Analysis of metabolic vulnerability by NMR

Abstract

Methods and systems for evaluating various health markers to differentiate between various health risks are disclosed. In one embodiment, the method includes evaluating biomarkers to determine the relative risk of premature total cause death in metabolic dystrophy-inflammatory syndrome (MMIS). A method can include obtaining NMR-derived measurements of GlycA, S-HDLP, branched chain amino acid (BCAA), and citric acid from a sample of a subject.

Description

[0001] field

[0002] The present disclosure generally relates to analysis of samples. The present disclosure may be particularly applicable to nuclear magnetic resonance (NMR) analysis of samples.

[0003] background

[0004] Currently, health care professionals and patients are interested in technologies that can evaluate the relative risk of death in patients suffering from certain disease states. Although some disease states may bring a higher risk of death, sometimes, a combination of one or more disease states may lead to an increased risk of death. However, the risk of death may not be limited to one disease state or a combination thereof. The increased risk of death may be attributed to various factors, which may be defined as risk factors in this article. Risk factors may include cholesterol levels, comorbidities, familial health history (e.g., diabetes, cancer, hypertension), weight, age, and other factors that may affect health. Therefore, health markers (further referred to as biomarkers) that may be associated with certain risk factors can be identified to help predictive models evaluate the increased risk of death in a population. Many risk factors may not be predictable when evaluating the risk of death, and may sometimes not be well correlated with the increased risk of death. For example, the first person who smokes may have a higher risk of lung cancer than the second non-smoker, and thus have a higher risk of death, but the second person may have a higher risk of death due to other reasons. Therefore, all-cause mortality is used as a term to further indicate the mortality rate of a population, regardless of the cause or risk. It has become necessary to identify certain biomarkers to help predict risk and evaluate the interest in all-cause mortality.

[0005] Although many disease states may bring risk of death, what is of particular interest is the syndrome called malnutrition-inflammatory complex syndrome (MICS). MICS may be associated with accelerated atherosclerosis and may predict death. MICS may also be defined as muscle atrophy associated with malnutrition-inflammatory syndrome. In MICS, individuals may or may not show appropriate risk factors that may lead clinicians to start a course of treatment. Therefore, it is necessary to analyze biomarkers that may be associated with all-cause mortality and / or MICS.

[0006] summary

[0007] Embodiments of the present disclosure include methods and systems that can evaluate a person's relative risk of premature all-cause death and / or provide a mortality risk stratification by evaluating the NMR spectrum of a biological sample from a patient sample. The biological sample from the patient sample can include at least blood, serum, saliva, urine, and sputum, from which one of the biomarkers can be extracted for NMR examination. In one embodiment, the analyte of interest can include a biomarker associated with MICS. In one embodiment, the analyte is analyzed using NMR. NMR, particularly proton NMR, is capable of detecting protons in a sample that are related to the structure of a compound in the sample. In essence, NMR can allow a user to predict the specific structure of a compound, or additionally use an internal standard to detect a specific analyte of interest.

[0008] It can be assumed that NMR biomarkers reflect the metabolic disorders behind protein energy consumption or metabolic malnutrition or can be its manifestation, rather than the nutritional deficiency aspect of malnutrition and its clinical diagnostic phenotype. Therefore, in many cases, MICS can be similar to MMIS (metabolic malnutrition-inflammatory syndrome). Many biomarkers can be analyzed to enhance the understanding of MICS / MMIS. For example, biomarkers that can be evaluated may include low serum albumin, C-reactive protein, GlycA, S-HDLP, citric acid, and branched chain amino acids (BCAA) including valine, leucine and isoleucine. These biomarkers can be parameters that can help determine the boundaries of MICS in patients, where low serum albumin represents a blood-bound globular protein produced by the liver and can help maintain volume in the bloodstream. Therefore, low levels of serum albumin may be associated with malnutrition and potential liver failure. More specifically, simultaneous measurement of GlycA and S-HDLP with small molecule metabolites (such as citric acid and BCAA) can define the parameters of metabolic malnutrition-inflammatory syndrome (MMIS). C-reactive protein is a protein also produced by the liver, which is produced in response to high inflammation in the body. GlycA is a biomarker also associated with systemic inflammation. S-HDLP can be defined as small high-density lipoprotein particles synthesized primarily in the liver. Citric acid is an organic molecule produced in response to respiration at the level of Kreb's cycle and can be involved in changing the pH of various fluids including serum. Finally, valine, leucine and isoleucine (e.g., branched-chain amino acids, BCAA) are part of a large group of nine amino acids (which can be referred to as essential amino acids). The human body does not produce the nine essential amino acids, which means that dietary habits may ultimately play a role in alleviating MICS.

[0009] In one embodiment, the user can obtain a sample of blood, serum or plasma from the subject to sample or analyze by nuclear magnetic resonance (NMR) spectroscopy. After sample collection and analysis, deconvolution of the NMR spectrum can be performed. In one embodiment, the deconvolution includes determining the presence and area (i.e., corresponding to concentration) of the signal corresponding to a certain biomarker. For example, the structure of GlycA is difficult to infer from the proton NMR spectrum, especially when one or more biomarkers overlap in the signal, but for convenience, considering that GlycA remains consistent in its structure, the proton position of the consistent part can be found and used to explain the entire molecule by calculation. Alternatively, when overlap is not possible, the consistent, single proton or a group of protons of the second biomarker can be used to identify the presence of one or more biomarkers. Although the proton spectrum can overlap when all existing biomarkers are detected simultaneously, the known, consistent position of the part within at least one biomarker can be easily detected, thereby allowing the user to infer the spectrum and simultaneously detect the presence of all biomarkers. The term "position" represents the x-axis of the proton NMR spectrum read in parts per million (ppm).

[0010] In one embodiment of the present disclosure, a method for determining the risk of death associated with MVX can be used, wherein MVX is referred to as the metabolic vulnerability index, and wherein the MVX score can be correlated with the risk of death. In addition, one embodiment of the present disclosure may include a method for calculating MVX, wherein MVX can be derived from the detection of the biomarkers low serum albumin, C-reactive protein, GlycA, S-HDLP, citric acid, valine, leucine, and isoleucine, and more particularly from the simultaneous measurement of GlycA, S-HDLP, citric acid, valine, leucine, and isoleucine (excluding low serum albumin and C-reactive protein). Another embodiment of the present disclosure may include a method for calculating MVX, wherein MVX can be derived from the detection of biomarkers including GlycA, S-HDLP, citric acid, valine, leucine, and isoleucine, and more particularly from the simultaneous measurement of six biomarkers GlycA, S-HDLP, citric acid, valine, leucine, and isoleucine. In addition, the multi-marker score can range from 1-100 and can be calculated using citric acid and three BCAAs to generate an index called the metabolic malnutrition index (MMX). Similarly, GlycA and S-HDLP can be used to generate an index called the inflammatory vulnerability index (IVX). When IVX and MMX are combined, MVX, a composite MICS (or MMIS) multi-marker, can be calculated. Combining biomarkers of malnutrition and inflammation may produce a total combined marker of the malocclusion-inflammatory complex syndrome (MICS) called MVX. When evaluated over a five-year period from one or more groups, MVX may have a strong correlation with mortality. Therefore, researchers are allowed to predict all-cause mortality in MICS based on the calculated IVX and MMX values ​​generated from proton NMR spectroscopy.

[0011] Embodiments of the present disclosure may include collecting demographic data from at least one or more cohorts of a population. The cohorts may include cohorts from Catheterization Genetics (referred to as CATHGEN), and may include cohorts from the Intermountain Heart collaborative study, with a combined total of more than eight thousand participants. Having a large number of patients may increase representativeness when generating statistical conclusions.

[0012] By reading the accompanying drawings and the detailed description of the preferred embodiments that follow (such description is only for the purpose of illustrating the present disclosure), those of ordinary skill in the art will appreciate the further features, advantages and details of the present disclosure. Features described with respect to one embodiment may be combined with other embodiments, although not specifically discussed together. That is, it should be noted that aspects of the present disclosure described with respect to one embodiment may be incorporated into different embodiments, although not specifically described in association therewith. That is, all embodiments and / or features of any embodiment may be combined in any manner and / or combination. The foregoing and other aspects of the present disclosure are explained in detail in the following specification.

[0013] BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a table showing baseline characteristics of the CATHGEN and Intermountain Heart cardiac catheterization cohorts according to one embodiment of the present disclosure.

[0015] Figure 2 is a table showing the association of IVX, MMX, and MVX with 5-year mortality in the CATHGEN and Intermountain Heart cohorts according to one embodiment of the present disclosure.

[0016] Figure 3 is a table showing the association of MVX with 5-year mortality in the CATHGEN patient subgroup according to one embodiment of the present disclosure.

[0017] Figure 4 is a diagram showing four graphs depicting the relative contribution of a given variable to the prediction of all-cause mortality and non-fatal MI in CATHGEN, calculated as a percentage of the total chi-square of the cox regression model, according to one embodiment of the present disclosure.

[0018] Figure 5 is a graph showing 5-year age-adjusted mortality by baseline IVX and MMX scores in CATHGEN according to one embodiment of the present disclosure. The number of subjects in each subgroup is given in parentheses. The synergy (interaction) between IVX and MMX is shown in that each has a much greater effect on the risk of death when the level of the other is higher rather than lower.

[0019] Figure 6 is a graph showing the cumulative incidence of death by baseline MVX score in the CATHGEN cohort according to one embodiment of the present disclosure.

[0020] Figure 7is a schematic diagram depicting various considerations that may be applicable to disease and death prevention according to one embodiment of the present disclosure.

[0021] Figure 8 is a table showing the calculation of sex-specific IVX, MMX, and MVX scores according to one embodiment of the present disclosure.

[0022] Fig. 9 is a table showing Spearman correlations in the CATHGEN and Intermountain Heart cohorts according to one embodiment of the present disclosure.

[0023] Fig.10 is a table showing the prognostic contribution of 15 covariates included in the cause-specific Cox model for all-cause mortality and non-fatal MI during 5 years of follow-up in CATHGEN according to one embodiment of the present disclosure.

[0024] Fig.11 is a table showing the prognostic contribution of six MVX variables in CATHGEN to cause-specific Cox models for all-cause mortality and non-fatal MI during 5 years of follow-up according to one embodiment of the present disclosure.

[0025] Fig.12 is a table showing the prognostic contribution of IVX and MMX in CATHGEN to cause-specific Cox models for all-cause mortality and non-fatal MI during 5 years of follow-up according to one embodiment of the present disclosure.

[0026] Fig.13 is a table showing the prognostic contribution of MVX in CATHGEN to cause-specific Cox models for all-cause mortality and non-fatal MI during 5 years of follow-up according to one embodiment of the present disclosure.

[0027] Fig.14 is a table showing the association of different HDL measurements in CATHGEN with 5-year mortality according to one embodiment of the present disclosure.Only small HDL particles (diameter below about 8.8 nm) are protective and are modified by GlycA.

[0028] Fig.15 is a table showing baseline characteristics of male and female participants in CATHGEN according to one embodiment of the present disclosure.

[0029] Fig.16 is a table showing the prognostic contribution of IVX and MMX in CATHGEN to the Cox model for all-cause mortality occurring within 1, 3, 5, and 6 to 10 years of follow-up according to one embodiment of the present disclosure.

[0030] Fig.17 is a table showing the prognostic contribution of MVX in CATHGEN to the Cox model for all-cause mortality occurring within 1, 3, 5, and 6 to 10 years of follow-up according to one embodiment of the present disclosure.

[0031] Fig.18 is a table showing the prognostic contribution of IVX and MMX in CATHGEN for cause-specific Cox models for different causes of death during 5 years of follow-up according to one embodiment of the present disclosure.

[0032] Fig.19 is a table showing the prognostic contribution of MVX in CATHGEN to cause-specific Cox models for different causes of death during 5 years of follow-up according to one embodiment of the present disclosure.

[0033] Fig. 20 is a table showing the association of MVX score with 5-year mortality in CATHGEN patient subgroups according to one embodiment of the present disclosure.

[0034] Fig.21 The set of graphs shows the relative contribution and associated sign of each variable to the prediction of all-cause mortality in the Intermountain Heart cohort, calculated as a percentage of the total chi-square of the Cox regression model, according to one embodiment of the present disclosure.

[0035] Fig. 22 The set of graphs shows the cumulative incidence of death by baseline MVX quintiles in the development of the CATHGEN cohort and the replication Intermountain Heart cohort according to one embodiment of the present disclosure.

[0036] The foregoing and other objects and aspects of the present disclosure are explained in detail in the following description.

[0037] Detailed Description

[0038] The terms used herein are only used for the purpose of describing specific embodiments, and are not intended to limit the present disclosure. The singular forms "one", "a kind of" and "said" used herein are also intended to include plural forms, unless the context clearly indicates otherwise. It is further understood that when used in this specification, the terms "comprising" and / or "including" specify the existence of stated features, integers, steps, operations, elements and / or components, but do not exclude the existence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. The term "and / or" used herein includes any and all combinations of one or more related listed items. Phrases used herein such as "between X and Y" and "between about X and Y" should be interpreted as including X and Y. Phrases used herein such as "between about X and Y" refer to "between about X and about Y". Phrases used herein such as "from about X to Y" refer to "from about X to about Y".

[0039] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those commonly understood by those of ordinary skill in the art to which the present disclosure belongs. It is further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the specification and the relevant technology, and should not be interpreted in an idealized or overly formalized meaning unless explicitly defined as such in this article. For the sake of brevity and / or clarity, well-known functions or configurations may not be described in detail.

[0040] It should be understood that, although the terms first, second, etc. can be used in this article to describe each element, component, region, layer and / or part, these elements, components, regions, layers and / or parts should not be limited by these terms. These terms are only used to distinguish an element, component, region, layer or part from another region, layer or part. Therefore, the first element, component, region, layer or part discussed below can be referred to as the second element, component, region, layer or part, without departing from the teaching of the present disclosure. Unless otherwise specifically noted, the order of operation (or step) is not limited to the order shown in the claims or drawings.

[0041] The term "programmatically" refers to operations directed by a computer program and / or software, a processor, or an ASIC to be performed. The term "electronic" and its derivatives refer to automated or semi-automated operations performed using devices having circuits and / or modules rather than by mental steps, and generally refer to operations performed programmatically. The terms "automated" and "automatic" mean that the operations can be performed with minimal or no human effort or input. The term "semi-automated" means that some input or activation by an operator is allowed, but the calculations and signal acquisition and calculation of the concentration of the ionized component are done electronically (usually programmatically) without manual input.

[0042] The term "about" means ± 10% of a specified value or number (mean or average).

[0043] The term "patient" or "subject" is used broadly and refers to an individual who provides a biological specimen or biological sample for testing or analysis.

[0044] The term "biological sample" refers to an in vitro blood, plasma, serum, cerebrospinal fluid, saliva, lavage fluid, sputum or tissue sample of a human or animal. Embodiments of the present disclosure may be particularly suitable for evaluating human plasma or serum biological samples, particularly GlycA (e.g., not found in urine). The plasma or serum sample may be fasting or non-fasting. In particular, the human biological sample may be most appropriately obtained from a patient via phlebotomy.

[0045] The term "GlycA" refers to a biomarker derived from measuring a composite NMR signal of a carbohydrate portion of an acute phase reactant, a glycoprotein containing an N-acetylglucosamine and / or N-acetylgalactosamine moiety, more specifically protons derived from 2-NAcGlc and 2-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 may 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 change. The GlycA NMR signal may include a subset of the NMR signal at a defined peak region so as to include only clinically relevant signal contributions and may exclude protein contributions to the signal within this region, as will be discussed further below. See US Patent Nos. 9,361,429, 9,470,771, and 9,792,410, the contents of which are hereby incorporated by reference as if fully set forth herein.

[0046] Chemical shift position (ppm) used herein represents the NMR spectrum of internal or external reference.In one embodiment, the position can be internally referenced to the CaEDTA signal at 2.519ppm.Therefore, the peak position discussed herein and / or claimed may change according to the generation or reference mode of chemical shift, as well known to those skilled in the art.Therefore, it should be clear that some described and / or claimed peak position has equivalent different peak positions in other corresponding chemical shifts, as well known to those skilled in the art.

[0047] The terms "population norm" and "standard" refer to values ​​defined by one or more large studies of higher risk patients, such as those recruited into the Catheterization Genetics (CATHGEN) cardiac catheterization and IntermountainHeart collaborative research biorepository, or other studies with large enough samples to represent the general population or target patient population. The disclosure of the present invention is not limited to the population values ​​in the CATHGEN or Intermountain Heart collaborative studies as currently defined normal or low-risk and high-risk population values, as levels may change over time. Therefore, reference ranges (e.g., quartiles or quintiles) associated with values ​​for a particular population in a risk segment can be provided and used to assess elevated or decreased levels and / or the risk of having a clinical disease state.

[0048] The term "clinical disease state" is used broadly and includes at-risk medical conditions that may indicate the need for medical intervention, treatment, therapeutic adjustment, or exclusion of a treatment (e.g., pharmaceutical drug) and / or monitoring. Identification of the likelihood of a clinical disease can allow the clinician to treat, delay, or inhibit the occurrence of the condition accordingly.

[0049] As used herein, the term "NMR spectroscopy" refers to the use of protons ( 1 H) Nuclear magnetic resonance spectroscopy techniques obtain data that can measure the corresponding parameters present in a biological sample (e.g., plasma or serum). "Measure" and its derivatives mean determining the level or concentration, and / or for certain lipoprotein subclasses, measuring their average particle size. The term "NMR-derived" refers to the measurement value associated with the calculation of the NMR signal / spectrum obtained by performing one or more scans of an in vitro biological sample using an NMR spectrometer.

[0050] The term "lowfield" refers to the area / position to the left of a peak / position / point on an NMR spectrum (relative to a higher ppm scale of reference). Conversely, the term "highfield" refers to the area / position to the right of a peak / position / point on an NMR spectrum.

[0051] The terms "mathematical model" and "model" are used interchangeably and, when used with "MVX," "Metabolic Vulnerability Index," or "risk," refer to a risk statistical model for evaluating a subject's risk of premature death in the future (usually within 1-12 years). The risk model may be or include any suitable model, including, but not limited to, one or more of a logistic regression model, a Cox proportional hazards regression model, a mixed model, or a hierarchical linear model. The risk model may provide a risk measure based on the probability of premature death within a determined time frame (usually within 1-12 years). The risk model may be particularly suitable for providing a risk stratification for patients with "intermediate risk," which is associated with a small to moderate likelihood of a clinical event occurring based on traditional risk factors. The MVX risk model may stratify the relative risk of premature death, as measured by standard χ2 and / or p-values ​​(the latter for a sufficiently representative study population).

[0052] The term "interaction parameter" refers to at least two different defined parameters combined as a (multiplied) product and / or ratio. Examples of interaction parameters include, but are not limited to (S-HDLP)(GlycA) and (protein)(citrate).

[0053] The term "multi-marker" refers to a multi-component biomarker.

[0054] The term "lipoprotein component" refers to a component in a mathematical risk model associated with lipoprotein particles, including the size and / or concentration of one or more lipoprotein subclasses (subtypes). A lipoprotein component may include any of the following: a lipoprotein particle subclass, concentration, size, ratio, and / or mathematical product (multiplication) of a lipoprotein parameter and / or a lipoprotein subclass measurement of a defined lipoprotein parameter or in combination with other parameters (such as GlycA).

[0055] The term "HDLP" means a high-density lipoprotein particle number measurement (e.g., HDLP number) that adds the particle concentrations of defined HDL subclasses. Total HDLP can be generated using a total high-density lipoprotein particle measurement that adds the concentrations (μmol / L) of all HDL subclasses (which can be divided into different size categories such as large, medium, and small according to size) ranging from about 7 nm (mean) to about 14 nm (mean), typically between 7.4-13.5 nm. In certain embodiments, HDL can be identified as a number of discrete size components, for example, 7 different sizes of HDLP subpopulations (H1-H7), ranging from the smallest HDLP size associated with H1 to the largest HDLP size associated with H7. In certain embodiments, the defined HDL particle subclasses include small HDL particles (S-HDLP). In certain embodiments, the S-HDLP can include HDL particle subclasses with diameters between about 7.3 nm (mean) and about 9.0 nm (mean).

[0056] The terms MICS (malnutrition-inflammatory complex syndrome) and MMIS (metabolic malnutrition-inflammatory syndrome) are used interchangeably and synonymously to describe "reverse epidemiology" whereby increases in conventional cardiovascular risk factors such as body mass index (BMI), serum cholesterol, and blood pressure may be associated with a decrease, rather than an increase, in cardiovascular and all-cause mortality.

[0057] The terms "sex" and "gender" are used interchangeably and synonymously herein. Thus, in many embodiments, examples, and figures, the terms "sex-specific" and "gender-specific" are used similarly and are used to describe individuals from each of one or more groups. Additionally, and more specifically, as used herein and for research and calculation purposes, "sex" and "gender" are limited to one of two options, namely male and female.

[0058] Unless otherwise indicated herein, abbreviations may be defined as follows: ASCVD refers to atherosclerotic cardiovascular disease, CAD refers to coronary artery disease, CHF refers to chronic heart failure, CKD refers to chronic kidney disease, IVX refers to inflammatory vulnerability index, MICS refers to malnutrition-inflammatory complex syndrome, MMIS refers to metabolic-malnutrition-inflammatory syndrome, NMR refers to nuclear magnetic resonance, S-HDLP refers to small high-density lipoprotein particles, MMX refers to metabolic malnutrition index, and MVX refers to metabolic vulnerability index.

[0059] lipoprotein

[0060] Lipoprotein can include multiple particles found in blood plasma, serum, whole blood and lymph, including triglyceride, cholesterol, phospholipid, sphingolipid and protein of various types and amounts. These various particles allow the originally hydrophobic lipid molecules in the blood to dissolve, and play the multiple functions related to lipid transport between lipolysis, lipogenesis and intestinal tract, liver, muscle tissue and adipose tissue. In blood and / or blood plasma, lipoprotein can be classified in a variety of ways, usually based on physical properties such as density or electrophoretic mobility or apolipoprotein content measured values, such as apoB or apoA-1, are respectively the main proteins in LDL and HDL.

[0061] Classification based on particle size determined by nuclear magnetic resonance can distinguish different lipoprotein particles based on size or size range. For example, NMR measurements can identify at least 15 different lipoprotein particle subtypes, including at least 7 high-density lipoprotein (HDL) subtypes, at least 3 low-density lipoprotein (LDL) subtypes, and at least 5 very low-density lipoprotein (VLDL) subtypes, the latter of which may also be referred to as TRL (triglyceride-rich lipoprotein).

[0062] Current analytical methods can allow NMR measurements that can provide concentrations of VLDL, LDL, and HDL subpopulations to measure small and large subpopulations of the respective groups. For example, to optimize the risk association with premature all-cause mortality, different sizes of HDL subpopulation groupings can be used, as will be discussed further below.

[0063] The NMR-derived estimated lipoprotein sizes indicated herein generally represent average measurements, but other size cutoffs may be used.

[0064] In a preferred embodiment, the MVX risk assessment model parameters may include NMR-derived measurements of deconvoluted signals associated with common NMR spectra of lipoproteins (and particularly HDL), wherein a defined deconvolution model is used to characterize the deconvoluted components of proteins and lipoproteins (including HDL, LDL, VLDL / TRL). This type of analysis can provide a rapid acquisition time of less than 2 minutes (typically between about 20 seconds and 90 seconds) and corresponding rapid programmatic calculations to generate measurements of model components, and then programmatically calculate one or more MVX risk scores using one or more defined risk models.

[0065] In addition, it should be noted that, although it can be considered that NMR measurements of lipoprotein particles are particularly suitable for the analysis described herein, it is contemplated that other techniques may be used now or in the future to measure these parameters, and the embodiments of the present disclosure are not limited to such measurement methods. It is also contemplated that different schemes utilizing NMR (e.g., including different deconvolution schemes) may be used to replace the deconvolution schemes described herein. See, for example, Kaess et al., The lipoprotein subfraction profile: heritability and identification of quantitative trait loci, J Lipid Res. Vol. 49, pp. 715-723 (2008); and Suna et al., 1H NMR metabolomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self-organizing maps, NMR Biomed. 2007; 20: 658-672. Flotation and ultracentrifugation using density-based separation techniques to evaluate lipoprotein particles and ion mobility analysis are alternative techniques for measuring lipoprotein subclass particle concentrations.

[0066] For example, the lipoprotein subclass groupings can be summed to determine the number of HDL or LDL particles according to certain specific embodiments of the present disclosure. It is worth noting that the "small, large and medium" size ranges indicated can be changed or redefined to expand or reduce their upper or lower limits, or even exclude certain ranges within the indicated ranges. The particle sizes mentioned above generally refer to average measurements, but other classification criteria can be used.

[0067] Embodiments of the present disclosure classify lipoprotein particles into subclasses grouped by size range, and the subclasses are based on the functional / metabolic relevance assessed by their relevance to lipids and metabolic variables. Thus, as noted above, the evaluation can measure more than 15 discrete lipoprotein particle subgroups (sizes). These discrete subgroups can be grouped into determined subclasses of VLDL / TRL and HDL and LDL. Intermediate density lipoprotein (IDL) can be combined with VLDL / TRL or LDL, or as a separate category in the size range between large LDL and small VLDL. For example, HDL subclass particles usually range (average) between about 7nm to about 15nm, more generally between about 7.3nm to about 14nm (e.g., 7.4nm-13.5nm). Total HDL concentration is the sum of the particle concentrations of each subgroup of its HDL subclass. The different subgroups of HDLP can be identified with numbers between 1-7, wherein "H1" represents the HDL subgroup of the smallest size, and "H7" is the HDL subgroup of the largest size. In certain embodiments, the defined HDL particle subclass comprises small HDL particles (S-HDLP). In certain embodiments, the S-HDLP may include a subclass of HDL particles having a diameter between about 7.3 nm (average) and about 9.0 nm (average).

[0068] BCAA

[0069] In certain 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 20150149095, which are incorporated herein by reference. The MVX model may include one or more BCAAs, including one or more of isoleucine, leucine, and valine (as discussed herein). In certain embodiments, one or more of the three BCAAs (valine, leucine, and isoleucine) may be quantified by NMR.

[0070] Ketone bodies

[0071] In certain embodiments, the MVX model can include a measurement of at least one ketone body (β-hydroxybutyrate, acetoacetate, acetone), which can be obtained by NMR analysis of the NMR spectrum of a biological sample. The NMR quantification of each of the three ketone bodies is based on its NMR signal amplitude, which comes from a separate deconvolution model for the three spectral regions in which the ketone body NMR signals appear. Due to the large overlap with signals from numerous lipoprotein subspecies and identified and unidentified small molecule metabolites, a deconvolution analysis can be used rather than simply integrating the ketone body signals. The amplitudes of the resulting β-hydroxybutyrate, acetoacetate, and acetone signals can be converted to μmol / L concentration units using conversion factors determined by spiking serum with known concentrations of ketone body stock solutions.

[0072] In one embodiment, a linear deconvolution model covering the spectral region from 1.07 to 1.33 ppm can be used to quantify the double peak of the β-hydroxybutyrate methyl signal appearing at approximately 1.16 and 1.15 ppm. This region can include overlapping interfering NMR signals from lipid fatty acid methylene protons of numerous TRL, LDL and HDL lipoprotein subspecies, serum protein signals, triplet signals from ethanol (at 1.13, 1.15 and 1.17 ppm), doublet signals from lactate (at 1.29 and 1.31 ppm), and doublet signals from unidentified metabolites (at 1.10 and 1.11 ppm), which only rarely appear in human serum samples. In one embodiment, the deconvolution model can include a library of 83 spectral components to accurately interpret the amplitude of the NMR signals from β-hydroxybutyrate and various interfering substances in serum.

[0073] In one embodiment, a linear deconvolution model that can cover the spectral region from 2.22 to 2.39 ppm can be used to quantify the single peak of the acetoacetate methyl signal appearing at about 2.24 ppm. This region can include overlapping interfering NMR signals of lipid fatty acid methylene protons from numerous TRL, LDL and HDL lipoprotein subspecies, serum protein signals, one or more octet signals from β-hydroxybutyrate (2.25 to 2.39 ppm), and signals from 3 unidentified metabolites appearing at 2.22, 2.30 and 2.35-2.41 ppm. In one embodiment, the deconvolution model can include a library of 82 spectral components to accurately explain the amplitude of the NMR signals from acetoacetate and various interfering substances in serum.

[0074] In one embodiment, a linear deconvolution model covering the spectral region from 2.14 to 2.22 ppm can be used to quantify the single peak of the acetone methyl signal appearing at 2.19 ppm. This region can include overlapping interfering NMR signals from lipid fatty acid methylene protons of numerous TRL, LDL and HDL lipoprotein subspecies, serum protein signals, and a single peak signal from an unidentified metabolite at 2.22 ppm. In one embodiment, the deconvolution model can include a library of 70 spectral components to accurately interpret the amplitude of the NMR signals from various interfering substances in acetone and serum.

[0075] GlycA

[0076] GlycA can be measured using a defined mathematical linear deconvolution model, as described in U.S. Pat. No. 9,470,771, which is incorporated herein by reference in its entirety. The GlycA measurement can be a unitless parameter, as assessed by NMR, by calculating the area under the peak area at a defined peak position in the NMR spectrum. In any case, GlycA measurements for a known population can be used to define levels or risks for certain subgroups, e.g., those with values ​​within the upper half of a defined range, including values ​​in the third and fourth quartiles, or the upper 3-5 quintiles, etc.

[0077] Citric Acid

[0078] In certain embodiments, the MVX model can include the measured value of citric acid, which can be obtained by the NMR analysis of the biological sample NMR spectrum. The NMR of citric acid can be quantitatively based on the NMR signal amplitude of three of the four members of the methylene proton quartet that appear at about 2.64, 2.60 and 2.48ppm, and the amplitude can be from the deconvolution model assuming linear baseline and variable offset. The fourth member of the citric acid signal quartet can appear at about 2.52ppm, and can overlap with the unimodal signal of CaEDTA as an internal chemical shift reference. Use the conversion factor determined by mixing the citric acid stock solution of known concentration in serum, the citric acid signal amplitude drawn can be converted to μmol / L concentration unit.

[0079] Serum Protein

[0080] In certain embodiments, the MVX model can include the measured value of serum protein, which can be obtained by NMR analysis of biological sample NMR spectrum. Alternatively, in the case of use, serum protein or serum albumin measured values ​​can be obtained by conventional means. The NMR quantification of serum protein can be based on the amplitude of the wide NMR signal from non-lipoprotein protein, and the amplitude comes from the linear deconvolution model covering the spectral region from 0.71 to 1.03ppm. This region includes overlapping interference NMR signals of lipid fatty acid methyl protons from numerous TRL, LDL and HDL lipoprotein subspecies, and those from branched-chain amino acids valine, leucine and isoleucine. In one embodiment, the deconvolution model can include a library of 66 spectral components to accurately explain the amplitude of the NMR signals from various interfering substances in serum protein and serum. The amplitude of the serum protein signal obtained can be reported in any unit of signal amplitude, or converted to molar concentration units using a conversion factor determined by mixing a known concentration of serum albumin stock solution into serum.

[0081] method

[0082] In one embodiment, a method comprises evaluating the NMR spectrum of at least one biomarker from a patient sample, the biomarker comprising: high-density lipoprotein (HDL), GlycA, branched-chain amino acids, ketones, citric acid or protein. In a further embodiment, multiple of the aforementioned biomarkers are evaluated. In another embodiment, a method comprises evaluating the NMR spectrum of at least one biomarker from a patient sample, the biomarker comprising high-density lipoprotein (HDL), GlycA, branched-chain amino acids or citric acid. In another embodiment, the evaluation results of the NMR of one or more of the aforementioned biomarkers are used to create a score representing the patient's risk of death and / or the relative risk of death compared to other patients.

[0083] In one embodiment of the present invention, high-density lipoprotein (HDL) biomarkers may include a variety of particles found in plasma, serum, whole blood and lymph, containing various types and amounts of triglycerides, cholesterol, phospholipids, sphingolipids and proteins. The HDL biomarkers may include HDL subclasses, including, but not limited to: large HDL particles (L-HDLP) containing a diameter of 9.5-12.0 nm; small HDL particles (S-HDLP) containing a diameter of 7.4-8.7 nm; etc.

[0084] In one embodiment, amino acids can be used to help generate the MVX index, where the branched chain amino acid biomarker can comprise amino acids including, but not limited to, valine, leucine, and / or isoleucine, or any combination thereof.

[0085] In one embodiment, the glycoprotein biomarker may comprise one or more acute phase glycoproteins, which may comprise at least GlycA.

[0086] In one embodiment, the organic biomarker may comprise at least a citric acid molecule.

[0087] In one embodiment, the lipoprotein particles may comprise high density lipoprotein (HDL) biomarkers, and more specifically small high density lipoprotein particles.

[0088] In one embodiment, the plasma protein biomarkers may comprise plasma protein subclasses, including, but not limited to, albumin.

[0089] In one embodiment, ketone body biomarkers may comprise a subset of ketone bodies including, but not limited to, acetone, acetoacetate, or beta-hydroxybutyrate.

[0090] As noted, in one embodiment of the invention the results of the evaluation of one or more biomarkers can be used to create a score representing the patient's risk of death and / or relative risk of death. U.S. Patent No. 11,156,621 (the disclosure of which is hereby incorporated by reference) includes a discussion of the "Metabolic Vulnerability Index" (MVX). The MVX score can be calculated using one or more NMR-derived measurements in one embodiment of the invention. The one or more NMR-derived measurements can include a proton NMR spectrum generated by at least one BCAA biomarker, at least one HDL biomarker, at least one glycoprotein biomarker, a citric acid biomarker, a plasma protein biomarker, and a ketone body biomarker, or a combination thereof.

[0091] In embodiments of the present disclosure, at least one or more biomarkers, when analyzed in NMR spectroscopy, provide data, alone or in combination, to illustrate a patient's risk of death and / or relative risk of death. When calculating MVX to confirm a patient's risk of death and / or relative risk of death, biomarkers and subclasses of biomarkers can be used. In yet other embodiments, when calculating MVX to confirm risk of death and / or relative risk of death, other biomarkers not included herein can be utilized and analyzed using NMR spectroscopy to generate an MVX score. Enhanced multi-marker performance can be achieved by including additional biomarkers.

[0092] Embodiments of the present disclosure include methods for determining marker levels associated with a person's risk of premature death. The method may include obtaining a sample from a human body and measuring GlycA, at least one high-density lipoprotein particle (HDLP) subclass, at least one branched-chain amino acid (BCAA), at least one ketone body, at least one serum protein, and citric acid. The method may further include obtaining a sample from a human body and measuring GlycA, at least one high-density lipoprotein particle (HDLP) subclass, at least one branched-chain amino acid (BCAA), and citric acid. In one embodiment, the high-density lipoprotein particles are small HDL particles (S-HDLP).

[0093] In certain embodiments, these measurements are used to generate MVX scores. In certain embodiments, the MVX score is determined using the following model: MVX = A + β1 * lnGlycA + β2 * lnS-HDLP + β4 * lnBCAA + β5 * ln ketone bodies. In certain embodiments, the MVX value is determined using the following model: MVX = A + β1 * lnGlycA + β2 * lnS-HDLP + β3 * (lnGlycA * lnS-HDLP) + β4 * lnBCAA + β5 * ln ketone bodies.

[0094] It should be noted that throughout this disclosure, A and β 1 -β n The empirical value of may vary depending on the model used. For example, β1 in the first equation for MVX above (i.e., the equation that does not include the term β3*(lnGlycA*lnS-HDLP)) will typically be a different value than β1 in the second equation above (i.e., the equation that includes this product term).

[0095] In certain embodiments, the method includes measuring at least one citric acid, at least one HDLP subclass, and at least one BCAA in addition to GlycA. In certain embodiments, the method includes measuring citric acid, GlycA, at least one HDLP subclass, and at least one BCAA simultaneously. In certain other embodiments, the method includes measuring at least one of citric acid and protein and GlycA, at least one HDLP subclass, at least one BCAA, and at least one ketone body. In certain embodiments, a measurement value including at least one of citric acid (citric acid) and serum protein (protein) is performed on subjects considered to have a high risk of death. In certain embodiments, the MVX value is determined using the following model: MVX=A+β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP)+β4*lnBCAA+β5*lnketone body+β6*lncitric acid+β7*lnprotein+β8*(lncitric acid*lnprotein).

[0096] In certain embodiments, the MVX value is defined as comprising an inflammatory vulnerability index (IVX) value and a metabolic malnutrition index (MMX) value. The values ​​MVX, IVX, and MMX can all be sex-specific. For example, MVX F (values ​​specific to females) can be defined as containing the female-specific values ​​MMX F and IVX F Alternatively, MVX M Can be a value specific to males and can be defined as containing the male-specific value MMX M and IVX M Generally, the MVX value can be defined to include the Inflammatory Vulnerability Index (IVX) value and the Metabolic Malnutrition Index (MMX) value, regardless of gender.

[0097] In certain embodiments, the measured values ​​of at least GlycA and at least one HDLP subclass are used to generate an inflammatory vulnerability index (IVX) value. In certain embodiments, the IVX value is determined using the following model: IVX = β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP). In other embodiments, IVX can be determined using the following model F Value: 9+(GlycA*-0.000187)+(S-HDLP*-0.3585)+((GlycA*S-HDLP)*0.000348). F The resulting scores may vary, but may include a range of 3.0 to 9.0 (inclusive), corresponding to scores of 1 to 100, respectively. In addition, IVX may be determined using the following model M :9+(GlycA*-0.00437)+(S-HDLP*-0.52307)+((GlycA*S-HDLP)*0.000817). in IVX M The resulting scores may vary, but may generally include a range of 1.4 to 7.6 (inclusive), corresponding to scores of 1 to 100, respectively.

[0098] In certain embodiments, the measured values ​​of at least one BCAA and at least one ketone body are used to generate a metabolic malnutrition index (MMX) value. In certain embodiments, the MMX value is determined using the following model: MMX=β4*lnBCAA+β5*lnketone body. The model can represent MMX1, and as discussed in detail herein, is a calculation for a population / subject that is considered to be at low risk.

[0099] In other embodiments, alternative MMX values ​​are generated using measured values ​​of at least one BCAA, at least one ketone body, citric acid, and protein. For example, in certain embodiments, the following model can be used to determine the MMX value: MMX = β4*lnBCAA + β5*lnketone body + β6*lncitric acid + β7*lnprotein. Alternatively, the following model can be used to determine the MMX value: MMX = β4*lnBCAA + β5*lnketone body + β6*lncitric acid + β7*lnprotein + β8*(lncitric acid *lnprotein). In such an embodiment, MMX can be described as follows: MMX = β9*MMX1 + β10*MMX2, wherein MMX1 is as described above, and MMX2 = β6*lncitric acid + β7*lnprotein + β8*(lncitric acid *lnprotein).

[0100] Measurements used to generate MMX values, including measurements of citrate and protein, are typically made in subjects and / or populations considered to be at high risk for CVD-related mortality (eg, subjects who have had a cardiovascular event or symptoms suggestive of potential CVD).

[0101] However, in other embodiments of the present disclosure, at least one BCAA and citric acid may be used to determine the MMX value. The MMX value may be sex-specific. Thus, the following model may be used to find the MMX F value:

[0102] ((4+(Leu*-0.03142)+(Leu 2 *0.0000893))*0.353)+

[0103] ((7+(Val*-0.03362)+(Val 2 *0.0000689))*0.684)+(Ileu*0.00332)+

[0104] ((1+(Citr*-0.0072)+(Citr 2 *0.0000573))*0.7135).

[0105] In MMX F The resulting scores can vary, but can generally range from ln(.8) to ln(1.8), inclusive, corresponding to scores of 1 to 100, respectively. Alternatively, the following model can be used to determine MMX m :

[0106] ((4+(Leu*-0.01594)+(Leu 2 *0.0000291))*1.076)+

[0107] ((7+(Val*-0.0239)+(Val 2 *0.00005))*0.414)+(Ileu*0.01265)+

[0108] ((1+(Citr*0.00906)+(Citr 2 *-0.0000126))*0.5881).

[0109] In MMX M The resulting scores may vary, but may generally include a range of ln(1.59) to ln(2.1), inclusive, corresponding to scores of 1 to 100, respectively.

[0110] Thus, in certain embodiments, the metabolic vulnerability index can be described as: MVX=βi*IVX+βm*MMX. For application to normal (i.e., low) risk patient populations (e.g., people who have not had a known cardiovascular event), the metabolic vulnerability index can be described as MVX1=βi*IVX+βm*MMX1 (where βi and βm may have unique values, depending on the model used). Conversely, for application to high-risk patient populations (e.g., people with known cardiovascular events), the metabolic vulnerability index can be described as MVX=βi*IVX+βm*MMX, where βi and βm may have unique values, depending on the model used, and MMX=β9*MMX1+β10*MMX2. In some cases, the βm of the low-risk model and the high-risk model are the same.

[0111] In certain embodiments, the metabolic vulnerability index for a female can be described as: MVX F =(IVX F *2.27278)+(lnMMX F *12.13511)+(IVX F *lnMMX F )*-1.09312, where the resulting score can vary, but can generally include 17 to 25 (inclusive), corresponding to a score of 1 to 100, respectively. In addition, the metabolic vulnerability index for men can be described as:

[0112] MVX M =(IVX M *3.54601)+(lnMMX M *14.41428)+(IVX F *lnMMX M )*-1.43438, where the resulting score can vary, but can generally include 27 to 34.2 (inclusive), corresponding to a score of 1 to 100, respectively.

[0113] For example, in some cases, MVX scores can be monitored in high-risk subjects or groups as a way to monitor the overall health of the subject and the risk of fatal cardiovascular events or death caused by other causes. Alternatively, MVX scores can be monitored in clinical trials of new drugs for high-risk groups as a way to monitor the efficacy of the test drug. For low-risk subjects, clinicians can choose to monitor MVX values ​​as a means of assessing overall health and well-being. In certain embodiments, MVX values ​​can be used as a guide to lifestyle changes that promote heart health.

[0114] system

[0115] Still other embodiments relate to a system. Certain embodiments of the present disclosure include systems capable of performing each method described herein. In certain embodiments, the system may include an NMR spectrometer configured to obtain an NMR spectrum and / or a spectrum of at least one signal comprising GlycA, at least one signal of at least one small high-density lipoprotein particle (S-HDLP) subclass, at least one signal of at least one branched-chain amino acid (BCAA) and at least one signal of at least one citric acid; and a processor, which determines a metabolic vulnerability index (MVX) value based on at least one signal of the measured GlycA, at least one small high-density lipoprotein particle (S-HDLP) subclass, at least one branched-chain amino acid (BCAA) and at least one citric acid, wherein the processor includes a memory or communicates with a memory.

[0116] In certain embodiments, 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. At least one processor can be configured to use at least one NMR spectrum to determine a metabolic vulnerability index score for a corresponding biological sample, which score is based on at least one defined premature death risk mathematical model, which can take into account at least one HDL subclass component measurement, at least one branched-chain amino acid measurement, GlycA measurement, citric acid measurement, and optionally ketone bodies and / or protein measurements obtained from at least one in vitro biological sample of the subject. In one embodiment, the HDLP subclass is a small HDLP (S-HDLP). In certain embodiments, the processor can be configured to calculate an MVX score based on the measurement 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*lnketone body. In certain 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 * ln ketone body. In certain 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 * ln ketone body + β6 * ln citric acid + β7 * ln protein + β8 * (ln citric acid * ln protein). In certain embodiments, the processor may be configured to calculate a sex-specific MVX score from one of the following models:

[0117] MVX F =(IVX F *2.27278)+(lnMMX F *12.13511)+(IVX F *lnMMX F )*-1.09312, where the resulting score can vary, but can typically include 17 to 25 (inclusive), corresponding to a score of 1 to 100, respectively. Or, for males:

[0118] MVX M =(IVX M *3.54601)+(lnMMX M *14.41428)+(IVX F *lnMMX M)*-1.43438, where the resulting score can vary, but can generally include 27 to 34.2 (inclusive), corresponding to a score of 1 to 100, respectively.

[0119] In certain embodiments, the processor may be configured to calculate an MVX score comprising an inflammatory vulnerability index (IVX) value and a metabolic malnutrition index (MMX) value. Thus, in certain embodiments, the processor may be configured to calculate an MVX score based on the following model: MVX=βi*IVX+βm*MMX. In these embodiments, the processor may be configured to calculate IVX values ​​using the following model: IVX=β1*lnGlycA+β2*lnS-HDLP+β3*(lnGlycA*lnS-HDLP). In certain embodiments, the processor may be configured to calculate MMX values ​​using the following model: MMX=β4*lnBCAA+β5*lnketone bodies; the model may represent MMX1. In certain embodiments, the processor may be configured to calculate MMX using the following model: MMX=β4*lnBCAA+β5*lnketone bodies+β6*lncitric acid+β7*lnprotein. In another embodiment, the processor can be configured to calculate MMX using the following model: MMX = β4 * ln BCAA + β5 * ln ketone body + β6 * ln citric acid + β7 * ln protein + β8 * (ln citric acid * ln protein). In such an embodiment, MMX can be described as follows: MMX = β9 * MMX1 + β10 * MMX2, wherein MMX1 is as described above, and MMX2 = β6 * ln citric acid + β7 * ln protein + β8 * (ln citric acid * ln protein).

[0120] In certain embodiments, the processor may be configured to calculate sex-specific MMX values ​​using the following model:

[0121] MMX F =

[0122] ((4+(Leu*-0.03142)+(Leu 2 *0.0000893))*0.353)+

[0123] ((7+(Val*-0.03362)+(Val 2 *0.0000689))*0.684)+(Ileu*0.00332)+

[0124] ((1+(Citr*-0.0072)+(Citr 2 *0.0000573))*0.7135).

[0125] In MMXF The resulting scores may vary, but may generally include a range of ln(.8) to ln(1.8), inclusive, corresponding to scores of 1 to 100, respectively.

[0126] MMX m =

[0127] ((4+(Leu*-0.01594)+(Leu 2 *0.0000291))*1.076)+

[0128] ((7+(Val*-0.0239)+(Val 2 *0.00005))*0.414)+(Ileu*0.01265)+

[0129] ((1+(Citr*0.00906)+(Citr 2 *-0.0000126))*0.5881).

[0130] In MMX M The resulting scores may vary, but may generally include a range of ln(1.59) to ln(2.1), inclusive, corresponding to scores of 1 to 100, respectively.

[0131] Still other embodiments relate to NMR systems. The system includes an NMR spectrometer; a flow probe in communication with the spectrometer; and at least one processor in communication with the spectrometer. At least one processor can be configured to obtain: (i) at least one NMR signal of a defined GlycA fitting region of an NMR spectrum associated with GlycA of a plasma or serum sample in the flow probe; (ii) at least one NMR signal of a defined citric acid fitting region of an NMR spectrum associated with a sample in the flow probe; (iii) at least one NMR signal of a defined BCAA fitting region of an NMR spectrum associated with a sample in the flow probe; and (iv) at least one NMR signal of an HDLP subclass parameter. The processor can be further configured to calculate measurements of GlycA, citric acid, at least one branched-chain amino acid, and an HDLP subclass parameter. The system can be further configured to calculate an MVX score using a defined mathematical model for all-cause mortality risk, which uses calculated measurements of GlycA, citric acid, at least one branched-chain amino acid, at least one HDLP subclass parameter, and optional serum proteins (proteins) and / or ketone bodies. In one embodiment, the HDLP subclass is a small HDLP (S-HDLP). In certain embodiments, the system can be further configured to calculate the MVX score based on the measured values ​​of GlycA, citric acid, at least one branched-chain amino acid, and at least one HDLP subclass. In certain embodiments, the system can be further configured to calculate the MVX score based on the measured values ​​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 * lnketone body. In certain embodiments, the system can 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 * lnketone body. In certain embodiments, the system can 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 * ln ketone bodies + β6 * ln citric acid + β7 * ln protein + β8 * (ln citric acid * ln protein). In certain embodiments, the system can be further configured to calculate an MVX score comprising an inflammatory vulnerability index (IVX) value and a metabolic malnutrition index (MMX) value. In certain embodiments, the system can be configured to calculate a sex-specific MVX score using sex-specific IVX and MMX from one of the following models:

[0132] MVX F =(IVX F *2.27278)+(lnMMX F *12.13511)+(IVX F *lnMMX F )*-1.09312, where the resulting score can vary, but can typically include 17 to 25 (inclusive), corresponding to a score of 1 to 100, respectively. Or, for males:

[0133] MVX M =(IVX M *3.54601)+(lnMMX M *14.41428)+(IVX F *lnMMX M )*-1.43438, where the resulting score can vary, but can generally include 27 to 34.2 (inclusive), corresponding to a score of 1 to 100, respectively.

[0134] Thus, in certain embodiments, the system may be further configured to calculate the MVX score based on the following model: MVX = βi * IVX + βm * MMX. In these embodiments, the system may be further configured to calculate the IVX value using the following model: IVX = β1 * lnGlycA + β2 * lnS-HDLP + β3 * (lnGlycA * lnS-HDLP). In certain embodiments, the system may be further configured to calculate the MMX value using the following model: MMX = β4 * lnBCAA + β5 * ln ketone body; the model may represent MMX1. In certain embodiments, the system may be further configured to calculate MMX using the following model: MMX = β4 * lnBCAA + β5 * ln ketone body + β6 * ln citric acid + β7 * ln protein. In another embodiment, the system can be further configured to calculate MMX using the following model: MMX = β4*lnBCAA + β5*lnketone body + β6*lncitrate + β7*lnprotein + β8*(lncitrate*lnprotein). In such an embodiment, MMX can be described as follows: MMX = β9*MMX1 + β10*MMX2, wherein MMX1 is as described above, and MMX2 = β6*lncitrate + β7*lnprotein + β8*(lncitrate*lnprotein).

[0135] Other methods

[0136] Additional aspects of the present disclosure relate to methods for monitoring patients to evaluate treatment or determine whether a patient is at risk of premature death. The method may include programmatically providing at least one defined MVX mathematical model disclosed herein, comprising a plurality of components, including at least one selected HDLP subclass, at least one branched-chain amino acid, citric acid, and GlycA, and optionally NMR-derived measurements of at least one protein or at least one ketone body. The method may further include programmatically deconvolving a spectrum comprising 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 a population norm associated with an increased risk of all-cause mortality; and / or (ii) whether the metabolic vulnerability index increases or decreases over time in response to treatment. In certain embodiments, the MVX score can be calculated based on the measured values ​​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 * lnketone body. In certain embodiments, the MVX score can be calculated using the following model: MVX = A + β1 * lnGlycA + β2 * lnS-HDLP + β3 * (lnGlycA * lnS-HDLP) + β4 * lnBCAA + β5 * lnketone body. In certain embodiments, the MVX score can be calculated using the following model: MVX = A + β1*lnGlycA + β2*lnS-HDLP + β3*(lnGlycA*lnS-HDLP) + β4*lnBCAA + β5*lnketone bodies + β6*lncitric acid + β7*lnprotein + β8*(lncitric acid*lnprotein). In certain embodiments, the MVX score calculation may include an inflammatory vulnerability index (IVX) value and a metabolic malnutrition index (MMX) value. Thus, in certain embodiments, the calculation of the MVX score can be based on the following model: MVX = βi*IVX + βm*MMX. In these embodiments, the calculation of the IVX value can use the following model: IVX = β1*lnGlycA + β2*lnS-HDLP + β3*(lnGlycA*lnS-HDLP). In certain embodiments, the calculation of the MMX value can use the following model: MMX = β4 * ln BCAA + β5 * ln ketone body; the model can represent MMX 1. In certain embodiments, the calculation of the MMX score can use the following model: MMX = β4 * ln BCAA + β5 * ln ketone body + β6 * ln citric acid + β7 * ln protein.In another embodiment, the calculation of the MMX score can use the following model: MMX = β4*lnBCAA + β5*lnketone body + β6*lncitric acid + β7*lnprotein + β8*(lncitric acid*lnprotein). In such an embodiment, MMX can be described as follows: MMX = β9*MMX1 + β10*MMX2, wherein MMX1 is as described above, and MMX2 = β6*lncitric acid + β7*lnprotein + β8*(lncitric acid*lnprotein).

[0137] Disclosed herein are methods and systems for determining a metabolic vulnerability index (MVX) of a subject. The method comprises predicting the probability of premature all-cause mortality in a patient with malnutrition-inflammatory complex syndrome (MICS), also known as metabolic malnutrition-inflammatory syndrome (MMIS), using a multivariate model of defined biomarkers.

[0138] One embodiment of the present disclosure utilizes a method for determining the level of a marker associated with a relative risk of premature death in a subject, the method comprising obtaining a sample from the subject; measuring one or more markers using NMR spectroscopy, wherein the markers may include GlycA, at least one high-density lipoprotein particle (HDLP) subclass, at least one branched-chain amino acid (BCAA), citric acid, and optionally at least one ketone body (ketone bodies) and at least one subset of serum proteins; determining a metabolic vulnerability index based on the NMR spectroscopy; repeating the sample processing over time; and at least evaluating whether MVX increases or decreases over time.

[0139] In one embodiment, the HDLP is a small HDLP (S-HDLP).

[0140] In certain embodiments, the MVX score can be sex specific. In certain embodiments, the MVX score can be calculated using IVX and MMX. In certain embodiments, the MMX score can be calculated using at least one BCAA and citric acid. In certain embodiments, the IVX score can be calculated using a GlycA measurement and a HDLP measurement.

[0141] In certain embodiments, IVX is sex specific. In the same embodiments, MMX is also sex specific. In certain embodiments, the HDLP is a small HDLP (S-HDLP), however, in other embodiments, the HDLP may be a medium or large HDLP (M-HDLP, L-HDLP).

[0142] In certain embodiments, the MVX score can 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*lnketone body.

[0143] In certain embodiments, the MVX score can be calculated using the following model: MVX = A + β1*lnGlycA + β2*lnS-HDLP + β3*(lnGlycA*lnS-HDLP) + β4*lnBCAA + β5*lnketone bodies. In these embodiments, MVX values ​​can be determined in subjects who are considered to have a low risk of cardiovascular events.

[0144] In certain embodiments, the MVX value may include a measurement of serum protein (albumin) and / or citric acid (citric acid).

[0145] In certain embodiments, the MVX value can be determined using the following model: MVX = A + β1*lnGlycA + β2*lnS-HDLP + β3*(lnGlycA*lnS-HDLP) + β4*lnBCAA + β5*lnketone bodies + β6*lncitric acid + β7*lnprotein + β8*(lncitric acid*lnprotein). In these embodiments, the MVX value can be determined in subjects who are considered to be at high risk for cardiovascular events.

[0146] In an alternative embodiment, the MVX value may comprise the Inflammatory Vulnerability Index (IVX) and / or the Metabolic Malnutrition Index (MMX) disclosed in more detail herein.

[0147] Thus, in certain embodiments, the calculation of the MVX score can be based on the following model: MVX = βi*IVX + βm*MMX. In these embodiments, the calculation of the IVX value can use the following model: IVX = β1*lnGlycA + β2*lnS-HDLP + β3*(lnGlycA*lnS-HDLP).

[0148] In certain embodiments, calculation of MMX values ​​can use the following model: MMX=β4*lnBCAA+β5*lnketone bodies; this model can represent MMX1 and be used for subjects considered to be at low risk for cardiovascular disease-related events.

[0149] In certain embodiments, the calculation of the MMX score can use the following model: MMX=β4*lnBCAA+β5*lnketone bodies+β6*lncitric acid+β7*lnprotein.

[0150] In another embodiment, the calculation of the MMX score can use the following model: MMX=β4*lnBCAA+β5*lnketone bodies+β6*lncitric acid+β7*lnprotein+β8*(lncitric acid*lnprotein).

[0151] In such embodiments, MMX can be described as follows: MMX = β9*MMX1 + β10*MMX2, wherein MMX1 is as described above and MMX2 = β6*ln citrate + β7*ln protein + β8*(ln citrate*ln protein); in these embodiments, MVX scores can be determined for subjects considered to be at high risk for cardiovascular disease-related events.

[0152] In certain embodiments, the BCAA may be at least one of leucine, isoleucine, or valine.

[0153] In certain embodiments, the ketone body may be at least one of acetone, acetoacetate, or β-hydroxybutyrate.

[0154] In certain embodiments, the measurement is performed by NMR. The metabolic vulnerability index can provide short-term (1 year) to long-term (12 years) premature death risk assessment. Subject follow-up can occur after 12 years, after 10 years and / or every 6 years, every 5 years, every 4 years, every 3 years, every 2 years or every 1 year. These risk assessments can generate MVX values ​​that are separated from traditional risk factors.

[0155] In certain embodiments, gender can be included as a factor in the MVX model.

[0156] In certain embodiments, age can be included as a factor in the MVX model. In other embodiments, the MVX model can exclude gender or age considerations to avoid false negatives or false positives based on data corruption, such as auxiliary data that is not directly related to the biological sample.

[0157] In certain embodiments, an MVX score is provided to a clinician based on data electronically associated with the sample or based on clinician or admitting laboratory input (e.g., a patient's fasting "F" or non-fasting "NF" and statin "S" or non-statin "NS" characterizations), which data may be provided on a label associated with the biological sample so as to be electronically associated with the sample at the NMR analyzer. Alternatively, the patient characterization data may be maintained in a computer database (remotely or via a server or other defined path) and may include a patient identifier, sample type, test type, etc. entered into an electronically associated file by a clinician or admitting laboratory that may be accessed or hosted by the admitting laboratory in communication with the NMR analyzer. The patient characterization data may allow the use of an appropriate MVX model for a particular patient.

[0158] It is contemplated that the Metabolic Vulnerability Index may be used to monitor subjects in clinical trials and / or drug therapy to identify medication incompatibilities, and / or to monitor for changes in risk status (positive or negative) that may be associated with a particular medication, the patient's lifestyle, etc., which may be patient-specific.

[0159] References to certain embodiments of the present disclosure include NMR systems capable of performing each of the methods described herein.

[0160] In certain embodiments, the NMR system may include an NMR spectrometer; a flow probe in communication with the spectrometer; and a processor in communication with the spectrometer, the processor being configured to obtain: (i) at least one NMR signal in a defined GlycA fit region of the NMR spectrum associated with GlycA of a plasma or serum sample in the flow probe; (ii) at least one NMR signal in a defined citrate fit region of the NMR spectrum associated with the sample in the flow probe; (iii) at least one NMR signal in a defined BCAA fit region of the NMR spectrum associated with the sample in the flow probe; and (iv) at least one NMR signal of at least one HDLP subclass; and optionally, at least one NMR signal of serum proteins (albumins) and / or ketone bodies.

[0161] In certain embodiments, the processor is further configured to calculate an MVX score based on measurements obtained by the spectrometer according to any embodiment of the invention disclosed herein.

[0162] Further embodiments of the present disclosure will now be described by way of the following non-limiting examples. Example

[0163] The present disclosure may be better understood by reference to the following non-limiting examples.

[0164] Example 1

[0165] A composite biomarker score, termed the metabolic vulnerability index (MVX), was derived from six metabolites simultaneously measured by a clinically deployed nuclear magnetic resonance (NMR) blood test that may reflect different etiologic aspects of the metabolic malnutrition-inflammatory syndrome. The MVX score provided surprisingly strong stratification of risk for all-cause mortality in two large, independent cohorts of cardiac catheterization patients who had not previously been suspected of being vulnerable to these metabolic dysnutrition-wasting syndromes. The contribution of MVX to a multivariate prediction model of 5-year mortality was greater than that of 15 risk factors, including age. The risk association was equally strong in men and women, young and old, overweight and underweight, and patients with and without comorbidities such as heart failure, renal dysfunction, diabetes, and hypertension. The consistency of the MVX risk association in higher- and lower-risk patient subgroups suggests that the impact of the metabolic malnutrition-inflammatory syndrome on survival may be more pervasive than previously thought.

[0166] The factors that may affect the risk of death can include malnutrition and inflammation and other potential factors. In addition, there may be an association between malnutrition-inflammation and muscle atrophy, thereby further increasing the risk of death. The muscle atrophy associated with malnutrition-inflammation can be most common in patients with chronic kidney disease (CKD) or chronic heart failure (CHF). The muscle atrophy associated with malnutrition-inflammation can also be observed in healthy and weak individuals and in patients with malignant tumors and liver diseases. The example of the name given to the syndrome can include protein energy consumption (PEW) (wherein cachexia can be a severe form), malnutrition-inflammatory atherosclerosis syndrome and malnutrition-inflammatory complex syndrome (MICS), and the term implies a collaborative relationship between systemic inflammation and protein energy malnutrition.

[0167] The subpopulation with a high prevalence of MICS may exhibit a "risk factor paradox," also referred to as "reverse epidemiology," whereby increases in traditional cardiovascular risk factors such as body mass index (BMI), serum cholesterol, and blood pressure may be associated with decreased (rather than increased) cardiovascular and all-cause mortality. It is not that the pathophysiological mechanisms contributing to cardiovascular disease are operative in this subpopulation, but that different superimposed etiologies appear to predominate, driving a reversal of multiple common risk factors associated with death. Thus, common risk factors for fatal versus nonfatal outcomes may be differentially affected in patients with MICS, and these factors may respond best to different therapeutic interventions.

[0168] The lack of simple quantitative and objective clinical assessment tools may be a factor that has hindered further recognition and understanding of MICS and its involvement in frailty syndromes. The assessment of the malnutrition component is particularly challenging, which is usually assessed using patient history, physical examination, and anthropomorphic evaluation. The main laboratory biomarkers of MICS are low serum albumin (reflecting malnutrition and inflammation) and elevated C-reactive protein (CRP), which can be nonspecific and therefore have limited clinical applicability.

[0169] All-cause mortality in the large, high-risk CATHGEN (CATHeterization GENetics) cardiac catheterization cohort may be independently associated with 2 novel biomarkers that may plausibly reflect the contribution of inflammation to the etiology of MICS. Both can be measured by nuclear magnetic resonance (NMR) spectroscopy as a clinical The first may be GlycA, a composite NMR signal of glycan residues from several acute phase glycoproteins, providing a sensitive and robust measure of systemic inflammation. The second may be the amount of small-sized high-density lipoprotein particles (S-HDLP), which appear to mediate several protective functions exerted by, among other things, bound anti-inflammatory and immune response proteins.

[0170] Laboratory methods

[0171] Fasting EDTA plasma samples were analyzed using the LP-4 algorithm on the LipoScience (now Labcorp, Morrisville, NC) NMR Profiler platform. Analysis. "Scans" (proton NMR spectroscopy) can produce one or more particle concentrations of several different size subclasses of triglyceride-rich lipoproteins (TRL), low-density lipoproteins (LDL), high-density lipoproteins (HDL), average TRL, LDL, and HDL particle sizes, as well as derived lipids (triglycerides and total, LDL, and HDL cholesterol), the inflammatory marker GlycA, the three branched-chain amino acids (valine, leucine, isoleucine), citric acid, plasma proteins, ketone bodies, and several other small molecule metabolites. In some cases, seven HDL particle subspecies with indicated estimated diameters (nm) were quantified: H7P (12nm), H6P (10.8nm), H5P (10.3nm), H4P (9.5nm), H3P (8.7nm), H2P (7.8nm), and H1P (7.4nm). For analytical purposes, identified subparticles were grouped into "large" (L-HDLP = H4P + H5P + H6P + H7P) and "small" (S-HDLP = H1P + H2P + H3P) HDL subclasses. In the Intermountain Heart cohort, lipids were measured by standardized chemistry analysis. Lipids and creatinine were measured by standardized chemistry analysis, and the estimated glomerular filtration rate (eGFR) was calculated using the 2021CKD-EPI equation. Chronic kidney disease (CKD) is defined as eGFR < 60 ml / min / 1.73 m 2 .

[0172] Study Group

[0173] For this study, consecutive patients who received cardiac catheterization at Duke University Medical Center for suspected ischemic heart disease between 2001 and 2011 were identified, and the patients were recruited into the CATHGEN biorepository, and had sufficient available frozen EDTA plasma (n=6,969). The final study group (n=5,876) excluded those lacking angiography (n=889), heart failure (n=98), creatinine (n=78) and BMI (n=28) information. Demographic data, medical history and angiography data were all from the Duke Cardiovascular Disease Database. Follow-up included determining mortality (confirmed by the National Death Index and the Social Security Death Index) and myocardial infarction (MI). Each patient was followed up longitudinally, and contact was made every year after 6 months of surgery and thereafter. The CATHGEN biorepository was monitored by the Duke University Institutional Review Board and approved on March 18, 2011. Before collecting blood samples, all research participants provided written informed consent. Incident events were defined as all-cause or cause-specific death or nonfatal MI at any time during follow-up, and time-to-time events were defined as the time from the time of cardiac catheterization at recruitment to death at any time point after recruitment. The median (IQR) follow-up was 6.2 (4.4-8.9) years. Cardiovascular death was defined as death from one of the following causes: MI, heart failure, sudden death, post-resuscitation, vascular causes, during or after cardiac surgery, or during cardiac catheterization. Non-cardiovascular death was defined as death from non-cardiac medical causes or non-cardiac causes related to the procedure. Unknown cause of death was defined as an unobserved or unknown cause of death. Coronary artery disease (CAD) was defined as the presence of at least 1 epicardial coronary vessel with clinically significant stenosis (≥75%) at the time of the index catheterization.

[0174] The second study population (n = 2,998) was from the Intermountain Heart Collaborative Study Cardiac Catheterization Registry, which included patients who underwent coronary angiography at LDS Hospital (Salt Lake City, UT) between September 2000 and September 2006. (2) Consecutive patients were included if they were ≥18 years of age, had at least 5 years of follow-up, and had adequate available frozen EDTA plasma and nonmissing clinical and laboratory variables. Patients provided informed consent before angiography, and the study was approved by the Intermountain Urban Center Regional Institutional Review Board on March 23, 2012. Incident events were all-cause deaths identified from hospital records, Utah State Department of Health records (death certificates), and the Social Security Administration Death Master File. Time to event was defined as the time from the time of recruitment cardiac catheterization to death at any time point after recruitment. The median (IQR) follow-up was 8.2 (6.9-9.2) years.

[0175] Statistical analysis

[0176] Continuous variables can be expressed as mean ± SD or median and interquartile range (IQR), and dichotomous variables can be expressed as percentages. The chi-square statistic and Student's t-test were used to compare the baseline characteristics of those who died and those who did not die during the 5-year follow-up. The Spearman correlation coefficient was used to assess the correlation between the selected variables. The association between NMR-measured lipoprotein and metabolite variables and all-cause mortality (and additional cause-specific mortality and non-fatal MI in CATHGEN) can be assessed using Cox proportional hazards models, adjusted for age, sex, race, smoking, diabetes, hypertension, BMI, total cholesterol, HDL cholesterol, triglycerides, eGFR, CAD, heart failure, previous MI, and family history of CAD. The assumption of proportional hazards was tested by incorporating time-varying covariates in the model. Of the several NMR measures (including plasma proteins (reverse) and ketone bodies) that were found to be significantly associated with all-cause mortality in CATHGEN when examined individually, only six (S-HDLP, GlycA, citrate, valine, leucine, isoleucine) made significant independent contributions to the joint prediction model. These can be combined into sex-specific IVX, MMX, and MVX multi-marker scores. An estimate of the relative importance of each predictor variable can be provided by its chi-square value as a percentage of the total chi-square value of the model. Estimates of relative importance may be very similar to those obtained by comparing the discriminant c-index of the complete model with the discriminant c-index of the model ignoring each variable. CATHGEN and Intermountain Heart statistical analyses can be performed using SAS version 9.4 by IS and SPSS version 22.0 by HTM, respectively. All reported P values ​​may be two-sided.

[0177] result

[0178] A study was conducted to evaluate the relative risk of death in patients from two large groups. Over a 5-year period, samples were collected from 5,876 patients in the CATHGEN group and 2,998 patients in the Intermountain Heart group. The samples optimally included blood samples drawn by phlebotomy. The samples were evaluated for the presence and amount of 8 health markers (referred to as biomarkers) using NMR spectroscopy. The 8 biomarkers included GlycA, at least one subclass of high-density lipoprotein particles (HDLP), three branched-chain amino acids (BCAA) (including valine, leucine and isoleucine), at least one ketone body, citric acid and at least one subset of serum proteins. From the biomarkers and at least 6 biomarkers (including GlycA, citrate, at least one high-density lipoprotein particle (HDLP) subclass and at least one BCCAA), three indices were calculated using previously acquired NMR instrument spectra: the metabolic malnutrition index (MMX), the inflammatory vulnerability index (IVX) and the malnutrition vulnerability index (MVX) as a result of the first two indices. The biomarkers were evaluated over a period of 5 years, and thus the indices were also evaluated. The MVX generated by MMX and IVX was followed for 5 years in two large cohorts to evaluate the risk of death in patients with malnutrition-inflammatory complex syndrome (MICS). Interestingly, of the 8 biomarkers, only 6 of the analyzed biomarkers (S-HDLP, GlycA, citrate, valine, leucine, isoleucine) made significant independent contributions to the joint prediction model.

[0179] Of the 5,876 CATHGEN patients and 2,888 Intermountain Heart patients evaluated, 1,000 (17%) and 441 (15.3%), respectively, died within 5 years. Figure 1 Baseline characteristics of the participants stratified by 5-year survival status are shown. In both groups, there was a high prevalence of hypertension, diabetes, heart failure, and angiographically confirmed CAD, with signs of a “risk factor paradox” (lower cholesterol, triglycerides, and BMI in individuals who died). Among NMR-measured metabolites presumably reflecting MICS, GlycA and citrate levels were higher, while S-HDLP, valine, and leucine levels were lower in patients who died. The associations between these biomarkers and selected risk factors were similar in both groups ( Fig. 9 In CATHGEN, six putative MICS-related biomarkers made a significant contribution to the multivariate prediction model for mortality, increasing the c-index from 0.690 to 0.761 ( Figure 4 , 10and 11). The strongest base model predictors, as estimated by the percentage contribution to the total chi-square of the model, were age (28%), renal function assessed by eGFR (27%), and heart failure (16%). The addition of the MMIS biomarker to the model substantially reduced the predictive contribution of heart failure and eGFR (to 3% each), which were replaced in importance by S-HDLP (25%) and GlycA (17%). The strong protective (inverse) association observed for small HDL particles only applied to particles below approximately 8.8 nm in diameter; concentrations of larger HDL particles and total HDL cholesterol (HDL-C) had negligible associations with mortality ( Fig.14 In the Intermountain Heart replication cohort, mortality associations for the six MMIS variables were similar to those in CATHGEN, but the major contributions of heart failure and eGFR were less attenuated (from 19% to 9% and from 32% to 13%, respectively), and GlycA (12%) and S-HDLP (12%) decreased in relative importance ( Fig.21 Given the complexity and multifactorial etiology of the metabolic dysfunction that underlies cachexia / sarcopenia / malnutrition and their associated mortality risk, it seems reasonable to generate 2 multi-marker “subscores” for mortality: the Inflammatory Vulnerability Index (IVX) combining GlycA and S-HDLP, and the Metabolic Malnutrition Index (MMX) combining valine, leucine, isoleucine, and citrate ( Figure 8 These were calculated by sex so that the scores for men and women were numerically similar, as they would not otherwise be attributable to sex differences in metabolite levels that are not associated with mortality risk. Specifically, citrate and GlycA levels were higher in women, while BCAA levels were lower ( Fig.15 ).

[0180] In CATHGEN, the predicted contribution of IVX (44%) is about twice that of MMX (20%) ( Figure 4 and 12 ), while in Intermountain Heart, IVX and MMX had comparable contributions to mortality risk of 18% and 12%, respectively ( Fig.21 ). In contrast to mortality prediction, the risk of nonfatal MI was much less influenced by MMIS-related NMR biomarkers and more dependent on comorbidities, with the combination of prevalent angiographic CAD and a history of prior MI making the major contribution (62–75%) in CATHGEN ( Figure 4 and 10 ).

[0181] Combining IVX and MMX yields a multi-marker of overall mortality risk called MVX (Metabolic Vulnerability Index). The calculation of MVX includes a product term (IVX * MMX), which takes into account the interaction (synergy) observed between the inflammatory and malnutrition components in MICS( Figure 8 ). A cross-classification plot of 5-year mortality by IVX and MMX tertiles( Figure 5 ) illustrates this interaction as follows: showing that the risk of death is higher in people with a high MMX score only when the IVX score is also high. The contribution of MVX to mortality prediction in CATHGEN (69%) is far greater than that of any of the other 15 risk factors in the model, including age (17%)( Figure 4 and 13 ). In Intermountain Heart, MVX also dominates, although less importantly, with its predictive contribution (31%) exceeding that of all variables except age (35%)( Fig.21 ).

[0182] There is a deviation from the proportional hazards assumption in the 5-year mortality models for IVX and MVX (p < 0.0001), but not in MMX. Therefore, a sensitivity analysis was performed, which compared models limited to deaths occurring within 1 year (n = 255), 3 years (n = 651), 5 years (n = 1000), and 6 to 10 years (n = 529) after enrollment in CATHGEN( Figure 16-17 ). The correlations of IVX and MVX with 1-year mortality were the strongest (IVX HR: 2.48; 95% CI 2.13 - 2.89; MVX HR: 3.00; 95% CI 2.60 - 3.45), and they remained the main predictors of deaths occurring after 5 years (IVX HR: 1.57; 95% CI 1.42 - 1.72; MVX HR: 1.53; 95% CI 1.39 - 1.68). In contrast, MMX showed an almost constant mortality association in the first 5 years of follow-up and weakened significantly over the long term. The contributions of MVX (82%) and age (10%) dominated the prediction of 1-year mortality and deaths occurring after 5 years (36% and 42% respectively).

[0183] Figure 2Associations of IVX, MMX, and MVX with 5-year all-cause mortality (adjusted for risk factors) in the CATHGEN and Intermountain Heart cohorts are shown, examined in quintiles and per 1 SD. The significant associations for all 3 multimarkers in CATHGEN were replicated in Intermountain Heart, but were slightly weaker (CATHGEN: MVX HR: 2.18; 95% CI 2.03-2.34, per 1 12.7 SD; Intermountain: MVX HR: 1.67; 95% CI 1.50-1.87, per 1 12.5 SD). Plots of cumulative mortality in subgroups of CATHGEN participants stratified by small differences in baseline MVX scores show a clear hierarchical relationship, with clinically meaningful mortality differences occurring not only in high MVX values, but also in lower MVX values ​​( Figure 6 ). Risk stratification by MVX quintiles in the Intermountain Heart cohort was comparable to that observed in CATHGEN ( Fig. 22 ).

[0184] Of the 1000 deaths that occurred over 5 years in CATHGEN, 379 (38%) were due to cardiovascular causes, 507 (51%) were due to noncardiovascular causes, and 114 (11%) were of unknown cause. Figure 18-19 Although some risk factors such as heart failure and diabetes predicted cardiovascular death, but not non-cardiovascular death, IVX, MMX, and MVX were similarly associated with the risk of death regardless of cause (for cardiovascular death, MVX HR: 2·02; 95% CI 1·80-2·26; for non-cardiovascular death, HR: 2·30; 95% CI 2·08-2·53).

[0185] Figure 3 and Fig. 20Comparative associations of MVX with all-cause mortality by sex, age, and in subgroups that differed in baseline BMI, hypertension, smoking, diabetes, heart failure, prior MI, CAD, or CKD status are provided. The MVX association was very similar in men (HR: 2·18; 95% CI 2·03-2·34) and women (HR: 2·22; 95% CI 1·97-2·50), and also in patients with and without risk factors that affect 5-year survival. The most striking example was that mortality was approximately twice as high in patients with prior heart failure and CKD as in those without prior heart failure and CKD, and this was also true in the lowest BMI (<22) category relative to the highest BMI (≥30) category. The MVX mortality association was equally strong in the two lowest-risk patient subgroups: the youngest (≤50 years) and the “healthiest” (without CAD, heart failure, diabetes, and CKD) (HR per SD, 2·61; 95% CI, 2·10-3·24 and 2·67; 95% CI, 2·18-3·28, respectively).

[0186] in conclusion

[0187] Complex and incompletely understood metabolic derangements associated with inflammation and protein energy wasting are important factors contributing to the increased risk of mortality in elderly patients and patients with chronic organ diseases burdened by the syndromes of cachexia, sarcopenia, malnutrition, and frailty. However, the relevance of these wasting syndromes to cardiovascular patients or the general population at lower risk is uncertain. Research has been hampered by the lack of objective clinical assessment tools for these intertwined syndromes of metabolic malnutrition and inflammation. This study aimed to determine the risk of mortality associated with the metabolic vulnerability index (MVX), a multimarker derived from six simultaneously measured serum biomarkers that may be associated with these metabolic dysbiosis syndromes, in two independent cohorts of cardiac catheterization patients. The results are encouraging, suggesting that survival may depend more on heretofore unrecognized etiologies than on those factors of vulnerability that contribute to disease development or are considered “causes” of death. If confirmed by future studies, treating the metabolic derangements underlying the overlapping syndromes of cachexia, sarcopenia, malnutrition, and frailty with anti-inflammatory, nutritional, or replacement therapies may provide greater survival benefits than targeting traditional disease risk factors.

[0188] Clinical NMR analysis was used to efficiently quantify six metabolites in plasma that may be associated with MMIS, and the derived IVX, MMX, and MVX multimarker scores were shown to exhibit strong hierarchical associations with all-cause mortality in two large cardiac catheterization cohorts. The associations were relatively strong for men and women, young and old, underweight and overweight patients, cardiovascular and non-cardiovascular causes of death, and for patients with and without angiographic evidence of CAD or other comorbidities such as heart failure, renal dysfunction, diabetes, and hypertension. These observations and the surprising finding that the MVX mortality association was equally strong in at least the two lowest risk patient subgroups (those ≤50 years of age, and phenotypically "healthy" ones without CAD, heart failure, CKD, or diabetes) suggest that MMIS may be of general relevance to mortality risk because it leads to greater or less metabolic vulnerability or resilience.

[0189] Figure 7 Conceptualizing the idea that the effects of MICS on survival are common regardless of cause of death, the long-term trajectory from health to disease states such as atherosclerotic cardiovascular disease (ASCVD) is shown on the left as being influenced by disease-specific risk factors (red), the targeting of which is the basis for successful prevention strategies (such as lowering serum cholesterol) to reduce the risk of fatal and nonfatal outcomes. This disease prevention strategy of targeting risk factors is analogous to a canoeist using the most appropriate tool (large paddle, etc.) to avoid being swept away by a waterfall. The suggestion, shown on the right (blue), is that later in the trajectory, when the risk of death is more proximal, the additive effects of MICS on metabolic vulnerability become a primary consideration. Under these altered circumstances (high MVX scores), the canoeist's survival is the primary concern and it would be best to adopt different risk mitigation options (helmets, etc.) that target inflammation and / or nutritional status to promote metabolic resilience.

[0190] This hypothesis was previously proposed to explain the "reverse epidemiology" in hemodialysis patients and other vulnerable patient subgroups, as well as the failure of prevention efforts to focus on conventional risk factors such as obesity, hypertension, and hypercholesterolemia, thereby substantially improving survival. This finding in patient subgroups not previously associated with MICS suggests that the contribution of malnutrition / inflammation to the risk of death has a certain degree of biological universality. Notably, the predictive contribution of MVX to the multivariable model of 5-year mortality was dominant, exceeding that of age and dwarfing the contributions of risk factors such as smoking, diabetes, and heart failure. It is not that these latter variables are unimportant in these patients; in CATHGEN, together they explained approximately 55% of the mortality prediction in the Cox model without MVX. Instead, adding MVX to the model significantly improved mortality prediction such that the 69% predictive contribution of MVX dwarfed the predictive contributions of the covariates. In contrast, the risk of non-fatal MI was relatively less affected by the MVX score and its components.

[0191] If MMIS plays a key role in the relatively short-term survival of cardiovascular patients, then a significant consequence is that there are questions about the composite endpoints of combined fatal and non-fatal events routinely used in ASCVD clinical trials. Previous criticisms of composite endpoints have focused on flaws in clinical interpretation and the weighting of "hard" and "soft" outcomes, but have never questioned the basic assumption that fatal and non-fatal ASCVD events have a common etiology. There is no doubt that they do, but if a more potent MMIS etiology is superimposed and dominates survival in the relatively short term, it would explain why MVX has an overwhelming impact on both cardiovascular and non-cardiovascular mortality, but a much smaller impact on non-fatal MI. From this perspective, the results of past clinical trials in which interventions did not equally affect the fatal and non-fatal components of the composite endpoint may merit reexamination.

[0192] Given the very strong associations of MVX and its MMX and IVX components with mortality, it is unclear why MICS has largely escaped clinical attention. A possible reason is the lack of clinically available serum markers specifically designed to detect coexisting protein-energy malnutrition / wasting and inflammation. The challenge lies in the inherent complexity of the overlap syndrome, with uncertainty as to which of the intertwined metabolic stresses are causal rather than phenotypic. These include chronic inflammation, endocrine disturbances, oxidative stress, excessive protein catabolism, acidemia, muscle anabolic resistance, defective mTOR signaling, elevated resting energy expenditure, and endothelial dysfunction. Given this complexity, it is not surprising that a multimarker index aggregating six simultaneously measured metabolites reflecting different aspects of the syndrome may have advantages over the common clinical markers of serum albumin and CRP. Separation of MVX into IVX and MMX components enables the potential future clinical goal of differentially treating the mortality risk associated with MICS according to the underlying cause of elevated MVX. Given the apparent synergistic effects of IVX and MMX and the uncertainty about how branched-chain amino acids and citrate are specifically involved, the arbitrariness of attributing IVX and MMX to “inflammatory” and “metabolic malnutrition” etiologies, respectively, deserves recognition.

[0193] The major effect of S-HDLP on IVX and MVX scores is of interest. Understanding how this small HDL subspecies relates to MICS may help determine which aspects of HDL multifunctionality influence lifespan and metabolic resilience. Current perceptions of the clinical importance of HDL (or lack thereof) are based almost entirely on studies of only one HDL biomarker, HDL cholesterol (HDL-C). Because the cholesterol content of large HDL particle subpopulations is much higher than that of small HDL particles, the clinical associations of HDL-C can mislead people about the protective role played by small HDL particles. This study provides a convincing example: the addition of HDL-C to the multivariate mortality model in CATHGEN did not increase the c-index at all, while the addition of S-HDLP did significantly increase the c-index. There was absolutely no association between the concentration of larger HDL and mortality. This clear demarcation of apparent HDL bioactivity and particle size is consistent with evidence that certain HDL functional activities (such as antioxidant, anti-inflammatory, and anti-infective) are mediated by protein and / or lipid species that are preferentially or exclusively present on small HDL particle subpopulations.

[0194] It is premature to predict how the MVX and its components, the MMX and IVX indices, might ultimately be used clinically, but several possibilities warrant future investigation. MVX has clear prognostic value for mortality, but it is unclear whether lowering MVX with anti-inflammatory, nutritional, or replacement therapies would prolong survival. At present, the best clinical application of MVX may be to supplement or expand the “disease burden / inflammatory profile” etiologic criteria used to diagnose and grade the severity of malnutrition, as it relates to the syndrome of cachexia, sarcopenia, and frailty. In this setting, MVX could provide a simple, quantitative, and objective measure of metabolic dysfunction that affects survival (for which there is an unmet need), both for prognosis and to help assess the efficacy and safety of therapeutic interventions. In terms of real-world clinical utility, the MVX, IVX, and MMX scores were calculated using data from the same NMRLipoProfile “scan” that is currently deployed in the United States for routine testing of patients for cardiometabolic risk by simultaneously assessing the lipidome, apolipoprotein B, GlycA, and the LP-IR insulin resistance score. Because the analysis uses no assay-specific reagents or other consumables, the MVX index can be generated with little or no incremental analytical cost.

[0195] Overall, the MVX score (i.e., the set of 6 NMR-measured biomarkers) reflects different aspects of the malnutrition-inflammatory syndrome and contributes more than standard cardiovascular risk factors and comorbidities to a 5-year mortality prediction model in 2 large cohorts of patients undergoing cardiac catheterization. Although speculative, these results suggest that fatal and nonfatal outcomes may be influenced by different etiologies and that the use of therapies targeting one or more components of the MMIS may improve survival. The dominant contribution of MVX measured in subjects at both high and low risk for cardiovascular events to mortality prediction in cardiovascular patients overall and in a relatively low-risk subgroup without preexisting disease suggests that the metabolic malnutrition-inflammatory syndrome may play a more pervasive etiological role in influencing survival than previously thought.

[0196] The foregoing is illustrative of the present disclosure and should not be construed as limiting thereof. Although several exemplary embodiments of the present disclosure have been described, those skilled in the art will readily recognize that many modifications may be made to the exemplary embodiments without substantially departing from the novel teachings and advantages of the present disclosure. Therefore, all such modifications are intended to be included within the scope of the present disclosure as defined by the claims. In the claims, when used, the "means + function" clause is intended to cover the structures described herein that perform the functions described, and not only structural equivalents, but also equivalent structures. Therefore, it should be understood that the foregoing is illustrative of the present disclosure and should not be construed as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims.

Claims

1. A method for monitoring the risk of metabolic malnutrition inflammatory syndrome in a subject, the method comprising: include: (a) obtaining a sample of blood, serum or plasma from the subject; (b) simultaneously measuring at least two biomarkers in the sample; (c) generating a metabolic vulnerability index (MVX) value from at least two simultaneously measured biomarkers; (d) determining a relative risk of premature death for said subject based on at least said MVX value; (e) repeating steps (a)-(d) at a later time point; and (f) evaluating the subject's relative risk of premature death and MVX value over time.

2. The method of claim 1, wherein the at least two biomarkers comprise two or more of the following: GlycA, at least one high-density lipoprotein particle (HDLP) subclass, citric acid, and branched chain amino acids (BCAAs).

3. The method of claim 2, wherein the HDLP subtype is small HDLP (S-HDLP).

4. The method of claim 2, wherein the BCAA is at least one of leucine, isoleucine, or valine.

5. The method of claim 1, wherein the MVX values ​​are sex-specific and comprise a sex-specific Inflammatory Vulnerability Index (IVX) and a sex-specific Metabolic Malnutrition Index (MMX).

6. The method of claim 1, wherein the MVX value is specific to female sex and is determined using the following model: MVX F =(IVX F *2.27278)+(lnMMX F *12.13511)+ (IVX F *lnMMX F )*-1.09312。 7. The method of claim 1, wherein the MVX value is specific to male sex and is determined using the following model: MVX M =(IVX M *3.54601)+(lnMMX M *14.41428)+ (IVX F *lnMMX M )*-1.43438。 8. The method of claim 5, wherein the measured values ​​of at least one of each BCAA and citric acid are used to generate a sex-specific metabolic malnutrition index (MMX) value.

9. The method of claim 5, wherein measurements of at least one HDLP subclass and GlycA are used to generate a sex-specific Inflammatory Vulnerability Index (IVX).

10. The method of claim 8, wherein the female sex-specific MMX value is determined using the following model: MMX F =((4+(Leu*-0.03142)+(Leu 2 *0.0000893))*0.353)+ ((7+(Col*-0.03362)+(Col 2 *0.0000689))*0.684)+(Ileu*0.00332)+((1+(Citr*-0.0072)+(Citr 2 *0.0000573))*0.7135)。 11. The method of claim 8, wherein the male sex-specific MMX value is determined using the following model: MMX m =((4+(Leu*-0.01594)+(Leu 2 *0.0000291))*1.076)+ ((7+(Col*-0.0239)+(Col 2 *0.00005))*0.414)+(Ileu*0.01265)+ ((1+(Citr*0.00906)+(Citr 2 *-0.0000126))*0.5881)。 12. The method of claim 9, wherein the IVX value specific to female sex is determined using the following model: 49 F =9+(GlycA*-0.000187)+(S-HDLP*-0.3585)+((GlycA*S- HDLP)*0.000348).

13. The method of claim 9, wherein the IVX value specific to male sex is determined using the following model: 49 M =9+(GlycA*-0.00437)+(S-HDLP*-0.52307)+((GlycA*S- HDLP)*0.000817).

14. The method of claim 1, wherein the MVX value is sex-specific and is determined in subjects considered to be at low risk for a cardiovascular event or in subjects considered to be at high risk for a cardiovascular event.

15. The method of claim 1, wherein the measuring is performed by nuclear magnetic resonance (NMR) spectroscopy.

16. The method of claim 1, wherein the metabolic vulnerability index (MVX) can provide a premature mortality risk assessment of short-term (1 year) to long-term (12 years) risk, and wherein evaluating the subject over time comprises following up the subject after 12 years, after 10 years, and / or every 6 years, every 5 years, every 4 years, every 3 years, every 2 years, or every 1 year.

17. A system, wherein include: an NMR spectrometer configured to simultaneously acquire an NMR spectrum and / or spectra of two or more biomarkers from a blood, serum, or plasma sample of the subject; and A processor for determining a metabolic vulnerability index (MVX) value based on the NMR spectrum and / or the spectra of two or more biomarkers, wherein the processor comprises or is in communication with a memory.

18. The system of claim 17, wherein the processor is configured to determine a sex-specific inflammatory vulnerability index (IVX) and a sex-specific metabolic malnutrition index (MMX).

19. The system of claim 17, wherein the NMR spectrum and / or the spectra of at least two biomarkers comprise two or more of: a signal for a GlycA biomarker, at least one signal for at least one high-density lipoprotein particle (HDLP) subclass biomarker, at least one signal for at least one branched chain amino acid (BCAA) biomarker, and at least one signal for a citric acid biomarker.

20. The system of claim 19, wherein the at least one HDLP species is a small HDLP (S-HDLP) species, and wherein the BCAA is at least one of leucine, isoleucine, or valine.

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