阅读说明：本技术 使用脂蛋白lp-z预测肝病死亡率的方法 (Method for predicting mortality of liver disease using lipoprotein LP-Z ) 是由 Z·G·蒋 J·D·奥特沃斯 I·沙劳罗瓦 E·J·耶亚拉加赫 M·A·康奈利 M·库里 于 2019-11-07 设计创作，主要内容包括：本文描述了在生物样品中通过NMR光谱法确定酒精性肝炎的患者死亡率的方法,且更具体地,基于血浆和血清中脂蛋白成分LP-Z确定Z指数得分的方法。(Described herein are methods of determining the mortality of patients with alcoholic hepatitis by NMR spectroscopy in a biological sample, and more particularly, methods of determining a Z-index score based on the lipoprotein components LP-Z in plasma and serum.)

1. A method of predicting mortality of a patient with alcoholic hepatitis, comprising:

acquiring an NMR spectrum of a biological sample obtained from a subject;

programmatically determining the concentration of LP-Z and total apoB-containing lipoprotein in the sample based on the NMR spectrum of the sample, wherein the NMR spectrum of the sample comprises LP-X and LP-Z; and

the Z-index score is calculated.

2. The method of claim 1, wherein the method obtaining step comprises:

generating a measured lipid signal profile for an NMR spectrum of a biological sample obtained from the subject; and

a calculated line shape of the sample was generated.

3. The method of claim 2, wherein the calculated linear shape is based on the derived concentrations of lipoprotein components including LP-X and LP-Z.

4. The method of claim 3, wherein the derived concentration of each of the lipoprotein components is a function of a reference spectrum of the component and a calculated reference coefficient.

5. The method of claim 2, wherein the generating step comprises calculating the reference coefficients for the calculated line shape based on a linear least squares fitting technique.

6. The method of claim 2, further comprising:

determining a correlation between an initial calculated profile of the sample and a measured profile of the sample; and

determining the presence of LP-Z based on the calculated profile if the correlation between the calculated profile and the measured profile of the sample is above a predetermined threshold.

7. The method of claim 1, wherein the Z-index score comprises the concentration of lipoproteins LP-Z, LDL and VLDL.

8. The method of claim 1, wherein said Z-index is the ratio of LP-Z concentration to total ApoB-containing lipoprotein concentration.

9. The method of claim 1, wherein the Z-index is calculated by the equation:

z index ([ LP-Z ])/([ VLDL ] + [ LDL ] + [ LP-Z ]).

10. The method of claim 1, wherein a Z-index greater than 0.6 predicts that patient death will occur within 90 days or less.

11. The method of claim 1, wherein the method predicts the likelihood of death of the patient within 90 days.

12. The method of claim 1, wherein the method predicts the likelihood of survival or patient response to treatment.

13. The method of claim 1, further comprising, prior to programmatically determining,

placing a sample of the subject in an NMR spectrometer;

deconvolving the NMR spectrum; and

NMR derived measurements of a plurality of selected lipoprotein parameters are calculated based on the deconvolved NMR spectra.

14. The method of claim 1, further comprising generating a report listing the concentration of lipoprotein components present in the sample and the likelihood of death.

15. The method of claim 1, wherein the biological sample is one of blood, serum, plasma, cerebrospinal fluid, or urine.

16. An NMR analyzer, comprising:

an NMR spectrometer;

a probe in communication with the spectrometer; and

a controller in communication with the spectrometer, the controller configured to obtain NMR signals of defined single peak regions of an NMR spectrum associated with LP-Z of a fluid sample in the probe and generate a patient report providing LP-Z levels.

17. The analyzer of claim 16, wherein the controller is in communication with at least one local or remote processor, wherein the at least one processor is configured to:

(i) obtaining a composite NMR spectrum of a fitted region of the fluid sample; and

(ii) deconvolving the composite NMR spectrum using a defined deconvolution model to generate LP-Z levels.

18. The analyzer of claim 17, wherein said deconvolution model comprises at least one of a High Density Lipoprotein (HDL) component, a Low Density Lipoprotein (LDL) component, a VLDL (very low density lipoprotein)/chylomicron component, LP-X, LP-Y, and LP-Z.

19. The analyzer of claim 16, wherein said probe is a flow probe.

20. The analyzer of claim 16, wherein said fluid sample is an in vitro plasma biological sample.

21. The analyzer of claim 16, wherein the fluid sample is a biological sample of blood, serum, plasma, cerebrospinal fluid, or urine.

Technical Field

Described herein are methods and systems for determining components in plasma and serum, and more particularly for determining lipoprotein components in plasma and serum.

Background

Alcoholic Hepatitis (AH) is a common cause of hospitalized patients with liver disease hospitalization in the united states. In the context of alcoholic liver disease, AH causes the most acute manifestations, with mortality rates of 5-10% in all patients and mortality rates of up to 30-50% in severe forms. Unlike other forms of liver failure, AH is characterized by severe coagulation defects (coagulopathy) and stasis of bile (cholestasis), which can occur without significant loss of hepatocytes or late fibrosis. The mechanism of this profound hepatocyte dysfunction in severe AH is still poorly understood. Conventional AH treatment is limited to withdrawal of alcohol, nutritional support and corticosteroids in selected patients for potential short-term benefit. Liver transplantation may be used for selected AH patients. Disease risk stratification is a key challenge in AH clinical management, and it remains difficult to predict the outcome of liver failure patients and select appropriate patient candidates for liver transplantation.

Several prediction strategies for AH have been investigated, including Maddrey Discriminant Function (DF), Glasgow Alcoholic Hepatitis Score (GAHS), age, serum bilirubin, INR and serum creatinine (ABIC) scores, and Lillie models. However, these scores do not reliably predict mortality and guide clinical decisions regarding liver transplantation. Another scoring system used to assess the severity of chronic liver disease is the end-stage liver disease Model (MELD). MELD is a score calculated from the International Normalized Ratio (INR) of serum creatinine, total bilirubin, prothrombin time and sodium concentration. MELD is generally a good predictor of 90-day mortality in cirrhosis patients due to various forms of chronic liver disease, and is commonly used to select liver transplant patients and rank patients on a liver transplant waiting list.

Despite having a high MELD score, a significant proportion of AH patients can recover with alcohol withdrawal and supportive care, unlike decompensated cirrhosis patients, where spontaneous recovery rarely occurs. Reliable prediction methods can help identify AH patients who will be candidates for organ transplantation.

One of the primary functions of the liver is to regulate lipid and lipoprotein metabolism. Secretion of very low density lipoprotein particles (VLDL), a triglyceride-rich lipoprotein, is one way in which hepatocytes can export triglycerides that accumulate within the cells. VLDL is metabolized to Low Density Lipoprotein (LDL), a cholesterol ester-rich particle. The conversion of VLDL to LDL in the circulation is dependent on a series of enzymes produced by the liver.

Recent data indicate that Nuclear Magnetic Resonance (NMR) spectroscopy can be used to identify and quantify LDL and abnormal lipoproteins, including LP-X and LP-Z. By accurately determining the presence and amount of lipoproteins in a biological sample and correlating lipoprotein levels to patient outcomes, predictive methods can be improved, ultimately improving patient care. Accordingly, there is a need for methods and systems for assays that accurately determine lipoproteins in plasma or serum samples and predict patient mortality. Described herein are novel methods and systems for accurately detecting and quantifying the amount of LP-Z in a biological sample using NMR spectroscopy and correlating the amount of LP-Z to patient mortality.

Disclosure of Invention

Methods and systems for accurately determining the presence and amount of LP-Z in a biological sample using NMR spectroscopy and generating a Z-index score to predict patient mortality are described herein. The present invention may be embodied in a variety of ways. In certain embodiments, the methods and systems comprise determining LP-Z in a subject or patient. In some embodiments, the method may predict a patient's response to therapy or the likelihood of death of the patient within 90 days.

In some embodiments, the method of predicting mortality in a subject with AH comprises the steps of: obtaining an NMR spectrum of a sample of plasma or serum obtained from the subject, and programmatically determining the presence of LP-Z and total apoB-containing lipoproteins in the sample based on the NMR spectrum of the sample. In some embodiments, the NMR spectrum of the sample may include all the subclasses of normal lipoproteins as well as abnormal lipoproteins LP-X, LP-Y and LP-Z. In certain embodiments, the method further comprises calculating a Z-index score. In some cases, a Z-index greater than 0.6 may correlate with alcoholic hepatitis mortality in 90 days or less.

Still other embodiments relate to NMR analyzers. The NMR analyzer may include an NMR spectrometer, a probe in communication with the spectrometer, and a controller in communication with the spectrometer, the controller configured to obtain NMR signals of defined single peak regions of an NMR spectrum associated with LP-Z of a fluid sample in the probe and generate a patient report providing LP-Z levels. In some embodiments, the probe may be a flow probe.

The controller may include or be in communication with at least one local or remote processor, wherein the at least one processor is configured to: (i) obtaining a composite NMR spectrum of a fitted region of the in vitro plasma biological sample; and (ii) deconvolving the composite NMR spectrum using a defined deconvolution model to generate LP-Z levels. In certain embodiments, the deconvolution model includes at least one of a High Density Lipoprotein (HDL) component, a Low Density Lipoprotein (LDL) component, a VLDL (very low density lipoprotein)/chylomicron component, LP-X and/or LP-Y and LP-Z.

Other features, advantages and details of the present invention will become apparent to those of ordinary skill in the art upon review of the following detailed description of the preferred embodiments, and the drawings, such description is merely illustrative of the invention. Features described with respect to one embodiment may be combined with other embodiments, although not specifically discussed with respect thereto. That is, it should be noted that aspects of the invention described with respect to one embodiment may be incorporated into a different embodiment, even if not specifically described with respect thereto. That is, features of all embodiments and/or any embodiment may be combined in any manner and/or combination. Accordingly, the applicant reserves the right to alter any originally filed claim or to file any new claim, including the right to be able to modify any originally filed claim to depend from and/or incorporate any feature of any other claim (although originally not claimed in this way). The above and other aspects of the invention are explained in detail in the specification set forth below.

Drawings

The disclosure may be better understood with reference to the following drawings, in which embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

Figure 1 shows an exemplary NMR spectrum of human serum.

Fig. 2 shows VLDL, LDL or HDL subclasses of an exemplary NMR spectrum.

FIG. 3 shows an exemplary plasma analysis using the LP-X deconvolution model, which includes reference signals for LP-X and LP-Z.

FIG. 4 shows exemplary LP-Z concentrations for healthy and hepatic patients as determined by NMR analysis.

Fig. 5 shows an exemplary Kaplan Meier curve for Z-index to predict 90-day survival in severe alcoholic hepatitis.

Fig. 6 shows exemplary repeated measurements of Z-index to predict 90-day survival in severe alcoholic hepatitis.

Figure 7 shows exemplary lipoprotein profiles in alcoholic hepatitis compared to healthy subjects.

Figure 8 shows the chemical structures of lipids and triglycerides.

Fig. 9 is a schematic diagram showing lipoprotein metabolism in healthy subjects.

FIG. 10 shows exemplary lipoprotein profiles for LP-X and LP-Z in alcoholic hepatitis.

Fig. 11 is a schematic diagram of a system for analyzing patient risk using a Z-index module and/or circuitry, according to an embodiment of the invention.

Detailed Description

In this application, plasma samples from patients with Alcoholic Hepatitis (AH) were studied for the relationship between LP-Z as determined by NMR spectroscopy and the mortality of AH patients was followed. Described herein are novel methods for accurately predicting mortality of AH patients based on the amount of LP-Z in a biological sample using NMR spectroscopy. The present invention may be embodied in a variety of ways.

In some embodiments, the methods and systems include determining LP-Z in a subject or patient. In some embodiments, the method may predict a patient's response to therapy or the likelihood of death of the patient within 90 days.

In some embodiments, the method of predicting mortality in a subject with AH comprises the steps of: obtaining an NMR spectrum of a sample of plasma or serum obtained from the subject, and programmatically determining the presence of LP-Z and apoB-containing lipoproteins in the sample based on the NMR spectrum of the sample, wherein the NMR spectrum of the sample comprises LP-X and LP-Z. In some embodiments, the NMR spectrum of the sample further includes LP-Y. In certain embodiments, the method further comprises calculating a Z-index score. In some cases, a Z-index greater than 0.6 may correlate with AH mortality in 90 days or less.

Lipoprotein Z (LP-Z) is a Low Density Lipoprotein (LDL) -like particle. As LDL, LP-Z carries one copy of apolipoprotein b (apob), with amphiphilic lipids on its surface and hydrophobic lipids in the particle core. The class referred to herein as LP-Z has been previously described as "highly triglyceride-rich LDL" (Kostner GM et al, Biochem J.1976; 157: 401-. Lipoprotein X (LP-X) is an abnormal multilamellar vesicle particle rich in phospholipids and unesterified cholesterol, which can be quantified by Nuclear Magnetic Resonance (NMR) spectroscopy. The presence of LP-X or LP-Z may not be detectable by the conventional lipidome (lipid panel).

Terms and definitions

Like numbers refer to like elements throughout. In the drawings, the thickness of some lines, layers, components, elements or features may be exaggerated for clarity. The dashed lines illustrate optional features or operations, unless indicated otherwise.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. As used herein, phrases such as "between X and Y" and "between about X and Y" should be interpreted to include X and Y. As used herein, phrases such as "between about X and Y" refer to "between about X and about V," as used herein, phrases such as "about X to Y" refer to "about X to about Y.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Well-known functions or constructions may not be described in detail for brevity and/or clarity.

The term "programmatically" means performed using computer programs and/or software, a processor, or ASIC-directed operations. The term "electronic" and its derivatives refer to automated or semi-automated operations performed using devices having circuits and/or modules, rather than via mental steps, and generally refers to operations performed in a programmed manner. The terms "automated" and "automated" mean that the operations can be performed with minimal or no manual labor or input. The term "semi-automated" means that the operator is allowed to make some input or activation, but the calculation and signal acquisition and calculation of the concentration of one or more ionizing components is done electronically (usually programmatically) without manual input. The term "about" refers to a specific value or number of +/-10% (mean or average).

The term "biological sample" refers to an extracorporeal blood, plasma, serum, CSF, saliva, lavage, sputum, urine or tissue sample of a human or animal. Embodiments of the present invention may be particularly suitable for evaluating biological samples of human plasma or serum. The plasma or serum sample may be fasting or non-fasting.

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

The term "clinical disease state" means a medical condition at risk that may indicate that a medical intervention, therapy adjustment, or exclusion of a certain therapy (e.g., a drug) and/or monitoring is appropriate. Identification of the likelihood of a clinical disease state may allow the clinician to treat the condition, delay or inhibit the onset of the condition accordingly. Examples of clinical disease states include, but are not limited to, CHD, CVD, stroke, type 2 diabetes, prediabetes, dementia, alzheimer's disease, cancer, arthritis, Rheumatoid Arthritis (RA), kidney disease, liver disease, lung disease, COPD (chronic obstructive pulmonary disease), peripheral vascular disease, congestive heart failure, organ transplant reactions and/or medical conditions associated with biological dysfunction in immunodeficiency, protein sorting, immune and receptor recognition, inflammation, pathogenicity, metastasis and other cellular processes.

Method for measuring LP-Z to determine Z index

Described herein are novel methods (i.e., assays) for characterizing LP-Z in a biological sample using NMR to diagnose or detect AH in a subject. In some embodiments, the methods can predict mortality in AH patients. The method may be embodied in a number of ways.

NMR spectroscopy has been used to simultaneously measure the full spectrum of circulating lipoproteins, including Very Low Density Lipoprotein (VLDL), Low Density Lipoprotein (LDL), and High Density Lipoprotein (HDL) particle subclasses, as well as abnormal lipoprotein particles such as LP-X and LP-Z, from in vitro plasma or serum samples. See, U.S. patent No. 4,933,844, U.S. patent No. 6,617,167, U.S. patent application No. 16/188,435 filed on 11/13/2018, the contents of which are incorporated herein by reference as if fully set forth herein. In some embodiments, the sample may be blood, serum, plasma, cerebrospinal fluid, or urine.

Generally, to evaluate lipoproteins in plasma and/or serum samples, the amplitudes of multiple NMR spectrum-derived signals within the chemical shift region of the NMR spectrum are derived by deconvolution of a complex methyl signal envelope (envelope) to produce subclass concentrations. Figure 1 shows an exemplary NMR spectrum of human serum with highlighted lipid methyl groups. Subclasses are represented by a number (usually more than 60) of discrete contributing subclass signals associated with NMR frequency and lipoprotein diameter. NMR evaluation can resolve the measured plasma NMR signals to produce concentrations of different subpopulations of lipoproteins of VLDL, LDL and HDL. For example, these subpopulations may be further characterized as being associated with a particular size range within the VLDL, LDL or HDL subclasses as shown in fig. 2. As shown in fig. 2, the subclass signals are combined to produce a measured signal. The subclass signal amplitudes derived by deconvolution can provide the concentration of each subclass.

In the past, the "higher" lipoprotein test panel available from LapCorp, Burlington, n.c. (e.g. the NMR lipofix lipoprotein test) typically included a total HDL particle (HDL-P) measurement summing the concentrations of all HDL subclasses and a total LDL particle (LDL-P) measurement summing the concentrations of all LDL subclasses. The LDL-P numbers indicate the concentration of those corresponding particles in nmol/L equivalent concentration units. The HDL-P number indicates the concentration of those corresponding particles expressed in. mu. mol/L equivalent concentration units.

Recently, NMR analysis using refined deconvolution models has been used to determine the concentrations of LP-X and LP-Z in biological samples. FIG. 3 shows an example of a good fit and small residual signal resulting from analysis of plasma from a patient with high bilirubin when using the LP-X deconvolution model of the reference signal, including LP-X, LP-Y and LP-Z.

NMR spectroscopy can be used to identify and quantify LP-Z in patients in which it accumulates, such as patients with Alcoholic Hepatitis (AH). As shown in figure 4, recent testing of plasma samples from AH patients using NMR-based methods developed by LabCorp to quantify the distribution of circulating lipoproteins in biological samples, indicates that exemplary AH patients carry significantly high levels of the abnormal lipoprotein LP-Z. In particular, the levels of LP-Z may be significantly higher in AH patients compared to healthy individuals (HC) or patients with other forms of chronic liver disease. In order to reliably use NMR for AH patient prognosis, the relationship of LP-Z, as determined by NMR, to patient mortality must be understood.

The AH test described above further confirms that in AH patients, the levels of both LP-Z and total apoB-containing lipoproteins are inversely correlated with liver synthesis function, as measured by INR. Although neither LP-Z nor total apoB-containing lipoprotein levels may have a robust correlation with mortality in patients with AH, these two parameters may mutually predict mortality. LP-Z and total apoB-containing lipoproteins (VLDL, LDL and LP-Z) can be used to simultaneously predict mortality. LP-Z can be positively correlated with mortality, while total apoB-containing lipoprotein can be negatively correlated with mortality. The novel biomarker Z-index described herein exploits these associations with patient mortality. The Z index can be calculated by the following equation:

wherein the concentration unit of the lipoprotein fraction is nmol/L.

The Z index may represent the proportion of abnormal lipoprotein LP-Z in apoB-containing lipoproteins, and may reflect the degree of liver damage that leads to disturbances of circulating lipoproteins in AH. The Z-index is highly predictive of short term mortality within 90 days. As shown by the exemplary Kaplan Meier curve of fig. 5, the Z-index can be robustly correlated with 90-day mortality. For every 1% increase in Z index, the mortality risk increases by 5% (95% CI 1.02-1.08, p = 0.001). The threshold value of the Z index was determined to be 0.6. At a Z-index of less than 0.6, only about 5% of patients are likely to die within 90 days of LP-Z identification (2 out of 38 test subjects in the data shown in FIG. 5). Conversely, when the Z-index is greater than 0.6, nearly 40% of patients can be expected to die within 90 days of LP-Z identification (of the data shown in fig. 5, 21 out of 53 test subjects die within 90 days).

The Z-index may be a more reliable predictor than the MELD score, which is the current standard for predicting liver failure patient outcomes. As shown in table 1, the Z index may be significantly better than the MELD score when predicting 90-day mortality in AH patients.

TABLE 1 multivariable Cox proportional Risk regression

Method	HR	95% CI	P value
				Z index (> 0.6)	8.4	1.9-36.4	0.004
MELD	1.0	0.9-1.2	0.5

The Z-index may also be a more reliable predictor than predicting other components of the AH outcome, as shown in table 2.

TABLE 2 confidence comparison for various strategies

Z index	≤0.6	＞0.6	P value
				Number of	38	53
INR	2.0±0.5	1.9±0.4	0.4
				Bilirubin	21.5±9.3	25.3±8.1	0.04
Creatinine	0.8±0.5	1.2±0.8	0.01

The Z index can be calculated using LP-Z and total apoB-containing lipoprotein concentration measured by NMR and can be used to effectively risk stratify patients with severe AH. An effective risk classification may be particularly useful to help distinguish patients at low risk of death from patients at high risk of death within 90 days. As shown in fig. 6, for example, the Z-index may be used as a repeated measure to predict the outcome. Z index in surviving people decreases by day 14, while Z index in dead people remains stable.

Although the disclosure herein discloses LP-Z and apoB-containing lipoproteins by NMR spectroscopy, one skilled in the art understands that the Z index is not specific to NMR spectroscopy. For example, the concentration of LP-Z can be estimated using agarose gel electrophoresis in combination with lipid staining using sudan black and Filipin. The concentration of ApoB can be measured by ELISA. Figure 7 shows that the exemplary lipoprotein profile in AH is unique in both sudan black and Filipin tests compared to the exemplary healthy subject (HC).

FIG. 8 shows lipoprotein structures and chemical structures of Phospholipids (PL), Cholesterol Esters (CE) and Triglycerides (TG) and Free Cholesterol (FC). Fig. 9 shows the lipid pathways in lipoprotein metabolism in healthy subjects. Most individuals (i.e., "normal" healthy subjects) have very low levels of LP-X or LP-Z or no LP-X or LP-Z. In contrast, variable amounts of LP-Y were found in both healthy and diseased individuals. In subjects exhibiting the presence of LP-X or LP-Z (e.g., subjects with obstructive jaundice or AH), LP-Z levels can be elevated to varying degrees.

The methyl lipid signals from LP-X, LP-Y and LP-Z each have a unique spectral shape and position in the NMR spectrum, unlike those of "normal" lipoprotein particles. A unique pattern of circulating lipoproteins, characterized by the accumulation of the abnormal lipoproteins LP-X and LP-Z, can exist in AH. Figure 10 shows an exemplary unique lipoprotein profile in AH patients. Elevated LP-X and LP-Z concentrations can distinguish between healthy patients and patients with liver disease, as determined by NMR analysis. These lipoproteins can be a valid biomarker for risk stratification of severe alcoholic hepatitis. The assays described herein utilize these unique spectral profiles to detect and quantify LP-X, LP-Y and LP-Z in serum or plasma samples.

In some embodiments, the method further comprises the step of generating a report listing the concentration of lipoprotein components present in the sample and the likelihood of death. In some embodiments, the method of diagnosing the presence or absence of LP-Z in a subject comprises the steps of: obtaining an NMR spectrum of a sample of plasma or serum obtained from the subject and programmatically determining the presence of LP-Z in the sample based on the NMR spectrum of the sample, wherein the NMR spectrum of the sample comprises LP-X, LP-Y and LP-Z. In some embodiments, the obtaining step of the method comprises (a) generating a measured lipid signal profile for an NMR spectrum of a plasma or serum sample obtained from the subject; and (b) generating a calculated profile of the sample, the calculated profile being based on derived concentrations of lipoprotein components that may be present in the sample, wherein the lipoprotein components include LP-X, LP-Y and LP-Z, the derived concentrations of each lipoprotein component being a function of a reference spectrum of the component and a calculated reference coefficient, wherein the three lipoprotein components at the calculated concentrations are LP-X, LP-Y and LP-Z.

In some embodiments, the method further comprises (c) determining a correlation between the initial calculated line shape of the sample and the measured line shape of the sample; and (d) determining the presence of LP-Z based on the calculated profile if the correlation between the calculated profile and the measured profile of the sample is above a predetermined threshold. In some embodiments, step (b) of the method comprises calculating the reference coefficients of the calculated line shape based on a linear least squares fitting technique. In some embodiments, the sample may be blood, serum, plasma, cerebrospinal fluid, or urine.

Referring now to fig. 11, it is contemplated that most, if not all, of the measurements may be made on or using system 10, which system 10 is in communication with or at least partially loaded on an NMR clinical analyzer 22 as described, for example, in U.S. patent No. 8,013,602, the contents of which are incorporated herein by reference as if fully set forth herein.

The system 10 may include a Z-index risk module 370 to collect data suitable for determining a Z-index. The system 10 may include an analysis circuit 20, the analysis circuit 20 including at least one processor 20p that may be loaded on an analyzer 22 or at least partially remote from the analyzer 22. If the latter, module 370 and/or circuitry 20 may be located, in whole or in part, on server 150. The server 150 may be provided using cloud computing, which includes providing computing resources on demand via a computer network. The resources may be embodied as various infrastructure services (e.g., computers, storage, etc.) as well as applications, databases, file services, email, and the like. In traditional computing models, both data and software are typically contained entirely within the user's computer; in cloud computing, a user's computer may contain little software or data (perhaps an operating system and/or a web browser), and may simply act as a display terminal for processes occurring on an external computer network. A cloud computing service (or aggregation of multiple cloud resources) may be generally referred to as a "cloud". Cloud storage may include a model of networked computer data storage in which data is stored on multiple virtual servers, rather than being hosted on one or more dedicated servers. The data transmission may be encrypted and may be accomplished via the internet using any suitable firewall to comply with industry or regulatory standards (e.g., HIPAA). The term "HIPAA" refers to the united states law defined by the Health Insurance Portability and Accountability Act. Patient data may include accession number or identifier, gender, age, and test data.

The analysis results may be sent to the patient, clinician site 50, health insurance agency 52, or pharmacy 51 via a computer network (e.g., the internet), via email, etc. The results may be sent directly from the analysis site or may be sent indirectly. The results may be printed out and sent by regular mail. This information may also be sent to a pharmacy and/or medical insurance company, or even a patient, who monitors prescriptions or drug use that may lead to an increased risk of adverse events, or issues medical alerts to prevent the prescription of contradictory medications. The results may be sent to the patient by e-mailing to a "home" computer or pervasive computing device (e.g., smart phone or laptop, etc.). For example, the results may be as an email attachment to the entire report or as a text message alert.

Illustrative embodiments of methods, systems, and analyzers

As used below, any reference to a method, system or analyzer will be understood as a reference to each of those methods, systems or analyzers individually (e.g., "illustrative embodiments 1-4" will be understood as "illustrative embodiments 1, 2, 3 or 4").

Illustrative embodiment 1 is a method of predicting mortality in a patient with alcoholic hepatitis comprising: acquiring an NMR spectrum of a biological sample obtained from a subject; programmatically determining the concentration of LP-Z and total apoB-containing lipoprotein in the sample based on the NMR spectrum of the sample, wherein the NMR spectrum of the sample comprises LP-X and LP-Z; and calculating a Z-index score.

Illustrative embodiment 2 is the method of any preceding or subsequent embodiment, wherein the obtaining step of the method comprises: generating a measured lipid signal profile for an NMR spectrum of a biological sample obtained from the subject; and generating a calculated line shape for the sample.

Illustrative embodiment 3 is the method of any preceding or subsequent embodiment, wherein the calculating the line shape is based on derived concentrations of lipoprotein components including LP-X and LP-Z.

Illustrative embodiment 4 is the method of any preceding or subsequent embodiment, wherein the derived concentration of each of the lipoprotein components is a function of a reference spectrum and a calculated reference coefficient for the component.

An illustrative embodiment 5 is the method of any preceding or subsequent embodiment, wherein the generating step comprises calculating the reference coefficients for the calculated line shape based on a linear least squares fitting technique.

Illustrative embodiment 6 is the method of any preceding or subsequent embodiment, further comprising: determining a correlation between an initial calculated profile of the sample and a measured profile of the sample; and determining the presence of LP-Z based on the calculated profile if the correlation between the calculated profile and the measured profile of the sample is above a predetermined threshold.

Illustrative embodiment 7 is the method of any preceding or subsequent embodiment, wherein the Z-index score comprises the concentration of lipoproteins LP-Z, LDL and VLDL.

Illustrative embodiment 8 is the method of any preceding or subsequent embodiment, wherein the Z-index is the ratio of LP-Z concentration to total ApoB-containing lipoprotein concentration.

Illustrative embodiment 9 is the method of any preceding or subsequent embodiment, wherein the Z-index is calculated by the equation:

z index ([ LP-Z ])/([ VLDL ] + [ LDL ] + [ LP-Z ]).

Illustrative embodiment 10 is the method of any preceding or subsequent embodiment, wherein a Z-index greater than 0.6 predicts that patient death will occur within 90 days or less.

Illustrative embodiment 11 is the method of any preceding or subsequent embodiment, wherein the method predicts the likelihood of death of the patient within 90 days.

An illustrative embodiment 12 is the method of any preceding or subsequent embodiment, wherein the method predicts the likelihood of survival or patient response to treatment.

Illustrative embodiment 13 is the method of any preceding or subsequent embodiment, further comprising, prior to programmatically determining,

placing a sample of the subject in an NMR spectrometer;

deconvolving the NMR spectrum; and

NMR derived measurements of a plurality of selected lipoprotein parameters are calculated based on the deconvolved NMR spectra.

Illustrative embodiment 14 is the method of any preceding or subsequent embodiment, further comprising generating a report listing the concentration of lipoprotein component present in the sample and the likelihood of death.

Illustrative embodiment 15 is the method of any preceding embodiment, wherein the biological sample is one of blood, serum, plasma, cerebrospinal fluid, or urine.

Illustrative embodiment 16 is an NMR analyzer comprising:

an NMR spectrometer;

a probe in communication with the spectrometer; and

a controller in communication with the spectrometer, the controller configured to obtain NMR signals of defined single peak regions of an NMR spectrum associated with LP-Z of a fluid sample in the probe and generate a patient report providing LP-Z levels.

The illustrative embodiment 17 is the analyzer of any preceding or subsequent embodiment, wherein the controller is in communication with at least one local or remote processor, wherein the at least one processor is configured to:

(i) obtaining a composite NMR spectrum of a fitted region of the fluid sample; and

(ii) deconvolving the composite NMR spectrum using a defined deconvolution model to generate LP-Z levels.

An illustrative embodiment 18 is the analyzer of any preceding or subsequent embodiment, wherein the deconvolution model includes at least one of a High Density Lipoprotein (HDL) component, a Low Density Lipoprotein (LDL) component, a VLDL (very low density lipoprotein)/chylomicron component, LP-X, LP-Y, and LP-Z.

An illustrative embodiment 19 is the analyzer of any preceding or subsequent embodiment, wherein the probe is a flow probe.

An illustrative embodiment 20 is the analyzer of any preceding or subsequent embodiment, wherein the fluid sample is an in vitro plasma biological sample.

Illustrative embodiment 21 is the analyzer of any preceding embodiment, wherein the fluid sample is a biological sample of blood, serum, plasma, cerebrospinal fluid, or urine.