related to the dual outcomes of cancer and mortality. [Figure 12] This table shows the predictive power and statistical significance of MVX as determined according to one embodiment of the present disclosure, evaluated by a multinomial logistic regression model for MESA participants, along with other parameters related to the dual outcomes of chronic kidney disease (CKD) and mortality. [Figure 13] A schematic diagram of the system for the MVX evaluation module and / or circuit according to embodiments of this disclosure is shown. [Figure 14] A schematic diagram of an NMR spectrometer according to an embodiment of this disclosure is shown. [Figure 15] A schematic diagram of a data processing system according to an embodiment of this disclosure is shown. [Figure 16] Examples of analyses used to develop INFX, MMX1, MMX2, and MMX scores in broad and different patient populations according to embodiments of this disclosure are shown. [Modes for carrying out the invention]

[0025] The above and other purposes and aspects of this disclosure are described in detail in the specification below.

[0026] The Disclosure will now be described in more detail below with reference to the accompanying drawings illustrating embodiments of the Disclosure. However, the Disclosure can be implemented in many different forms and should not be construed as being limited to the embodiments described herein. Rather, these embodiments are provided to give the Disclosure consistency and completeness and to adequately convey the scope of the Disclosure to those skilled in the art.

[0027] Throughout the drawings, the same reference numbers indicate the same elements. In the drawings, some line thicknesses, layers, components, elements, or features may be emphasized for clarity. Dashed lines indicate any feature or behavior unless otherwise specified.

[0028] A. Definitions and Terms The terms used herein are for the purpose of describing only specific embodiments and are not intended to limit the disclosure. Unless the context clearly indicates otherwise, both singular and plural terms are intended herein. Furthermore, the terms “including” and / or “containing” as used herein describe the presence of a described feature, integer, process, operation, element, and / or component, but are not intended to exclude the presence or addition of one or more other features, integers, processes, operations, elements, components, and / or groups thereof. The terms “and / or” as used herein include any and all combinations of one or more related items listed. Phrases such as “between X and Y” and “about between X and Y” as used herein are understood to include X and Y. Phrases such as “about between X and Y” as used herein mean “about between X and about Y.” Phrases such as “from about X to Y” as used herein mean “from about X to about Y.”

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

[0030] In this specification, when describing various elements, components, regions, layers, and / or divisions, we may use terms such as "first," "second," etc., but it should be understood that these elements, components, regions, layers, and / or divisions are not limited by these terms. These terms are used solely to distinguish one element, component, region, layer, or division from another region, layer, or division. Accordingly, the first element, component, region, layer, or division discussed below may be called the second element, component, region, layer, or division without departing from the teachings of this disclosure. The order of operations (or processes) is not limited to the order shown in the claims or drawings unless otherwise specified.

[0031] The term "programmatically" means that an operation is performed using 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 processes, and usually refer to operations that are performed programmatically. The terms "automated" and "automated" mean that an operation can be performed with minimal work or without manual work or input. The term "semi-automated" means that an operator can perform some inputs or activations, but calculations and signal acquisition and the calculation of ionized component concentrations are performed electronically, usually programmatically, without requiring manual input.

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

[0033] The term "patient" is used in a broad sense to refer to an individual that 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 portion of an acute-phase reactant glycoprotein containing N-acetylglucosamine and / or N-acetylgalactosamine moieties, more specifically from the protons of two NAcGlc and two NAcGal methyl groups. The GlycA signal is centered at approximately 2.00 ppm in the plasma NMR spectrum at approximately 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. Therefore, if the temperature of the test sample changes, the GlycA peak region may also change. The GlycA NMR signal may include a subset of the NMR signal in a defined peak region so as to include only the clinically relevant signal contributors, and protein contributors to the signal in this region may be excluded, as will be discussed further below. See the contents of U.S. Patents Nos. 9,361,429, 9,470,771, and 9,792,410, which are incorporated herein by reference in their entirety as if they were full quotations.

[0035] As used herein, a chemical shift position (ppm) refers to an NMR spectrum referenced internally or externally. In one embodiment, the position may be referenced internally within the CaEDTA signal at 2.519 ppm. Accordingly, the prominent peak positions discussed and / or claimed herein may vary depending on how the chemical shift is generated or referenced, as is well known to those skilled in the art. Therefore, 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 an in vitro blood, plasma, serum, CSF, saliva, lavage fluid, sputum, or tissue sample from a human or animal. Embodiments of this disclosure are particularly suited to evaluating human plasma or serum biological samples for GlycA (e.g., not found in urine). Plasma or serum samples may be fasted or not fasted.

[0037] The terms “population norm” and “standard” refer to values ​​defined by mean-risk patients, such as those enrolled in the Framingham Progeny Study or the Multiethnic Study of Atherosclerosis (MESA), or by large studies (including multiple studies) of high-risk patients, such as those enrolled in the Catheter Genetics (CATHGEN) cardiac catheterization biorepository, or by other studies with sufficient samples to represent the general population or target patient population. In this specification, the terms “low-risk” (or “normal-risk”) mean individuals who do not have a known cardiovascular event. Such populations are also known as primary prevention populations. For example, MESA is a low-risk population. In this specification, 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, this disclosure is not limited to population values ​​in MESA or CATHGEN, as currently defined normal, low-risk, and high-risk population values ​​or levels may change over time. Therefore, a reference range may be provided and used to assess elevated or reduced levels and / or risk of a clinical disease state, associated with values ​​from a defined population in a risk segment (e.g., quartiles or quintiles).

[0038] The term “clinical disease state” is used broadly to include dangerous medical conditions that indicate it is appropriate to have medical intervention, treatment, modification of treatment, or elimination and / or observation of a particular treatment (e.g., a drug). By identifying the possibility of a clinical disease, clinicians can treat, delay, or prevent the onset of symptoms accordingly.

[0039] In this specification, the term "NMR spectral analysis" refers to proton ( 1 H) This means obtaining data that allows for the measurement of each parameter present in a biological sample, e.g., plasma or serum, using nuclear magnetic resonance spectroscopy. "Measure" and its derivatives refer to determining levels or concentrations and / or, for a particular lipoprotein subclass, measuring its average particle size. The term "NMR-derived" means that the associated measurement is calculated using NMR signals / spectrums derived from one or more scans of an in vitro biological sample in an NMR spectrometer.

[0040] The term "downfield" refers to the region / location on the NMR spectrum to the left of a particular peak / position / point (where the ppm scale is higher compared to a baseline). Conversely, the term "upfield" refers to the region / location on the NMR spectrum to the right of a particular peak / position / 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 the future risk of premature death in a subject, typically within 1 to 12 years. A risk model may include, but is not limited to, one or more suitable models, such as logistic regression models, Cox proportional hazards regression models, mixed models, or hierarchical linear models. A risk model can provide a measure of risk based on the probability of premature death within a defined timeframe, typically within 1 to 12 years. Risk models are particularly well-suited for risk stratification for patients with “intermediate risk” associated with a small to moderate chance of having a clinical event based on conventional risk factors. The MVX risk model can stratify the relative risk of premature death, such as that measured by standard χ² and / or p-values ​​(the latter with a well-represented study population).

[0042] The term "interaction parameter" refers to at least two distinct 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-indicator" refers to a multi-component biometric indicator.

[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. The lipoprotein component may include the (integrated) mathematical results of the lipoprotein particle's subclass, concentration, size, ratio and / or lipoprotein parameters, and / or defined lipoprotein parameters, or lipoprotein subclass measurements in combination with other parameters, such as GlycA.

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

[0046] As used herein, "untreated biological sample" refers to a biological sample that, unlike sample preparation for mass spectrometry, is not subjected to any process that would physically or chemically alter it after it is obtained (however, buffers and diluents may be used). Therefore, once a biological sample is obtained, components derived from the biological sample are not altered or removed. For example, once a serum biological sample is obtained, the serum is not subjected to any process that would remove components from the serum. In some embodiments, the untreated biological sample is not subjected to a filtration and / or ultrafiltration process.

[0047] B. Method for determining the MVX of a subject This specification discloses methods and systems for determining a subject's metabolic vulnerability index (MVX). These methods include predicting a patient's chance of premature death by utilizing a multivariate model of defined biomarkers. The methods include the steps of: obtaining a sample from a 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 the subject's metabolic vulnerability index value 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, the MVX value may be determined for subjects who are considered to be at low risk of cardiovascular events.

[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, the MVX value may be determined for subjects who are thought to be at high risk of cardiovascular events.

[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 can be denoted as MMX1 and may be used for subjects who appear to be at low risk for cardiovascular disease-related events. In some embodiments, the calculation of the MMX score may be as follows: 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 embodiments, MMX may be written as follows: MMX = β9 * MMX1 + β10 * MMX2, where MMX1 is as described above, and MMX2 = β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein) In these embodiments, the MVX score may be determined for subjects who appear to be at high risk of cardiovascular disease-related events.

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

[0051] The Metabolic Vulnerability Index (MVX) can provide a short-term (1 year) to long-term (12 years) assessment of premature death risk. These risk assessments can generate MVX values ​​separated from conventional risk factors.

[0052] Figure 1 shows a schematic diagram of one embodiment of a method for determining a subject's MVX value. This method may include an initial step of obtaining a nuclear magnetic resonance (NMR) spectrum of a plasma or serum sample obtained from the subject. Next, a calculated linear shape may be generated for the sample, which is based on the derived concentrations of lipoproteins and metabolite components potentially present in the sample (each derived concentration being a function of the reference spectrum and calculated reference coefficients for that component). This method can be concluded to determine 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 subjects during routine and simple screening, and to initiate diagnosis and treatment of conditions associated with premature death, or to prevent subjects from receiving potentially harmful drugs.

[0053] Figure 2 is a schematic diagram illustrating one example of the use of MVX to determine a patient's response to treatment. Using a multivariate model, patients can be evaluated at the start of or during clinical trials, and during treatment(s), to identify or observe drug development and / or anti-inflammatory, anti-obesity, or other drug or nutritional treatment candidates.

[0054] In some embodiments, the measurements are obtained by acquiring the NMR signal of an in vitro plasma or serum patient sample and determining the NMR-derived concentration measurements of HDL particle subclasses, GlycA, and / or multiple metabolic malnutrition biomarkers such as ketone bodies and BCAAs.

[0055] Embodiments of this disclosure provide a risk assessment of early all-cause mortality in patients using a multi-parameter (multivariate) model of defined predictive biometrics.

[0056] A multivariate risk assessment model may include, but is not limited to, S-HDLP, at least one ketone body, at least one defined branched-chain amino acid, and at least one defined HDLP element such as GlycA. Furthermore, the risk assessment model may include one or more citrates and serum proteins.

[0057] The multivariate model may include at least one of the following NMR measurements: GlycA, branched-chain amino acids, citrates, ketone bodies, total protein, and HDLP elements (e.g., subclasses) derived from the same NMR spectrum.

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

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

[0060] Embodiments of this disclosure appear particularly suitable for stratifying the risk of patients with similar conventional risk factors. In summary, it is conceivable that the MVX score can be used to stratify the relative risk of premature death. The MVX score can stratify the risk of premature death of patients with the same age, sex, blood pressure, and BMI, independently of these clinical factors.

[0061] Embodiments of this disclosure can assess the risk of premature death in patients within a 1 to 12-year timeframe using multiple risk model parameters.

[0062] As described above, embodiments of the present disclosure may include biometric indicators associated with inflammation (e.g., INFX) and metabolic malnutrition (e.g., MMX), as shown in Table 1. In some embodiments, the MVX score may be defined as including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value. In some embodiments of this disclosure, a method for determining the level of an index associated with the relative risk of premature death in a subject may 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), at least one ketone body, and optionally at least one citrate and serum protein; generating an Inflammation Index (INFX) value using the GlycA and at least one HDLP subclass measurements; generating at least one Metabolic Malnutrition Index (MMX) value using the at least one BCAA, at least one ketone body, and optionally protein and citrate measurements; and determining a Metabolic Vulnerability Index (MVX) value based on the INFX and MMX values. [Table 1]

[0063] Inflammation can be associated with many different disease conditions, including, but not limited to, cardiovascular disease (CVD). Inflammation is also thought to modulate HDL function. See, for example, Fogelman, "When Good Cholesterol Goes Bad," Nature Medicine, 2004. The carbohydrate components of glycoproteins can play biological roles in protein sorting, immunity and receptor recognition, inflammation, and other cellular processes.

[0064] As disclosed herein, the MVX model may include at least two inflammation indicators, e.g., GlycA and S-HDLP, and at least two metabolic malnutrition bioindicators, e.g., 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, and BMI, and in the case of antihypertensive drugs, the MVX mathematical model can generate MVX values ​​independently of any clinical parameters.

[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 biometrics 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 conventional Framingham or other risk assessments, can be used relatively easily as a screening tool, and may allow for the identification of individuals at risk earlier than conventional 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 embodiments of the present disclosure. In total, 1,263 out of 6,971 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) had a premature death rate more than 10 times higher 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 were largely independent of age and sex.

[0069] In some embodiments, MVX can be used to stratify mortality risk in high-risk populations, such as in the CATHGEN study, as shown in Figure 3.

[0070] As disclosed herein, the MVX value is calculated using multiple parameters, including at least GlycA, S-HDLP, at least one branched-chain amino acid, and at least one ketone body. The parameters may also include citrate and protein measurements as well as various interaction parameters. Figure 4 is a table showing the predictive power (χ²) and statistical significance (p-value) of conventional risk factors and MVX-related parameters in the Cox proportional hazards prediction model for mortality in the CATHGEN study population. In this table, "BCAA" is the sum of the concentrations of three branched-chain amino acids (valine, leucine, and isoleucine), and "Ketone bodies" is the sum of the concentrations of three ketone bodies (β-hydroxybutyrate, acetacetate, and acetone). Using the six MVX parameters shown in Figure 4 (including interaction parameters for lnGlycA*lnS-HDLP and lnCitrate*lnProtein), the MVX score used for 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). The MVX value may also be decomposed into two components: estimated inflammatory sites, which can be estimated by the Inflammation Index (INFX) parameter, and estimated malnutrition sites, which can be estimated by the Metabolic Malnutrition Index (MMX) parameter. Figure 5 shows the predictive power and statistical significance of these parameters, and two tables showing the MVX of the Cox predictive model for mortality during the 5-year follow-up period in the CATHGEN study. The model on the left includes INFX and MMX parameters, while the model on the right includes MVX parameters.

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

[0072] In some embodiments, MVX is calculated independently of conventional risk assessment parameters. Figure 7 is a table showing the predictive power provided by the likelihood ratio chi-squared statistic of the Cox predictive model for mortality over three follow-up periods in the CATHGEN study population, either for 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).

[0073] In some embodiments, MVX can be used to stratify mortality risk among low-risk populations, as shown in Figure 8. The left table in Figure 8 is a graph showing the cumulative mortality rate over 12 years for female participants in an MESA study stratified by quintiles of low-risk MVX scores (e.g., MVX1) according to embodiments of the present disclosure. Of 3,581 female MESA participants, 412 died during the 12-year follow-up period. The right table in Figure 8 is a graph showing the cumulative mortality rate over 12 years for male participants in an MESA study stratified by quintiles of MVX1 scores according to embodiments of the present disclosure. Of 3,198 male MESA participants, 554 died during the 12-year follow-up period.

[0074] In some embodiments, MVX is thought to be able to predict the relative risk of premature death regardless of the disease state(s) considered to be the “cause” of death. Figure 9 is a table showing the predictive power and statistical significance of MVX (e.g., MVX1) and other parameters for four different CVDs, along with the mortality outcomes of MESA (low risk) participants as evaluated by a multinomial logistic regression model. Three of the four outcomes are generally referred to as the composite “CVD” outcome: left column: non-fatal CVD events that did not result in death during follow-up (n=432); second column from the left: non-fatal CVD events that resulted in death later 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 early or concurrent CVD (n=610). The results show that MVX highly predicts three outcomes, including death (p<0.0001), but does not predict CVD in cases without death or subsequent failure to die (p=0.50). Therefore, the predictions of the fatal and non-fatal components of the composite CVD outcome by MVX are substantially different, suggesting that the etiologies of non-fatal and fatal CVD are more heterogeneous than believed, and raising questions about theories that combine fatal and non-fatal CVD into a single outcome.

[0075] Furthermore, as shown in Figures 10 to 12, mortality rates caused by or considered to be indirectly caused by other diseases other than CVD are also predicted by MVX. Figure 10 is a table showing the predictive power and statistical significance of MVX (e.g., MVX1), other parameters for the combination of outcomes for congestive heart failure (CHF), and the mortality rate of MESA (low-risk) participants as assessed by a multinomial logistic regression model. Figure 11 is a table showing the predictive power and statistical significance of MVX, other parameters for the combination of outcomes for cancer, and the mortality rate of MESA participants as assessed by a multinomial logistic regression model. Figure 12 is a table showing the predictive power and statistical significance of MVX, other parameters for the combination of outcomes for chronic kidney disease (CKD), and the mortality rate of MESA participants as assessed by a multinomial logistic regression model.

[0076] In some embodiments, sex 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 sex or age so as not to generate false negatives or false positives based on data corruption of such auxiliary data that is not directly related to the biological sample, for example.

[0077] In some embodiments, the MVX score is provided to the clinician based on data electronically correlated with the sample, or based on patient characterizations such as fasting "F" or non-fasting "NF" or statin "S" or non-statin "NS," which can be entered by the clinician or intake lab, for example, on a label associated with the biological sample at an NMR analyzer. Alternatively, patient characterization data can be stored in a computer database (remotely, via a server, or other defined route) and may include patient identifiers, sample types, test types, etc., which are entered into an electronic correlation file by the clinician or intake lab and are accessible or manageable by the intake lab communicating with the NMR analyzer. The patient characterization data can also be used to make a suitable MVX model available for a particular patient.

[0078] The metabolic vulnerability index can be used to observe subjects in clinical trials and / or drug therapies, to identify drug conflicts, and / or to observe changes in risk status (positive or negative) associated with specific medications or the patient's lifestyle, which may be unique to each patient.

[0079] Lipoprotein Lipoproteins comprise a wide variety of particles found in plasma, serum, whole blood, and lymph, including various types and amounts of triglycerides, cholesterol, phospholipids, sphingolipids, and proteins. These diverse 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 generally been classified in many ways based on physical properties such as density or electrophoresis, or on apolipoprotein content, e.g., apoB or apoA-1, and the major proteins in LDL and HDL, respectively.

[0080] Classification based on particle size 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 for the generation of measurements for subpopulations of different sizes from NMR measurements that reveal the concentrations of VLDL, LDL, and HDL subpopulations. For example, to optimize the risk association with early all-cause mortality, grouping of HDL subpopulations by different sizes, as discussed further below, may be used.

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

[0083] In a preferred embodiment, the MVX risk assessment model parameters may include NMR-derived measurements of the analytical signal, particularly associated with HDL, and the common NMR spectrum of the lipoprotein, using a defined analytical model that characterizes the analytical components of proteins and lipoproteins, including HDL, LDL, and VLDL / TRL. This type of analysis allows for rapid acquisition times of less than two minutes, typically around 20 to 90 seconds, and correspondingly, rapid program calculations that can generate measurements of the model elements, as well as the program calculation of one or more MVX risk scores using one or more defined risk models.

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

[0085] The grouping of lipoprotein subclasses may be summed, for example, according to certain embodiments of this disclosure, to determine the number of HDL or LDL particles. The prominent "small, large, medium" size ranges may be modified, or redefined by broadening or narrowing their upper or lower limits, or certain ranges within the prominent range may be excluded. The above particle sizes typically refer to average measurements, but other classifications may be used.

[0086] Embodiments of this disclosure classify lipoprotein particles into subclasses grouped by size range based on their function / metabolic relevance, which is assessed by their correlation with lipids and metabolic variables. Thus, as described above, the assessment can determine 15 distinct subpopulations (sizes) of lipoprotein particles. These distinct subpopulations can be grouped into subclasses defined with respect to VLDL / TRL and HDL and LDL. Intermediate-density lipoproteins (IDLs) can be combined with VLDL / TRL or LDL, or combined as a separate category in the size range between large LDL and small VLDL. For example, HDL subclass particles are typically in the range of about 7 nm to about 15 nm (on average), more commonly about 7.3 nm to about 14 nm (e.g., 7.4 nm to 13.5 nm). Total HDL concentration is the sum of the particle concentrations of each subpopulation of that HDL subclass. Different subpopulations of HDLP can be identified by numbers from 1 to 7, where "H1" represents the smallest-sized HDL subpopulation and "H7" represents the largest-sized HDL subpopulation. In some embodiments, the defined subclass of HDL particles includes small HDL particles (S-HDLP). In some embodiments, S-HDLP may include HDL particle subclasses having a diameter of about 7.3 nm (average) to about 9.0 nm (average). BCAA

[0087] In some embodiments, the MVX model includes a measurement 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 isoleucine, leucine, and valine (as described herein). In some embodiments, one or more of a 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, acetacetate, acetone) that can be obtained via NMR analysis of the NMR spectrum of a biological sample. The NMR quantification of each of the three ketone bodies is based on their NMR signal amplitudes derived from separate analytical models specialized for the three spectral regions in which the ketone body NMR signals appear. Extensive overlap with signals from numerous lipoprotein variants and specific, unspecified small molecule metabolites necessitates analytical analysis rather than simple integration of the ketone body signals. The derived amplitudes of the β-hydroxybutyrate, acetacetate, and acetone signals may be converted to μmol / L concentration units using conversion factors determined by spiking serum with stock ketone body solutions of known concentrations.

[0089] In one embodiment, the binotate β-hydroxybutyrate methyl signals appearing at approximately 1.16 and 1.15 ppm are quantified using a linear analysis model encompassing a spectral region from 1.07 ppm to 1.33 ppm. This region includes overlapping interference NMR signals from lipid fatty acid methylene protons of numerous TRL, LDL, and HDL lipoprotein subspecies, serum protein signals, triplet signals from ethanol (1.13, 1.15, and 1.17 ppm), binotate signals from lactate (1.29 and 1.31 ppm), and binotate signals from unspecified metabolites that rarely appear in human serum samples (1.10 and 1.11 ppm). In one embodiment, the analysis model includes a library of 83 spectral components to accurately determine the amplitude of NMR signals from β-hydroxybutyrate and various interfering substances in serum.

[0090] In one embodiment, the singlet acetacetate-methyl signal appearing at approximately 2.24 ppm is quantified using a linear analysis model encompassing the spectral region from 2.22 ppm to 2.39 ppm. This region includes overlapping interference NMR signals from lipid fatty acid methylene protons of numerous TRL, LDL, and HDL lipoprotein subspecies, serum protein signals, octet signals from β-hydroxybutyrate (2.25 to 2.39 ppm), and signals from three unspecified metabolites appearing at 2.22, 2.30, and 2.35 to 2.41 ppm. In one embodiment, the analysis model includes a library of 82 spectral components to accurately determine the amplitude of NMR signals from various interfering substances in acetacetate and serum.

[0091] In one embodiment, the singlet acetonemethyl signal appearing at approximately 2.19 ppm is quantified using a linear analysis model encompassing the spectral region from 2.14 ppm to 2.22 ppm. This region includes overlapping interference NMR signals from lipid fatty acid methylene protons of numerous TRL, LDL, and HDL lipoprotein subspecies, serum protein signals, and a singlet signal from an unspecified metabolite at 2.22 ppm. In one embodiment, the analysis model includes a library of 70 spectral components to accurately determine the amplitude of 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. GlycA measurements may be unitless parameters, such as those evaluated by NMR, by calculating the area under the peak region at defined peak positions in the NMR spectrum. In either case, GlycA measurements on a known population (e.g., MESA) can be used to define the level or risk of specific subgroups, such as those having values ​​within the upper half of a defined range, including values ​​in the third and fourth quartiles, or the top three to five quintiles.

[0093] citrate In some embodiments, the MVX model includes citrate measurements that can be obtained via NMR analysis of the NMR spectrum of a biological sample. NMR quantification of citrate is based on the amplitudes of three of the four members of the methylene proton quartet, appearing at approximately 2.64, 2.60, and 2.48 ppm, derived from an analytical model assuming a linear baseline and variable offset. A 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 criterion. The derived citrate signal amplitudes can be converted to concentrations in μmol / L using conversion factors determined by spiking serum with stock citrate solutions of known concentrations.

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

[0095] System for measuring C.MVX Referring to some embodiments of this disclosure, a system is provided that can perform each of the methods described herein. In some embodiments, the system may include: an NMR spectrometer configured to acquire an NMR spectrum (including multiple) 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 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 or communicating with memory. The system may further include an NMR spectrometer configured to acquire an NMR spectrum (or more) containing 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] Referring to some embodiments of this disclosure, an NMR system is provided that can perform each of the methods described herein. In some embodiments, the NMR system may include an NMR spectrometer, a flow probe communicating with the spectrometer, and a processor communicating with the spectrometer, configured to acquire: (i) at least one NMR signal of a defined GlycA-matched 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 of a defined ketone body-matched region of the NMR spectrum associated with the sample in the flow probe; (iii) at least one NMR signal of a defined BCAA-matched region of the NMR spectrum associated with the 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 acquired by the spectrometer according to any of the embodiments of the invention disclosed herein.

[0097] Some flowcharts and block diagrams in this specification illustrate the architecture, functionality, and operation of possible implementations of the analysis model and evaluation system and / or program according to the present invention. In this regard, each block in a flowchart or block diagram represents a module, segment, operation, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions described within a block may occur in a different order than shown in the diagram. For example, two consecutively shown blocks may actually be executed substantially simultaneously, or, depending on the associated functionality, the blocks may sometimes be executed in reverse order.

[0098] Referring here to Figure 13, it is conceivable that most, if not all, measurements can be performed in or using a system 10 that communicates with, or at least partially incorporates, an NMR clinical analyzer 22, for example, as described in U.S. Patent No. 8,013,602, which is incorporated herein by reference, as fully described herein. The analyzer 22 includes a spectrometer and a sample handler system.

[0099] 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, GlycA, BCAAs, ketone bodies, and, but is not limited to, HDLP subpopulations such as S-HDLP and / or citrate and / or protein. In some embodiments, the processor may be configured to calculate an MVX score based on the measured values ​​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 can 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 compute MMX 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 described above, and MMX2 = β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein).

[0100] The system may include at least one processor that may be mounted on the analyzer 22, or an analysis circuit 20 that includes at least one processor at least partially remote from the analyzer 22. In the latter case, the processor / analysis circuit 20 may be located entirely or partially on a server. The server may be provided using cloud computing, which includes providing on-demand computing resources over a computer network. Resources may be embodied as various infrastructure services (e.g., computers, storage devices, etc.), as well as applications, databases, file services, email, etc. In the conventional model of computing, both data and software are usually entirely contained within the user's computer, whereas in cloud computing, the user's computer may have little to no software or data (perhaps an operating system and / or a web browser), and requires little more than the functionality of a display terminal for processes performed on a network of external computers. Cloud computing services (or a collection of multiple cloud resources) are sometimes commonly referred to as the “cloud.” Cloud storage devices may include a model of networked computer data storage where data is stored on multiple virtual servers rather than being hosted on one or more dedicated servers. Data transfer can be encrypted and carried out over the internet using any suitable firewall to comply with industrial or regulatory standards such as HIPAA. The term "HIPAA" refers to U.S. law as defined by the Health Insurance Portability and Accountability Act. Patient data may include access numbers or identifiers, gender, age, and trial data.

[0101] The results of the MVX assessment may be transmitted to patients, clinician sites, health insurance agents, or pharmacies via computer networks such as the Internet 227, including by email. The results may be transmitted directly or indirectly from the analysis site. The results may also be printed and sent by conventional mail. This information can also be transmitted to pharmacies and / or health insurance companies, or even to patients, to distribute medical warnings to monitor prescriptions or drug use that may increase the risk of adverse events, or to prevent the prescription of conflicting medications. The results may be emailed to patients on their "home" computers or to widely used computer devices such as smartphones or notepads. The results may be, for example, an email attachment of a summary report or a text message warning.

[0102] For example, one or more electronic devices associated with different users, such as a clinician site, a patient, and / or a trial or lab site, may be configured to access an electronic analysis circuit that communicates with the display of each electronic device. The analysis circuit can be hosted on a server and provided to various devices via an internet portal or a downloadable app or other computer program. The circuit may be configured to allow a user, for example a clinician, to input one or more of the following: (i) the patient's conventional risk factor values, (ii) the patient's conventional risk factor values ​​and personal vulnerability index score, or (iii) the personal vulnerability index score. The circuit can automatically add different data fields based on the patient identifier or other password used during sign-in, or it can allow the user to input both the MVX score and conventional factor measurements for each patient. The analysis circuit can be configured to track changes in the MVX score over time and generate electronic reports that can be sent to clinicians, patients, or other users. The analysis circuit can also send notifications regarding recommendations for retesting, follow-up tests, etc. For example, if the MVX risk score rises, for instance, in an intermediate risk category, or exceeds a low risk value, the circuit can notify the clinician that further testing is appropriate, or it can notify the patient to consult with their doctor about which testing is appropriate or whether it would be desirable to increase the observation interval for a follow-up MVX trial. The analysis circuit may generate a risk assessment pathway or analysis to provide realistic information that stratifies the future risk of early all-cause mortality for patients with the same conventional risk factor values. The electronic analysis circuit may be installed on a server in the cloud, or otherwise accessible via the internet, or may be associated with different client architectures as will be understood by those skilled in the art. Thus, clinicians, patients, or other users can generate customized reports on risk assessment or obtain risk stratification information.

[0103] Figure 14 shows an example of MVX measurement using NMR. Several embodiments of this disclosure comprise an NMR system capable of performing each of the methods described herein. In some embodiments, the NMR system may include an NMR spectrometer, a flow probe communicating with the spectrometer, and a processor communicating with the spectrometer, configured to acquire: (i) at least one NMR signal of a defined GlycA-matched 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 of a defined ketone body-matched region of the NMR spectrum associated with the sample in the flow probe; (iii) at least one NMR signal of a defined BCAA-matched region of the NMR spectrum associated with the 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 acquired by the spectrometer according to any of the embodiments of the invention disclosed herein.

[0104] Referring to Figure 14, a system 207 for obtaining and calculating the linear shape of a selected sample is shown. System 207 includes an NMR spectrometer 22 for performing NMR measurements of the sample. In one embodiment, the spectrometer 22 is configured so that NMR measurements for the proton signal are performed at 400 MHz, while in other embodiments, measurements may be performed at frequencies between 200 MHz and approximately 900 MHz, or other suitable frequencies. Other frequencies corresponding to desired operable magnetic field strengths may be used. Typically, a proton flow probe is installed, along with a temperature controller 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 slow device I / O ports that connect to the operating elements of the spectrometer 22 via a pulse control and interface circuit 216. These elements include an RF transmitter 217 that generates RF excitation pulses of duration, frequency, and magnitude as instructed by at least one digital signal processor mounted on or communicable to the digital computer 211, and an RF power amplifier 218 that amplifies the pulses and connects them to an RE transmitting coil 219 surrounding the sample cell 220 and / or flow probe 220. The NMR signal generated by the sample excited in the presence of a 9.4 Tesla polarization magnetic field generated by the superconducting magnet 221 is received by coil 222 and applied to RF receiver 223. The amplified and filtered NMR signal is demodulated in 224, and the resulting quadrature signal is applied to the interface circuit 216, where it is digitized and input via the digital computer 211. The processor and / or analyzer circuit 20 in Figures 13 and 14, and / or the multi-parameter MVX risk module 350 in Figures 13 and 15, can be located in one or more processors associated with a digital computer 211 and / or secondary computer 213 or other computers that may be located in a facility or remotely, 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 which 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 a database associated with one or more servers. The computer 213 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, which is stored in its memory or, under the direction of a program accessible to the computer 213, processes the chemical shift spectrum in accordance with the teachings of the present invention and generates a report which can be output to the 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 computer display screens, electronic notepads, and smartphones can also be used 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, the printer 226 may be directly connected to the digital computer 211. Other interfaces and output devices may also be used, as is well known to those skilled in the art.

[0108] A particular embodiment of the present invention aims to provide a method, system, and / or computer program product that uses MVX assessment, which may be particularly useful in automated screening tests for clinical disease conditions and / or in vitro risk assessment for screening biological samples.

[0109] Embodiments of this disclosure may take the form of an embodiment of the entire software or an embodiment combining software and hardware, which are generally referred to herein as “circuits” or “modules.”

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

[0111] Computer-enabled or computer-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, devices, or propagation media. More specific examples (non-exclusive list) of computer-readable media include electrical connections having one or more lines, portable computer diskettes, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, and portable compact disk read-only memory (CD-ROM). Furthermore, computer-enabled or computer-readable media may include, for example, paper or another suitable medium on which a program is printed, so that the program can be electronically captured via optical scanning of the paper or other medium, and then, if necessary, compiled, interpreted, or processed in a suitable manner and further stored in computer memory.

[0112] Computer program code for performing the operations of the Disclosure may be written in an object-oriented programming language such as Java 7, Smalltalk, Python, LabVIEW, C++, or Visual Basic. However, computer program code for performing the operations of the Disclosure may also be written in a conventional procedural programming language such as the C programming language or assembly language. The program code may run entirely on the user's computer, partially on the user's computer, 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), wide area network (WAN), or secure area network (SAN), or the connection may be to an external computer (for example, via the Internet using an Internet service provider).

[0113] Figure 15 is a block diagram of an exemplary embodiment of a data processing system 305 showing a system, method, and computer program product according to an embodiment of the present invention. The processor 310 may have a memory 314 via an address / data bus 348 or communicate with the memory 314. The processor 310 may be any commercially available microprocessor or a custom microprocessor. The memory 314 represents an overall hierarchy of memory devices, including software and data used to realize the functionality of the data processing system 305. The memory 314 may include, but is not limited to, devices of types such as cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

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

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

[0116] The I / O device driver 358 typically includes software routines accessed by the application program 354 via the operating system 352 to communicate with devices such as I / O data ports, data storage devices 356, and specific memory 314 components and / or image acquisition systems 225. The application program 354 is an example of a program that implements various features of the data processing system 305 and may include at least one application that supports operation according to embodiments of the present invention. Finally, the data 356 represents static and dynamic data used by the application program 354, the operating system 352, the I / O device driver 358, and other software programs that may reside in memory 314.

[0117] The present invention is illustrated, for example, with reference to module 350, which is an application program in Figure 15, but as those skilled in the art will understand, other configurations can also be used while benefiting from the teachings of the present invention. Accordingly, the present invention should not be construed as being limited to the configuration of Figure 15, and 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 a clinical disease state or risk, and / or to determine whether a therapeutic intervention is desirable, and / or to indicate the effectiveness of a treatment, or even unintended therapeutic outcomes.

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

[0120] Examples Example 1 The Metabolic Vulnerability Index (MVX) mathematical model was developed using data collected from the CATHGEN trial population of 6,936 participants. Conventional clinical parameters, including age, race, sex, smoking status, hypertension, diabetes, 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. The Cox proportional hazards prediction model for mortality was used to generate the predictive power (χ²) and statistical significance (p-value) of each parameter, as shown in Figures 4 and 5. In this case, all enumerated parameters were used in the prediction model to generate the predictive power of each parameter, but the MVX of the model alone is a very strong, statistically significant predictor of mortality risk, as shown in Figure 7.

[0121] Using an MVX mathematical model generated from the Cox proportional hazards prediction model for mortality, MVX scores (1-100) were created for each participant in the CATHGEN study. These MVX scores were then used to subdivide the population into nine subgroups, and as shown in Figure 3, the cumulative mortality rate for each subgroup over the five-year follow-up period increased in direct proportion to the increase in the MVX score.

[0122] Figure 16 shows an example of how various models of INFX, MMX1, MMX2, and MMX can be used to develop numerical scores as shown in Figure 3. For application to normal (i.e., non-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 A1. How can we determine the level of an indicator associated with the relative risk of premature death in a subject? Obtaining samples from subjects, This involves 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 for generating a Metabolic Vulnerability Index (MVX) value using the measured values ​​of GlycA, the at least one HDLP subclass, the at least one BCAA, and the at least one ketone body. A3. Methods A1-A2 in which the HDLP subclass is a small HDLP (S-HDLP). A4. The aforementioned MVX value is, MVX=A+β1*lnGlycA+β2*lnS-HDLP+β4*lnBCAA+β5*lnKetoneBody A2 or A3 method determined using the model. A5. The MVX value is calculated as follows: MVX = A + β1*lnGlycA + β2*lnS-HDLP + β3*(lnGlycA*lnS-HDLP) + β4*lnBCAA + β5*lnKetoneBody A2 or A3 method determined using the model. A6. The MVX value is determined by the method in A5 for subjects who are considered to be at low risk for cardiovascular events. A7. The method of A1 or A2, further comprising measuring at least one of citrate and serum protein. A8. The method of A7, in which at least one of citrate and serum protein levels is measured in subjects who appear to be at high risk for CVD-related death. A9. The MVX value is determined by the methods A7 to A8, 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. Any one of the 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 for generating an Inflammation Index (INFX) value using the measured values ​​of GlycA and at least one HDLP subclass. A12.INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) Methods A10 to A11 for determining the INFX value using the model. A13. The method of A10 for generating a metabolic malnutrition index (MMX) value using the at least one BCAA, the at least one ketone body, and optionally protein and citrate measurements. A14. The above metabolic malnutrition index (MMX) value is calculated as MMX = β4*lnBCAA + β5*lnKetoneBody + β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein) Methods A10 and A13 are defined as follows. A15. Any one of the methods A10, A13, and A14, wherein the metabolic malnutrition index (MMX) includes 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 in A15 or A16, where MMX1 = β4*lnBCAA + β5*lnKetoneBody. A18. The method of A15 to A16, where MMX2 = β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein). A19. Any one of the methods A15 to A17 in which the MVX value is determined using the model MVX1 = βi * INFX + βm * MMX1. A20.MVX1 is the method of A19 used to determine subjects who appear to be at low risk for CVD-related events. A21. The method of A15, in which the MVX value is determined using the model MVX = βi*INFX + βm*MMX, where MMX = β9*MMX1 + β10*MMX2. A22.MVX is the method of A21 used to determine subjects who appear to be at high risk for CVD-related events. A23. Any one of the embodiments wherein the BCAA is at least one of leucine, isoleucine, or valine. A24. Any one of the embodiments wherein the ketone body is at least one of acetone, acetacetate, or beta-hydroxybutyrate. A25. The measurement is performed by any one of the embodiments described above, using NMR. B1. How to determine the level of an indicator associated with the relative risk of premature death in a subject? Obtaining a sample from the aforementioned subject, The measurement involves 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 citrate and serum protein. Using GlycA and measurements of at least one HDLP subclass, an Inflammation Index (INFX) value is generated, Using the aforementioned at least one BCAA, the aforementioned at least one ketone body, and optionally the aforementioned measurements of protein and citrate, generate at least one metabolic malnutrition index (MMX) value. This may include determining the Metabolic Vulnerability Index (MVX) value based on the INFX value and the MMX value. B2. The method of B1, wherein the HDLP subclass is a small HDLP (S-HDLP). B3. The INFX value is determined by the methods B1 to B2, using the model INFX = β1*lnGlycA + β2*lnS - HDLP + β3*(lnGlycA*lnS - HDLP). B4. The metabolic malnutrition index (MMX) value is defined by the methods of B1 to B2, where MMX = β4*lnBCAA + β5*lnKetoneBody + β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein). B5. Any one of Embodiments B1 to B4, wherein the metabolic malnutrition index (MMX) includes a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. Method B5, where B6.MMX = β9*MMX1 + β10*MMX2. Methods B5 and B6, where B7.MMX1 = β4*lnBCAA + β5*lnKetoneBody. Methods B5 and B6, where B8.MMX2 = β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein). B9. Methods B5 to B7, in which the MVX value is determined using the model MVX1 = βi * INFX + βm * MMX1. Method B9 is used to determine if a subject is considered to be at low risk for CVD-related events (B10.MVX1). B11. Any of the methods B5 to B8 used to determine the MVX value using the model MVX = βi * INFX + βm * MMX, where MMX = β9 * MMX1 + β10 * MMX2. B12. The method used in B11 to determine if a subject is at high risk for CVD-related events. B13. Any one of B1 to B12 wherein the BCAA is at least one of leucine, isoleucine, or valine. B14. Any one of B1 to B13, wherein the ketone body is at least one of acetone, acetacetate, or beta-hydroxybutyrate. B15. The measurement is performed by any one of the methods B1 to B14, which is performed by NMR. C1. A system that performs any one of the embodiments described above. An NMR spectrometer configured to acquire an NMR spectrum (including multiple) comprising at least one signal for D1.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, A system comprising a processor for determining a metabolic vulnerability index (MVX) value based on at least one signal measured for GlycA, at least one high-density lipoprotein particle (HDLP) subclass, at least one branched-chain amino acid (BCAA), and at least one ketone body, wherein the processor includes or communicates with memory. D2. The system of D1 further comprising an NMR spectrometer configured to acquire an NMR spectrum (one or more) containing at least one signal for serum protein and / or citrate, and a processor that determines a metabolic vulnerability index (MVX) value based on the at least one signal measured for serum protein and citrate. D3. Systems D1 to D2 in which the HDLP subclass is a small HDLP (S-HDLP). D4. The aforementioned processor further comprises MVX = A + β1 * ln GlycA + β2 * ln S - HDLP + β4 * ln BCAA + β5 * ln KetoneBody A D1 or D3 system 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 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, either system D1 or D3. D6. The processor is further configured to calculate the MVX value using one of the systems D1 to D3, which is a system of D7. The processor is further configured to calculate MVX values, including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value, in any one of the systems D1 to D6. D8. The processor is further configured in the system of D7 to calculate the INFX value using the model 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 * ln BCAA + β5 * ln Ketone Body + β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein). D10. The processor is further configured to calculate a metabolic malnutrition index (MMX) including a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value, any one of the systems D7 to D9. D11. The system of D10, wherein the processor is further configured to calculate the MMX value using the model MMX = β9 * MMX1 + β10 * MMX2. D12. The processor is further configured in the D10 to D11 systems to calculate the MMX1 value using the model MMX1 = β4 * ln BCAA + β5 * ln KetoneBody. D13. The above processor further has MMX2 = β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein) A D10 to D11 system configured to calculate MMX2 values ​​using the model. D14. The above processor further has MVX1 = βi * INFX + βm * MMX1 One of the D10 to D12 systems configured to calculate the MVX value using the model. D15. The above processor further comprises MVX = βi * INFX + βm * MMX, where MMX = β9 * MMX1 + β10 * MMX2 A system D10 or any one of D11 through D13 configured to calculate the MVX value using the model. 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, acetacetate, or beta-hydroxybutyrate. E1. NMR spectrometer and, A flow probe communicating with the aforementioned spectrometer, A processor communicating with a spectrometer configured to obtain: (i) at least one NMR signal for a defined GlycA-matched region of the NMR spectrum associated with GlycA of a blood plasma or serum sample in a flow probe; (ii) at least one NMR signal for a defined ketone body-matched region of the NMR spectrum associated with the sample in the flow probe; (iii) at least one NMR signal for a defined BCAA-matched region of the NMR spectrum associated with the sample in the flow probe; (iv) at least one NMR signal for at least one HDLP subclass-matched region of the NMR spectrum associated with the sample in the flow probe; and optionally, at least one NMR signal for the serum protein and / or citrate-matched region(s) of the NMR spectrum associated with the sample in the flow probe; An NMR system equipped with the following features. E2. The system of E1, wherein the HDLP subclass is a small HDLP (S-HDLP). E3. Systems E1 to E2, wherein the processor is further configured to calculate an MVX score based on GlycA, at least one ketone body, at least one branched-chain amino acid, and the measured values ​​of at least one HDLP subclass, using the formula MVX = A + β1*lnGlycA + β2*lnS - HDLP + β4*lnBCAA + β5*lnKetoneBody. E4. The processor is further configured in the E1 to E2 systems 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 comprises MVX = A + β1 * lnGlycA + β2 * lnS-HDLP + β3 * (lnGlycA * lnS-HDLP) + β4 * lnBCAA + β5 * lnKetoneBody + β6 * lnCitrate + β7 * lnProtein + β8 * (lnCitrate * lnProtein) One of the E1 through E4 systems configured to calculate the MVX value using the model. E6. The processor is further configured to calculate MVX values, including an Inflammation Index (INFX) value and a Metabolic Malnutrition Index (MMX) value, in any one of the E1 to E5 systems. E7. The system of E6, wherein the processor is further configured to calculate the INFX value using the model 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 * ln BCAA + β5 * ln KetoneBody + β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein). E9. The processor is further configured to calculate a metabolic malnutrition index (MMX) including a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value, any one of the systems E6 to E7. E10. The above processor further has MMX = β9 * MMX1 + β10 * MMX2 An E9 system configured to calculate MMX values ​​using the model. E11. Systems E9 to E10 in which the processor is further configured to calculate the MMX1 value using the model MMX1 = β4 * ln BCAA + β5 * ln KetoneBody. E12. The E9 to E10 systems, wherein the processor is further configured to calculate the MMX2 value using the model MMX2 = β6 * ln Citrate + β7 * ln Protein + β8 * (ln Citrate * ln Protein). E13. The processor is further configured to calculate the MVX value using the model MVX1 = βi * INFX + βm * MMX1, which is one of the systems E9 to E11. E14. The processor is further configured to calculate the MVX value using the model MVX = βi * INFX + βm * MMX, where MMX = β9 * MMX1 + β10 * MMX2, in any one of the systems E10 to E13. E15. Any one of the systems E1 to E14, wherein the BCAA is at least one of leucine, isoleucine, or valine. E16. Any one of the systems from E1 to E15, wherein the ketone body is at least one of acetone, acetacetate, or beta-hydroxybutyrate. F1. A method for observing a patient, Obtaining a sample from the aforementioned subject, The measurement 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 / or serum protein in the sample, Based on the aforementioned measurements, the metabolic vulnerability index (MVX) value is determined, A method comprising assessing whether the MVX value is at least above a defined level of the population norm associated with an increased risk of all-cause mortality. F2. Method F1, wherein the HDLP subclass is a small HDLP (S-HDLP). F3. The MVX value is determined using the F1 or F2 method, which uses the model MVX = A + β1*lnGlycA + β2*lnS-HDLP + β4*lnBCAA + β5*lnKetoneBody. F4. The MVX value is determined using the F1 or F2 method, which uses the model MVX = A + β1 * lnGlycA + β2 * lnS - HDLP + β3 * (lnGlycA * lnS - HDLP) + β4 * lnBCAA + β5 * lnKetoneBody. F5. Methods F1 to F4 for determining the MVX value in subjects who are considered to be at low risk for cardiovascular events. F6. The method of F1, in which at least one measurement of citrate and serum protein is performed in subjects who appear to be at high risk for CVD-related death. F7. The MVX value is determined using the F1 or F2 method, which uses 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. Methods F1 to F7, wherein the MVX value is defined as including the Inflammation Index (INFX) value and the Metabolic Malnutrition Index (MMX) value. A method of F8 for generating an Inflammation Index (INFX) value using F9.GlycA and the measured values ​​of at least one HDLP subclass. Methods F8 and F9 for determining the INFX value using the model F10.INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP). F11. The method of F8 for generating a metabolic malnutrition index (MMX) value using the at least one BCAA, the at least one ketone body, and optionally protein and citrate measurements. F12. The metabolic malnutrition index (MMX) value is defined by either F8 or F11, using the formula MMX = β4*lnBCAA + β5*lnKetoneBody + β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein). F13. The metabolic malnutrition index (MMX) is one of the methods F8 or F11, wherein the metabolic malnutrition index (MMX) includes a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. Method F13, where F14.MMX = β9*MMX1 + β10*MMX2. Either method F13 or F14, where F15.MMX1 = β4*lnBCAA + β5*lnKetoneBody. F16.MMX2 = β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein), which is either F14 or F13. F17. The MVX value is determined by any one of the methods F13 to F15 using the model MVX1 = βi * INFX + βm * MMX1. F18.MVX1 is the F17 method used to determine subjects who appear to be at low risk for CVD-related events. F19. The MVX value is determined using the method of F13, which employs the model MVX = βi * INFX + βm * MMX, where MMX = β9 * MMX1 + β10 * MMX2. F20.MVX is a method of F19 used to determine whether a subject is at high risk for CVD-related events. F21. Any one of F1 to F20 wherein the BCAA is at least one of leucine, isoleucine, or valine. F22. Any one of F1 to F21 wherein the ketone body is at least one of acetone, acetacetate, or beta-hydroxybutyrate. F23. The measurement is performed by any one of the methods F1 to F21, which is performed by NMR. G1. A method for observing a patient, (a) Obtaining samples from subjects, (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 citrate and serum protein in the sample. (c) Determining the metabolic vulnerability index (MVX) value based on the above measurements, (d) Repeating steps (a) through (c) at a later point, (e) A method comprising evaluating whether at least the MVX value has increased or decreased over time. G2. The method of G1, wherein the HDLP subclass is a small HDLP (S-HDLP). G3. The MVX value is determined by the G1 or G2 method using the model MVX = A + β1*lnGlycA + β2*lnS-HDLP + β4*lnBCAA + β5*lnKetoneBody. G4. The MVX value is determined using the G1 or G2 method, which uses the model MVX = A + β1 * lnGlycA + β2 * lnS - HDLP + β3 * (lnGlycA * lnS - HDLP) + β4 * lnBCAA + β5 * lnKetoneBody. G5. One of the methods G1 to G4 for determining the MVX value in subjects who are considered to be at low risk for cardiovascular events. G6. A G1 or G2 method performed in subjects suspected to be at high risk for CVD-related death, in which at least one of citrate and serum protein levels is measured. G7. The MVX value is determined using one of the following methods: G1, G2, or G6, 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. The MVX value is defined as any one of the methods G1 to G7, which includes the Inflammation Index (INFX) value and the Metabolic Malnutrition Index (MMX) value. The G8 method for generating an Inflammation Index (INFX) value using G9.GlycA and the measured values ​​of at least one HDLP subclass. G10.INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) Methods G8 to G9 for determining INFX values ​​using the model. G11. The method of G8 for generating a metabolic malnutrition index (MMX) value using the at least one BCAA, the at least one ketone body, and optionally protein and citrate measurements. G12. The Metabolic Malnutrition Index (MMX) value is calculated as follows: MMX = β4*lnBCAA + β5*lnKetoneBody + β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein) Either G8 or G11, as defined. G13. Any one of the methods G8, G11, or G12, wherein the metabolic malnutrition index (MMX) includes a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. G14. The method of G13, where MMX = β9*MMX1 + β10*MMX2. G15.MMX1 = β4*lnBCAA + β5*lnKetoneBody, as in the G13 method. G16. The method of G13, where MMX2 = β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein). G17. The MVX value is determined using the method of G15, which involves the model MVX1 = βi*INFX + βm*MMX1. G18.MVX1 is the G17 method used to determine whether a subject is at low risk for CVD-related events. G19. The MVX value is determined using the method of G13, which employs the model MVX = βi*INFX + βm*MMX, where MMX = β9*MMX1 + β10*MMX2. G20.MVX is the G19 method used to determine whether a subject is 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, acetacetate, or beta-hydroxybutyrate. G23. The measurement described above is performed by the method of G1, using NMR.

[0124] The foregoing is illustrative of the Disclosure and should not be construed as limiting the Invention. While several exemplary embodiments of the Disclosure have been described, those skilled in the art will readily understand that many modifications are possible in the exemplary embodiments without substantially departing from the novel teachings and merits of the Disclosure. Therefore, all such modifications are intended to fall within the scope of the Disclosure as defined in the claims. Where the Means-plus-Function clause is used in the claims, it is intended to encompass structures described herein as performing the listed functions not only for structural equivalents but also for equivalent structures. Therefore, the foregoing is illustrative of the Disclosure and should not be construed as limiting to any particular embodiment disclosed, and modifications to the disclosed embodiments, as well as other embodiments, are intended to fall within the scope of the appended claims. The Disclosure is defined by the appended claims and the equivalents of the claims contained herein. [Note 1] A method for determining the level of an indicator associated with the relative risk of premature death in a subject, Obtaining samples from subjects, A method comprising 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. [Note 2] The method described in Appendix 1 for generating a metabolic vulnerability index (MVX) value using the GlycA, the at least one HDLP subclass, the at least one BCAA, and the at least one ketone body measurement. [Note 3] The method according to either item 1 or 2, wherein the HDLP subclass is a small HDLP (S-HDLP). [Note 4] The method described in either Appendix 2 or 3, wherein the MVX value is determined using the model MVX = A + β1 * ln GlycA + β2 * ln S - HDLP + β4 * ln BCAA + β5 * ln KetoneBody. [Note 5] The aforementioned MVX value is determined using the method described in either item 2 or 3 of Appendix 2, using the model MVX = A + β1 * lnGlycA + β2 * lnS - HDLP + β3 * (lnGlycA * lnS - HDLP) + β4 * lnBCAA + β5 * lnKetoneBody. [Note 6] The method described in Appendix 5, wherein the MVX value is determined in subjects who are considered to be at low risk for cardiovascular events. [Note 7] The method according to Appendix 1, 2, or 3, further comprising measuring at least one of citrate and serum protein. [Note 8] The method according to Appendix 7, wherein at least one of the citrate and serum protein measurements is performed in subjects who appear to be at high risk for CVD-related death. [Note 9] The aforementioned MVX value is determined using the method described in either Appendix 7 or 8, which 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). [Note 10] The method according to any one of the appendices 1 to 9, wherein the MVX value includes the Inflammation Index (INFX) value and the Metabolic Malnutrition Index (MMX) value. [Note 11] The method described in Appendix 10 for generating an Inflammation Index (INFX) value using the measured values ​​of GlycA and at least one HDLP subclass. [Note 12] INFX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP) The method according to either Appendix 10 or 11 for determining the INFX value using the model. [Note 13] The method described in Appendix 10 for generating a metabolic malnutrition index (MMX) value using the at least one BCAA, the at least one ketone body, and optionally protein and citrate measurements. [Note 14] The aforementioned metabolic malnutrition index (MMX) is calculated as follows: MMX = β4*lnBCAA + β5*lnKetoneBody + β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein) The method described in either item 10 or 13 of the appendix, as defined above. [Note 15] The method according to any one of the appendices 10, 13, or 14, wherein the metabolic malnutrition index (MMX) includes a first metabolic malnutrition index MMX1 value and a second metabolic malnutrition index MMX2 value. [Note 16] The method described in Appendix 15, where MMX = β9 * MMX1 + β10 * MMX2. [Note 17] The method described in either item 15 or 16 of the appendix, wherein MMX1 = β4*lnBCAA + β5*lnKetoneBody. [Note 18] The method described in either item 15 or 16 of Appendix 15, wherein MMX2 = β6*lnCitrate + β7*lnProtein + β8*(lnCitrate*lnProtein). [Note 19] The method described in any one of the appendices 15 to 17, wherein the aforementioned MVX value is determined using the model MVX1 = βi * INFX + βm * MMX1. [Note 20] MVX1 is determined by the method described in Appendix 19 for subjects who appear to be at low risk for CVD-related events. [Note 21] The method described in Appendix 15, wherein the MVX value is determined using the model MVX = βi*INFX + βm*MMX, where MMX = β9*MMX1 + β10*MMX2. [Note 22] MVX is determined by the method described in Appendix 21 for subjects who appear to be at high risk for CVD-related events. [Note 23] The method according to any one of the appendices 1 to 22, wherein the BCAA is at least one of leucine, isoleucine, or valine. [Note 24] The method according to any one of the claims 1 to 23, wherein the ketone body is at least one of acetone, acetacetate, or beta-hydroxybutyrate. [Note 25] The measurement is performed by NMR according to any one of the methods described in Appendix 1 to 24. [Note 26] The method for determining the level of an indicator associated with the relative risk of premature death in a subject is: Obtaining a sample from the aforementioned subject, The measurement involves 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 citrate and serum protein. Using GlycA and measurements of at least one HDLP subclass, an Inflammation Index (INFX) value is generated, Using the aforementioned at least one BCAA, the aforementioned at least one ketone body, and optionally the aforementioned measurements of protein and citrate, generate at least one metabolic malnutrition index (MMX) value. A method comprising determining the Metabolic Vulnerability Index (MVX) value based on INFX and MMX values. [Note 27] The method described in Appendix 26, wherein the HDLP subclass is a small HDLP (S-HDLP). [Note 28] The measurement is performed by NMR according to the method of either Appendix 26 or 27. [Note 29] A system that performs any one of the items in Appendix 1 through 28. [Note 30] An NMR spectrometer configured to acquire an NMR spectrum (including multiple) 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, A processor that determines a metabolic vulnerability index (MVX) value based on 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, A processor is a system that includes memory or communicates with memory. [Note 31] Furthermore, an NMR spectrometer configured to acquire NMR spectra and / or multiple spectra containing at least one signal for serum protein and / or citrate, The system according to Appendix 30, comprising a processor that determines a metabolic vulnerability index (MVX) value based on at least one signal measured for the serum protein and the citrate. [Note 32] The system described in either item 30 or 31 of Appendix 30, wherein the aforementioned HDLP subclass is a small HDLP (S-HDLP). [Note 33] The system according to any one of Appendix 30 to 32, wherein the processor is further configured to calculate an MVX score based on GlycA, at least one ketone body, at least one branched-chain amino acid, and the measured values ​​of the at least one HDLP subclass, using the formula MVX = A + β1*lnGlycA + β2*lnS - HDLP + β4*lnBCAA + β5*lnKetoneBody. [Note 34] The aforementioned MVX value is determined using the system described in any one of the appendices 30 to 32, 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). [Note 35] The method according to any one of Appendix 30 to 32, wherein the MVX value includes the Inflammation Index (INFX) value and the Metabolic Malnutrition Index (MMX) value. [Note 36] NMR spectrometer and A flow probe communicating with the aforementioned spectrometer, A processor communicating with a spectrometer configured to acquire: (i) at least one NMR signal from a defined GlycA-matched 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 from a defined ketone body-matched region of the NMR spectrum associated with the sample in the flow probe; (iii) at least one NMR signal from a defined BCAA-matched region of the NMR spectrum associated with the 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. An NMR system including... [Note 37] The method according to Appendix 36, wherein the processor is further configured to calculate the amounts of GlycA, the at least one ketone body, the at least one branched-chain amino acid, and the HDLP subclass. [Note 38] The system described in either Appendix 36 or 37, wherein the aforementioned HDLP subclass is a small HDLP (S-HDLP). [Note 39] The aforementioned processor further comprises MVX = A + β1 * ln GlycA + β2 * ln S - HDLP + β4 * ln BCAA + β5 * ln KetoneBody A system according to any one of Appendix 36 to 38, configured to calculate an MVX score based on GlycA, at least one ketone body, at least one branched-chain amino acid, and at least one of the HDLP subclasses, using the formula. [Note 40] The aforementioned MVX value is determined using the system described in any of the appendices 36 to 38, which 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). [Note 41] The method described in Appendix 40, wherein the MVX value is defined as including the Inflammation Index (INFX) value and the Metabolic Malnutrition Index (MMX) value. [Note 42] A method of observing patients, Obtaining a sample from the aforementioned subject, The following measures the 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 citrate and serum protein in the aforementioned sample. Based on the aforementioned measurements, the metabolic vulnerability index (MVX) value is determined, A method comprising assessing whether the MVX value is at least above a defined level of the population norm associated with an increased risk of all-cause mortality. [Note 43] The method described in Appendix 42, wherein the HDLP subclass is a small HDLP (S-HDLP). [Note 44] The measurement is performed by NMR according to the method of any one of the appendices 42 to 43. [Note 45] A method of observing patients, (a) Obtaining samples from subjects, (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 citrate and serum protein in the sample. (c) Determining the metabolic vulnerability index (MVX) value based on the above measurements, (d) Repeating steps (a) through (c) at a later point, (e) A method comprising evaluating whether at least the MVX value has increased or decreased over time. [Note 46] The method described in Appendix 45, wherein the HDLP subclass is a small HDLP (S-HDLP). <00009​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​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 Status Modules 375 Risk Prediction Module 378 All-Cause Mortality Risk Assessment Module

Claims

1. An NMR spectrometer configured to acquire an NMR spectrum (including multiple) 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 comprises a processor that determines a metabolic vulnerability index (MVX) value based on 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, A processor is a system that includes memory or communicates with memory.

2. Furthermore, an NMR spectrometer configured to acquire an NMR spectrum and / or multiple spectra containing at least one signal for serum protein and / or citrate, The system according to claim 1, comprising a processor that determines a metabolic vulnerability index (MVX) value based on at least one signal measured for the serum protein and the citrate.

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

4. The system according to any one of claims 1 or 2, wherein the BCAA is at least one of leucine, isoleucine, or valine.

5. The system according to any one of claims 1, 3, or 4, wherein the processor is further configured to calculate an MMVX score based on acquired NMR signals of GlycA, at least one ketone body, at least one branched-chain amino acid, and the at least one HDLP subclass using the formula MMVX = A + β1 * ln GlycA + β2 * ln S - HDLP + β4 * ln BCAA + β5 * ln KetoneBody.

6. The system according to any one of claims 2 to 4, wherein the MVS value is determined using the model MVS = A + β1 * lnGlycA + β2 * lnS - HDLP + β3 * (lnGlycA * lnS - HDLP) + β4 * lnBCAA + β5 * lnKetoneBody + β6 * lnCitrate + β7 * lnProtein + β8 * (lnCitrate * lnProtein).

7. The system according to any one of claims 1 to 2, wherein the MVS value is defined as including an inflammation index (INFX) value and a metabolic malnutrition index (MMX) value.

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