WO2025166184A1 - Markers and methods for detecting fatty liver disease - Google Patents

Markers and methods for detecting fatty liver disease

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Publication number
WO2025166184A1
WO2025166184A1 PCT/US2025/014066 US2025014066W WO2025166184A1 WO 2025166184 A1 WO2025166184 A1 WO 2025166184A1 US 2025014066 W US2025014066 W US 2025014066W WO 2025166184 A1 WO2025166184 A1 WO 2025166184A1
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WIPO (PCT)
Prior art keywords
subject
score
hete
eet
masld
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PCT/US2025/014066
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French (fr)
Inventor
Edward A. Dennis
Oswald Quehenberger
Aaron ARMANDO
Rohit LOOMBA
Arun J. SANYAL
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Virginia Commonwealth University
University of California Berkeley
University of California San Diego UCSD
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Virginia Commonwealth University
University of California Berkeley
University of California San Diego UCSD
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Application filed by Virginia Commonwealth University, University of California Berkeley, University of California San Diego UCSD filed Critical Virginia Commonwealth University
Publication of WO2025166184A1 publication Critical patent/WO2025166184A1/en
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Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A61P1/16—Drugs for disorders of the alimentary tract or the digestive system for liver or gallbladder disorders, e.g. hepatoprotective agents, cholagogues, litholytics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/08—Hepato-biliairy disorders other than hepatitis
    • G01N2800/085—Liver diseases, e.g. portal hypertension, fibrosis, cirrhosis, bilirubin

Definitions

  • the invention relates in general to materials and methods to quantitate markers to determine liver disease.
  • BACKGROUND According to recent data from the National Health and Nutrition Examination Survey (NHANES), the prevalence of adult obesity in the United States significantly increased to 42.8% in 2018. If one includes less severe forms of obesity, this number increases such that approximately 66% of the adult general population is either overweight or obese. This has been associated with numerous adverse health consequences including the development of cardiovascular disease, type 2 diabetes mellitus (T2DM) and several cancers.
  • T2DM type 2 diabetes mellitus
  • MASLD metabolic disfunction- associated steatotic liver disease
  • NAFLD nonalcoholic fatty liver disease
  • MASH metabolic dysfunction associated steatotic liver
  • MAFL metabolic dysfunction associated steatotic liver
  • MASH metabolic dysfunction-associated steatohepatitis
  • FIB-4 Fibrosis-4
  • FIB-4 is a commonly used laboratory aid based on age, AST (aspartate aminotransferase), ALT (alanine aminotransferase) and platelet counts; it was developed to evaluate the presence of underlying advanced fibrosis and its use in the context of MASLD assessment is also for fibrosis assessment. This does not, however, provide any insight on the presence of steatosis the hallmark of MASLD.
  • liver biopsy Confirmation of steatotic liver disease requires liver biopsy and either measurement of the continuous attenuation parameter by transient elastography or MRI based methods.
  • a liver biopsy is an invasive procedure with potentially serious side effects.
  • Transient elastography or MRI are not always widely available and are expensive creating a barrier for assessment of MASLD, which starts by assessment of clinical risk factors.
  • the disclosure provides a non-invasive method for detecting a presence, an absence, a type, or a stage of fatty liver disease in subject, the method comprising: (a) providing or obtaining a biological sample from the subject; (b) measuring the presence or level of one or more molecules in the biological sample or in a constituent of the biological sample, wherein the one or more molecules are selected from: Group A: 4-HDoHE, 5-HETE, 9-HODE, 12- HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10- diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and/or EPA; Group B: SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:
  • the method does not comprise collecting a biopsy from the subject.
  • the subject is an animal.
  • the subject is a human.
  • the type of fatty liver disease comprises metabolic dysfunction- associated steatotic liver disease (MASLD), metabolic dysfunction- associated steatohepatitis (MASH), nonalcoholic fatty liver disease (NAFLD), Nonalcoholic Steatohepatitis (NASH), alcoholic fatty liver disease (AFLD), Alcoholic Steatohepatitis (ASH), or any combination thereof.
  • measuring the presence or level of the one or more molecules comprises using gas or liquid chromatography-mass spectrometry or direct-infusion mass spectrometry.
  • the method is used as a non-invasive point of care diagnostic tool.
  • the biological sample comprises blood plasma.
  • the method comprises measuring 14-HDoHE, DHA, 12-HETE, 12-HEPE, 4HDoHE, EPA, 15-HETE, 11,12-EET, 5,6-EET, 14,15- EET, 15-HETrE, 9,10-dHOME, 5-HETE, adrenic acid and 9-HODE.
  • the method comprises measuring 1 to 5 markers selected from the group consisting of 4-HDoHE, 5-HETE, 9-HODE, 12- HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10- diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and EPA.
  • the disclosure also provides a method of determining whether a subject has or is at risk of having MASLD, the method comprising measuring the level of markers selected from the group consisting of 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15- HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and EPA; wherein when 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12- HETE, 14-HDoHE, 15-HETE, 15-HETrE, or adrenic acid are above a cutoff value, their score is 1, and wherein when 9,10-diHOME, 11,12- EET, 5,6-EET, 14,15-EET, DHA, or EPA fall below a cutoff value, their score is also 1, summing the total of the scores wherein if Attorney Docket No.00015-438WO
  • the cutoff values are as set forth in Table 1.
  • the method further comprises measuring complex lipids selected from the group consisting of SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3, DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, and PI 18:0/20:4, wherein when SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0,
  • the cutoff values for the complex lipids are set forth in Table 2B.
  • the method does not comprise collecting a biopsy from the subject.
  • the subject is an animal.
  • the animal is a human.
  • measuring the presence or level of the one or more molecules comprises using gas or liquid chromatography-mass spectrometry or direct-infusion mass spectrometry.
  • the method is used as a non-invasive point of care diagnostic tool.
  • the biological sample comprises blood plasma.
  • the disclosure also provides a method of determining a NAS score for a subject, the method comprising measuring a panel of markers including PC 16:0/16:1, PC 14:0_16:1, PI 16:0/16:1, PI 18:0/16:1, PI 16:0/16:0, SM d18:1/n20:0, SM d18:0/n18:0, SM d18:0/n22:0, 9,10-diHOME, PC 17:0/20:4, PC 18:2/20:4, PC 18:1/22:6, PI 18:0/20:4, PP-E P16:0/22:6 and 11,12-diHETrE, wherein when PC 16:0/16:1, PC 14:0_16:1, PI 16:0/16:1, PI 18:0/16:1, PI 16:0/16:0, SM d18:1/n20:0, SM d18:0/n18:0, SM d18:0/n22:0, and
  • the disclosure also provides a method of determining if a subject has or is at risk of having liver fibrosis, the method comprising measuring a panel of markers including PC 16:0/16:0, PC 14:0/20:4, PC 18:1/18:1, PC16:0/22:5, PC 18:1/22:6, PI 18:0/18:2, SM d18:0/n20:0, Cer d18:0/n16:0, Cer d18:0/n18:0, 19,20-diHDPA, SM d16:1/n22:0, and SM d18:2/n23:0.
  • a panel of markers including PC 16:0/16:0, PC 14:0/20:4, PC 18:1/18:1, PC16:0/22:5, PC 18:1/22:6, PI 18:0/18:2, SM d18:0/n20:0, Cer d18:0/n16:0, Cer d18:0/n18:0, 19,20-diHDPA, SM
  • the disclosure also provides a method of treating a subject, the method comprising performing a method as described herein, wherein the subject having MASLD, MASH, high NAS or fibrosis is treated with a GLP-1 agonist, resmetirom and/or bariatric surgery.
  • the disclosure provides a non-invasive method for detecting a presence, an absence, a type, or a stage of fatty liver disease in subject, the method comprising providing or obtaining a biological sample from the subject; measuring the presence or level of one or more molecules in the biological sample or in a constituent of the biological sample, wherein the one or more molecules are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 biomarkers from Table 3 or Table 4.
  • the one or more molecules are selected from: 4-HDoHE, 5- HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14-15-EET, DHA, EPA, and a combination thereof.
  • the one or more molecules are analyzed for an increase and/or a decrease in levels compared to a normal control.
  • the method measures whether there is an increase in one or more molecules selected from 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid and any combination of the foregoing and/or a decrease in one or more molecules selected from 9,10- diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, EPA and any combination of the foregoing.
  • the method does not comprise collecting a biopsy from the subject.
  • the subject is an animal such a human.
  • the type of fatty liver disease comprises metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), non- alcoholic fatty liver disease (NAFLD), non-alcoholic Steatohepatitis (NASH), alcoholic fatty liver disease (AFLD), Alcoholic Steatohepatitis (ASH), or any combination thereof.
  • measuring the presence or level of the one or more molecules comprises using gas or liquid chromatography-mass spectrometry or direct-infusion mass spectrometry.
  • the method is used as a non-invasive point of care diagnostic tool.
  • the biological sample comprises blood plasma
  • the method comprises separating the blood plasma from the blood sample.
  • the method further comprises using bioinformatics for analyzing the sample.
  • the method comprises measuring 14- HDoHE, DHA, 12-HETE, 12-HEPE, 4-HDoHE, EPA, 15-HETE, 11,12-EET, 5,6- EET, 14,15-EET, 15-HETrE, 9,10-dHOME, 5-HETE, adrenic acid and 9- HODE.
  • the method comprises measuring 1-5 biomarkers from Table 3 or Table 4.
  • the method comprises measuring 1-15 or 1-20 biomarker from Table 3 or Table 4.
  • the biological sample is selected from the group consisting of blood, blood plasma and blood serum.
  • Figure 1 shows a correlation heat map of eicosanoids in control and MASLD plasma. Similarities between variables detected in the MASLD dataset were calculated using Spearman’s correlation. Shorter distances in the dendrograms indicate stronger relationships between the variables. Dark squares shows a positive correlation between variables; light squares shows a negative correlation between the variables.
  • Figure 2 shows a graph of eicosanoid changes (positive and negative changes) in MASLD. Shown are the top 25 eicosanoids with the most robust changes in MASLD.
  • Figure 3 shows a Partial Least Square analysis. Shown is the Scores Plot of PLS-DA for the 32 eicosanoids that were present in at least 80% of the samples, The analysis shows a clear separation between the control and MASLD groups. The partial overlap represents patients with very mild disease.
  • Figure 4A-C shows biomarker analysis for MASLD. For analysis, all 32 eicosanoids present in at least 80% of plasma were used.
  • A Multivarite ROC analysis of 32 eicosanoids using Random Forests as the classification method.
  • B Average importance of the top 20 eicosanoids using Univariate AUROC as ranking method.
  • C Predicted class probability and chart for each corresponding confusion matrix.
  • Figure 5A-C shows another model for the diagnosis of MASLD. Fifteen eicosanoids were selected to optimally establish a panel that best distinguishes between normal controls and MASLD.
  • A Multivariate AUROC analysis of 15 eicosanoid panel using Random Forests as the classification method.
  • B Average importance using Univariate AUROC as ranking method.
  • C Predicted class probability chart for each corresponding confusion matrix.
  • Figure 6A-B provides a Box Plots of Selected Eicosanoid Panel.
  • Figure 8A-C shows (A) Area under the curve score of 0.998 achieved using random forests as the classification. 358 Complex Lipid Analytes that were present in at least 80% of all samples were included. (B) 15 selected complex lipid analytes for the final MASLD panel, ranked by univariate AUC scores. Boxes on the right indicate whether an analyte increases or decreases with MASLD. (C) Two-dimensional separation of control and MASLD patients predicting class probabilities by random forests. The chart indicates a complete separation of the two groups with no overlap. [0021] Figure 9 provides a Box Plots of a complex lipid panel.
  • FIG. 10A-C shows (A) Area under the curve score of 1 indicates a complete separation of the two groups even at a 95% confidence level as shown. Random forests is used as the classification. (B) 15 selected complex lipid analytes for the final Attorney Docket No.00015-438WO1 MASLD panel, ranked by univariate AUC scores. Boxes on the right indicate whether an analyte increases or decreases with MASLD. (C) Two-dimensional separation of control and MASLD patients predicting class probabilities by random forests. The chart indicates a complete separation of the two groups.
  • Figure 11A-C shows (A) Area under the curve score of 1 indicates a complete separation of the two groups even at a 95% confidence level as shown. Random forests is used as the classification. (B) All 30 analytes that consist of eicosanoids or complex lipids, ranked by univariate AUC scores. Boxes on the right indicate whether an analyte increases or decreases with MASLD. (C) Two-dimensional separation of control and MASLD patients predicting class probabilities by random forests. The chart indicates a complete and robust separation of the two groups. [0024] Figure 12 shows data for 30 analytes used in the lipidomic score separated by whether they increase or decrease in MASLD.
  • a score of 1 is given for each analyte above their respective cutoff for analytes that increase and below their respective cutoff for analytes that decrease.
  • the analyte scores are summed to make lipidomic score.
  • the cutoff values were identified by univariate AUC analysis which best separates the two groups for each analyte. Shown are the percent accuracies of each of the groups based off of the lipidomic score cutoff used. Score cutoffs of 12-14 give 100% probability of correct assignment to each group as shown in the grey line. Percentage values are made by dividing the number of correct assignments over the total patients in each group for each cutoff value.
  • Figure 13A-C provides (A) Area under the curve of 0.73 comparing patients with low NAS scores (1-3) compared to high NAS scores (4-8). Random forests is used as the classification. (B) 15 lipid analytes that consist of eicosanoids or complex lipids, ranked by univariate AUC scores. The boxes on the right indicate whether each analyte increases or decreases with high NAS scores.
  • FIG. 14 shows analytes selected for the final lipidomic NAS panel. Patients were grouped by their NASH activity score via biopsy. NAS Low are values between 1-3 and NAS High are values between 4-8. Shown are the trends that correlate with NAS grouping. The data are expressed as pmol/ml. Outliers higher than 1.5 times the interquartile range of the 3rd quartile were removed for visualization.
  • Figure 15 provides analytes selected for the final NAS lipidomic panel.
  • FIG. 16 shows examples of analytes included in the final NAS lipidomic panel that separate the patients from the controls for each of the components that are combined to make the NAS biopsy score. The data are expressed as pmol/ml. Outliers higher than 1.5 times the interquartile range of the 3 quartile were removed for visualization.
  • Figure 17A-E provides (A) 15 analytes in used in the NAS lipidomic score separated by whether they increase or decrease with high NAS biopsy scores.
  • a cutoff of 8 means a patient with a score of 8-15 or above is identified as high NAS (4-8) and 1-7 is identified as low NAS (1-3).
  • this cutoff value 80.8% of patients were correctly assigned Attorney Docket No.00015-438WO1 with low MAS scores and 68.3% of patients with high MAS scores.
  • the overall probability of correctly assigning a patient is 74.5%.
  • E 15 analytes are in used in the lipidomic score separated by whether they increase or decrease in high NAS biopsy. A score of 1 is given for each analyte above their respective cutoff for analytes that increase and below their respective cutoff for analytes that decrease.
  • FIG. 18A-C provides (A) Area under the curve of 0.77 comparing patients with low fibrosis scores (0-1) compared to high fibrosis scores (2-4). Random forests is used as the classification.
  • Figure 19 shows analytes selected for the final fibrosis lipidomic panel where patients are separated into 2 groups by fibrosis stage. Fib Low are values between 0-1 and Fib High are value between 2-4. Shown are the trends with fibrosis groups. The data are expressed as pmol/ml.
  • Figure 20 shows analytes selected for the final fibrosis lipidomic panel where patients are separated by fibrosis stage via biopsy. Shown are trends with fibrosis stages. The data are expressed as pmol/ml. Outliers higher than 1.5 times the interquartile range of the 3 quartile were removed for visualization.
  • Attorney Docket No.00015-438WO1 [0033]
  • Figure 21A-E shows (A) 12 analytes in used in the lipidomic score separated by whether they increase or decrease with high fibrosis scores. The cutoff values shown were identified by univariate AUC analysis which best separates the two groups for each analyte.
  • a cutoff of 7 means a patient with a score of 7-12 is identified as high Fib (2-4) and 1-6 is identified as low Fib (0-1). Using this cutoff value 75.6% of patients were correctly assigned with low Fib4 scores and 72.5% of patients with high Fib4 scores. The overall probability of correctly assigning a patient is 74.0%.
  • E 12 analytes are in used in the lipidomic score separated by whether they increase or decrease in high fibrosis biopsy. A score of 1 is given for each analyte above their respective cutoff for analytes that increase and below their respective cutoff for analytes that decrease. The analyte scores are summed to make lipidomic score.
  • cutoff values were identified by univariate AUC analysis which best separates the two groups for each analyte. Shown are the percent accuracies of each of the groups based off of the lipidomic score cutoff used. Probability of correct assignment to each group as shown in the grey line. Percentage values are made by dividing the number of correct assignments over the total patients in each group for each cutoff value.
  • an eicosanoid includes a plurality of such eicosanoids and reference to “the subject” includes reference to one or more subjects and so forth.
  • Attorney Docket No.00015-438WO1 [0035]
  • the use of “or” means “and/or” unless stated otherwise.
  • “comprise,” “comprises,” “comprising” “include,” “includes,” and “including” are interchangeable and not intended to be limiting.
  • any values provided in a range of values include both the upper and lower bounds, and any values contained within the upper and lower bounds.
  • Biomarker means a compound that is differentially present (i.e., increased or decreased) in a biological sample from a subject or a group of subjects having a first phenotype (e.g., having a disease) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having the disease).
  • a biomarker may be differentially present at any level, but is generally present at a level that is increased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least Attorney Docket No.00015-438WO1 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more; or is generally present at a level that is decreased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by
  • biomarker level and “level” refer to a measurement that is made using any analytical method for detecting the biomarker in a biological sample and that indicates the presence, absence, absolute amount or concentration, relative amount or concentration, titer, a level, an expression level, a ratio of measured levels, or the like, of, for, or corresponding to the biomarker in the biological sample.
  • level depends on the specific design and components of the particular analytical method employed to detect the biomarker.
  • biomarker panel refers to a set biomarkers that are informative for predicting liver disease, and in particular embodiments, informative for predicting liver disease progression.
  • expression levels of the set of biomarkers in the biomarker panel can be informative for predicting liver disease progression.
  • a biomarker panel can include two, three, four, five, six, seven, eight, nine, ten eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty one, twenty two, twenty three, twenty four, twenty five, twenty six, twenty seven, twenty eight, twenty nine or thirty biomarkers.
  • a “control” is meant any useful reference used to diagnose MASH, MAFLD or liver fibrosis.
  • the control can be any sample, standard, standard curve, or level that is used for comparison purposes.
  • the control may be a negative control (e.g., a sample or level from a subject diagnosed as not having MAFLD, MASH, or liver Attorney Docket No.00015-438WO1 fibrosis, e.g., a healthy subject) or a positive control (e.g., a sample or level from a subject clinically diagnosed as having MAFLD, MASH, or liver fibrosis).
  • "detecting" or "determining” with respect to a biomarker level includes the use of both the instrument used to observe and record a signal corresponding to a biomarker level and the material(s) required to generate that signal.
  • the level is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.
  • “Diagnose”, “diagnosing”, “diagnosis”, and variations thereof refer to the detection, determination, or recognition of a health status or condition of an individual on the basis of one or more signs, symptoms, data, or other information pertaining to that individual.
  • the health status of an individual can be diagnosed as healthy/normal (i.e., a diagnosis of the absence of a disease or condition) or diagnosed as ill/abnormal (i.e., a diagnosis of the presence, or an assessment of the characteristics, of a disease or condition).
  • diagnosis encompass, with respect to a particular disease or condition, the initial detection of the disease; the characterization or classification of the disease; the detection of the progression, remission, or recurrence of the disease; and the detection of disease response after the administration of a treatment or therapy to the individual.
  • the diagnosis of metabolic disfunction-associated steatotic liver disease (MASLD) includes distinguishing individuals who have MASLD from individuals who do not.
  • the term “mammal” encompasses both humans and non-humans and includes but is not limited to humans, non-human primates, canines, felines, murines, bovines, equines, and porcines.
  • the term “obtaining a dataset associated with a sample” encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample and processing the sample to experimentally determine the data.
  • the Attorney Docket No.00015-438WO1 phrase also encompasses receiving a set of data, e.g., from a third party that has processed the sample to experimentally determine the dataset.
  • a “reference level” or “reference sample level” of a biomarker means a level of the biomarker that is indicative of a particular disease state, phenotype, or predisposition to developing a particular disease state or phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or predisposition to developing a particular disease state or phenotype, or lack thereof.
  • a “positive" reference level of a biomarker means a level that is indicative of a particular disease state or phenotype.
  • a “negative" reference level of a biomarker means a level that is indicative of a lack of a particular disease state or phenotype.
  • a “reference level” of a biomarker may be an absolute or relative amount or concentration of the biomarker, a presence or absence of the biomarker, a range of amount or concentration of the biomarker, a minimum and/or maximum amount or concentration of the biomarker, a mean amount or concentration of the biomarker, and/or a median amount or concentration of the biomarker; and, in addition, “reference levels” of combinations of biomarkers may also be ratios of absolute or relative amounts or concentrations of two or more biomarkers with respect to each other.
  • Appropriate positive and negative reference levels of biomarkers for a particular disease state, phenotype, or lack thereof may be determined by measuring levels of desired biomarkers in one or more appropriate subjects, and such reference levels may be tailored to specific populations of subjects (e.g., a reference level may be age-matched or gender- matched so that comparisons may be made between biomarker levels in samples from subjects of a certain age or gender and reference levels for a particular disease state, phenotype, or lack thereof in a certain age or gender group).
  • control level of a target molecule refers to the level of the target molecule in the same sample type from an individual that does not have the disease or condition, or from an individual that is not suspected of having the disease or condition.
  • a "control level" of a target molecule need not be determined each time the present methods are carried out, and may be a previously determined level that is used as a reference or threshold to determine whether the level in a particular sample is higher or lower than a normal level.
  • a control level in a method described herein is the level that has been observed in one or more subjects (i.e., a population) without MASLD. In some embodiments, a control level in a method described herein is the level that has been observed in one or more subjects with MASLD, but not a particular subset or species falling under MASLD. In some embodiments, a control level in a method described herein is the average or mean level, optionally plus or minus a statistical variation that has been observed in a plurality of normal subjects, or subjects with MASLD.
  • sample can include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, such as a blood sample, taken from a subject, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision, or intervention or other means known in the art.
  • Examples of an aliquot of body fluid include amniotic fluid, aqueous humor, bile, lymph, breast milk, interstitial fluid, blood, blood plasma, cerumen (earwax), Cowper’s fluid (pre-ejaculatory fluid), chyle, chyme, female ejaculate, menses, mucus, saliva, urine, vomit, tears, vaginal lubrication, sweat, serum, semen, sebum, pus, pleural fluid, cerebrospinal fluid, synovial fluid, intracellular fluid, and vitreous humour.
  • Metabolic dysfunction-associated steatotic liver disease (MASLD), previously referred to as non-alcoholic fatty liver disease (NAFLD), represents a spectrum of disease occurring in the absence of alcohol abuse. It is characterized by the presence of steatosis (fat in the liver) and may represent a hepatic manifestation of the metabolic syndrome (including obesity, diabetes and Attorney Docket No.00015-438WO1 hypertriglyceridemia).
  • MASLD is linked to insulin resistance, it causes liver disease in adults and children and may ultimately lead to cirrhosis (Skelly et al., J Hepatol., 35: 195-9, 2001; Chitturi et al., Hepatology, 35(2):373-9, 2002).
  • the severity of MASLD ranges from the relatively benign isolated predominantly macrovesicular steatosis (i.e., nonalcoholic fatty liver (NAFL)) to non-alcoholic steatohepatitis (NASH) (Angulo et al., J Gastroenterol Hepatol, 17 Suppl:S186-90, 2002).
  • NASH non-alcoholic steatohepatitis
  • Angulo et al., J Gastroenterol Hepatol, 17 Suppl:S186-90, 2002 Angulo et al., J Gastroenterol Hepatol, 17 Suppl:S186-90, 2002.
  • NASH is characterized by the histologic presence of steatosis, cytological ballooning, scattered inflammation and pericellular fibrosis (Contos et al., Adv Anat Pathol., 9:37-51, 2002).
  • Hepatic fibrosis resulting from NASH may progress to cirrhosis
  • the degree of insulin resistance correlates with the severity of MASLD (NAFLD), being more pronounced in patients with MASH (NASH) than with simple fatty liver (Sanyal et al., Gastroenterology, 120(5):1183-92, 2001).
  • NASH MASLD
  • Sanyal et al., Gastroenterology, 120(5):1183-92, 2001 insulin-mediated suppression of lipolysis occurs and levels of circulating fatty acids increase.
  • Two factors associated with MASH include insulin resistance and increased delivery of free fatty acids to the liver. Insulin blocks mitochondrial fatty acid oxidation. The increased generation of free fatty acids for hepatic re-esterification and oxidation results in accumulation of intrahepatic fat and increases the liver’s vulnerability to secondary insults.
  • NASH gamma-glutamyltransferase
  • gamma-GT gamma-glutamyltransferase
  • fasting levels of plasma insulin cholesterol and triglyceride.
  • Macrovesicular steatosis represents hepatic accumulation of triglycerides, and this in turn is due to an imbalance between the delivery and utilization of free fatty acids to the liver.
  • triglyceride will accumulate and act as a reserve energy source.
  • stored triglycerides in adipose
  • Oxidation of fatty acids will yield energy for utilization.
  • Obesity is the most common risk factor for MASLD. Whereas 75% of the general population is overweight or obese, only 30-40% of the general population have MASLD. Also, it has been reported that up to 20% of individuals with MASLD are lean and would thus be missed by current practice guidelines. There is therefore a need for a diagnostic test that can be used in routine clinical settings to identify who has MASLD so that appropriate secondary tests to assess disease activity and fibrosis can be performed for risk stratification and clinical decision making. [0057] Lipidomic analysis of plasma provides a “snapshot” of the state of lipid metabolism in the body.
  • MASLD MASLD
  • Free, unesterified polyunsaturated fatty acids such as arachidonic acid (AA), adrenic acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) can undergo enzymatic or non-enzymatic oxidation and other modifications to give rise to prostaglandins, leukotrienes and various other forms of oxygenated metabolites.
  • AA arachidonic acid
  • EPA eicosapentaenoic acid
  • DHA docosahexaenoic acid
  • These lipid metabolites, including their precursor fatty acids are collectively referred to as “eicosanoids”.
  • Bioactive lipids include a number of molecules whose concentrations or presence affect cellular function. Bioactive lipids, as used herein, include phospholipids, sphingolipids, lysophospholipids, ceramides, diacylglycerol, eicosanoids, steroid hormones and the like.
  • Eicosanoids and related metabolites are a group of structurally diverse metabolites that derive from the oxidation of polyunsaturated acids (PUFAs) including arachidonic acid (AA), linoleic acid, alpha and gamma linolenic acid, dihomo gamma linolenic acid, eicosapentaenoic acid and docosahexaenoic acid. They are locally acting bioactive signaling lipids that regulate a diverse set of homeostatic and inflammatory processes.
  • PUFAs polyunsaturated acids
  • eicosanoids and other oxylipins are of great clinical interest and lipidomics is now widely used to screen effectively for potential disease biomarkers.
  • the biosynthesis of eicosanoids and oxylipins involves the action of multiple enzymes organized into a complex and intertwined lipid-anabolic network.
  • the enzymatic formation of eicosanoids requires free fatty acids as substrates; thus, the pathway is initiated by the hydrolysis of phospholipids (PLs) by Attorney Docket No.00015-438WO1 phospholipase A upon physiological stimuli.
  • the hydrolyzed PUFAs are then processed by three enzyme systems: cyclooxygenases (COX), lipoxygenases (LOX), and cytochrome P450 enzymes (CYP450).
  • COX cyclooxygenases
  • LOX lipoxygenases
  • CYP450 cytochrome P450 enzymes
  • Each of these enzyme systems produces unique collections of oxygenated metabolites that function as end-products or as intermediates for a cascade of downstream enzymes.
  • the resulting eicosanoids exhibit diverse biological activities, half-lives and utilities in regulating many physiological processes in health and disease including the immune response, inflammation, and homeostasis.
  • non-enzymatic processes can produce oxidized PUFA metabolites via free radical reactions giving rise to isoprostanes and other oxidized fatty acids.
  • the eicosanoid biosynthetic pathway includes over 100 bioactive lipids and relevant enzymes organized into a complex and intertwined lipid-signaling network.
  • Biosynthesis of polyunsaturated fatty acid (PUFA) derived lipid mediators is initiated via the hydrolysis of phospholipids by phospholipase A (PLA) upon physiological stimuli.
  • PUFA polyunsaturated fatty acid
  • PUFA arachidonic acid
  • DGLA dihomo-gamma-linolenic acid
  • EPA eicosapentaenoic acid
  • DHA docosahexaenoic acid
  • LOX lipoxygenases
  • COX cyclooxygenases
  • cytochrome P450s producing three distinct lineages of oxidized lipid classes.
  • Eicosanoids which are key regulatory molecules in metabolic syndromes and the progression of hepatic steatosis to MASLD, act either as anti-inflammatory agents or as pro-inflammatory agents. Convincing evidence for a causal role of lipid peroxidation in steatohepatitis has not been unequivocally established; however, a decade of research has strongly suggested that these processes occur and that oxidative-stress is associated with hepatic toxicity and injury.
  • MASLD encompasses a wide spectrum of histological cases associated with hepatic fat over-accumulation Attorney Docket No.00015-438WO1 that range from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH/MASH).
  • NAFL nonalcoholic fatty liver
  • NASH/MASH nonalcoholic steatohepatitis
  • the severity of MASLD can be distinguished by evidence of cytological ballooning, inflammation, and higher degrees of scarring and fibrosis.
  • NASH/MASH is a serious condition, and approximately 10-25% of inflicted patients eventually develop advanced liver disease, cirrhosis, and hepatocellular carcinoma.
  • Alterations in lipid metabolism may give rise to hepatic steatosis due to increased lipogenesis, defective peroxisomal and mitochondrial ⁇ -oxidation, and/or a lower ability of the liver to export lipids resulting in changes in fatty acids and/or eicosanoids.
  • Some studies have highlighted the role of triacylglycerol, membrane fatty acid composition, and very low density lipoprotein (VLDL) production in the development of NASH and associated metabolic syndromes.
  • VLDL very low density lipoprotein
  • Cyclooxygenase-2 (COX-2) a key enzyme in eicosanoid metabolism, is abundantly expressed in NASH/MASH, which promotes hepatocellular apoptosis in rats.
  • oxidized lipid products of LA including 9-hydroxyoctadienoic acid (9-HODE), 13-HODE, 9-oxooctadienoic acid (9-oxoODE), and 13-oxoODE as well as of arachidonic acid 5-hydroxyeicosa-tetraenoic acid (5- HETE), 8-HETE, 11-HETE, and 15-HETE are linked to histological severity in MASLD.
  • Free fatty acids are cytotoxic; thus the majority of all fatty acids in mammalian systems are esterified to phospholipids and glycerolipids as well as other complex lipids.
  • Eicosanoids act locally in an autocrine or paracrine fashion and signal by binding to G-protein-coupled receptors or act intracellularly via various peroxisome proliferator-activating receptors. For optimal biological activity, these mediators need to be present in their free, non-esterified form.
  • a number of studies reported that a portion of eicosanoids are naturally esterified and can also be contained in cell membrane lipids, including PLs, in the form of esters.
  • esterified eicosanoids may be signaling molecules in Attorney Docket No.00015-438WO1 their own right or serve as a cellular reservoir for the rapid release upon cell stimulation.
  • Two potential mechanisms for the formation of eicosanoids- containing PLs have been proposed: (i) direct oxidation of PUFAs on the intact PLs, and (ii) re-acylation of preformed free oxylipins into lysoPLs. Cyclooxygenases require free fatty acid as substrate and show little activity toward PUFAs in intact PLs.
  • prostaglandins are first formed enzymatically and then incorporated into PLs by the sequential actions of long-chain acyl-CoA synthases and lysophospholipid acyltransferases. Additionally, preformed fatty acid epoxides, including the regioisomers of epoxyeicosatrienoic acid (EET), are effectively incorporated primarily into the phospholipid fraction of cellular lipids, presumably via CoA- dependent mechanisms.
  • LOX mammalian 12/15 lipoxygenase
  • the endocannabinoid 2- arachidonylglycerol is a substrate for COX-2 and is metabolized to prostaglandin H2 glycerol ester as effectively as free AA.
  • the final products derived from this direct PL oxygenation pathway include esterified prostaglandins (PGs) as well as 11-HETE and 15-HETE.
  • PGs esterified prostaglandins
  • PUFAs contained in PLs can also be oxidized by non-enzymatic reactions. Free radical peroxidation reactions observed under conditions of oxidative stress can freely proceed on intact PLs resulting in the formation of isoprostanes.
  • the gold standard technique for the diagnosis of NASH/MASH is a liver biopsy examination, which is recognized as the only reliable method to evaluate the presence and extent of necro-inflammatory changes, presence of ballooning and fibrosis in liver.
  • liver biopsy is an invasive procedure with possible serious complications and limitations. Reliable noninvasive methods are therefore needed to avoid the sampling risks. It is proposed that differences in plasma levels of eicosanoids and complex lipids can be used to identify MASLD and MASH.
  • Attorney Docket No.00015-438WO1 [0069] This disclosure provides a study on 301 MASLD patients and 48 controls as well as a validation study.
  • the patient population represented the entire spectrum of disease progression from simple steatosis with no inflammation to end-stage liver disease.
  • the disclosure identifies a panel of bioactive lipids that are able to accurately identify MASLD at any stage; mild, moderate, or severe, with or without inflammation and fibrosis, and with or without underlying obesity.
  • 77 eicosanoid metabolites and 25 complex lipids were detectable in at least one of the plasma samples.
  • a metabolite to be of clinical relevance as a biomarker, it was present consistently and in measurable amounts.
  • the disclosure demonstrates that metabolites that were present in 80% of all control and patient plasma samples were included in further analysis.
  • cirrhosis of the liver can readily Attorney Docket No.00015-438WO1 be diagnosed, and the false negative classification should not pose any limitation in a clinical setting.
  • a panel comprising fifteen lipid metabolites (see Table 1 and Table 4) that accurately predict MASLD were used.
  • a strategy was developed to determine a MASLD LIPIDOMICS SCORE, which predicts the presence of MASLD with high accuracy based on lipidomics analysis of ⁇ 50 ul of plasma.
  • the panel can be carried out as part of or in conjunction with variables including BMI, diabetes and cardiovascular disease, gender, ethnicity, and age as well as liver enzymes and lipid levels, including matched controls.
  • the research also provides a panel of 15 complex lipids that accurately predict MASLD (see Table 2B).
  • the complex lipid panel can be used alone or in combination with the eicosanoid panel and may further include variables including BMI, diabetes and cardiovascular disease, gender, ethnicity, and age as well as liver enzymes and lipid levels, including matched controls.
  • Table 1 Eicosanoid lipidomics panel scoring chart for MASLD
  • Table 2B provides an additional panel or standalone panel of 15 complex lipids that can be used to identify subjects having or at risk of having MASLD. [0076] Table 2A – Top 25 Univariate analyses of Complex Lipids
  • sphingolipids Sphingomyelin and Ceramide
  • m indicates a mono-, di-, or tri-hydroxy sphingoid base
  • n indicates there are no hydroxy groups present on the fatty acid where an “h” indicates a hydroxy fatty acid.
  • the disclosure provides methods and compositions useful for determining the risk or probability a subject will have MASLD and/or MASH.
  • a test sample is obtained from the subject.
  • the sample can be obtained by the individual or by a third party, e.g., a medical professional.
  • the test sample is tested to determine values of one or more markers by performing a marker quantification assay.
  • the marker quantification assay determines quantitative expression values of one or more biomarkers from the test sample.
  • the marker quantification assay may be an immunoassay, and more specifically, a multi-plex immunoassay. In other embodiments, the markers are measured by mass spectroscopy, high-performance liquid chromatography and the like.
  • the levels of various biomarkers can be obtained in a single run using a single test sample obtained from the subject.
  • the quantified expression values of the biomarkers are provided to a computer system and/or medical professional.
  • a computer system includes one or more computers, embodied as a computer system. Therefore, in various embodiments, the steps described in reference to the computer system are performed in silico.
  • the computer system analyzes the received biomarker expression values from a marker quantification assay to generate an assessment of disease or disease progression in the subject.
  • the marker quantification assay and the computer system can be employed by different parties. For example, a first party performs the marker quantification assay that then provides the results to a second party which implements the computer system analysis.
  • the first party may be a clinical laboratory that obtains test samples from subjects and performs the assay on the test samples.
  • the second party receives the expression values of biomarkers resulting from the performed assay and analyzes the expression values using the computer system.
  • the computer system can comprise a model training module, a model deployment module, and a training data store.
  • Each of the components of the computer system is hereafter described in reference to two phases: 1) a training phase and 2) a deployment phase.
  • the training phase refers to the building and training of one or more predictive models based on training data that includes quantitative expression values of Attorney Docket No.00015-438WO1 biomarkers obtained from individuals that are known to be healthy, in a state of quiescence, in a state of remission, or in an earlier state of disease progression (e.g., mild/moderate MASLD as opposed to MASH) or individuals that are known to have disease activity, in a state of exacerbation, in a state of relapse, or in a more advanced state of disease progression (e.g., MASH as opposed to MASLD). Therefore, the predictive models are trained to predict disease activity in a subject based on quantitative biomarker values.
  • a predictive model is applied to quantitative biomarker values from a test sample obtained from a subject of interest in order to generate a prediction of disease activity in the subject of interest.
  • the components of the computer system are applied during one of the training phase and the deployment phase.
  • the model training module and training data store are applied during the training phase whereas the model deployment module is applied during the deployment phase.
  • the training phase and the deployment phase can be performed to enable continuously trained models.
  • the model training module can train a model that the model deployment module can subsequently deploy. The same model can undergo additional training by the model training module (e.g., continuously trained using, for example, new training data that is obtained).
  • the components of the computer system can be performed by different parties depending on whether the components are applied during the training phase or the deployment phase.
  • the training and deployment of the predictive model are performed by different parties.
  • the model training module and training data store applied during the training phase can be employed by a first party (e.g., to train a predictive model) and the model deployment module applied during the deployment phase can be performed by a second party (e.g., to deploy the predictive model).
  • Attorney Docket No.00015-438WO1 [0086]
  • the model training module trains one or more predictive models using training data comprising values of biomarkers.
  • the training data may be stored in the training data store.
  • the computer system generates the training data comprising expression values of biomarkers by analyzing biomarker expression values in test samples.
  • the computer system obtains the training data comprising values of biomarkers from a third party. The third party may have analyzed test samples to determine the biomarker values.
  • the training data comprising expression values of biomarkers are derived from clinical subjects.
  • the training data can be values of biomarkers that were measured from test samples obtained from clinical subjects.
  • the training model retrieves the training data from a training data store and randomly partitions the training data into a training set and a test set.
  • 70% of the training data may be partitioned into the training set and the other 30% can be partitioned into the test set.
  • Other proportions of training set and test set may be implemented.
  • the training set is used to train predictive models whereas the test set is used to validate the predictive models.
  • the predictive model is any one of a regression model (e.g., linear regression, logistic regression, or polynomial regression), decision tree, random forest, support vector machine, Naive Bayes model, k-means cluster, or neural network (e.g., feed-forward networks, convolutional neural networks (CNN), deep neural networks (DNN), autoencoder neural networks, generative adversarial networks, or recurrent networks (e.g., long short-term memory networks (LSTM), bi-directional recurrent networks, deep bidirectional recurrent networks), linear mixed effects (LME) model, or any combination thereof.
  • the predictive model can be a stacked classifier that includes both a linear regression and decision tree.
  • the predictive model can be trained using a machine learning implemented method, such as any one of a linear regression algorithm, logistic regression algorithm, decision tree algorithm, support vector machine classification, Naive Bayes classification, Attorney Docket No.00015-438WO1 K-Nearest Neighbor classification, random forest algorithm, deep learning algorithm, gradient boosting algorithm, and dimensionality reduction techniques such as manifold learning, principal component analysis, factor analysis, autoencoder regularization, and independent component analysis, or combinations thereof.
  • the cellular disease model is trained using supervised learning algorithms, unsupervised learning algorithms, semi- supervised learning algorithms (e.g., partial supervision), weak supervision, transfer, multi-task learning, or any combination thereof.
  • the predictive model has one or more parameters, such as hyperparameters or model parameters.
  • Hyperparameters are generally established prior to training. Examples of hyperparameters include the learning rate, depth or leaves of a decision tree, number of hidden layers in a deep neural network, number of clusters in a k-means cluster, penalty in a regression model, and a regularization parameter associated with a cost function.
  • Model parameters are generally adjusted during training. Examples of model parameters include weights associated with nodes in layers of neural network, support vectors in a support vector machine, and coefficients in a regression model.
  • the model parameters of the cellular disease model are trained (e.g., adjusted) using the training data to improve the predictive capacity of the cellular disease model.
  • the importance of each biomarker for a disease activity endpoint is determined by using a method including one of random forest (RF), gradient boosting (GBM), extreme gradient boosting (XGB), or LASSO algorithms.
  • RF random forest
  • GBM gradient boosting
  • XGB extreme gradient boosting
  • the model training module may generate a variable importance plot that depicts the importance of each candidate biomarker.
  • the random forest algorithm may provide, for each candidate biomarker, 1) a mean decrease in model accuracy and 2) a mean decrease in a Gini coefficient which is a measure of how much each candidate biomarker contributes to the homogeneity of nodes and leaves in the random forest.
  • each candidate biomarker is dependent on one or both of the mean decrease Attorney Docket No.00015-438WO1 in model accuracy and mean decrease in Gini coefficient.
  • Each of GBM, XGB, and LASSO can also be used to rank the importance of each candidate biomarker based on an influence value. Therefore, the model training module can generate a ranking of each of candidate biomarkers using one of the methods including RF, GBM, XGB, or LASSO.
  • Each predictive model is iteratively trained using, as input, the quantitative values of the markers for each individual. For example, one iteration involves providing a training example that includes the quantitative value of biomarkers for a particular individual.
  • Each predictive model is trained on an indication (e.g., the positive or negative result).
  • the model deployment module analyzes quantitative biomarker values from a test sample obtained from a subject of interest by applying a trained predictive model.
  • the subject has not previously been diagnosed with a disease and therefore, the deployment of the predictive model enables in silico diagnosis of the disease based on the quantitative biomarker values derived from the subject.
  • the subject has been previously diagnosed with a disease.
  • the deployment of the predictive model enables in silico prediction of disease activity (e.g., disease progression) based on the quantitative biomarker values derived from the subject.
  • the quantitative biomarker values are provided as input to the predictive model.
  • the predictive model analyzes the quantitative biomarker values and outputs an assessment of disease activity (e.g., disease progression).
  • the predicted score can then be informative of the disease activity.
  • the predicted score can enable the classification of the subject into one of multiple disease progression categories (e.g., one of mild/moderate disease progression or severe progression).
  • the assessment of disease activity is a predicted score representing the learned combination of the quantitative biomarker values.
  • the predicted score represents an aggregation of the quantitative values and therefore, is not directly dependent on solely one biomarker value.
  • the assessment of disease activity is a predicted score that may be informative of the disease activity in the subject.
  • the predicted score outputted by the prediction model is compared to one or more reference scores to determine a measure of the disease activity.
  • Reference scores refer to previously determined scores, further described below as “healthy scores” or “diseased scores,” that correspond to diseased patients or non-diseased patients.
  • the one or more scores may be “healthy scores” corresponding to healthy patients, a patient’s own baseline at a prior timepoint when the patient did not exhibit disease activity (e.g., longitudinal analysis), patients clinically diagnosed with the disease but not exhibiting disease activity, or a threshold score (e.g., a cutoff).
  • the one or more scores may be “diseased scores” corresponding to diseased patients, a patient’s own score indicating disease activity at a prior timepoint, or a threshold score (e.g., a cutoff).
  • the threshold score can correspond to healthy patients and can be generated by training a predictive model using expression values of biomarkers from healthy patients.
  • the threshold score can correspond to diseased patients and can be generated by training a predictive model using expression values of biomarkers from the diseased patients.
  • a threshold score corresponding to healthy patients can be lower than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 5% lower than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 10% lower than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 15% lower than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 20% lower than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 25% lower than a threshold score corresponding to diseased patients.
  • the Attorney Docket No.00015-438WO1 threshold score corresponding to healthy patients can be at least 50% lower than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 75% lower than a threshold score corresponding to diseased patients.
  • a threshold score corresponding to healthy patients can be higher than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 5% higher than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 10% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 15% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 20% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 25% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 50% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 75% higher than a threshold score corresponding to diseased patients.
  • the threshold score corresponding to healthy patients can be at least 100% higher than a threshold score corresponding to diseased patients.
  • the predicted score outputted by the prediction model is compared to one or both of the threshold score corresponding to healthy patients and threshold score corresponding to diseased patients, and based on the comparison, a measure of the disease activity is determined.
  • the predicted score outputted by the prediction model can be compared to a healthy score.
  • the subject can be classified as having the disease if the predicted score of the subject is significantly different (e.g., p-value ⁇ 0.05) in comparison to the healthy score.
  • the predicted Attorney Docket No.00015-438WO1 score outputted by the prediction model can be compared to the diseased score.
  • the subject can be classified as not having the disease if the predicted score of the subject is significantly different (e.g., p-value ⁇ 0.05) in comparison to the diseased score.
  • the predicted score outputted by the prediction model is compared to both the healthy score and the diseased score.
  • the subject can be classified as having the disease if the predicted score of the subject is significantly different (e.g., p-value ⁇ 0.05) in comparison to the healthy scores and not significantly different (e.g., p-value >0.05 in comparison to the diseased scores for patients that have been diagnosed with the disease.
  • the subject can undergo treatment. In other words, the assessment can guide the treatment of the subject.
  • the subject can be administered a therapeutic intervention to treat the disease.
  • the assessment of disease activity corresponds to a likely response to a therapy provided to the subject.
  • the predicted score outputted by the prediction model can be compared to a score corresponding to individuals previously determined to be responsive to a therapy (e.g., clinically determined to be responsive to the therapy).
  • the subject can be classified as being a responder if the predicted score of the subject is significantly different (e.g., p-value ⁇ 0.05) in comparison to the score corresponding to individuals previously determined to not be responsive to the therapy.
  • the subject can be classified as being a responder if the predicted score of the subject is not significantly different (e.g., p-value >0.05) in comparison to the score corresponding to individuals previously determined to be responsive to the therapy.
  • the predicted score outputted by the prediction score is compared to a score corresponding to individuals previously determined to be non-responders.
  • the subject can be classified as a non-responder if the predicted score of the subject is not significantly different (e.g., p-value >0.05) from the score corresponding to individuals previously determined to be non- responders.
  • the subject can be classified as a non-responder if the Attorney Docket No.00015-438WO1 predicted score of the subject is significantly different (e.g., p- value ⁇ 0.05) from the score corresponding to individuals previously determined to be responders.
  • the predicted score outputted by the prediction model is compared to both a score corresponding to individuals previously determined to be responders and a score corresponding to individuals previously determined to be non-responders.
  • the subject can be classified as being a responder if the predicted score of the subject is significantly different (e.g., p-value ⁇ 0.05) in comparison to the score corresponding to individuals previously determined to be non- responders and not significantly different (e.g., p-value >0.05) in comparison to the score corresponding to individuals previously determined to be responders.
  • the assessment of disease activity is a classification of disease progression (e.g., mild/moderate disease versus severe disability).
  • the predicted score outputted by the prediction model can be compared to one or both scores corresponding to individuals previously identified as having mild/moderate disease and corresponding to individuals previously identified as having severe disability.
  • a measure of the disease progression or level predicted by the predictive model provides additional utility for managing the disease activity in the patient.
  • the measure of the disease progression or level predicted by the predictive model is useful for selecting a candidate therapeutic or for determining the effectiveness of a previously administered therapeutic.
  • the measure of disease progression or level predicted by the predictive model for a patient can be compared to a prior measure of disease activity to determine whether a therapeutic administered to the patient is demonstrating efficacy.
  • a biomarker panel further incorporates one or more subject attributes.
  • subject attributes can include an age of the subject, the gender of the subject, a disease duration experienced by the subject, racial/ethnic identity, weight, height, body mass index (BMI), and socioeconomic status.
  • BMI body mass index
  • the sample can undergo centrifugation (e.g., pelleting or density gradient centrifugation) to separate larger and/or more dense entities in the sample (e.g., cells and other macromolecules) from the biomarkers.
  • centrifugation e.g., pelleting or density gradient centrifugation
  • Other examples include filtration (e.g., ultrafiltration) to phase separate the biomarkers from other portions of the sample.
  • the sample from a subject can be processed to produce a sub-sample with a fraction of biomarkers that were in the sample.
  • producing a fraction of biomarkers can involve performing a protein fractionation procedure.
  • protein fractionation procedures include chromatography (e.g., gel filtration, ion exchange, hydrophobic chromatography, or affinity chromatography).
  • the fractionation procedure can involve affinity purification or immunoprecipitation where biomarkers are bound by specific antibodies.
  • Such antibodies can be immobilized on a support, such as a magnetic particle or nanoparticle or a plate.
  • the sample from the subject is processed to extract biomarkers from the sample and further processed to produce a sub-sample with a fraction of extracted biomarkers. Altogether, this enables a purified sub-sample of biomarkers that are of particular interest.
  • an assay for evaluating levels of the biomarkers of particular interest can be more accurate and of higher quality.
  • a therapeutic agent is provided to an individual prior to and/or subsequent to obtaining the sample from the individual and determining quantitative values of one or more markers in the obtained sample.
  • Attorney Docket No.00015-438WO1 [00110] Accordingly, the disclosure provides a method of diagnosing or assessing the risk that a subject has MASLD, the method comprising measuring the levels of 15 eicosanoids and/or 15 complex lipids as set forth in Table 1 (see also Table 4) and/or complex lipids as set forth in Table 2B.
  • a method of the disclosure comprises obtaining a sample from a subject, measuring the amount of eicosanoids and optionally, or alternatively, complex lipids in the sample as set forth in Tables 1 and 2B, respectively.
  • the sample is processed by spiking the sample with an internal standard.
  • the amount of eicosanoids are identified such that if they exceed a cutoff as set forth in Table 1, the subject is identified as having or at risk of having MASLD.
  • the method of the disclosure comprises determining the level of one or more eicosanoids and/or complex lipids in a sample of a patient.
  • the sample is a plasma sample.
  • eicosanoids and lipids may be made by any suitable lipid assay technique, such as a high throughput technique including, but not limited to, spectrophotometric analysis (e.g., colorimetric sulfo-phospho-vanillin (SPV) assessment method of Cheng et al., Lipids, 46(1):95-103 (2011)).
  • spectrophotometric analysis e.g., colorimetric sulfo-phospho-vanillin (SPV) assessment method of Cheng et al., Lipids, 46(1):95-103 (2011).
  • SPV colorimetric sulfo-phospho-vanillin
  • Other analytical methods suitable for detection and quantification of lipid content will be known to those in the art including, without limitation, ELISA, NMR, UV-Vis or gas-liquid chromatography, HPLC, UPLC and/or MS or RIA methods enzymatic based chromogenic methods.
  • methods were used to measure the “free” oxylipins present in plasma, not those appearing after Attorney Docket No.00015-438WO1 alkaline hydrolysis (see, Feldstein et al.).
  • the sum total of esterified and free oxylipins are used by treating the sample with alkali (e.g., KOH).
  • alkali e.g., KOH
  • eicosanoids and specifically PGs are sensitive to alkaline-induced degradation.
  • experiments can be performed to minimize degradation of lipid metabolites during alkaline treatment and to identify specific eicosanoids and related oxidized PUFAs that are released intact from esterified lipids and which can be quantitatively measured.
  • the disclosure provides methods, kits and compositions useful identifying MASLD.
  • the disclosure provides methods of identifying subject having or at risk of having MASLD. Such methods will help in the early onset and treatment of disease.
  • the methods reduce biopsy risks associated with liver biopsies currently used in diagnosis.
  • the methods and compositions comprise unmodified and/or modified eicosanoids and PUFAs in the diagnosis.
  • the biomarkers are manipulated from their natural state by chemical modifications to provide a derived biomarker that is measured and quantitated.
  • the amount of a specific biomarker can be compared to normal standard sample levels (i.e., those lacking any liver disease) or can be compared to levels obtained from a diseased population (e.g., populations with clinically diagnosed MASLD).
  • Lipids are extracted from the sample, as detailed further in the Examples. The identity and quantity of bioactive lipids, eicosanoids and/or PUFA metabolites in the extracted lipids is first determined and then compared to suitable controls (e.g., a sample indicative of a subject with no liver disease, a sample indicative of a subject with MASLD and/or a sample indicative of a subject with NASH).
  • the method comprises obtaining a sample from a subject (e.g., a plasma sample), extracting the bioactive lipids in the sample and determining the levels of one or more of 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14-15-EET, DHA, EPA, and any combination thereof.
  • a sample from a subject e.g., a plasma sample
  • 4-HDoHE 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14-15-EET, DHA, EPA, and any combination thereof.
  • a composite scoring algorithm was used to distinguish between control and MASLD. This was accomplished by converting eicosanoid abundance to a binary system. To create the composite score the univariate ROC results were used that provide the cutoff values that best separate controls from MASLD. These values were applied across the selected eicosanoid panel to generate the binary value of 0 or 1. If the levels of any of the eicosanoids including 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE or adrenic acid were above the cutoff, their score was 1.
  • any of the eicosanoids including 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA or EPA fall below the cutoff, their score is also 1. If any of the metabolites in any of the samples did not follow this pattern, they received a score of 0. Using this algorithm, a composite score was constructed to separate controls from MASLD. The composite score is he sum total of the binary value of each of the analytes that follows this framework. Using this approach, a composite score of 6+ was optimal to diagnose MASLD. This means that at least 6 metabolites of the 15 metabolites must be present at concentrations indicative for MASLD to classify the sample as MASLD.
  • the method comprises measuring a ratio of peak areas between endogenous eicosanoids and matching deuterated internal eicosanoids.
  • the method can further comprise measuring one or more (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or all 15) complex lipids identified in Attorney Docket No.00015-438WO1 Table 2B. The measurements are compared to a control or reference level (e.g., levels associated with a subject lacking MASLD), wherein a statistically significant difference in the markers is indicative of MASLD.
  • the reference level will be a reference level for the particular type of measurement used.
  • a score of 1 is given for each analyte above their respective cutoff for analytes that increase and below their respective cutoff for analytes that decrease.
  • the analyte scores are summed to make lipidomic score.
  • the cutoff values were identified by univariate AUC analysis which best separates the two groups for each analyte. Score cutoffs of 12- 14 give 100% probability of correct assignment to each group. Percentage values are made by dividing the number of correct assignments over the total patients in each group for each cutoff value.
  • the disclosure also provides a method whereby a subject identified as having MASLD using the methods and compositions provided herein are further screened for MASH.
  • the disclosure also provides a 15 marker panel for distinguishing high NAS scores from low NAS scores.
  • the panel comprises the markers in Table 5 and 6.
  • a subject identified as having MASLD by the methods herein may further have a sample (either existing or newly obtain) analyzed by extracting bioactive Attorney Docket No.00015-438WO1 eicosanoids, lipids and markers in the sample and determining the levels of one or more of analytes set forth in Table 5 and Table 6.
  • the disclosure provides a substantially non-invasive method of predicting or assessing the risk of progression of liver disease in a patient comprising obtaining a plasma sample from a subject and optionally treating the plasma sample with alcohol to dissolve free eicosanoids and free polyunsaturated fatty acid (fPUFA) to obtain free-dissolved eicosanoids and free-dissolved fPUFAs; purifying bioactive lipids including eicosanoids and complex lipids; measuring the level of eicosanoids selected from the group consisting of one or more of 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15- HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, EPA, or any combination thereof; determining the area under receiver operating characteristic curve (AUROC) based upon a ratio of the levels of the bioactive lipids matched with deuterated
  • the AUROC is about at least 0.8, at least about 0.9, or at least about 0.99.
  • the disclosure provides a substantially non-invasive method of predicting or assessing the risk of progression of liver disease in a patient diagnosed with liver disease comprising obtaining a plasma sample from a subject, spiking deuterated internal standards into each sample and primary standards used to generate a standard curve and optionally treating the plasma sample with alcohol to dissolve free eicosanoids and free polyunsaturated fatty acid (fPUFA) to obtain free-dissolved eicosanoids and free-dissolved fPUFAs; purifying bioactive lipids including eicosanoids and complex lipids; measuring the level of eicosanoids selected from the group consisting of one or more of 4- HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15- HETrE, adrenic acid, 9,10-diHOME, 11,12
  • the liver disease is a MASLD.
  • the AUROC is about at least 0.8, at least about 0.9, or at least about 0.99.
  • cutoffs are as set forth in Table 7. [00130] Table 7: 30 analytes in used in the lipidomic score separated by whether they increase or decrease in MASLD. The cutoff values shown were identified by univariate AUC analysis which best separates the two groups for each analyte.
  • the disclosure also provides a panel of lipids for assessing liver fibrosis.
  • cutoffs were used to define the level of the lipid(s) that provide diagnostic potential.
  • Attorney Docket No.00015-438WO1 Table 8 provides a list of lipids and cutoff values.
  • Table 8 provides a list of lipids and cutoff values.
  • the cutoff values shown were identified by univariate AUC analysis which best separates the groups for each analyte. For example, using a cutoff of 7 means a patient with a score of 7-12 is identified as high Fibrosis and 1-6 is identified as low Fibrosis. Using this cutoff value assign 75.6% of patients with low Fibrosis scores and 72.5% of patients with high Fibrosis scores. The overall probability of correctly assigning a patient is 74.0%.
  • MASLD NIDDK NASH Clinical Research Network study cohort. It included samples from individuals who participated in the non-interventional DB1 and DB2 registry (NCT01030484) and also baseline samples from individuals who participated in the PIVENS trial (NCT00063622) and the FLINT trial (NCT01265498). For the hypothesis building study, baseline samples from patients enrolled in these trials and who did not undergo further treatment were used. For the Validation Study, baseline samples from the FLINT trial were used.
  • Plasma samples were obtained within 90 days of an evaluable liver biopsy which was confirmed to demonstrate MASLD.
  • the liver histology was evaluated in a masked manner by the pathology committee of the NASH CRN using a validated protocol and the presence of MASLD and its individual histological features documented using the NASH CRN histological classification system.
  • the histological spectrum extended from steatosis alone to steatohepatitis with varying stages of fibrosis.
  • Blood samples were obtained in all cases in fasted state followed by plasma separation, aliquoting and freezing within 2-3 hours using a pre-specified protocol. Samples were frozen and stored at -70° C at individual clinical centers and then transferred on dry ice to the NIDDK biorepository.
  • Controls were collected by similar procedures and were defined by a normal clinical examination, normal liver enzymes and Attorney Docket No.00015-438WO1 functions and a magnetic resonance imaging-proton density fat fraction (MRI-PDFF) assessed liver fat content ⁇ 5% and magnetic resonance elastography (MRE) assessed liver stiffness ⁇ 2.5% based upon previously published thresholds. They were identified at a single center and characterized for purposes of this analysis (Table 9). For the Validation Study, a separate set of control samples were collected at a later time and selected by the same criteria.
  • MRI-PDFF magnetic resonance imaging-proton density fat fraction
  • MRE magnetic resonance elastography
  • Eicosanoids were analyzed by UPLC-MS as previously described (Quehenberger et al., J. Lipid Res., 51:3299-3305, 2010; and Quenhenberger et al., J. Lipid Res., 59:2436-2445, 2018).
  • aliquots of 50 ul plasma samples were diluted to 900 ul with PBS and spiked with a mixture of 26 deuterated ISTDs in 100 ul of ethanol.
  • the eicosanoids were extracted using Strata-X reversed-phase SPE columns (8B-S100-UBJ, Phenomenex). Columns were activated with 3 ml of 100% methanol and then equilibrated with 3 ml of water containing 10% ethanol.
  • the columns were washed with 10% methanol to remove impurities, and the metabolites were then eluted with 1 ml of 100% methanol and stored at -80°C to prevent metabolite degradation.
  • the eluent was dried under vacuum and re- dissolved in 50 ul of the UPLC solvent A (water/acetonitrile/acetic acid (60:40:0.02; v/v/v)) for UPLC/MS/MS analysis.
  • UPLC solvent A water/acetonitrile/acetic acid (60:40:0.02; v/v/v)
  • All eicosanoids were quantified by the stable isotope dilution method. Briefly, identical amounts of ISTDs were added to each sample and to all the PSTDs. Nine-point standard curves were generated for each of the 136 PSTDs, ranging from 0.03 ng to 10 ng.
  • AUROC area under receiver operating characteristics curve
  • Controls had normal liver enzymes and hepatic synthetic functions that were significantly different from the patients with MASLD (p ⁇ 0.001 for AST and ALT, p ⁇ 0.02 for bilirubin).
  • 34 had steatosis while 44 had borderline steatohepatitis and 223 had definite steatohepatitis.
  • Eicosanoid profile in plasma A comprehensive analysis of circulating eicosanoids was performed in plasma from controls and MASLD patients. In all, 65 eicosanoid metabolites were detectable in at least one of the samples and the distribution of their concentration in controls and in those with MASLD was calculated. Several of these metabolites were present in the plasma only at low levels and their presence in circulation was inconsistent.
  • a composite scoring algorithm was developed that can be used to distinguish between control and MASLD. To accomplish this, the cutoff values for each of the eicosanoids in the panel that distinguish between controls and MASLD were established. Because the values of some of the metabolites, especially the fatty acids, are orders of magnitudes higher than those of the eicosanoid metabolites, it is impractical to use averages as a composite value. Small percentage differences in the plasma fatty acids that are three to four orders of magnitude more abundant than eicosanoids would skew the classification disproportionally. Moreover, some of the eicosanoids are increased in MASLD and some are increased in the controls.

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Abstract

The disclosure provides methods for identifying metabolic disfunction-associated steatotic liver disease (MASLD) in a subject. The disclosure also provides methods for identifying metabolic disfunction-associated steatohepatitis (MASH) and fibrosis in a subject.

Description

Attorney Docket No.00015-438WO1 MARKERS AND METHODS FOR DETECTING FATTY LIVER DISEASE CROSS REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority of U.S. Provisional Appl. No. 63/548,769, filed February 1, 2024, U.S. Provisional Appl. No. 63/645,630, filed May 10, 2024, and U.S. Provisional Appl. No. 63/685,636, filed August 21, 2024, the disclosures of which are incorporated herein by reference for all purposes. STATEMENT REGARDING FEDERALLY SPONSORED R&D [0002] This invention was made with Government support under Grant No. DK105961 awarded by the National Institutes of Health. The Government has certain rights in the invention. FIELD OF THE INVENTION [0003] The invention relates in general to materials and methods to quantitate markers to determine liver disease. BACKGROUND [0004] According to recent data from the National Health and Nutrition Examination Survey (NHANES), the prevalence of adult obesity in the United States significantly increased to 42.8% in 2018. If one includes less severe forms of obesity, this number increases such that approximately 66% of the adult general population is either overweight or obese. This has been associated with numerous adverse health consequences including the development of cardiovascular disease, type 2 diabetes mellitus (T2DM) and several cancers. Another key outcome of excess adiposity and insulin resistance is the development of metabolic disfunction- associated steatotic liver disease (MASLD), which has increased globally to greater than 30% of the adult population. Of note, this condition was until recently referred to as nonalcoholic fatty liver disease (NAFLD). The two histological phenotypes of NAFLD, i.e., a nonalcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH) are now referred to as metabolic dysfunction associated steatotic liver (MASL) and metabolic dysfunction-associated steatohepatitis (MASH), respectively. MASH tends to progress to cirrhosis more frequently than MAFL and should therefore be especially targeted for therapeutic intervention. Attorney Docket No.00015-438WO1 [0005] The current approach for clinical evaluation of MASLD begins with assessment of a FIB-4 (Fibrosis-4) score followed by additional testing including transient elastography in those with clinical risk factors. FIB-4 is a commonly used laboratory aid based on age, AST (aspartate aminotransferase), ALT (alanine aminotransferase) and platelet counts; it was developed to evaluate the presence of underlying advanced fibrosis and its use in the context of MASLD assessment is also for fibrosis assessment. This does not, however, provide any insight on the presence of steatosis the hallmark of MASLD. Confirmation of steatotic liver disease requires liver biopsy and either measurement of the continuous attenuation parameter by transient elastography or MRI based methods. A liver biopsy is an invasive procedure with potentially serious side effects. Transient elastography or MRI are not always widely available and are expensive creating a barrier for assessment of MASLD, which starts by assessment of clinical risk factors. SUMMARY [0006] The disclosure provides a non-invasive method for detecting a presence, an absence, a type, or a stage of fatty liver disease in subject, the method comprising: (a) providing or obtaining a biological sample from the subject; (b) measuring the presence or level of one or more molecules in the biological sample or in a constituent of the biological sample, wherein the one or more molecules are selected from: Group A: 4-HDoHE, 5-HETE, 9-HODE, 12- HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10- diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and/or EPA; Group B: SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3, DG34:316:1_18:2, DG 36:418:2_18:2, PC 37:417:0/20:4, PC 38:518:1/20:4, PI 18:0/18:1, and PI 18:0/20:4; Group C: PC 16:0/16:1, PC 14:0_16:1, PI 16:0/16:1, PI 18:0/16:1, PI 16:0/16:0, SM d18:1/n20:0, SM d18:0/n18:0, SM d18:0/n22:0, 9,10-diHOME, PC 17:0/20:4, PC 18:2/20:4, PC 18:1/22:6, PI 18:0/20:4, PP-E P16:0/22:6 and 11,12-diHETrE; and/or Group D: PC 16:0/16:0, PC 14:0/20:4, PC 18:1/18:1, PC16:0/22:5, PC 18:1/22:6, PI 18:0/18:2, SM d18:0/n20:0, Cer d18:0/n16:0, Cer d18:0/n18:0, 19,20-diHDPA, SM d16:1/n22:0, and SM d18:2/n23:0, wherein Group A and B are determinative of MASLD, Attorney Docket No.00015-438WO1 Group C is determinative of MASH and Group D is determinative of liver fibrosis. In one embodiment, when Group A 4-HDoHE, 5-HETE, 9- HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, or adrenic acid are above a cutoff value, their score is 1, and wherein when 9,10- diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, or EPA fall below a cutoff value, their score is also 1. In another embodiment, the method does not comprise collecting a biopsy from the subject. In still another embodiment, the subject is an animal. In still another embodiment, the subject is a human. In yet another embodiment, the type of fatty liver disease comprises metabolic dysfunction- associated steatotic liver disease (MASLD), metabolic dysfunction- associated steatohepatitis (MASH), nonalcoholic fatty liver disease (NAFLD), Nonalcoholic Steatohepatitis (NASH), alcoholic fatty liver disease (AFLD), Alcoholic Steatohepatitis (ASH), or any combination thereof. In another embodiment, measuring the presence or level of the one or more molecules comprises using gas or liquid chromatography-mass spectrometry or direct-infusion mass spectrometry. In still another embodiment, the method is used as a non-invasive point of care diagnostic tool. In yet another embodiment, the biological sample comprises blood plasma. In still another embodiment, the method comprises measuring 14-HDoHE, DHA, 12-HETE, 12-HEPE, 4HDoHE, EPA, 15-HETE, 11,12-EET, 5,6-EET, 14,15- EET, 15-HETrE, 9,10-dHOME, 5-HETE, adrenic acid and 9-HODE. In another embodiment, the method comprises measuring 1 to 5 markers selected from the group consisting of 4-HDoHE, 5-HETE, 9-HODE, 12- HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10- diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and EPA. [0007] The disclosure also provides a method of determining whether a subject has or is at risk of having MASLD, the method comprising measuring the level of markers selected from the group consisting of 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15- HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and EPA; wherein when 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12- HETE, 14-HDoHE, 15-HETE, 15-HETrE, or adrenic acid are above a cutoff value, their score is 1, and wherein when 9,10-diHOME, 11,12- EET, 5,6-EET, 14,15-EET, DHA, or EPA fall below a cutoff value, their score is also 1, summing the total of the scores wherein if Attorney Docket No.00015-438WO1 the score is 6 or greater the subject is classified as having MALSD. In one embodiment, the cutoff values are as set forth in Table 1. In another embodiment, the method further comprises measuring complex lipids selected from the group consisting of SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3, DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, and PI 18:0/20:4, wherein when SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3 are above a cutoff value, their score is 1, and wherein when DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, PI 18:0/20:4 fall below a cutoff value, their score is also 1, summing the total of the scores wherein if the score is 6 or greater the subject is further classified as having MALSD. In another embodiment, the cutoff values for the complex lipids are set forth in Table 2B. In another embodiment, the method does not comprise collecting a biopsy from the subject. In another embodiment, the subject is an animal and in some embodiments, a human. In one embodiment, measuring the presence or level of the one or more molecules comprises using gas or liquid chromatography-mass spectrometry or direct-infusion mass spectrometry. In one embodiment, the method is used as a non- invasive point of care diagnostic tool. In still another embodiment, the biological sample comprises blood plasma. [0008] The disclosure also provides a method of determining whether a subject has or is at risk of having MASLD, the method comprising measuring the level of markers selected from the group consisting of SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3, DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, and PI 18:0/20:4, wherein when SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3 are above a cutoff value, their score is 1, and wherein when DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, PI 18:0/20:4 fall below a cutoff value, their score is also 1, summing the total of the scores wherein if the score is 6 or greater Attorney Docket No.00015-438WO1 the subject is classified as having MALSD. In one embodiment, the cutoff values for the complex lipids are set forth in Table 2B. In still another embodiment, the method does not comprise collecting a biopsy from the subject. In yet another embodiment, the subject is an animal. In a further embodiment, the animal is a human. In another embodiment, measuring the presence or level of the one or more molecules comprises using gas or liquid chromatography-mass spectrometry or direct-infusion mass spectrometry. In still another embodiment, the method is used as a non-invasive point of care diagnostic tool. In another embodiment, the biological sample comprises blood plasma. [0009] The disclosure also provides a method of determining a NAS score for a subject, the method comprising measuring a panel of markers including PC 16:0/16:1, PC 14:0_16:1, PI 16:0/16:1, PI 18:0/16:1, PI 16:0/16:0, SM d18:1/n20:0, SM d18:0/n18:0, SM d18:0/n22:0, 9,10-diHOME, PC 17:0/20:4, PC 18:2/20:4, PC 18:1/22:6, PI 18:0/20:4, PP-E P16:0/22:6 and 11,12-diHETrE, wherein when PC 16:0/16:1, PC 14:0_16:1, PI 16:0/16:1, PI 18:0/16:1, PI 16:0/16:0, SM d18:1/n20:0, SM d18:0/n18:0, SM d18:0/n22:0, and 9,10-diHOME are above a cutoff value in Table 6 each marker above the cutoff is assigned a score of 1 and wherein when PC 17:0/20:4, PC 18:2/20:4, PC 18:1/22:6, PI 18:0/20:4, PP-E P 16:0/22:6 and 11,12-diHETrE are below a cutoff value in Table 6 each marker below the cutoff is assigned a score of 1, wherein when the total score is 6 or more the subject has a high NAS Score. [0010] The disclosure also provides a method of determining if a subject has or is at risk of having liver fibrosis, the method comprising measuring a panel of markers including PC 16:0/16:0, PC 14:0/20:4, PC 18:1/18:1, PC16:0/22:5, PC 18:1/22:6, PI 18:0/18:2, SM d18:0/n20:0, Cer d18:0/n16:0, Cer d18:0/n18:0, 19,20-diHDPA, SM d16:1/n22:0, and SM d18:2/n23:0. In one embodiment, when PC 16:0/16:0, PC 14:0/20:4, PC 18:1/18:1, PC 16:0/22:5, PC 18:1/22:6, PI 18:0/18:2, SM d18:0/n20:0, Cer d18:0/n16:0, Cer d18:0/n18:0, and 19,20-diHDPA are above a cutoff value as set forth in Table 8 each marker above the cutoff is scored as 1, and wherein when each of SM d16:1/n22:0, and SM d18:2/n23:0 is below the cutoff value of Table 8 Attorney Docket No.00015-438WO1 each marker is scored as 1, such that when the total score is 7 or greater the subject has or is at risk of having liver fibrosis. [0011] The disclosure also provides a method of treating a subject, the method comprising performing a method as described herein, wherein the subject having MASLD, MASH, high NAS or fibrosis is treated with a GLP-1 agonist, resmetirom and/or bariatric surgery. [0012] The disclosure provides a non-invasive method for detecting a presence, an absence, a type, or a stage of fatty liver disease in subject, the method comprising providing or obtaining a biological sample from the subject; measuring the presence or level of one or more molecules in the biological sample or in a constituent of the biological sample, wherein the one or more molecules are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 biomarkers from Table 3 or Table 4. In another embodiment, the one or more molecules are selected from: 4-HDoHE, 5- HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14-15-EET, DHA, EPA, and a combination thereof. In another embodiment, the one or more molecules are analyzed for an increase and/or a decrease in levels compared to a normal control. In another embodiment, the method measures whether there is an increase in one or more molecules selected from 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid and any combination of the foregoing and/or a decrease in one or more molecules selected from 9,10- diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, EPA and any combination of the foregoing. In one embodiment, the method does not comprise collecting a biopsy from the subject. In another or further embodiment, the subject is an animal such a human. In still another or further embodiment, the type of fatty liver disease comprises metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), non- alcoholic fatty liver disease (NAFLD), non-alcoholic Steatohepatitis (NASH), alcoholic fatty liver disease (AFLD), Alcoholic Steatohepatitis (ASH), or any combination thereof. In yet another or further embodiment, measuring the presence or level of the one or more molecules comprises using gas or liquid chromatography-mass spectrometry or direct-infusion mass spectrometry. In still another Attorney Docket No.00015-438WO1 or further embodiment, the method is used as a non-invasive point of care diagnostic tool. In yet another or further embodiment, the biological sample comprises blood plasma, and wherein the method comprises separating the blood plasma from the blood sample. In still another embodiment of any one of the foregoing embodiments, the method further comprises using bioinformatics for analyzing the sample. In another embodiment, the method comprises measuring 14- HDoHE, DHA, 12-HETE, 12-HEPE, 4-HDoHE, EPA, 15-HETE, 11,12-EET, 5,6- EET, 14,15-EET, 15-HETrE, 9,10-dHOME, 5-HETE, adrenic acid and 9- HODE. In still another embodiment, the method comprises measuring 1-5 biomarkers from Table 3 or Table 4. In yet still another embodiment, the method comprises measuring 1-15 or 1-20 biomarker from Table 3 or Table 4. In still another embodiment, the biological sample is selected from the group consisting of blood, blood plasma and blood serum. BRIEF DESCRIPTION OF THE DRAWINGS [0013] Figure 1 shows a correlation heat map of eicosanoids in control and MASLD plasma. Similarities between variables detected in the MASLD dataset were calculated using Spearman’s correlation. Shorter distances in the dendrograms indicate stronger relationships between the variables. Dark squares shows a positive correlation between variables; light squares shows a negative correlation between the variables. [0014] Figure 2 shows a graph of eicosanoid changes (positive and negative changes) in MASLD. Shown are the top 25 eicosanoids with the most robust changes in MASLD. [0015] Figure 3 shows a Partial Least Square analysis. Shown is the Scores Plot of PLS-DA for the 32 eicosanoids that were present in at least 80% of the samples, The analysis shows a clear separation between the control and MASLD groups. The partial overlap represents patients with very mild disease. [0016] Figure 4A-C shows biomarker analysis for MASLD. For analysis, all 32 eicosanoids present in at least 80% of plasma were used. (A) Multivarite ROC analysis of 32 eicosanoids using Random Forests as the classification method. (B) Average importance of the top 20 eicosanoids using Univariate AUROC as ranking method. (C) Predicted class probability and chart for each corresponding confusion matrix. Attorney Docket No.00015-438WO1 [0017] Figure 5A-C shows another model for the diagnosis of MASLD. Fifteen eicosanoids were selected to optimally establish a panel that best distinguishes between normal controls and MASLD. (A) Multivariate AUROC analysis of 15 eicosanoid panel using Random Forests as the classification method. (B) Average importance using Univariate AUROC as ranking method. (C) Predicted class probability chart for each corresponding confusion matrix. [0018] Figure 6A-B provides a Box Plots of Selected Eicosanoid Panel. (A) Data for fifteen eicosanoids that were selected to optimally establish a panel that best distinguishes between normal controls and MASLD are shown as Box Plots. The data are expressed as pmol/ml plasma. Outliers with values more than 1.5 times the inter-quartile range above the 3rd quartile have been removed from the graphs for visualization, but all values were included in the calculation of the median. (B) Data of fifteen eicosanoids in the Validation Study. [0019] Figure 7 provides the partial least squares discriminant analysis (PLSDA) which shows almost complete separation of control and MASLD groups using 358 Complex Lipid Analytes that were present in at least 80% of the data points of the analysis. [0020] Figure 8A-C shows (A) Area under the curve score of 0.998 achieved using random forests as the classification. 358 Complex Lipid Analytes that were present in at least 80% of all samples were included. (B) 15 selected complex lipid analytes for the final MASLD panel, ranked by univariate AUC scores. Boxes on the right indicate whether an analyte increases or decreases with MASLD. (C) Two-dimensional separation of control and MASLD patients predicting class probabilities by random forests. The chart indicates a complete separation of the two groups with no overlap. [0021] Figure 9 provides a Box Plots of a complex lipid panel. Data for fifteen complex lipids that were selected to optimally establish a panel that best distinguishes between normal controls and MASLD are shown as Box Plots. [0022] Figure 10A-C shows (A) Area under the curve score of 1 indicates a complete separation of the two groups even at a 95% confidence level as shown. Random forests is used as the classification. (B) 15 selected complex lipid analytes for the final Attorney Docket No.00015-438WO1 MASLD panel, ranked by univariate AUC scores. Boxes on the right indicate whether an analyte increases or decreases with MASLD. (C) Two-dimensional separation of control and MASLD patients predicting class probabilities by random forests. The chart indicates a complete separation of the two groups. [0023] Figure 11A-C shows (A) Area under the curve score of 1 indicates a complete separation of the two groups even at a 95% confidence level as shown. Random forests is used as the classification. (B) All 30 analytes that consist of eicosanoids or complex lipids, ranked by univariate AUC scores. Boxes on the right indicate whether an analyte increases or decreases with MASLD. (C) Two-dimensional separation of control and MASLD patients predicting class probabilities by random forests. The chart indicates a complete and robust separation of the two groups. [0024] Figure 12 shows data for 30 analytes used in the lipidomic score separated by whether they increase or decrease in MASLD. A score of 1 is given for each analyte above their respective cutoff for analytes that increase and below their respective cutoff for analytes that decrease. The analyte scores are summed to make lipidomic score. The cutoff values were identified by univariate AUC analysis which best separates the two groups for each analyte. Shown are the percent accuracies of each of the groups based off of the lipidomic score cutoff used. Score cutoffs of 12-14 give 100% probability of correct assignment to each group as shown in the grey line. Percentage values are made by dividing the number of correct assignments over the total patients in each group for each cutoff value. For example, using a cutoff of 13 means a patient with a score of 13-30 is identified as MASLD and 1-12 is identified as control. Using this cutoff value completely separates the two groups with 100% probability of correct assignment. [0025] Figure 13A-C provides (A) Area under the curve of 0.73 comparing patients with low NAS scores (1-3) compared to high NAS scores (4-8). Random forests is used as the classification. (B) 15 lipid analytes that consist of eicosanoids or complex lipids, ranked by univariate AUC scores. The boxes on the right indicate whether each analyte increases or decreases with high NAS scores. (C) Two- dimensional separation of NAS Low and NAS High patients predicting Attorney Docket No.00015-438WO1 class probabilities by random forests. The chart shows 193 correctly assigned patients and 108 incorrectly assigned patients. [0026] Figure 14 shows analytes selected for the final lipidomic NAS panel. Patients were grouped by their NASH activity score via biopsy. NAS Low are values between 1-3 and NAS High are values between 4-8. Shown are the trends that correlate with NAS grouping. The data are expressed as pmol/ml. Outliers higher than 1.5 times the interquartile range of the 3rd quartile were removed for visualization. [0027] Figure 15 provides analytes selected for the final NAS lipidomic panel. Patients were grouped by their NASH activity score via biopsy. Shown are the trends that correlate with NASH activity score. The data are expressed as pmol/ml. Outliers higher than 1.5 times the interquartile range of the 3 quartile were removed for visualization. [0028] Figure 16 shows examples of analytes included in the final NAS lipidomic panel that separate the patients from the controls for each of the components that are combined to make the NAS biopsy score. The data are expressed as pmol/ml. Outliers higher than 1.5 times the interquartile range of the 3 quartile were removed for visualization. [0029] Figure 17A-E provides (A) 15 analytes in used in the NAS lipidomic score separated by whether they increase or decrease with high NAS biopsy scores. The cutoff values shown were identified by univariate AUC analysis which best separates the two groups for each analyte. (B) Comparison of NAS scores by biopsy and NAS lipidomic scores. The dotted line is a lipidomic score cutoff of 8 which best separates low NAS scores of 1-3 compared to high NAS scores of 4-8. (C) Percent Accuracy of each of the groups based off of the lipidomic score cutoff used. A score cutoff of 7 gives 74.5% probability of correct assignment to each group as shown in the grey line. (D) Percentage values of each lipidomic score cutoff for each of the two groups and the probability of correctly assigning a patient for each cutoff is shown at the bottom row. For example, using a cutoff of 8 means a patient with a score of 8-15 or above is identified as high NAS (4-8) and 1-7 is identified as low NAS (1-3). Using this cutoff value 80.8% of patients were correctly assigned Attorney Docket No.00015-438WO1 with low MAS scores and 68.3% of patients with high MAS scores. The overall probability of correctly assigning a patient is 74.5%. (E) 15 analytes are in used in the lipidomic score separated by whether they increase or decrease in high NAS biopsy. A score of 1 is given for each analyte above their respective cutoff for analytes that increase and below their respective cutoff for analytes that decrease. The analyte scores are summed to make lipidomic score. The cutoff values were identified by univariate AUC analysis which best separates the two groups for each analyte. Shown are the percent accuracies of each of the groups based off of the lipidomic score cutoff used. Probability of correct assignment to each group as shown in the grey line. Percentage values are made by dividing the number of correct assignments over the total patients in each group for each cutoff value. [0030] Figure 18A-C provides (A) Area under the curve of 0.77 comparing patients with low fibrosis scores (0-1) compared to high fibrosis scores (2-4). Random forests is used as the classification. (B) 12 lipid analytes that consist of eicosanoids and complex lipids, ranked by univariate AUC scores. The boxes on the right indicate whether each analyte increases or decreases with higher fibrosis scores. (C) Two-dimensional separation of Fib Low and Fib High patients predicting class probabilities by random forests. The chart shows 219 correctly assigned patients and 82 incorrectly assigned patients. [0031] Figure 19 shows analytes selected for the final fibrosis lipidomic panel where patients are separated into 2 groups by fibrosis stage. Fib Low are values between 0-1 and Fib High are value between 2-4. Shown are the trends with fibrosis groups. The data are expressed as pmol/ml. Outliers higher than 1.5 times the interquartile range of the 3 quartile were removed for visualization. [0032] Figure 20 shows analytes selected for the final fibrosis lipidomic panel where patients are separated by fibrosis stage via biopsy. Shown are trends with fibrosis stages. The data are expressed as pmol/ml. Outliers higher than 1.5 times the interquartile range of the 3 quartile were removed for visualization. Attorney Docket No.00015-438WO1 [0033] Figure 21A-E shows (A) 12 analytes in used in the lipidomic score separated by whether they increase or decrease with high fibrosis scores. The cutoff values shown were identified by univariate AUC analysis which best separates the two groups for each analyte. (B) Comparison of Fibrosis scores by biopsy and fibrosis lipidomic scores. The red dotted line is a lipidomic score cutoff of 7 which best separates Fibrosis biopsy scores of 0-1 compared with 2-4. (C) Percent Accuracy of each of the groups based off of the lipidomic score cutoff used. A score cutoff of 7 gives 74.0% probability of correct assignment to each group as shown in the grey line. (D) Percentage values of each lipidomic score cutoff for each of the two groups and the probability of correctly assigning a patient for each cutoff is shown at the bottom row. For example, using a cutoff of 7 means a patient with a score of 7-12 is identified as high Fib (2-4) and 1-6 is identified as low Fib (0-1). Using this cutoff value 75.6% of patients were correctly assigned with low Fib4 scores and 72.5% of patients with high Fib4 scores. The overall probability of correctly assigning a patient is 74.0%. (E) 12 analytes are in used in the lipidomic score separated by whether they increase or decrease in high fibrosis biopsy. A score of 1 is given for each analyte above their respective cutoff for analytes that increase and below their respective cutoff for analytes that decrease. The analyte scores are summed to make lipidomic score. The cutoff values were identified by univariate AUC analysis which best separates the two groups for each analyte. Shown are the percent accuracies of each of the groups based off of the lipidomic score cutoff used. Probability of correct assignment to each group as shown in the grey line. Percentage values are made by dividing the number of correct assignments over the total patients in each group for each cutoff value. DETAILED DESCRIPTION [0034] As used herein and in the appended claims, the singular forms "a”, "an”, and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "an eicosanoid" includes a plurality of such eicosanoids and reference to "the subject" includes reference to one or more subjects and so forth. Attorney Docket No.00015-438WO1 [0035] Also, the use of “or” means “and/or” unless stated otherwise. Similarly, “comprise,” “comprises,” “comprising” “include,” “includes,” and “including” are interchangeable and not intended to be limiting. [0036] It is to be further understood that where descriptions of various embodiments use the term “comprising,” those skilled in the art would understand that in some specific instances, an embodiment can be alternatively described using language “consisting essentially of” or “consisting of.” [0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice of the disclosed methods and compositions, the exemplary methods, devices and materials are described herein. [0038] Any publications discussed above and throughout the text are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the inventors are not entitled to antedate such disclosure by virtue of prior disclosure. [0039] As used herein, the term “about” refers to a value that is within 10% above or below the value being described, typically within about 5% or about 1% of the value being described. [0040] As used herein, any values provided in a range of values include both the upper and lower bounds, and any values contained within the upper and lower bounds. [0041] "Biomarker" means a compound that is differentially present (i.e., increased or decreased) in a biological sample from a subject or a group of subjects having a first phenotype (e.g., having a disease) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having the disease). A biomarker may be differentially present at any level, but is generally present at a level that is increased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least Attorney Docket No.00015-438WO1 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more; or is generally present at a level that is decreased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, or by 100% (i.e., absent). Typically a biomarker is differentially present at a level that is statistically significant. [0042] As used herein, "biomarker level" and "level" refer to a measurement that is made using any analytical method for detecting the biomarker in a biological sample and that indicates the presence, absence, absolute amount or concentration, relative amount or concentration, titer, a level, an expression level, a ratio of measured levels, or the like, of, for, or corresponding to the biomarker in the biological sample. The exact nature of the "level" depends on the specific design and components of the particular analytical method employed to detect the biomarker. [0043] The term “biomarker panel” refers to a set biomarkers that are informative for predicting liver disease, and in particular embodiments, informative for predicting liver disease progression. For example, expression levels of the set of biomarkers in the biomarker panel can be informative for predicting liver disease progression. In various embodiments, a biomarker panel can include two, three, four, five, six, seven, eight, nine, ten eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty one, twenty two, twenty three, twenty four, twenty five, twenty six, twenty seven, twenty eight, twenty nine or thirty biomarkers. [0044] By a “control” is meant any useful reference used to diagnose MASH, MAFLD or liver fibrosis. The control can be any sample, standard, standard curve, or level that is used for comparison purposes. The control may be a negative control (e.g., a sample or level from a subject diagnosed as not having MAFLD, MASH, or liver Attorney Docket No.00015-438WO1 fibrosis, e.g., a healthy subject) or a positive control (e.g., a sample or level from a subject clinically diagnosed as having MAFLD, MASH, or liver fibrosis). [0045] As used herein, "detecting" or "determining" with respect to a biomarker level includes the use of both the instrument used to observe and record a signal corresponding to a biomarker level and the material(s) required to generate that signal. In various embodiments, the level is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like. [0046] "Diagnose", "diagnosing", "diagnosis", and variations thereof refer to the detection, determination, or recognition of a health status or condition of an individual on the basis of one or more signs, symptoms, data, or other information pertaining to that individual. The health status of an individual can be diagnosed as healthy/normal (i.e., a diagnosis of the absence of a disease or condition) or diagnosed as ill/abnormal (i.e., a diagnosis of the presence, or an assessment of the characteristics, of a disease or condition). The terms "diagnose", "diagnosing", "diagnosis", etc., encompass, with respect to a particular disease or condition, the initial detection of the disease; the characterization or classification of the disease; the detection of the progression, remission, or recurrence of the disease; and the detection of disease response after the administration of a treatment or therapy to the individual. The diagnosis of metabolic disfunction-associated steatotic liver disease (MASLD) includes distinguishing individuals who have MASLD from individuals who do not. [0047] The term “mammal” encompasses both humans and non-humans and includes but is not limited to humans, non-human primates, canines, felines, murines, bovines, equines, and porcines. [0048] The term “obtaining a dataset associated with a sample” encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample and processing the sample to experimentally determine the data. The Attorney Docket No.00015-438WO1 phrase also encompasses receiving a set of data, e.g., from a third party that has processed the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from at least one database or at least one publication or a combination of databases and publications. A dataset can be obtained by one of skill in the art via a variety of known ways including stored on a storage memory. [0049] A "reference level" or “reference sample level” of a biomarker means a level of the biomarker that is indicative of a particular disease state, phenotype, or predisposition to developing a particular disease state or phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or predisposition to developing a particular disease state or phenotype, or lack thereof. A "positive" reference level of a biomarker means a level that is indicative of a particular disease state or phenotype. A "negative" reference level of a biomarker means a level that is indicative of a lack of a particular disease state or phenotype. A "reference level" of a biomarker may be an absolute or relative amount or concentration of the biomarker, a presence or absence of the biomarker, a range of amount or concentration of the biomarker, a minimum and/or maximum amount or concentration of the biomarker, a mean amount or concentration of the biomarker, and/or a median amount or concentration of the biomarker; and, in addition, "reference levels" of combinations of biomarkers may also be ratios of absolute or relative amounts or concentrations of two or more biomarkers with respect to each other. Appropriate positive and negative reference levels of biomarkers for a particular disease state, phenotype, or lack thereof may be determined by measuring levels of desired biomarkers in one or more appropriate subjects, and such reference levels may be tailored to specific populations of subjects (e.g., a reference level may be age-matched or gender- matched so that comparisons may be made between biomarker levels in samples from subjects of a certain age or gender and reference levels for a particular disease state, phenotype, or lack thereof in a certain age or gender group). Such reference levels may also be tailored to specific techniques that are used to measure levels of biomarkers in biological samples (e.g., LC-MS, GC-MS, etc.), where Attorney Docket No.00015-438WO1 the levels of biomarkers may differ based on the specific technique that is used. A "control level" of a target molecule refers to the level of the target molecule in the same sample type from an individual that does not have the disease or condition, or from an individual that is not suspected of having the disease or condition. A "control level" of a target molecule need not be determined each time the present methods are carried out, and may be a previously determined level that is used as a reference or threshold to determine whether the level in a particular sample is higher or lower than a normal level. In some embodiments, a control level in a method described herein is the level that has been observed in one or more subjects (i.e., a population) without MASLD. In some embodiments, a control level in a method described herein is the level that has been observed in one or more subjects with MASLD, but not a particular subset or species falling under MASLD. In some embodiments, a control level in a method described herein is the average or mean level, optionally plus or minus a statistical variation that has been observed in a plurality of normal subjects, or subjects with MASLD. [0050] The term “sample” can include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, such as a blood sample, taken from a subject, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision, or intervention or other means known in the art. Examples of an aliquot of body fluid include amniotic fluid, aqueous humor, bile, lymph, breast milk, interstitial fluid, blood, blood plasma, cerumen (earwax), Cowper’s fluid (pre-ejaculatory fluid), chyle, chyme, female ejaculate, menses, mucus, saliva, urine, vomit, tears, vaginal lubrication, sweat, serum, semen, sebum, pus, pleural fluid, cerebrospinal fluid, synovial fluid, intracellular fluid, and vitreous humour. [0051] Metabolic dysfunction-associated steatotic liver disease (MASLD), previously referred to as non-alcoholic fatty liver disease (NAFLD), represents a spectrum of disease occurring in the absence of alcohol abuse. It is characterized by the presence of steatosis (fat in the liver) and may represent a hepatic manifestation of the metabolic syndrome (including obesity, diabetes and Attorney Docket No.00015-438WO1 hypertriglyceridemia). MASLD is linked to insulin resistance, it causes liver disease in adults and children and may ultimately lead to cirrhosis (Skelly et al., J Hepatol., 35: 195-9, 2001; Chitturi et al., Hepatology, 35(2):373-9, 2002). The severity of MASLD ranges from the relatively benign isolated predominantly macrovesicular steatosis (i.e., nonalcoholic fatty liver (NAFL)) to non-alcoholic steatohepatitis (NASH) (Angulo et al., J Gastroenterol Hepatol, 17 Suppl:S186-90, 2002). NASH is characterized by the histologic presence of steatosis, cytological ballooning, scattered inflammation and pericellular fibrosis (Contos et al., Adv Anat Pathol., 9:37-51, 2002). Hepatic fibrosis resulting from NASH may progress to cirrhosis of the liver or liver failure, and in some instances may lead to hepatocellular carcinoma. [0052] The degree of insulin resistance (and hyperinsulinemia) correlates with the severity of MASLD (NAFLD), being more pronounced in patients with MASH (NASH) than with simple fatty liver (Sanyal et al., Gastroenterology, 120(5):1183-92, 2001). As a result, insulin- mediated suppression of lipolysis occurs and levels of circulating fatty acids increase. Two factors associated with MASH (NASH) include insulin resistance and increased delivery of free fatty acids to the liver. Insulin blocks mitochondrial fatty acid oxidation. The increased generation of free fatty acids for hepatic re-esterification and oxidation results in accumulation of intrahepatic fat and increases the liver’s vulnerability to secondary insults. [0053] The prevalence of MASLD in children is unknown because of the requirement of histologic analysis of liver in order to confirm the diagnosis (Schwimmer et al., Pediatrics, 118(4):1388-93, 2006). However, estimates of prevalence can be inferred from pediatric obesity data using hepatic ultra-sonongraphy and elevated serum transaminase levels and the knowledge that 85% of children with MASLD are obese. Data from the National Health and Nutrition Examination Survey has revealed a threefold rise in the prevalence of childhood and adolescent obesity over the past 35 years; data from 2000 suggests that 14-16% children between 6-19 yrs age are obese with a BMI >95% (Fishbein et al., J Pediatr. Gastroenterol. Nutr., 36(1):54-61, 2003). Attorney Docket No.00015-438WO1 [0054] In patients with histologically proven MASLD, serum hepatic aminotransferases, specifically alanine aminotransferase (ALT), levels are elevated from the upper limit of normal to 10 times this level (Schwimmer et al., J Pediatr., 143(4):500-5, 2003; Rashid et al., J Pediatr Gastroenterol Nutr., 30(1):48-53, 2000). The ratio of ALT/AST (aspartate aminotransferase) is >1 (range 1.5 – 1.7) which differs from alcoholic steatohepatitis where the ratio is generally <1. Other abnormal serologic tests that may be abnormally elevated in MASH (NASH) include gamma-glutamyltransferase (gamma-GT) and fasting levels of plasma insulin, cholesterol and triglyceride. [0055] The exact mechanism by which MASLD develops into MASH (NASH) remains unclear. Because insulin resistance is associated with both MASLD and MASH, it is postulated that other additional factors are also required for MASH (NASH) to arise. This is referred to as the “two-hit” hypothesis (Day CP. Best Pract. Res. Clin. Gastroenterol., 16(5):663-78, 2002) and involves, firstly, an accumulation of fat within the liver and, secondly, the presence of large amounts of free radicals with increased oxidative stress. Macrovesicular steatosis represents hepatic accumulation of triglycerides, and this in turn is due to an imbalance between the delivery and utilization of free fatty acids to the liver. During periods of increased calorie intake, triglyceride will accumulate and act as a reserve energy source. When dietary calories are insufficient, stored triglycerides (in adipose) undergo lipolysis and fatty acids are released into the circulation and are taken up by the liver. Oxidation of fatty acids will yield energy for utilization. [0056] Obesity is the most common risk factor for MASLD. Whereas 75% of the general population is overweight or obese, only 30-40% of the general population have MASLD. Also, it has been reported that up to 20% of individuals with MASLD are lean and would thus be missed by current practice guidelines. There is therefore a need for a diagnostic test that can be used in routine clinical settings to identify who has MASLD so that appropriate secondary tests to assess disease activity and fibrosis can be performed for risk stratification and clinical decision making. [0057] Lipidomic analysis of plasma provides a “snapshot” of the state of lipid metabolism in the body. The development of MASLD is Attorney Docket No.00015-438WO1 closely linked to metabolic syndrome and altered systemic metabolism including delivery of a lipotoxic load of fatty acids to the liver, leading to inflammation, another hallmark of MASLD. Free, unesterified polyunsaturated fatty acids such as arachidonic acid (AA), adrenic acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) can undergo enzymatic or non-enzymatic oxidation and other modifications to give rise to prostaglandins, leukotrienes and various other forms of oxygenated metabolites. These lipid metabolites, including their precursor fatty acids, are collectively referred to as “eicosanoids”. This class of lipids are highly bioactive and can also be secreted from the liver into the circulation actively via lipoproteins or extracellular vesicles (exosomes) or through vascular leakage. Circulating eicosanoids provide a diagnostic signature reflective of the underlying presence of MASLD. [0058] Bioactive lipids include a number of molecules whose concentrations or presence affect cellular function. Bioactive lipids, as used herein, include phospholipids, sphingolipids, lysophospholipids, ceramides, diacylglycerol, eicosanoids, steroid hormones and the like. Eicosanoids and related metabolites, sometimes referred to as oxylipins, are a group of structurally diverse metabolites that derive from the oxidation of polyunsaturated acids (PUFAs) including arachidonic acid (AA), linoleic acid, alpha and gamma linolenic acid, dihomo gamma linolenic acid, eicosapentaenoic acid and docosahexaenoic acid. They are locally acting bioactive signaling lipids that regulate a diverse set of homeostatic and inflammatory processes. Given the important regulatory functions in numerous physiological and pathophysiological states, the accurate measurement of eicosanoids and other oxylipins is of great clinical interest and lipidomics is now widely used to screen effectively for potential disease biomarkers. [0059] The biosynthesis of eicosanoids and oxylipins involves the action of multiple enzymes organized into a complex and intertwined lipid-anabolic network. Generally, the enzymatic formation of eicosanoids requires free fatty acids as substrates; thus, the pathway is initiated by the hydrolysis of phospholipids (PLs) by Attorney Docket No.00015-438WO1 phospholipase A upon physiological stimuli. The hydrolyzed PUFAs are then processed by three enzyme systems: cyclooxygenases (COX), lipoxygenases (LOX), and cytochrome P450 enzymes (CYP450). Each of these enzyme systems produces unique collections of oxygenated metabolites that function as end-products or as intermediates for a cascade of downstream enzymes. The resulting eicosanoids exhibit diverse biological activities, half-lives and utilities in regulating many physiological processes in health and disease including the immune response, inflammation, and homeostasis. Additionally, non-enzymatic processes can produce oxidized PUFA metabolites via free radical reactions giving rise to isoprostanes and other oxidized fatty acids. [0060] The eicosanoid biosynthetic pathway includes over 100 bioactive lipids and relevant enzymes organized into a complex and intertwined lipid-signaling network. Biosynthesis of polyunsaturated fatty acid (PUFA) derived lipid mediators is initiated via the hydrolysis of phospholipids by phospholipase A (PLA) upon physiological stimuli. These PUFA including arachidonic acid (AA), dihomo-gamma-linolenic acid (DGLA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) are then processed by three enzyme systems: lipoxygenases (LOX), cyclooxygenases (COX) and cytochrome P450s, producing three distinct lineages of oxidized lipid classes. These enzymes are all capable of converting free arachidonic acid and related PUFA to their specific metabolites and exhibit diverse potencies, half-lives and utilities in regulating inflammation and signaling. Additionally, non-enzymatic processes can result in oxidized PUFA metabolites including metabolites from the essential fatty acids linoleic (LA) and alpha-linolenic acid (ALA). [0061] Eicosanoids, which are key regulatory molecules in metabolic syndromes and the progression of hepatic steatosis to MASLD, act either as anti-inflammatory agents or as pro-inflammatory agents. Convincing evidence for a causal role of lipid peroxidation in steatohepatitis has not been unequivocally established; however, a decade of research has strongly suggested that these processes occur and that oxidative-stress is associated with hepatic toxicity and injury. As discussed above, MASLD encompasses a wide spectrum of histological cases associated with hepatic fat over-accumulation Attorney Docket No.00015-438WO1 that range from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH/MASH). The severity of MASLD can be distinguished by evidence of cytological ballooning, inflammation, and higher degrees of scarring and fibrosis. Hence, NASH/MASH is a serious condition, and approximately 10-25% of inflicted patients eventually develop advanced liver disease, cirrhosis, and hepatocellular carcinoma. [0062] Alterations in lipid metabolism may give rise to hepatic steatosis due to increased lipogenesis, defective peroxisomal and mitochondrial β-oxidation, and/or a lower ability of the liver to export lipids resulting in changes in fatty acids and/or eicosanoids. Some studies have highlighted the role of triacylglycerol, membrane fatty acid composition, and very low density lipoprotein (VLDL) production in the development of NASH and associated metabolic syndromes. [0063] Cyclooxygenase-2 (COX-2), a key enzyme in eicosanoid metabolism, is abundantly expressed in NASH/MASH, which promotes hepatocellular apoptosis in rats. Others have reported that oxidized lipid products of LA including 9-hydroxyoctadienoic acid (9-HODE), 13-HODE, 9-oxooctadienoic acid (9-oxoODE), and 13-oxoODE as well as of arachidonic acid 5-hydroxyeicosa-tetraenoic acid (5- HETE), 8-HETE, 11-HETE, and 15-HETE are linked to histological severity in MASLD. [0064] Free fatty acids are cytotoxic; thus the majority of all fatty acids in mammalian systems are esterified to phospholipids and glycerolipids as well as other complex lipids. Similarly, oxygenated metabolites of fatty acids can exist either in their free form or esterified to complex lipids. [0065] Eicosanoids act locally in an autocrine or paracrine fashion and signal by binding to G-protein-coupled receptors or act intracellularly via various peroxisome proliferator-activating receptors. For optimal biological activity, these mediators need to be present in their free, non-esterified form. However, a number of studies reported that a portion of eicosanoids are naturally esterified and can also be contained in cell membrane lipids, including PLs, in the form of esters. The role of esterified eicosanoids is not clear, but they may be signaling molecules in Attorney Docket No.00015-438WO1 their own right or serve as a cellular reservoir for the rapid release upon cell stimulation. [0066] Two potential mechanisms for the formation of eicosanoids- containing PLs have been proposed: (i) direct oxidation of PUFAs on the intact PLs, and (ii) re-acylation of preformed free oxylipins into lysoPLs. Cyclooxygenases require free fatty acid as substrate and show little activity toward PUFAs in intact PLs. A number of subsequent studies support the concept that prostaglandins are first formed enzymatically and then incorporated into PLs by the sequential actions of long-chain acyl-CoA synthases and lysophospholipid acyltransferases. Additionally, preformed fatty acid epoxides, including the regioisomers of epoxyeicosatrienoic acid (EET), are effectively incorporated primarily into the phospholipid fraction of cellular lipids, presumably via CoA- dependent mechanisms. [0067] In contrast, mammalian 12/15 lipoxygenase (LOX) can act directly on PLs to generate esterified HETE isomers including esterified 12-HETE and 15-HETE. Similarly, the endocannabinoid 2- arachidonylglycerol is a substrate for COX-2 and is metabolized to prostaglandin H2 glycerol ester as effectively as free AA. The final products derived from this direct PL oxygenation pathway include esterified prostaglandins (PGs) as well as 11-HETE and 15-HETE. PUFAs contained in PLs can also be oxidized by non-enzymatic reactions. Free radical peroxidation reactions observed under conditions of oxidative stress can freely proceed on intact PLs resulting in the formation of isoprostanes. [0068] At the present time, the gold standard technique for the diagnosis of NASH/MASH is a liver biopsy examination, which is recognized as the only reliable method to evaluate the presence and extent of necro-inflammatory changes, presence of ballooning and fibrosis in liver. However, liver biopsy is an invasive procedure with possible serious complications and limitations. Reliable noninvasive methods are therefore needed to avoid the sampling risks. It is proposed that differences in plasma levels of eicosanoids and complex lipids can be used to identify MASLD and MASH. Attorney Docket No.00015-438WO1 [0069] This disclosure provides a study on 301 MASLD patients and 48 controls as well as a validation study. The patient population represented the entire spectrum of disease progression from simple steatosis with no inflammation to end-stage liver disease. The disclosure identifies a panel of bioactive lipids that are able to accurately identify MASLD at any stage; mild, moderate, or severe, with or without inflammation and fibrosis, and with or without underlying obesity. In all, 77 eicosanoid metabolites and 25 complex lipids were detectable in at least one of the plasma samples. For a metabolite to be of clinical relevance as a biomarker, it was present consistently and in measurable amounts. The disclosure demonstrates that metabolites that were present in 80% of all control and patient plasma samples were included in further analysis. The application of such a stringent algorithm reduced the panel of potential biomarkers to 32 eicosanoid metabolites, which were futher reduced to 15 eicosanoids and 15 complex lipids. [0070] To avoid overfitting of a statistical model in the biomarker search, a method was used to avoid an over-representation of a specific pathway. Typically metabolites of a specific pathway are all increased or decreased if the underlying biosynthetic pathway is up- or down-regulated. Thus, multiple eicosanoid metabolites derived from a single pathway should not be considered as independent variables. To address this issue, a correlation analysis was used (Figure 1). As can be seen, several correlation clusters became apparent. For example, a high degree of correlation was found between some hydroxylated fatty acids. These presumably are derived from the LOX pathway. It was reasoned that if the activity of the LOX pathway is changed in MASLD, then all metabolites within this pathway would change accordingly. Using all 32 metabolites in the AUROC analysis gave a perfect AUC of 0.999 (CI = 0.99-1.0). However, using the entire dataset, a partial overlap in the Scores Plot of the PLSDA was observed. Some of the patients either presented with very mild disease or had progressed significantly to cirrhosis. At that stage, the liver loses much of the fat content, and the metabolic characteristics of the liver change significantly. In general, cirrhosis of the liver can readily Attorney Docket No.00015-438WO1 be diagnosed, and the false negative classification should not pose any limitation in a clinical setting. [0071] To improve the diagnostic performance, a panel comprising 12 eicosanoids and three free fatty acids was selected from the top 20 metabolites that were identified to be predictive for MASLD by univariant AUROC analysis. The panel was highly predictive for MASLD with an AUROC of 0.999 (95% CI = 0.986–1.0). As shown in Figure 5, of the 48 controls and of the 301 MASLD patients, only one control was incorrectly classified. [0072] A panel comprising fifteen lipid metabolites (see Table 1 and Table 4) that accurately predict MASLD were used. Furthermore, a strategy was developed to determine a MASLD LIPIDOMICS SCORE, which predicts the presence of MASLD with high accuracy based on lipidomics analysis of ~50 ul of plasma. The panel can be carried out as part of or in conjunction with variables including BMI, diabetes and cardiovascular disease, gender, ethnicity, and age as well as liver enzymes and lipid levels, including matched controls. [0073] Moreover, the research also provides a panel of 15 complex lipids that accurately predict MASLD (see Table 2B). The complex lipid panel can be used alone or in combination with the eicosanoid panel and may further include variables including BMI, diabetes and cardiovascular disease, gender, ethnicity, and age as well as liver enzymes and lipid levels, including matched controls. [0074] Table 1: Eicosanoid lipidomics panel scoring chart for MASLD
Attorney Docket No.00015-438WO1 embodiment, Table 2B provides an additional panel or standalone panel of 15 complex lipids that can be used to identify subjects having or at risk of having MASLD. [0076] Table 2A – Top 25 Univariate analyses of Complex Lipids
Attorney Docket No.00015-438WO1 [0077] Table 2B - 15 complex lipids:
Attorney Docket No.00015-438WO1 Complex Lipi d composition. e headgroup for the Class (e.g., PC for phosphatidylcholine, Cer for Ceramide, etc.). The fatty acids chains are labeled so that the first number designates the total number of carbons and the second number after the colon indicates the total number of double bonds. The slash (/) indicate the 2 fatty acids that are present in the sn1 and sn2 position in this molecule with the sn1 position being first. The underscore (_) between the fatty acids indicates that the exact fatty acid composition was identified, but the position of each fatty acid could not be resolved, i.e., sn1 and sn2. For sphingolipids (Sphingomyelin and Ceramide) the “m”, “d” or “t” indicates a mono-, di-, or tri-hydroxy sphingoid base and the “n” indicates there are no hydroxy groups present on the fatty acid where an “h” indicates a hydroxy fatty acid. [0078] The disclosure provides methods and compositions useful for determining the risk or probability a subject will have MASLD and/or MASH. In various embodiments, a test sample is obtained from the subject. The sample can be obtained by the individual or by a third party, e.g., a medical professional. Examples of medical professionals include physicians, emergency medical technicians, nurses, first responders, phlebotomist, nurse practitioners, Attorney Docket No.00015-438WO1 surgeons, dentists, and any other obvious medical professional as would be known to one skilled in the art. [0079] The test sample is tested to determine values of one or more markers by performing a marker quantification assay. The marker quantification assay determines quantitative expression values of one or more biomarkers from the test sample. The marker quantification assay may be an immunoassay, and more specifically, a multi-plex immunoassay. In other embodiments, the markers are measured by mass spectroscopy, high-performance liquid chromatography and the like. The levels of various biomarkers can be obtained in a single run using a single test sample obtained from the subject. The quantified expression values of the biomarkers are provided to a computer system and/or medical professional. [0080] Generally, a computer system includes one or more computers, embodied as a computer system. Therefore, in various embodiments, the steps described in reference to the computer system are performed in silico. The computer system analyzes the received biomarker expression values from a marker quantification assay to generate an assessment of disease or disease progression in the subject. [0081] In various embodiments, the marker quantification assay and the computer system can be employed by different parties. For example, a first party performs the marker quantification assay that then provides the results to a second party which implements the computer system analysis. For example, the first party may be a clinical laboratory that obtains test samples from subjects and performs the assay on the test samples. The second party receives the expression values of biomarkers resulting from the performed assay and analyzes the expression values using the computer system. [0082] In one embodiment, the computer system can comprise a model training module, a model deployment module, and a training data store. [0083] Each of the components of the computer system is hereafter described in reference to two phases: 1) a training phase and 2) a deployment phase. More specifically, the training phase refers to the building and training of one or more predictive models based on training data that includes quantitative expression values of Attorney Docket No.00015-438WO1 biomarkers obtained from individuals that are known to be healthy, in a state of quiescence, in a state of remission, or in an earlier state of disease progression (e.g., mild/moderate MASLD as opposed to MASH) or individuals that are known to have disease activity, in a state of exacerbation, in a state of relapse, or in a more advanced state of disease progression (e.g., MASH as opposed to MASLD). Therefore, the predictive models are trained to predict disease activity in a subject based on quantitative biomarker values. During the deployment phase, a predictive model is applied to quantitative biomarker values from a test sample obtained from a subject of interest in order to generate a prediction of disease activity in the subject of interest. [0084] In some embodiments, the components of the computer system are applied during one of the training phase and the deployment phase. For example, the model training module and training data store are applied during the training phase whereas the model deployment module is applied during the deployment phase. In various embodiments, the training phase and the deployment phase can be performed to enable continuously trained models. For example, the model training module can train a model that the model deployment module can subsequently deploy. The same model can undergo additional training by the model training module (e.g., continuously trained using, for example, new training data that is obtained). Therefore, as the model is continuously trained, it can exhibit improved prediction capacity when analyzing samples during deployment. [0085] In various embodiments, the components of the computer system can be performed by different parties depending on whether the components are applied during the training phase or the deployment phase. In such scenarios, the training and deployment of the predictive model are performed by different parties. For example, the model training module and training data store applied during the training phase can be employed by a first party (e.g., to train a predictive model) and the model deployment module applied during the deployment phase can be performed by a second party (e.g., to deploy the predictive model). Attorney Docket No.00015-438WO1 [0086] During the training phase, the model training module trains one or more predictive models using training data comprising values of biomarkers. The training data may be stored in the training data store. In various embodiments, the computer system generates the training data comprising expression values of biomarkers by analyzing biomarker expression values in test samples. In various embodiments, the computer system obtains the training data comprising values of biomarkers from a third party. The third party may have analyzed test samples to determine the biomarker values. [0087] In various embodiments, the training data comprising expression values of biomarkers are derived from clinical subjects. For example, the training data can be values of biomarkers that were measured from test samples obtained from clinical subjects. [0088] In some embodiments, the training model retrieves the training data from a training data store and randomly partitions the training data into a training set and a test set. As an example, 70% of the training data may be partitioned into the training set and the other 30% can be partitioned into the test set. Other proportions of training set and test set may be implemented. As such, the training set is used to train predictive models whereas the test set is used to validate the predictive models. [0089] In various embodiments, the predictive model is any one of a regression model (e.g., linear regression, logistic regression, or polynomial regression), decision tree, random forest, support vector machine, Naive Bayes model, k-means cluster, or neural network (e.g., feed-forward networks, convolutional neural networks (CNN), deep neural networks (DNN), autoencoder neural networks, generative adversarial networks, or recurrent networks (e.g., long short-term memory networks (LSTM), bi-directional recurrent networks, deep bidirectional recurrent networks), linear mixed effects (LME) model, or any combination thereof. For example, the predictive model can be a stacked classifier that includes both a linear regression and decision tree. [0090] The predictive model can be trained using a machine learning implemented method, such as any one of a linear regression algorithm, logistic regression algorithm, decision tree algorithm, support vector machine classification, Naive Bayes classification, Attorney Docket No.00015-438WO1 K-Nearest Neighbor classification, random forest algorithm, deep learning algorithm, gradient boosting algorithm, and dimensionality reduction techniques such as manifold learning, principal component analysis, factor analysis, autoencoder regularization, and independent component analysis, or combinations thereof. In various embodiments, the cellular disease model is trained using supervised learning algorithms, unsupervised learning algorithms, semi- supervised learning algorithms (e.g., partial supervision), weak supervision, transfer, multi-task learning, or any combination thereof. [0091] In various embodiments, the predictive model has one or more parameters, such as hyperparameters or model parameters. Hyperparameters are generally established prior to training. Examples of hyperparameters include the learning rate, depth or leaves of a decision tree, number of hidden layers in a deep neural network, number of clusters in a k-means cluster, penalty in a regression model, and a regularization parameter associated with a cost function. Model parameters are generally adjusted during training. Examples of model parameters include weights associated with nodes in layers of neural network, support vectors in a support vector machine, and coefficients in a regression model. The model parameters of the cellular disease model are trained (e.g., adjusted) using the training data to improve the predictive capacity of the cellular disease model. [0092] In some embodiments, the importance of each biomarker for a disease activity endpoint (e.g., disease progression endpoint) is determined by using a method including one of random forest (RF), gradient boosting (GBM), extreme gradient boosting (XGB), or LASSO algorithms. For example, if using random forest algorithms, the model training module may generate a variable importance plot that depicts the importance of each candidate biomarker. Specifically, the random forest algorithm may provide, for each candidate biomarker, 1) a mean decrease in model accuracy and 2) a mean decrease in a Gini coefficient which is a measure of how much each candidate biomarker contributes to the homogeneity of nodes and leaves in the random forest. In one scenario, the importance of each candidate biomarker is dependent on one or both of the mean decrease Attorney Docket No.00015-438WO1 in model accuracy and mean decrease in Gini coefficient. Each of GBM, XGB, and LASSO, can also be used to rank the importance of each candidate biomarker based on an influence value. Therefore, the model training module can generate a ranking of each of candidate biomarkers using one of the methods including RF, GBM, XGB, or LASSO. [0093] Each predictive model is iteratively trained using, as input, the quantitative values of the markers for each individual. For example, one iteration involves providing a training example that includes the quantitative value of biomarkers for a particular individual. Each predictive model is trained on an indication (e.g., the positive or negative result). [0094] During the deployment phase, the model deployment module analyzes quantitative biomarker values from a test sample obtained from a subject of interest by applying a trained predictive model. In some embodiments, the subject has not previously been diagnosed with a disease and therefore, the deployment of the predictive model enables in silico diagnosis of the disease based on the quantitative biomarker values derived from the subject. In some embodiments, the subject has been previously diagnosed with a disease. Here, the deployment of the predictive model enables in silico prediction of disease activity (e.g., disease progression) based on the quantitative biomarker values derived from the subject. [0095] In various embodiments, the quantitative biomarker values are provided as input to the predictive model. The predictive model analyzes the quantitative biomarker values and outputs an assessment of disease activity (e.g., disease progression). The predicted score can then be informative of the disease activity. For example, the predicted score can enable the classification of the subject into one of multiple disease progression categories (e.g., one of mild/moderate disease progression or severe progression). In various embodiments, the assessment of disease activity (e.g., disease progression) is a predicted score representing the learned combination of the quantitative biomarker values. Generally, the predicted score represents an aggregation of the quantitative values and therefore, is not directly dependent on solely one biomarker value. Attorney Docket No.00015-438WO1 [0096] In various embodiments, the assessment of disease activity is a predicted score that may be informative of the disease activity in the subject. In various embodiments, the predicted score outputted by the prediction model is compared to one or more reference scores to determine a measure of the disease activity. Reference scores refer to previously determined scores, further described below as “healthy scores” or “diseased scores,” that correspond to diseased patients or non-diseased patients. For example, the one or more scores may be “healthy scores” corresponding to healthy patients, a patient’s own baseline at a prior timepoint when the patient did not exhibit disease activity (e.g., longitudinal analysis), patients clinically diagnosed with the disease but not exhibiting disease activity, or a threshold score (e.g., a cutoff). As another example, the one or more scores may be “diseased scores” corresponding to diseased patients, a patient’s own score indicating disease activity at a prior timepoint, or a threshold score (e.g., a cutoff). As one example, the threshold score can correspond to healthy patients and can be generated by training a predictive model using expression values of biomarkers from healthy patients. As another example, the threshold score can correspond to diseased patients and can be generated by training a predictive model using expression values of biomarkers from the diseased patients. [0097] In various embodiments, a threshold score corresponding to healthy patients can be lower than a threshold score corresponding to diseased patients. For example, the threshold score corresponding to healthy patients can be at least 5% lower than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 10% lower than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 15% lower than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 20% lower than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 25% lower than a threshold score corresponding to diseased patients. As another example, the Attorney Docket No.00015-438WO1 threshold score corresponding to healthy patients can be at least 50% lower than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 75% lower than a threshold score corresponding to diseased patients. [0098] In various embodiments, a threshold score corresponding to healthy patients can be higher than a threshold score corresponding to diseased patients. For example, the threshold score corresponding to healthy patients can be at least 5% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 10% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 15% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 20% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 25% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 50% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 75% higher than a threshold score corresponding to diseased patients. As another example, the threshold score corresponding to healthy patients can be at least 100% higher than a threshold score corresponding to diseased patients. Thus, in particular embodiments, the predicted score outputted by the prediction model is compared to one or both of the threshold score corresponding to healthy patients and threshold score corresponding to diseased patients, and based on the comparison, a measure of the disease activity is determined. [0099] In one embodiment, the predicted score outputted by the prediction model can be compared to a healthy score. The subject can be classified as having the disease if the predicted score of the subject is significantly different (e.g., p-value <0.05) in comparison to the healthy score. In one embodiment, the predicted Attorney Docket No.00015-438WO1 score outputted by the prediction model can be compared to the diseased score. The subject can be classified as not having the disease if the predicted score of the subject is significantly different (e.g., p-value <0.05) in comparison to the diseased score. In some embodiments, the predicted score outputted by the prediction model is compared to both the healthy score and the diseased score. For example, the subject can be classified as having the disease if the predicted score of the subject is significantly different (e.g., p-value <0.05) in comparison to the healthy scores and not significantly different (e.g., p-value >0.05 in comparison to the diseased scores for patients that have been diagnosed with the disease. In various embodiments, depending on the classification of the subject, the subject can undergo treatment. In other words, the assessment can guide the treatment of the subject. For example, if the subject is classified as having the disease, the subject can be administered a therapeutic intervention to treat the disease. [00100] In various embodiments, the assessment of disease activity corresponds to a likely response to a therapy provided to the subject. In one embodiment, the predicted score outputted by the prediction model can be compared to a score corresponding to individuals previously determined to be responsive to a therapy (e.g., clinically determined to be responsive to the therapy). The subject can be classified as being a responder if the predicted score of the subject is significantly different (e.g., p-value <0.05) in comparison to the score corresponding to individuals previously determined to not be responsive to the therapy. The subject can be classified as being a responder if the predicted score of the subject is not significantly different (e.g., p-value >0.05) in comparison to the score corresponding to individuals previously determined to be responsive to the therapy. In one embodiment, the predicted score outputted by the prediction score is compared to a score corresponding to individuals previously determined to be non-responders. The subject can be classified as a non-responder if the predicted score of the subject is not significantly different (e.g., p-value >0.05) from the score corresponding to individuals previously determined to be non- responders. The subject can be classified as a non-responder if the Attorney Docket No.00015-438WO1 predicted score of the subject is significantly different (e.g., p- value <0.05) from the score corresponding to individuals previously determined to be responders. In some embodiments, the predicted score outputted by the prediction model is compared to both a score corresponding to individuals previously determined to be responders and a score corresponding to individuals previously determined to be non-responders. For example, the subject can be classified as being a responder if the predicted score of the subject is significantly different (e.g., p-value <0.05) in comparison to the score corresponding to individuals previously determined to be non- responders and not significantly different (e.g., p-value >0.05) in comparison to the score corresponding to individuals previously determined to be responders. [00101] In various embodiments, the assessment of disease activity is a classification of disease progression (e.g., mild/moderate disease versus severe disability). Thus, in such embodiments, the predicted score outputted by the prediction model can be compared to one or both scores corresponding to individuals previously identified as having mild/moderate disease and corresponding to individuals previously identified as having severe disability. [00102] In various embodiments, a measure of the disease progression or level predicted by the predictive model provides additional utility for managing the disease activity in the patient. As one example, the measure of the disease progression or level predicted by the predictive model is useful for selecting a candidate therapeutic or for determining the effectiveness of a previously administered therapeutic. [00103] In various embodiments, the measure of disease progression or level predicted by the predictive model for a patient can be compared to a prior measure of disease activity to determine whether a therapeutic administered to the patient is demonstrating efficacy. [00104] In various embodiments, a biomarker panel further incorporates one or more subject attributes. For example, subject attributes can include an age of the subject, the gender of the subject, a disease duration experienced by the subject, racial/ethnic identity, weight, height, body mass index (BMI), and socioeconomic status. Attorney Docket No.00015-438WO1 [00105] In various embodiments, prior to implementation of a marker assay a sample obtained from a subject can be processed. In various embodiments, processing the sample enables the implementation of the marker quantification assay to more accurately evaluate levels of one or more biomarkers in the sample. [00106] In various embodiments, the sample from a subject can be processed to extract biomarkers from the sample. In one embodiment, the sample can undergo phase separation to separate the biomarkers from other portions of the sample. For example, the sample can undergo centrifugation (e.g., pelleting or density gradient centrifugation) to separate larger and/or more dense entities in the sample (e.g., cells and other macromolecules) from the biomarkers. Other examples include filtration (e.g., ultrafiltration) to phase separate the biomarkers from other portions of the sample. [00107] In various embodiments, the sample from a subject can be processed to produce a sub-sample with a fraction of biomarkers that were in the sample. In various embodiments, producing a fraction of biomarkers can involve performing a protein fractionation procedure. One example of protein fractionation procedures include chromatography (e.g., gel filtration, ion exchange, hydrophobic chromatography, or affinity chromatography). In particular embodiments, the fractionation procedure can involve affinity purification or immunoprecipitation where biomarkers are bound by specific antibodies. Such antibodies can be immobilized on a support, such as a magnetic particle or nanoparticle or a plate. [00108] In various embodiments, the sample from the subject is processed to extract biomarkers from the sample and further processed to produce a sub-sample with a fraction of extracted biomarkers. Altogether, this enables a purified sub-sample of biomarkers that are of particular interest. Thus, implementing an assay for evaluating levels of the biomarkers of particular interest can be more accurate and of higher quality. [00109] In various embodiments, a therapeutic agent is provided to an individual prior to and/or subsequent to obtaining the sample from the individual and determining quantitative values of one or more markers in the obtained sample. Attorney Docket No.00015-438WO1 [00110] Accordingly, the disclosure provides a method of diagnosing or assessing the risk that a subject has MASLD, the method comprising measuring the levels of 15 eicosanoids and/or 15 complex lipids as set forth in Table 1 (see also Table 4) and/or complex lipids as set forth in Table 2B. For example, a method of the disclosure comprises obtaining a sample from a subject, measuring the amount of eicosanoids and optionally, or alternatively, complex lipids in the sample as set forth in Tables 1 and 2B, respectively. In one embodiment, the sample is processed by spiking the sample with an internal standard. In still another embodiment, the amount of eicosanoids are identified such that if they exceed a cutoff as set forth in Table 1, the subject is identified as having or at risk of having MASLD. The method of the disclosure comprises determining the level of one or more eicosanoids and/or complex lipids in a sample of a patient. In one embodiment, the sample is a plasma sample. The determination of eicosanoids and lipids may be made by any suitable lipid assay technique, such as a high throughput technique including, but not limited to, spectrophotometric analysis (e.g., colorimetric sulfo-phospho-vanillin (SPV) assessment method of Cheng et al., Lipids, 46(1):95-103 (2011)). Other analytical methods suitable for detection and quantification of lipid content will be known to those in the art including, without limitation, ELISA, NMR, UV-Vis or gas-liquid chromatography, HPLC, UPLC and/or MS or RIA methods enzymatic based chromogenic methods. Lipid extraction may also be performed by various methods known to the art, including the conventional method for liquid samples described in Bligh and Dyer, Can. J. Biochem. Physiol., 37, 911 (1959). [00111] As described below and elsewhere herein ultrahigh performance liquid chromatography-mass spectrometry (UPLC-MS) protocols are described to demonstrate that plasma levels of oxylipins can be used as biomarkers to identify subjects having or at risk of having MASLD. In this method, a panel of oxylipins that, when used together, can discriminate controls from MASLD with a high degree of certainty. [00112] The disclosure includes the measurements of bioactive lipids. In some embodiments, methods were used to measure the “free” oxylipins present in plasma, not those appearing after Attorney Docket No.00015-438WO1 alkaline hydrolysis (see, Feldstein et al.). In other embodiment, the sum total of esterified and free oxylipins are used by treating the sample with alkali (e.g., KOH). [00113] For example, eicosanoids and specifically PGs are sensitive to alkaline-induced degradation. Thus, experiments can be performed to minimize degradation of lipid metabolites during alkaline treatment and to identify specific eicosanoids and related oxidized PUFAs that are released intact from esterified lipids and which can be quantitatively measured. [00114] The disclosure provides methods, kits and compositions useful identifying MASLD. In addition, the disclosure provides methods of identifying subject having or at risk of having MASLD. Such methods will help in the early onset and treatment of disease. Moreover, the methods reduce biopsy risks associated with liver biopsies currently used in diagnosis. The methods and compositions comprise unmodified and/or modified eicosanoids and PUFAs in the diagnosis. As such, in some instances the biomarkers are manipulated from their natural state by chemical modifications to provide a derived biomarker that is measured and quantitated. The amount of a specific biomarker can be compared to normal standard sample levels (i.e., those lacking any liver disease) or can be compared to levels obtained from a diseased population (e.g., populations with clinically diagnosed MASLD). [00115] Lipids are extracted from the sample, as detailed further in the Examples. The identity and quantity of bioactive lipids, eicosanoids and/or PUFA metabolites in the extracted lipids is first determined and then compared to suitable controls (e.g., a sample indicative of a subject with no liver disease, a sample indicative of a subject with MASLD and/or a sample indicative of a subject with NASH). [00116] The disclosure demonstrates that out of 2 (65,536 combination) possible combinations of 16 lipids, 20 models were developed based upon a review of the bioactive lipids present in control and MASLD subject. Table 3 provides a list of the top 25 eicosanoids for the diagnosis of MASLD. [00117] Table 3: Attorney Docket No.00015-438WO1 Biomarker AUROC 95% CI p value Log2 FC 14-HDoHE 0.95 0.92-0.97 0.16 -5.2 Attorney Docket No.00015-438WO1 9-HODE 0.68 0.60-0.74 0.28 -2.5 [00119] Accordingly, in one method of the disclosure, the method comprises obtaining a sample from a subject (e.g., a plasma sample), extracting the bioactive lipids in the sample and determining the levels of one or more of 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14-15-EET, DHA, EPA, and any combination thereof. [00120] To be useful for clinical applications a composite scoring algorithm was used to distinguish between control and MASLD. This was accomplished by converting eicosanoid abundance to a binary system. To create the composite score the univariate ROC results were used that provide the cutoff values that best separate controls from MASLD. These values were applied across the selected eicosanoid panel to generate the binary value of 0 or 1. If the levels of any of the eicosanoids including 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE or adrenic acid were above the cutoff, their score was 1. If any of the eicosanoids, including 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA or EPA fall below the cutoff, their score is also 1. If any of the metabolites in any of the samples did not follow this pattern, they received a score of 0. Using this algorithm, a composite score was constructed to separate controls from MASLD. The composite score is he sum total of the binary value of each of the analytes that follows this framework. Using this approach, a composite score of 6+ was optimal to diagnose MASLD. This means that at least 6 metabolites of the 15 metabolites must be present at concentrations indicative for MASLD to classify the sample as MASLD. One will recognize that this scoring system can be dynamically tuned by adjusting the individual metabolite cutoff values and/or adjusting the composite scoring. A similar approach was use for complex lipids. [00121] In another embodiment, the method comprises measuring a ratio of peak areas between endogenous eicosanoids and matching deuterated internal eicosanoids. In another embodiment, the method can further comprise measuring one or more (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or all 15) complex lipids identified in Attorney Docket No.00015-438WO1 Table 2B. The measurements are compared to a control or reference level (e.g., levels associated with a subject lacking MASLD), wherein a statistically significant difference in the markers is indicative of MASLD. Moreover, it will be recognized that the reference level will be a reference level for the particular type of measurement used. [00122] In one embodiment, when the top 15 eicosanoids were used and the top 15 complex lipids (30 biomarkers), A score of 1 is given for each analyte above their respective cutoff for analytes that increase and below their respective cutoff for analytes that decrease. The analyte scores are summed to make lipidomic score. The cutoff values were identified by univariate AUC analysis which best separates the two groups for each analyte. Score cutoffs of 12- 14 give 100% probability of correct assignment to each group. Percentage values are made by dividing the number of correct assignments over the total patients in each group for each cutoff value. For example, using a cutoff of 13 means a patient with a score of 13-30 is identified as MASLD and 1-12 is identified as control. Using this cutoff value completely separates the two groups with 100% probability of correct assignment. [00123] The disclosure also provides a method whereby a subject identified as having MASLD using the methods and compositions provided herein are further screened for MASH. The disclosure also provides a 15 marker panel for distinguishing high NAS scores from low NAS scores. The panel comprises the markers in Table 5 and 6. [00124] Table 5: Panel to Distinguish High NAS from Low NAS
Attorney Docket No.00015-438WO1 [00126] In a further embodiment, a subject identified as having MASLD by the methods herein may further have a sample (either existing or newly obtain) analyzed by extracting bioactive Attorney Docket No.00015-438WO1 eicosanoids, lipids and markers in the sample and determining the levels of one or more of analytes set forth in Table 5 and Table 6. [00127] The disclosure provides a substantially non-invasive method of predicting or assessing the risk of progression of liver disease in a patient comprising obtaining a plasma sample from a subject and optionally treating the plasma sample with alcohol to dissolve free eicosanoids and free polyunsaturated fatty acid (fPUFA) to obtain free-dissolved eicosanoids and free-dissolved fPUFAs; purifying bioactive lipids including eicosanoids and complex lipids; measuring the level of eicosanoids selected from the group consisting of one or more of 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15- HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, EPA, or any combination thereof; determining the area under receiver operating characteristic curve (AUROC) based upon a ratio of the levels of the bioactive lipids matched with deuterated internal standards of the same metabolite. In another or further embodiment, the AUROC is about at least 0.8, at least about 0.9, or at least about 0.99. [00128] In another embodiment, the disclosure provides a substantially non-invasive method of predicting or assessing the risk of progression of liver disease in a patient diagnosed with liver disease comprising obtaining a plasma sample from a subject, spiking deuterated internal standards into each sample and primary standards used to generate a standard curve and optionally treating the plasma sample with alcohol to dissolve free eicosanoids and free polyunsaturated fatty acid (fPUFA) to obtain free-dissolved eicosanoids and free-dissolved fPUFAs; purifying bioactive lipids including eicosanoids and complex lipids; measuring the level of eicosanoids selected from the group consisting of one or more of 4- HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15- HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, EPA, and any combination thereof; calculating the ratio between endogenous metabolite and matching deuterated internal standards, converting the ratios to absolute amounts by linear regression, determining the area under receiver operating characteristic curve (AUROC) based upon a ratio of the levels of the bioactive lipids matched with deuterated internal standards of the same metabolite. Attorney Docket No.00015-438WO1 In one embodiment, the liver disease is a MASLD. In another or further embodiment, the AUROC is about at least 0.8, at least about 0.9, or at least about 0.99. [00129] When a combination of eicosanoids and complex lipids are used in assessing a subject for MASLD, cutoffs are as set forth in Table 7. [00130] Table 7: 30 analytes in used in the lipidomic score separated by whether they increase or decrease in MASLD. The cutoff values shown were identified by univariate AUC analysis which best separates the two groups for each analyte. [00131] The disclosure also provides a panel of lipids for assessing liver fibrosis. As with the prior panels, cutoffs were used to define the level of the lipid(s) that provide diagnostic potential. Attorney Docket No.00015-438WO1 For example, Table 8 provides a list of lipids and cutoff values. For the 12 analytes used in the lipidomic score the cutoff values shown were identified by univariate AUC analysis which best separates the groups for each analyte. For example, using a cutoff of 7 means a patient with a score of 7-12 is identified as high Fibrosis and 1-6 is identified as low Fibrosis. Using this cutoff value assign 75.6% of patients with low Fibrosis scores and 72.5% of patients with high Fibrosis scores. The overall probability of correctly assigning a patient is 74.0%. [00132] Table 8: [00133] It is to be understood that while the disclosure has been described in conjunction with specific embodiments thereof, that the foregoing description as well as the examples which follow are intended to illustrate and not limit the scope of the disclosure. Other aspects, advantages and modifications within the scope of the disclosure will be apparent to those skilled in the art to which the disclosure. Attorney Docket No.00015-438WO1 EXAMPLES Example 1 [00134] Study Population. All human plasma samples were collected as part of clinical studies and/or trials which abided by the policies of the institutional review boards of the institution responsible for the samples and NASH CRN protocols and were supplied for analysis as de-identified samples. The work abides by the Declaration of Helsinki principles. [00135] A retrospective-prospective analysis of plasma samples from adult patients with varying phenotypes of MASLD (n = 301) was performed and compared to controls (n = 48). The MASLD population was derived from the NIDDK NASH Clinical Research Network study cohort. It included samples from individuals who participated in the non-interventional DB1 and DB2 registry (NCT01030484) and also baseline samples from individuals who participated in the PIVENS trial (NCT00063622) and the FLINT trial (NCT01265498). For the hypothesis building study, baseline samples from patients enrolled in these trials and who did not undergo further treatment were used. For the Validation Study, baseline samples from the FLINT trial were used. Plasma samples were obtained within 90 days of an evaluable liver biopsy which was confirmed to demonstrate MASLD. The liver histology was evaluated in a masked manner by the pathology committee of the NASH CRN using a validated protocol and the presence of MASLD and its individual histological features documented using the NASH CRN histological classification system. The histological spectrum extended from steatosis alone to steatohepatitis with varying stages of fibrosis. [00136] Blood samples were obtained in all cases in fasted state followed by plasma separation, aliquoting and freezing within 2-3 hours using a pre-specified protocol. Samples were frozen and stored at -70° C at individual clinical centers and then transferred on dry ice to the NIDDK biorepository. Samples were transmitted from the biorepository to the laboratory for analysis on dry ice. Thus, there were no instances of freeze-thaw prior to the analysis of the samples for the current study. [00137] Controls were collected by similar procedures and were defined by a normal clinical examination, normal liver enzymes and Attorney Docket No.00015-438WO1 functions and a magnetic resonance imaging-proton density fat fraction (MRI-PDFF) assessed liver fat content <5% and magnetic resonance elastography (MRE) assessed liver stiffness < 2.5% based upon previously published thresholds. They were identified at a single center and characterized for purposes of this analysis (Table 9). For the Validation Study, a separate set of control samples were collected at a later time and selected by the same criteria. [00138] Table 9 – Patient characteristics by MASLD status (N=349): Mean (± SD) or N (%) Control MASLD patients P* (N = 48) (N = 301) 35-54 18 (38%) 145 (48%) 55-74 23 (48%) 125 (42%) Sex, male 22 (46%) 102 (34%) 0.08 Race <0.001 Non-Hispanic white 14 (29%) 234 (78%) Non-Hispanic black 4 (8%) 6 (2%) Hispanic 25 (52%) 33 (11%) Other 5 (10%) 28 (9%) BMI (kg/m2) 30.1 (±4.1) 34.6 (±6.3) <0.001 BMI category <0.001 Underweight 0 (0%) 0 (0%) Normal 0 (0%) 9 (3% Overweight 26 (54%) 58 (19%) Obese 22 (46%) 233 (78%) Type 2 diabetes 10 (21%) 144 (48%) <0.001 Total Cholesterol (mg/dL) 181 (±39) 187 (±43) 0.4 Triglycerides (mg/dL) 105 (±42) 182 (±210) 0.01 LDL (mg/dL) 107 (±33) 111 (±38) 0.5 HDL(mg/dL) 53 (±15) 44 (±12) <0.001 Bilirubin, total (mg/dL) 0.5 (±0.2) 0.6 (±0.3) 0.02 Aspartate aminotransferase, AST (U/L) 19 (±5) 57 (±37) <0.001 Alanine aminotransferase, ALT (U/L) 17 (±5) 77 (±52) <0.001 Alkaline phosphatase, ALP (U/L) 81 (±29) 82 (±27) 0.8 Fibrosis stage† 0. None 48 (100%) 44 (15%) 1a. Mild, zone 3 perisinusoidal 31 (10%) 1b. Moderate, zone 3, perisinusoidal 40 (13%) 1c. Portal/periportal only 8 (3%) 2. Zone 3 and periportal, any combination 73 (24%) 3. Bridging 84 (28%) 4. Cirrhosis 21 (7%) MASH stage† Attorney Docket No.00015-438WO1 Not MASLD 48 (100%) 0 (0%) 0. MASLD, not MASH 0 (0%) 34 (11%) 1a. borderline MASH, zone 3 pattern 0 (0%) 43 (14%) 1b. borderline MASH, zone 1 periportal pattern 0 (0%) 1 (<1%) 2. Definite MASH 0 (0%) 223 (74%) Time difference between lab exam and biopsy 45 (±118) (day) * P-value from student t-test for continuous variables and Fisher’s exact test for Date of lab exam – date of biopsy. [00139] Reagents. All solvents were ultra-performance LC (UPLC) grade or better and were purchased from Thermo Fisher Scientific (Waltham, MA). All primary standards (PSTDs) for standard curves (136 individual standards) and deuterated internal standards (ISTDs) (26 deuterated standards) for eicosanoid (EIC) analysis were purchased from Cayman Chemicals (Ann Arbor, MI) or Enzo Life Sciences (Farmingdale, NY). [00140] Lipidomic Analysis of Ecosanoids. Eicosanoids were analyzed by UPLC-MS as previously described (Quehenberger et al., J. Lipid Res., 51:3299-3305, 2010; and Quenhenberger et al., J. Lipid Res., 59:2436-2445, 2018). For isolation, aliquots of 50 ul plasma samples were diluted to 900 ul with PBS and spiked with a mixture of 26 deuterated ISTDs in 100 ul of ethanol. The eicosanoids were extracted using Strata-X reversed-phase SPE columns (8B-S100-UBJ, Phenomenex). Columns were activated with 3 ml of 100% methanol and then equilibrated with 3 ml of water containing 10% ethanol. After loading the samples, the columns were washed with 10% methanol to remove impurities, and the metabolites were then eluted with 1 ml of 100% methanol and stored at -80°C to prevent metabolite degradation. Prior to analysis, the eluent was dried under vacuum and re- dissolved in 50 ul of the UPLC solvent A (water/acetonitrile/acetic acid (60:40:0.02; v/v/v)) for UPLC/MS/MS analysis. [00141] The separation of individual metabolites was performed on an Acquit UPLC system (Waters, Milford, MA), equipped with a C18 BEH shield column (2.1×100 nm; 1.7 um; Waters), as described previously (Quenhenberger et al., J. Lipid Res., 59:2436-2445, 2018). Briefly, 10 ul of purified samples were injected and separated using a binary buffer system consisting of buffer A (described above) and buffer B Attorney Docket No.00015-438WO1 composed of acetonitrile/2-propanol (50/50, v/v). At a flow rate of 0.5 ml/min, buffer A was held at 100% for 1 min followed by a gradient over 3 min to 55% buffer B, then further increased over 1.5 min to 100% buffer B and kept at this level for 0.5 min. The starting conditions were reconstituted in 1 min. The column was kept at 40°C and the samples at 4°C. [00142] The eluting metabolites were analyzed by mass spectrometry (MS). For data collection, the UPLC was interfaced with a Sciex 6500 QTRAP hybrid triple quadrupole mass spectrometer (SCIEX, Redwood City, CA). The MS was operated in the negative ionization mode using a scheduled multiple reaction monitoring (MRM) method. The source settings were as follows: curtain gas (CUR = 20 psi), nebulizer gas (GS1 = 30 psi), turbo heater gas (GS2 = 20 psi), electrospray voltage (TEM = -4,500 V), source temperature 500°C, and collision gas (CAD = medium). [00143] All eicosanoids were quantified by the stable isotope dilution method. Briefly, identical amounts of ISTDs were added to each sample and to all the PSTDs. Nine-point standard curves were generated for each of the 136 PSTDs, ranging from 0.03 ng to 10 ng. To calculate the amount of each eicosanoid in a sample, ratios of peak areas between endogenous eicosanoids and matching deuterated internal eicosanoids were calculated. Ratios were converted to absolute amounts by linear regression analysis of the standard curves. Currently, most eicosanoids are characterized at low femtomole levels. [00144] To avoid to the extent possible any degradation of eicosanoids or non-enzymatic oxidation of PUFAs during sample preparation, the samples were thawed only once and all preparations were performed immediately on ice. For this purpose, pure standards were subjected to the extraction procedure or used directly for UPLC-MS analysis. There was no observable significant differences in eicosanoid recovery and thus it was concluded that eicosanoids were not degraded or formed during the analytical process. Furthermore, a set of plasma quality control samples were analyzed over a period of three years. The samples were periodically thawed for analysis. It was found that prolonged storage over three years at -80℃ with a Attorney Docket No.00015-438WO1 single thaw cycle did not significantly affect the integrity of the sample. [00145] Statistical analysis. In all, 77 analytes were detectable in any of the control or patient samples. Of these, any metabolite that was present in less than 80% of the samples was remove. It was rationalized that if metabolites are present in less than 80% of the patient samples, they could not be used meaningfully in clinical practice. Any non-detectable values were replaced with 1/5 of the minimum value for each analyte. The remaining 28 eicosanoids and PUFAs were used for data processing. Statistical analyses were performed by MetaboAnalyst 5.0. The peak area of each metabolite was used without normalization and associations of the lipidomics features with the patient phenotype were determined by partial least squares discriminant analysis (PLS-DA). To determine the significance of the differences between the control and patient group, T-Test analysis was performed. For identifying potential biomarkers and to evaluate their performances, multivariate area under receiver operating characteristics curve (AUROC) analysis based on the Random Forest for classification and univariate AUROC for feature ranking was performed. [00146] Patient characteristics. A total of 349 individuals including 301 with MASLD and 48 normal controls without MASLD were studied (Table 9). The ages of participants with MASLD were not statistically significant than those of the controls (mean ages 52.3 vs. 51.4 years (p<0.3). The distribution of ages in the two groups was also not statistically significantly different. Although as expected, the controls had a lower BMI (30.1 vs. 34.6 kg/m, p< 0.001), 46% of the controls were obese. In contrast, 78% of MASLD subjects were obese. The prevalence of type 2 diabetes was also higher in those with MASLD (48% vs 21%, p< 0.001), as expected. [00147] Controls had normal liver enzymes and hepatic synthetic functions that were significantly different from the patients with MASLD (p< 0.001 for AST and ALT, p<0.02 for bilirubin). Amongst those with MASLD, 34 had steatosis while 44 had borderline steatohepatitis and 223 had definite steatohepatitis. About two thirds of patients with MASLD had some degree of fibrosis (as noted in Table 9); the fibrosis stages were relatively evenly distributed Attorney Docket No.00015-438WO1 between stages with 26%, 24%, 28% and 7% spread through stages 1,2,3 and 4, respectively. [00148] Eicosanoid profile in plasma. A comprehensive analysis of circulating eicosanoids was performed in plasma from controls and MASLD patients. In all, 65 eicosanoid metabolites were detectable in at least one of the samples and the distribution of their concentration in controls and in those with MASLD was calculated. Several of these metabolites were present in the plasma only at low levels and their presence in circulation was inconsistent. To increase the reliability of the individual metabolites to distinguish between normal controls and MASLD, only eicosanoids that were present in at least 80% of the patients were included. It was rationalized that if metabolites are present in less than 80% of the patient samples, they could not be used meaningfully in clinical practice. This stringency decreased the dataset to 30 analytes, which were used for all subsequent statistical analyses. (Table 10). [00149] Table 10: Eicosanoids in the plasma of healthy controls and MASLD patients. Only analytes that were present in at least 80% of the samples are shown. Analyte control (N = 48) MASLD patients (N = 301) Mean SE Mean SE P value pmol/ml pmol/ml tetranor 12-HETE 0.14 0.01 0.26 0.03 0.12 11-HETE 0.42 0.02 33.4 16.5 0.43 16 HDoHE 0.23 0.02 11.9 4.9 0..35 5-HETE 1.76 0.12 214.0 80.7 0.30 4 HDoHE 0.09 0.01 47.2 19.4 0.34 9-HOTrE 0.24 0.02 1.58 0.49 0.28 5-HETrE 0.08 0.01 4.20 1.61 0.31 15-HETE 0.53 0.02 26.1 12.0 0.40 13-HODE 19.2 1.7 84.2 24.4 0.29 15-HETrE 0.18 0.01 9.15 4.42 0.42 8-HETE 0.36 0.02 24.8 11.8 0.41 8-HETrE 0.15 0.01 9.7 4.5 0.40 12-HETE 2.22 0.34 42.1 10.9 0.15 12-HEPE 0.15 0.02 3.58 1.13 0.23 14 HDoHE 0.43 0.05 15.6 4.3 0.16 9-HODE 21.1 1.8 115.4 34.8 0.28 18-HETE 0.31 0.02 0.55 0.09 0.29 5,6-EET 0.07 0.01 0.03 0.004 <0.001 11,12-EET 0.12 0.01 0.06 0.01 <0.001 14,15-EET 0.56 0.04 1.05 0.31 0.54 Attorney Docket No.00015-438WO1 19,20-DiHDPA 2.26 0.12 2.48 0.12 0.45 5,6-diHETrE 0.48 0.03 0.58 0.07 0.55 8,9-diHETrE 0.43 0.02 0.39 0.02 0.43 11,12-diHETrE 0.79 0.04 0.98 0.04 0.06 14,15-diHETrE 0.87 0.03 0.90 0.03 0.65 9,10-diHOME 5.54 0.72 3.23 0.24 <0.001 12,13-diHOME 7.51 0.82 5.96 0.33 0.08 Arachidonic Acid 6626 347.9 7915 290.3 0.08 Adrenic Acid 1058 77.1 2013 82.4 <0.001 EPA 9110 832.8 3417 187.1 <0.001 DHA 12637 844.3 4801 153.5 <0.001 20cooh AA 23.1 2.6 10.0 0.9 <0.001 [00150] To test for collinearities between the variables and to identify characteristic patterns within the dataset, a correlation matrix was constructed (Figure 1). As can be seen, a high degree of correlation was found between specific hydroxylated fatty acids including 5-, 8-, 11-, and 15-HETE. A notable exception was 12-HETE, which did not fall into this general group but correlated with some hydroxylated w-3 fatty acids including 12-HEPE and 14-HDoHE. Additionally, most of hydroxylated fatty acids also showed significant correlation, as well as their pre-curso epoxides are derived from the cytochrome P450 pathway. [00151] Establishing a model to distinguish between MASLD and controls without MASLD. To examine if any of the changes across the eicosanoid profile are sufficient to discriminate between MASLD and controls, a supervised partial least-square discriminant analysis (PLS-DA) was performed. The score blot shown in Figure 3 indicated that the changes in the plasma eicosanoid levels were sufficient to segregate MASLD from controls (Table 1). [00152] Experiments were then performed to examine whether any of the variables correlated with MASLD. Table 3 lists the top 25 variables that correlated with the disease. Figure 2 shows that 14- HDoHE and 12-HETE showed a strong positive correlation. In contrast, several of the epoxides including 5,6-EET and 11,12-EET showed a strong negative correlation, as did several of the fatty acids. [00153] The performance of the features selected by PLS-DA was tested by AUROC analysis using the Random Forest approach (Figure 4). As can be seen, using all 32 variables, the performance is almost perfect at 0.999. Using the confusion matrix, only one control and Attorney Docket No.00015-438WO1 one MASLD patient were misclassified. The top 20 eicosanoids with the best discriminatory power to distinguish MASLD from controls were identified. [00154] Selection of a final eicosanoid panel for MASLD and clinical performance of the panel. Several of the detectable eicosanoids were present in plasma at low levels and showed considerable variabilities. Moreover, a number of eicosanoids displayed high degrees of collinearity and thus were not further considered as independent variables. Thus, the tope 15 eicosanoids that best distinguished between normal controls and MASLD were used to further develop an “Eicosanoid Lipidomics Panel” and a model to diagnose MASLD (Table 4). Their individual diagnostic test performances were assessed using AUROC (Figure 5). The panel demonstrated an AUROC of 0.999 with a confidence interval CI of 95%. The predicted class probabilities and the confusion matrix show that out of 48 controls and 301 MASLD patients, only one control was misclassified The reliability of the model was further tested by analyzing the performance of each of the 15 analytes that comprise the panel. As shown in the Figure 6, the levels of each individual analyte were significantly different between healthy controls and MASLD. [00155] Composite score for diagnostic application. To be useful for clinical applications, a composite scoring algorithm was developed that can be used to distinguish between control and MASLD. To accomplish this, the cutoff values for each of the eicosanoids in the panel that distinguish between controls and MASLD were established. Because the values of some of the metabolites, especially the fatty acids, are orders of magnitudes higher than those of the eicosanoid metabolites, it is impractical to use averages as a composite value. Small percentage differences in the plasma fatty acids that are three to four orders of magnitude more abundant than eicosanoids would skew the classification disproportionally. Moreover, some of the eicosanoids are increased in MASLD and some are increased in the controls. For these reasons a binary system was used to calculate a score that best separates controls from MASLD. [00156] To create the composite score, the absolute cutoff values for each of the eicosanoids in the panel was established. The Attorney Docket No.00015-438WO1 univariate ROC results were used that provide the cutoff values that best separate controls from MASLD (Table 1). These values were applied across the selected eicosanoid panel to generate the binary value of 0 or 1. For each sample, if the levels of any of the eicosanoids including 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14- HDoHE, 15-HETE. 15-HETrE, or adrenic acid were above the cutoff, their score was 1. Conversely, some of the eicosanoid metabolites decrease in MASLD. If any of the eicosanoids, including 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, or EPA fall below the cutoff, their score is also 1. If any of the metabolites in any of the samples did not follow this pattern, they received a score of 0. Using this algorithm, a composite score was constructed to separate controls from MASLD. The composite score is the sum total of the binary value of each of the analytes that follows this framework. Using this approach, a composite score of 6+ was optimal to diagnose MASLD (Table 6). This means that at least 6 metabolites of the 15 biomarkers must be present at concentrations indicative for MASLD to classify the sample as MASLD. Using the composite score of 6+, one can achieve a predictive accuracy of 99.4%. This stringency identifies all MASLD patients but misaligns 2 of the controls. Using a composite score of 5+ reduces the stringency and misaligns 5 controls to the MASLD group. Increasing the stringency to 7+ misclassifies 7 MASLD samples to the control category. Nonetheless, this “MASLD LIPIDOMICS SCORE” scoring system can be dynamically tuned in future studies by either adjusting the individual metabolite cutoff values and/or adjusting the composite scoring. [00157] Validation Study. To validate the findings, a separate cohort consisting of 122 MASLD samples and 30 control samples was analyzed. The characteristics by MASLD status is shown in Table 11 and the mean eicosanoid values in the previously described fifteen analyte panel are listed in Table 12. Figure 7 shows the Validation Study box plots for the 15 eicosanoids that were selected in the original study. The eicosanoid panel scoring chart for MASLD in the Validation Study is shown in Table 13. In the Validation Study, the same cutoff values were used that were determined in the original study. Using these values and the MASLD LIPIDOMICS SCORE of 6+, Attorney Docket No.00015-438WO1 there were 3 false positives among the controls and 1 false negative among the MASLD cohort. [00158] Table 11 – Validation Study subject characteristics by MASLD status (N=152) *P-value from student t-test for continuous variable and Fisher’s exact test for categorical variable. †Biopsy not done for controls. Date of lab exam – date of biopsy. Attorney Docket No.00015-438WO1 [00159] Table 12 – Validation Study Eicosanoids in the plasma of controls and MASLD patients. Only analytes that were in the selected fifteen analyte panel are shown for the Validation Study. [00160] Table 13 – Eicosanoid Panel Scoring Chart for MASLD in Validation Study. The same cutoff values were used for the Validation Study as for the original study. Attorney Docket No.00015-438WO1 [00161] Numerous modifications and variations in the invention as set forth in the above illustrative examples are expected to occur to those skilled in the art. Consequently only such limitations as appear in the appended claims should be placed on the invention.

Claims

Attorney Docket No.00015-438WO1 What is claimed: 1. A non-invasive method for detecting a presence, an absence, a type, or a stage of fatty liver disease in subject, the method comprising: a) providing or obtaining a biological sample from the subject; b) measuring the presence or level of one or more molecules in the biological sample or in a constituent of the biological sample, wherein the one or more molecules are selected from: Group A: 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and/or EPA; Group B: SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3, DG34:316:1_18:2, DG 36:418:2_18:2, PC 37:417:0/20:4, PC 38:518:1/20:4, PI 18:0/18:1, and PI 18:0/20:4; Group C: PC 16:0/16:1, PC 14:0_16:1, PI 16:0/16:1, PI 18:0/16:1, PI 16:0/16:0, SM d18:1/n20:0, SM d18:0/n18:0, SM d18:0/n22:0, 9,10-diHOME, PC 17:0/20:4, PC 18:2/20:4, PC 18:1/22:6, PI 18:0/20:4, PP-E P16:0/22:6 and 11,12-diHETrE; and/or Group D: PC 16:0/16:0, PC 14:0/20:4, PC 18:1/18:1, PC16:0/22:5, PC 18:1/22:6, PI 18:0/18:2, SM d18:0/n20:0, Cer d18:0/n16:0, Cer d18:0/n18:0, 19,20-diHDPA, SM d16:1/n22:0, and SM d18:2/n23:0, wherein Group A and B are determinative of MASLD, Group C is determinative of MASH and Group D is determinative of liver fibrosis. 2. The method of claim 1, wherein when Group A 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, or adrenic acid are above a cutoff value, their score is 1, and wherein when 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, or EPA fall below a cutoff value, their score is also 1. 3. The method of claim 1, wherein the method does not comprise collecting a biopsy from the subject. Attorney Docket No.00015-438WO1 4. The method of claim 1, wherein the subject is an animal. 5. The method of claim 1, wherein the subject is a human. 6. The method of claim 1, wherein the type of fatty liver disease comprises metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), nonalcoholic fatty liver disease (NAFLD), Nonalcoholic Steatohepatitis (NASH), alcoholic fatty liver disease (AFLD), Alcoholic Steatohepatitis (ASH), or any combination thereof. 7. The method of claim 1, wherein measuring the presence or level of the one or more molecules comprises using gas or liquid chromatography-mass spectrometry or direct-infusion mass spectrometry. 8. The method of claim 1, wherein the method is used as a non- invasive point of care diagnostic tool. 9. The method of claim 1, wherein the biological sample comprises blood plasma. 10. The method of claim 1, wherein the method comprises measuring 14-HDoHE, DHA, 12-HETE, 12-HEPE, 4HDoHE, EPA, 15-HETE, 11,12-EET, 5,6-EET, 14,15-EET, 15-HETrE, 9,10-dHOME, 5-HETE, adrenic acid and 9-HODE. 11. The method of claim 1, wherein the method comprises measuring 1 to 5 markers selected from the group consisting of 4-HDoHE, 5- HETE, 9-HODE, 12-HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and EPA. 12. A method of determining whether a subject has or is at risk of having MASLD, the method comprising measuring the level of markers selected from the group consisting of 4-HDoHE, 5-HETE, 9-HODE, 12- HEPE, 12-HETE, 14-HDoHE, 15-HETE, 15-HETrE, adrenic acid, 9,10- diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, and EPA; Attorney Docket No.00015-438WO1 wherein when 4-HDoHE, 5-HETE, 9-HODE, 12-HEPE, 12-HETE, 14- HDoHE, 15-HETE, 15-HETrE, or adrenic acid are above a cutoff value, their score is 1, and wherein when 9,10-diHOME, 11,12-EET, 5,6-EET, 14,15-EET, DHA, or EPA fall below a cutoff value, their score is also 1, summing the total of the scores wherein if the score is 6 or greater the subject is classified as having MALSD. 13. The method of claim 12, wherein the cutoff values are as set forth in Table 1. 14. The method of claim 12, further comprising measuring complex lipids selected from the group consisting of SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3, DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, and PI 18:0/20:4, wherein when SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3 are above a cutoff value, their score is 1, and wherein when DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, PI 18:0/20:4 fall below a cutoff value, their score is also 1, summing the total of the scores wherein if the score is 6 or greater the subject is further classified as having MALSD. 15. The method of claim 12, wherein the cutoff values for the complex lipids are set forth in Table 2B. 16. The method of claim 12, wherein the method does not comprise collecting a biopsy from the subject. 17. The method of claim 12, wherein the subject is an animal. 18. The method of claim 12, wherein the subject is a human. 19. The method of claim 12, wherein measuring the presence or level of the one or more molecules comprises using gas or liquid Attorney Docket No.00015-438WO1 chromatography-mass spectrometry or direct-infusion mass spectrometry. 20. The method of claim 12, wherein the method is used as a non- invasive point of care diagnostic tool. 21. The method of claim 12, wherein the biological sample comprises blood plasma. 22. A method of determining whether a subject has or is at risk of having MASLD, the method comprising measuring the level of markers selected from the group consisting of SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3, DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, and PI 18:0/20:4, wherein when SM d18:0/n18:0, Cer d18:0/n22:0, TG 16:0_16:0_18:2, TG 18:0_18:0_18:0, TG 18:0_18:1_18:2, DG 16:0_18:0, DG 18:0_18:0, CE 18:2, CE 20:3 are above a cutoff value, their score is 1, and wherein when DG 16:1_18:2, DG 18:2_18:2, PC 17:0/20:4, PC 18:1/20:4, PI 18:0/18:1, PI 18:0/20:4 fall below a cutoff value, their score is also 1, summing the total of the scores wherein if the score is 6 or greater the subject is classified as having MALSD. 23. The method of claim 22, wherein the cutoff values for the complex lipids are set forth in Table 2B. 24. The method of claim 22, wherein the method does not comprise collecting a biopsy from the subject. 25. The method of claim 22, wherein the subject is an animal. 26. The method of claim 22, wherein the subject is a human. 27. The method of claim 22, wherein measuring the presence or level of the one or more molecules comprises using gas or liquid Attorney Docket No.00015-438WO1 chromatography-mass spectrometry or direct-infusion mass spectrometry. 28. The method of claim 22, wherein the method is used as a non- invasive point of care diagnostic tool. 29. The method of claim 22, wherein the biological sample comprises blood plasma. 30. A method of determining a NAS score for a subject, the method comprising measuring a panel of markers including PC 16:0/16:1, PC 14:0_16:1, PI 16:0/16:1, PI 18:0/16:1, PI 16:0/16:0, SM d18:1/n20:0, SM d18:0/n18:0, SM d18:0/n22:0, 9,10-diHOME, PC 17:0/20:4, PC 18:2/20:4, PC 18:1/22:6, PI 18:0/20:4, PP-E P16:0/22:6 and 11,12- diHETrE, wherein when PC 16:0/16:1, PC 14:0_16:1, PI 16:0/16:1, PI 18:0/16:1, PI 16:0/16:0, SM d18:1/n20:0, SM d18:0/n18:0, SM d18:0/n22:0, and 9,10-diHOME are above a cutoff value in Table 6 each marker above the cutoff is assigned a score of 1 and wherein when PC 17:0/20:4, PC 18:2/20:4, PC 18:1/22:6, PI 18:0/20:4, PP-E P 16:0/22:6 and 11,12-diHETrE are below a cutoff value in Table 6 each marker below the cutoff is assigned a score of 1, wherein when the total score is 6 or more the subject has a high NAS Score. 31. A method of determining if a subject has or is at risk of having liver fibrosis, the method comprising measuring a panel of markers including PC 16:0/16:0, PC 14:0/20:4, PC 18:1/18:1, PC16:0/22:5, PC 18:1/22:6, PI 18:0/18:2, SM d18:0/n20:0, Cer d18:0/n16:0, Cer d18:0/n18:0, 19,20-diHDPA, SM d16:1/n22:0, and SM d18:2/n23:0. 32. The method of claim 31, wherein when PC 16:0/16:0, PC 14:0/20:4, PC 18:1/18:1, PC 16:0/22:5, PC 18:1/22:6, PI 18:0/18:2, SM d18:0/n20:0, Cer d18:0/n16:0, Cer d18:0/n18:0, and 19,20-diHDPA are above a cutoff value as set forth in Table 8 each marker above the cutoff is scored as 1, and wherein when each of SM d16:1/n22:0, and SM d18:2/n23:0 is below the cutoff value of Table 8 each marker Attorney Docket No.00015-438WO1 is scored as 1, such that when the total score is 7 or greater the subject has or is at risk of having liver fibrosis. 33. The method of claim 1, 12, 22, 31 or 32, wherein the subject having MASLD, MASH, high NAS or fibrosis is treated with a GLP-1 agonist, resmetirom and/or bariatric surgery.
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