Methods for determining whether a subject has or is at risk for developing a disease or condition

Abstract

Disclosed are methods for determining whether a subject has or is at risk for developing a disease or condition. Also disclosed are methods for determining whether a subject is at risk for developing an infectious disease or a complication thereof.

Description

[Technical Field]

[0001] The present disclosure relates generally to methods for determining whether a subject has or is at risk for developing a disease or condition. [Background technology]

[0002] Various biomarkers may be useful for predicting whether a subject has a particular disease or condition, or whether they are at risk of developing that disease or condition. Such biomarkers may be measured from various biological samples, for example, from blood samples or other biological fluids. However, when obtaining quantitative measurements of biomarkers, certain sample types may be more complicated than others. Also, for example, biomarkers may be needed to predict serious diseases that lead to hospitalization or even death, such as serious infectious diseases and their complications, such as sepsis, pneumonia, and other lower respiratory tract infections. Summary of the Invention [Means for solving the problem]

[0003] Disclosed is a method for determining whether a subject has or is at risk for developing a disease or condition, the method comprising: determining a quantitative value of at least one biomarker in a biological sample obtained from the subject relative to a quantitative value of albumin in the biological sample; comparing the quantitative value of said at least one biomarker with a control sample or control value; and An increase or decrease in the quantitative value of the at least one biomarker compared to the control sample or control value indicates that the subject has or is at increased risk of developing the disease or condition.

[0004] Also disclosed is a method for determining whether a subject is at risk for developing an infectious disease or a complication thereof, the method comprising: In the biological sample obtained from the subject, Glycoprotein acetyl ·albumin, Omega-6 fatty acids, Monounsaturated fatty acids, ·Saturated fatty acids, Omega-3 fatty acids, Apolipoprotein A1 (ApoA1), Docosahexaenoic acid (DHA), Leucine, Acetic acid, Alanine, Apolipoprotein B (ApoB), ·glutamine, Isoleucine, Linoleic acid, Phenylalanine, Tyrosine, -Unsaturation degree of fatty acids, Balin, histidine, Cholesterol in HDL (HDL-C), ·glucose, Acetoacetic acid, 3-hydroxybutyric acid, LDL cholesterol (LDL-C), Lactic acid, · Triglycerides in LDL (LDL-TG), pyruvate, or Cholesterol in VLDL (VLDL-C) determining a quantitative value of at least one biomarker among comparing the quantitative value of said at least one biomarker with a control sample or control value; and An increase or decrease in the quantitative value of the at least one biomarker compared to the control sample or control value indicates that the subject is at increased risk of developing the infectious disease or a complication thereof. [Brief explanation of the drawings]

[0005] The accompanying drawings, which are included to provide a further understanding of the embodiments and constitute a part of this specification, illustrate various embodiments.

[0006] [Figure 1a] FIG. 1a shows the relationship between baseline biomarker levels and future development of type 2 diabetes when the biomarkers are analyzed in absolute concentrations and when scaled relative to the concentration of albumin. [Figure 1b] Figure 1b shows the relationship between baseline biomarker levels and future development of severe pneumonia when the biomarkers were analyzed in absolute concentrations and when scaled relative to the concentration of albumin. [Figure 2a] FIG. 2a shows the relationship between baseline biomarker levels and future development of pneumonia (severe or non-severe) when biomarker concentrations were analyzed as absolute concentrations. [Figure 2b] Figure 2b shows the relationship between baseline biomarker levels and future development of other lower respiratory diseases (acute bronchitis, acute bronchiolitis, and unspecified acute lower respiratory infections) when biomarker concentrations were analyzed as absolute concentrations. [Figure 2c] Figure 2c shows the relationship between baseline biomarker levels and future development of sepsis when biomarker concentrations were analyzed in absolute concentrations. [Figure 3a] Figure 3a shows the relationship between baseline biomarker levels for three multi-biomarker scores and future development of severe pneumonia for the entire study population and for the following subgroups of individuals: study participants without chronic respiratory or cardiometabolic disease at baseline, in different age groups (divided into age tertiles of the study population), and by gender. The relationship between baseline biomarker levels for the three different multi-biomarker scores and severe pneumonia occurring within two years of the blood sample being taken is also shown. [Figure 3b]Figure 3b shows the risk gradient of future severe pneumonia by percentile of the three multi-biomarker scores (top half of the figure) and the cumulative risk for severe pneumonia after blood samples were drawn at various quantiles of the three multi-biomarker scores (bottom half of the figure). DETAILED DESCRIPTION OF THE INVENTION

[0007] In one aspect, a method of determining whether a subject has or is at risk for developing a disease or condition is disclosed.

[0008] This method is determining a quantitative value of at least one biomarker in a biological sample obtained from the subject relative to a quantitative value of albumin in the biological sample; comparing the quantitative value of said at least one biomarker with a control sample or control value; and An increase or decrease in the quantitative value of the at least one biomarker compared to the control sample or control value indicates that the subject has or is at increased risk of developing the disease or condition.

[0009] In a second aspect, a method for determining whether a subject is at risk for developing an infectious disease or a complication thereof is disclosed, the method comprising: In the biological sample obtained from the subject, Glycoprotein acetyl ·albumin, Omega-6 fatty acids, Monounsaturated fatty acids, ·Saturated fatty acids, Omega-3 fatty acids, Apolipoprotein A1 (ApoA1), Docosahexaenoic acid (DHA), Leucine, Acetic acid, Alanine, Apolipoprotein B (ApoB), ·glutamine, Isoleucine, Linoleic acid, Phenylalanine, Tyrosine, -Unsaturation degree of fatty acids, Balin, histidine, Cholesterol in HDL (HDL-C), ·glucose, Acetoacetic acid, 3-hydroxybutyric acid, LDL cholesterol (LDL-C), Lactic acid, · Triglycerides in LDL (LDL-TG), pyruvate, or Cholesterol in VLDL (VLDL-C) determining a quantitative value of at least one biomarker (or a plurality of biomarkers, e.g., at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or all) of the above; comparing the quantitative value of said at least one biomarker with a control sample or control value; and An increase or decrease in the quantitative value of the at least one biomarker compared to the control sample or control value indicates that the subject is at increased risk of developing the infectious disease or a complication thereof.

[0010] Any embodiment described below may be understood as relating to either (or both) of the above aspects.

[0011] The biological sample may be a dried blood sample, a dried sample obtainable from blood, or a biological sample obtainable from a dried blood sample, although in the context of this specification, the biological sample does not necessarily have to be a dried blood sample or obtainable from a dried blood sample, as described in more detail below.

[0012] The dried blood sample may be a dried whole blood sample. However, this term may be understood to encompass, at least in some embodiments, dried samples containing serum and / or plasma (not necessarily whole blood). The term "dried blood sample" or "dried sample obtainable from blood" may, in some embodiments, refer to a dried sample obtainable from separated blood.

[0013] The dried blood sample or dried sample obtainable from blood may be obtainable from a subject's fingertip, from sampling capillary blood from a subject's upper arm, and / or by venipuncture of a subject, although there may be additional or alternative locations and / or methods of obtaining blood.

[0014] The method may include determining quantitative values ​​of a plurality of biomarkers in the biological sample. Accordingly, any reference herein to "at least one biomarker" may be understood to refer to a plurality of biomarkers. For example, a plurality of biomarkers may include 2, 3, 4, 5, 6, 7, 8, 9, 10, or more (i.e., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10) biomarkers. Accordingly, the term "plurality of biomarkers" may be understood herein to refer to any number (more than one) of biomarkers. Accordingly, the term "plurality of biomarkers" may be understood to refer to any number (more than one) of the biomarkers described herein and / or a combination or subset of the biomarkers described herein. Determining a plurality of biomarkers may increase the accuracy of prediction of whether a subject has or is at risk for developing a disease or condition. Generally, the greater the number of biomarkers, the more accurate or predictive the method may be. The multiple biomarkers may be measured from the same biological sample or from separate biological samples, using the same analytical method or different analytical methods, hi one embodiment, the multiple biomarkers may be a panel of multiple biomarkers.

[0015] In at least one embodiment, in the context of the present specification, the phrase "comparing the quantitative value of a biomarker to a control sample or control value" may be understood as referring to comparing the quantitative value(s) of the biomarker(s) individually or as a plurality of biomarkers (e.g., when a risk score is calculated from quantitative values ​​of multiple biomarkers) to the control sample or control value(s), depending, for example, on whether a quantitative value of a single (individual) biomarker or a plurality of biomarkers is determined, as will be understood by one of skill in the art.

[0016] In one embodiment, the method comprises: In a biological sample obtained from a subject, the following is performed on the quantitative value of albumin in the biological sample: Glycoprotein acetyl Omega-6 fatty acids, Monounsaturated fatty acids, ·Saturated fatty acids, Omega-3 fatty acids, Apolipoprotein A1 (ApoA1), Docosahexaenoic acid (DHA), Leucine, Acetic acid, Alanine, Apolipoprotein B (ApoB), ·glutamine, Isoleucine, Linoleic acid, Phenylalanine, Tyrosine, -Unsaturation degree of fatty acids, Balin, histidine, Cholesterol in HDL (HDL-C), ·glucose, Acetoacetic acid, 3-hydroxybutyric acid, LDL cholesterol (LDL-C), Lactic acid, · Triglycerides in LDL (LDL-TG), pyruvate, or Cholesterol in VLDL (VLDL-C) The method includes determining the quantitative value of at least one biomarker (or a plurality of biomarkers, e.g., at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or all) of the above.

[0017] In one embodiment, the method comprises determining a quantitative value of glycoprotein acetyl.

[0018] In one embodiment, the method comprises determining a quantitative value of albumin.

[0019] In one embodiment, the method comprises determining a quantitative value of omega-6 fatty acids.

[0020] In one embodiment, the method comprises determining a quantitative value of monounsaturated fatty acids.

[0021] In one embodiment, the method comprises determining a quantitative value of saturated fatty acids.

[0022] In one embodiment, the method comprises determining a quantitative value of an omega-3 fatty acid.

[0023] In one embodiment, the method comprises determining a quantitative value of apolipoprotein A1 (ApoA1).

[0024] In one embodiment, the method comprises determining a quantitative value of docosahexaenoic acid (DHA).

[0025] In one embodiment, the method comprises determining a quantitative value of leucine.

[0026] In one embodiment, the method includes determining a quantitative value of acetic acid.

[0027] In one embodiment, the method comprises determining a quantitative value of alanine.

[0028] In one embodiment, the method comprises determining a quantitative value of apolipoprotein B (ApoB).

[0029] In one embodiment, the method includes determining a quantitative value of glutamine.

[0030] In one embodiment, the method comprises determining a quantitative value of isoleucine.

[0031] In one embodiment, the method comprises determining a quantitative value of linoleic acid.

[0032] In one embodiment, the method comprises determining a quantitative value of phenylalanine.

[0033] In one embodiment, the method comprises determining a quantitative value of tyrosine.

[0034] In one embodiment, the method comprises determining a quantitative value of the degree of unsaturation of the fatty acid.

[0035] In one embodiment, the method includes determining a quantitative value of valine.

[0036] In one embodiment, the method comprises determining a quantitative value of histidine.

[0037] In one embodiment, the method comprises determining a quantitative value of cholesterol in HDL (HDL-C).

[0038] In one embodiment, the method includes determining a quantitative value of glucose.

[0039] In one embodiment, the method includes determining a quantitative value of acetoacetic acid.

[0040] In one embodiment, the method comprises determining a quantitative value of 3-hydroxybutyrate.

[0041] In one embodiment, the method comprises determining a quantitative value of cholesterol in LDL (LDL-C).

[0042] In one embodiment, the method includes determining a quantitative value of lactate.

[0043] In one embodiment, the method comprises determining a quantitative value of triglycerides in LDL (LDL-TG).

[0044] In one embodiment, the method includes determining a quantitative value of pyruvate.

[0045] In one embodiment, the method comprises determining a quantitative value of cholesterol in VLDL (VLDL-C).

[0046] In one embodiment, the method comprises: In a biological sample obtained from a subject, the following is performed on the quantitative value of albumin in the biological sample: Omega-6 fatty acids, Monounsaturated fatty acids, saturated fatty acids, or Omega-3 fatty acids The method includes determining the quantitative value of at least one biomarker (or a plurality of biomarkers, for example, at least two, or at least three, or at least four, i.e., all) of the above.

[0047] In one embodiment, the method comprises: In a biological sample obtained from a subject, the following is performed on the quantitative value of albumin in the biological sample: Apolipoprotein A1 (ApoA1), Docosahexaenoic acid (DHA), Leucine, Acetic acid, Alanine, Apolipoprotein B (ApoB), ·glutamine, Isoleucine, Linoleic acid, Phenylalanine, Tyrosine, -Unsaturation degree of fatty acids, Valine, or Histidine The method includes determining the quantitative value of at least one biomarker (or a plurality of biomarkers, e.g., at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, i.e., all) of the above.

[0048] In one embodiment, the method comprises: In a biological sample obtained from a subject, the following is performed on the quantitative value of albumin in the biological sample: Cholesterol in HDL (HDL-C), ·glucose, Acetoacetic acid, 3-hydroxybutyric acid, LDL cholesterol (LDL-C), Lactic acid, · Triglycerides in LDL (LDL-TG), pyruvate, or Cholesterol in VLDL (VLDL-C) The method includes determining the quantitative value of at least one biomarker (or a plurality of biomarkers, e.g., at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, i.e., all) among the above.

[0049] In one embodiment, particularly of the second aspect, the method comprises: determining a quantitative value of glycoprotein acetyl in a biological sample obtained from the subject; and / or In a biological sample obtained from a subject, Omega-6 fatty acids, Monounsaturated fatty acids, saturated fatty acids, or Omega-3 fatty acids and / or determining the quantitative value of at least one biomarker of In a biological sample obtained from a subject, Apolipoprotein A1 (ApoA1), Docosahexaenoic acid (DHA), Leucine, Acetic acid, Alanine, Apolipoprotein B (ApoB), ·glutamine, Isoleucine, Linoleic acid, Phenylalanine, Tyrosine, -Unsaturation degree of fatty acids, Valine, or Histidine and / or determining the quantitative value of at least one biomarker of In a biological sample obtained from a subject, Cholesterol in HDL (HDL-C), ·glucose, Acetoacetic acid, 3-hydroxybutyric acid, LDL cholesterol (LDL-C), Lactic acid, · Triglycerides in LDL (LDL-TG), pyruvate, or Cholesterol in VLDL (VLDL-C) The method includes determining a quantitative value of at least one biomarker among the above.

[0050] The subject may be a human. Additionally or alternatively, the subject may be an animal, such as a mammal, such as a non-human primate, dog, cat, horse, sheep, goat, cow, rabbit, pig, and / or rodent, such as a mouse or rat, or any other species.

[0051] In the context of this specification, the term "biomarker" may refer to a biomarker, e.g., a chemical or molecular marker, that may be found to be associated with a disease or condition or the risk of having or developing that disease or condition. It does not necessarily refer to a biomarker that has been statistically well validated as having specific validity in a clinical setting. A biomarker may be a metabolite, compound, lipid, protein, moiety, functional group, composition, combination of two or more metabolites and / or compounds, their (measurable or measured) amounts, ratios or other values ​​derived therefrom, or in principle any measurement that reflects a chemical and / or biological component that may be found to be associated with a disease or condition or the risk of having or developing it. Biomarkers and any combinations thereof, optionally combined with additional measures, may be used to suggest or measure biological processes specific to and / or indicative of a particular disease or condition, e.g., infectious disease or its complications, sepsis, pneumonia, other lower respiratory infections, or diabetes, or the risk of developing same. Any "ratio" of two biomarkers, or of at least one biomarker, to albumin may refer to the ratio of the quantitative values ​​of the biomarkers, or the ratio of the quantitative value of the biomarker to the quantitative value of albumin, or the ratio of the quantitative value of the biomarker to another measure (e.g., the quantitative value of a metabolite or compound, or the total quantitative value of metabolites or compounds of the same class), or any combination thereof.

[0052] When the quantitative value of at least one biomarker is determined relative to the quantitative value of albumin in a biological sample, it may be possible to obtain a relatively accurate quantitative value of the at least one biomarker. In other words, the (initial) quantitative value of the at least one biomarker (or the (initial) quantitative values ​​of multiple biomarkers) may be normalized to the quantitative value of albumin or scaled to the quantitative value of albumin. This may also be possible when there is a potential dilution effect, for example, when using a dried blood sample that is subsequently diluted before determining the quantitative value of the at least one biomarker, and / or when the source of the sample can be such that a dilution effect can occur, for example, when using a blood sample that can be obtained from a subject's fingertip. A fingertip sample may contain additional fluids, such as interstitial fluid, in addition to the blood collected from the fingertip, the relative volumes of which may be difficult to control.

[0053] It may therefore be possible to accurately measure at least one biomarker from a dried sample, such as a dried blood sample, and the ratio of such measures may be considered to correspond well with values ​​obtained from venous blood.

[0054] However, in some embodiments, particularly certain embodiments according to the second aspect, it may be possible to determine the quantitative value of at least one biomarker relative to the quantitative value of a scaling biomarker (other than albumin) in the biological sample. Alternatively or additionally, certain other markers may also be useful for normalizing or scaling the quantitative value of the at least one biomarker.

[0055] The quantitative value of the at least one biomarker relative to the quantitative value of albumin in the biological sample may be determined, for example, by calculating the ratio of the initial quantitative value of the at least one biomarker in the biological sample to the quantitative value of albumin. Additional mathematical transformations may also be used, for example, for presentation of the results, for example, to a subject.

[0056] The method may further include determining whether the subject has or is at risk of developing a disease or condition using a calculated risk score, hazard ratio, and / or predicted absolute risk based on the quantitative value of at least one biomarker or plurality of biomarkers relative to the quantitative value of albumin.

[0057] This risk score and / or hazard ratio and / or predicted absolute risk may be calculated based on any multiple, combination or subset of the biomarkers described herein.

[0058] The risk score and / or hazard ratio and / or predicted absolute risk may be calculated, for example, as shown in the following examples. For example, using an appropriate method, multiple biomarkers measured, for example, using NMR spectroscopy, may be combined using a regression algorithm and multivariate analysis, and / or machine learning analysis. Prior to regression analysis or machine learning, any missing values ​​in the biomarkers may be imputed with the average value of each biomarker for the data set. Several biomarkers, for example, 10, that may be most associated with the development of a disease or condition may be selected for use in the prediction model. Other modeling approaches may be used to calculate risk scores and / or hazard ratios and / or predicted absolute risks based on a subset of individual biomarkers, i.e., multiple biomarkers.

[0059] An increased or decreased risk score, hazard ratio, odds ratio, and / or predicted absolute and / or relative risk may indicate that the subject is at increased risk of developing the disease or condition.

[0060] In the context of this specification, the terms "glycoprotein acetyl," "glycoprotein acetylation," or "GlycA" may refer to the abundance of circulating glycosylated proteins and / or nuclear magnetic resonance spectroscopy (NMR) signals that represent the abundance of circulating glycosylated proteins, i.e., N-acetylated glycoproteins. Glycoprotein acetyl may include signals from multiple different glycoproteins, including, for example, alpha-1-acid glycoprotein, alpha-1 antitrypsin, haptoglobin, transferrin, and / or alpha-1 antichymotrypsin. Glycoprotein acetyl and methods for measuring them are described, for example, in Kettunen et al., 2018, Circ Genom Precis Med. 11:e002234 and Soininen et al., 2009, Analyst 134, 1781-1785.

[0061] In the present context, the term "albumin" may be understood to refer to serum albumin (often referred to as blood albumin). This is albumin found in the blood of vertebrates. For example, human albumin (human serum albumin) is encoded by the ALB gene. Serum albumin is the most abundant blood protein in mammals. Albumin is a globular, water-soluble, non-glycosylated serum protein with an approximate molecular weight of 65,000 daltons. Measurement of albumin using NMR has been described, for example, in publications by Kettunen et al., 2012, Nature Genetics 44, 269-276; Soininen et al., 2015, Circulation: Cardiovascular Genetics 8, 192-206 (DOI: 10.1161 / CIRCGENETICS.114.000216) and Wuertz et al., 2017, American Journal of Epidemiology 186(9), 1084-1096 (DOI: 10.1093 / aje / kwx016). Albumin may also be measured by various other methods, for example, in clinical settings. Examples of such methods may include dye-binding methods such as bromocresol green and bromocresol purple.

[0062] In the present context, the term "HDL" refers to high density lipoprotein.

[0063] In the present context, the term "LDL" refers to low density lipoprotein.

[0064] In the present context, the term "VLDL" refers to very low density lipoproteins.

[0065] In the present context, the term "omega-6 fatty acid" may refer to all omega-6 fatty acids. Omega-6 fatty acids are polyunsaturated fatty acids. In omega-6 fatty acids, the last double bond in the fatty acid chain is the sixth bond counting from the methyl end.

[0066] In the present context, the term "monounsaturated fatty acids" (MUFA) may refer to all monounsaturated fatty acids. Monounsaturated fatty acids have one double bond in their fatty acid chain. MUFA may include (mainly) omega-9 and omega-7 fatty acids. Oleic acid (18:1ω-9), palmitoleic acid (16:1ω-7), and cis-vaccenic acid (18:1ω-7) are examples of common MUFAs in human serum.

[0067] In the present context, the term "saturated fatty acids" (SFA) may refer to all saturated fatty acids. Saturated fatty acids may be or may include fatty acids that do not have double bonds in their structure. Palmitic acid (16:0) and stearic acid (18:0) are examples of abundant SFAs in human serum.

[0068] In the present context, the term "omega-3 fatty acid" may refer to all omega-3 fatty acids. Omega-3 fatty acids are polyunsaturated fatty acids. In omega-3 fatty acids, the last double bond in the fatty acid chain is the third bond counting from the methyl end.

[0069] For any or all fatty acid indicators, including omega-3, DHA, LA, MUFA, SFA, this fatty acid indicator, e.g., total omega-6 fatty acids, includes blood (or serum / plasma) free fatty acids, bound fatty acids, and esterified fatty acids, which may be esterified to glycerol, e.g., triglycerides, diglycerides, monoglycerides, or phosphoglycerides, or to cholesterol, e.g., cholesterol esters.

[0070] In the context of this specification, the term "apolipoprotein" may refer to apolipoprotein molecules that are amphipathic proteins and are important structural components in the surface region of lipoprotein particles. They may include apolipoprotein B (ApoB) and apolipoprotein A1 (ApoA1).

[0071] In the context of this specification, the term "degree of unsaturation of fatty acids" may be understood as referring to the number of double bonds in all fatty acids, for example the average number of double bonds in all fatty acids.

[0072] In the context of this specification, the phrase "cholesterol in HDL", or cholesterol in any other lipoprotein class such as LDL or VLDL, may be understood as referring to the total cholesterol in said lipoprotein class or subfraction.

[0073] In the present context, the term "quantitative value" may refer to any quantitative value that characterizes the amount and / or concentration of a biomarker. For example, it may be the amount or concentration of a biomarker in a biological sample, or a signal derived from nuclear magnetic spectroscopy (NMR) or other methods suitable for quantitatively detecting a biomarker. Such a signal may indicate or correlate with the amount or concentration of a biomarker. A quantitative value may also be a quantitative value calculated from one or more signals derived from an NMR or other measurement. A quantitative value may additionally or alternatively be measured using various techniques. Such methods may include mass spectrometry (MS), gas chromatography combined with MS, high-performance liquid chromatography alone or combined with MS, immunoturbidimetry, ultracentrifugation, ion mobility, enzymatic analysis, colorimetric or fluorometric analysis, immunoblot analysis, immunohistochemical methods (e.g., in situ methods based on antibody detection of metabolites), and immunoassays (e.g., ELISA). Examples of various methods are provided below. The method used to determine the quantitative value(s) in a subject must be the same as the method used to determine the quantitative value(s) in a control subject(s) or control sample(s).

[0074] In the present context, the term "quantitative value of albumin" may refer to any quantitative value that characterizes the amount and / or concentration of albumin. For example, this quantitative value of albumin may be the amount or concentration of albumin in a biological sample, or may be a signal derived from nuclear magnetic spectroscopy (NMR) or other methods suitable for quantitatively detecting albumin. Such a signal may indicate or correlate with the amount or concentration of albumin. The quantitative value of albumin may also be a quantitative value calculated from one or more signals derived from NMR or other measurements. The quantitative value may additionally or alternatively be measured using various techniques. Such methods may include mass spectrometry (MS), gas chromatography combined with MS, high-performance liquid chromatography alone or combined with MS, immunoturbidimetry, ultracentrifugation, ion mobility, enzymatic analysis, colorimetric or fluorometric analysis, immunoblot analysis, immunohistochemical methods (e.g., in situ methods based on antibody detection), and immunoassays (e.g., ELISA). Examples of various methods are provided below. The method used to determine the quantitative value(s) in a subject must be the same as the method used to determine the quantitative value(s) in a control subject(s) or control sample(s).

[0075] In the context of this specification, the term "control subject" may refer to a subject who is known to be free of a disease or condition and / or who is known to be not at risk of having or developing a disease or condition. A control subject may be a matched control subject.

[0076] The disease may be an infectious disease or a complication thereof.

[0077] In one embodiment, the disease is a serious infectious disease, a serious infection or a (serious) complication thereof.

[0078] In one embodiment, the infectious disease or complication thereof is sepsis.

[0079] In one embodiment, the infectious disease or complication thereof is pneumonia.

[0080] In one embodiment, the infectious disease or complication thereof is another lower respiratory disease (ie, a lower respiratory tract disease).

[0081] In the context of this specification, the term "pneumonia" may be understood to refer to inflammation of one or both of a subject's lungs. Pneumonia may be caused by, for example, bacterial, fungal, and / or viral infection. The infection may involve inflammation of the air sacs in one or both of the subject's lungs. The signs and symptoms of pneumonia may vary from mild to severe or potentially life-threatening, depending on factors such as the type of microorganism causing the infection, the age and / or overall health of the subject. Pneumonia is the leading cause of death from infectious diseases worldwide.

[0082] In one embodiment, the pneumonia is severe pneumonia, which can include or result in, for example, hospitalization and / or death.

[0083] In one embodiment, the method comprises determining a quantitative value of glycoprotein acetyl.

[0084] Glycoprotein acetyl may be useful for determining whether a subject is at risk for developing an infectious disease or a complication thereof, such as sepsis, pneumonia, including severe pneumonia, or other lower respiratory tract infections.

[0085] In one embodiment, the disease or complication thereof is diabetes, such as type 2 diabetes.

[0086] The method may further include determining whether the subject is at risk of developing an infectious disease or a complication thereof using the risk score, hazard ratio, and / or predicted absolute risk calculated based on the quantitative values ​​of the at least one biomarker or plurality of biomarkers.

[0087] This risk score, hazard ratio, and / or predicted absolute risk may be calculated based on any multiple, combination, or subset of the biomarkers described herein.

[0088] Risk scores, hazard ratios, and / or predicted absolute risks may be calculated, for example, as shown in the following examples. For example, using an appropriate method, multiple biomarkers measured, for example, using NMR spectroscopy, may be combined using a regression algorithm and multivariate analysis, and / or machine learning analysis. Prior to regression analysis or machine learning, any missing values ​​in the biomarkers may be imputed with the average value of each biomarker for the data set. Several biomarkers, for example, 10, that may be most associated with the development of a disease or condition may be selected for use in the predictive model. Other modeling approaches may be used to calculate risk scores, hazard ratios, and / or predicted absolute risks based on a subset of individual biomarkers, i.e., multiple biomarkers.

[0089] In one embodiment, the method for determining whether a subject has or is at risk for developing pneumonia comprises: determining a quantitative value of at least one biomarker in a biological sample obtained from the subject relative to a quantitative value of albumin in the biological sample; comparing the quantitative value of said at least one biomarker with a control sample or control value; Including, An increase or decrease in the quantitative value of the at least one biomarker as compared to the control sample or control value indicates that the subject has or is at high risk of developing pneumonia.

[0090] In one embodiment of the above embodiments, the at least one biomarker may include or be glycoprotein acetyl.

[0091] In one embodiment of the above aspect, the biological sample may be a dried blood sample or may be obtainable from a dried blood sample, however, the biological sample may alternatively or additionally be any other biological sample, such as any biological sample described herein.

[0092] The quantitative or initial quantitative value of at least one biomarker or biomarkers, and / or the quantitative value of albumin, can be determined by nuclear magnetic spectroscopy (NMR), e.g. 1 The biomarkers may be measured using H-NMR. At least one additional biomarker or multiple additional biomarkers may also be measured using NMR. NMR may provide a particularly efficient and rapid method for simultaneously measuring biomarkers, including multiple biomarkers, and can provide quantitative values ​​for them. Moreover, NMR typically requires little sample pretreatment or preparation. Biomarkers measured by NMR can be effectively measured on large samples using assays for blood (serum or plasma) NMR metabolomics previously published by Soininen et al., 2015, Circulation: Cardiovascular Genetics 8, 192-206 (DOI: 10.1161 / CIRCGENETICS.114.000216), Soininen et al., 2009, Analyst 134, 1781-1785, and Wuertz et al., 2017, American Journal of Epidemiology 186(9), 1084-1096 (DOI: 10.1093 / aje / kwx016). This provides data on 228 biomarkers per sample, as described in detail in the above scientific papers.

[0093] In one embodiment, the (initial) quantitative value of at least one biomarker and / or the quantitative value of albumin is determined using nuclear magnetic spectroscopy.

[0094] However, quantification of the various biomarkers described herein may also be performed by techniques other than NMR, for example, mass spectrometry (MS), enzymatic methods, antibody-based detection methods, or other biochemical or chemical methods may be contemplated, depending on the biomarker.

[0095] For example, glycoprotein acetylation can be measured or approximated by immunoturbidimetric assays of alpha-1-acid glycoprotein, haptoglobin, alpha-1-antitrypsin, and transferrin (e.g., as described in Ritchie et al., 2015, Cell Syst. 28;1(4):293-301).

[0096] For example, monounsaturated fatty acids and polyunsaturated fatty acids can be quantified (i.e., their quantitative values ​​may be determined) by serum total fatty acid composition using gas chromatography (e.g., as described in Jula et al., 2005, Arterioscler Thromb Vase Biol 25, 1952-1959).

[0097] Cholesterol in lipoprotein fractions can be quantified using high performance liquid chromatography.

[0098] Apolipoprotein B, apolipoprotein A1, and glucose can be quantified by an enzymatic clinical chemistry analyzer such as the ROCHE COBAS 6000.

[0099] In the context of this specification, the term "sample" or "biological sample" may refer to any biological sample obtained from a subject or a group or population of subjects. Samples may be fresh, frozen, or dried.

[0100] The biological sample (particularly in embodiments in which the biological sample is not a dried blood sample) may include or be, for example, a blood sample, a plasma sample, a serum sample, or a sample derived therefrom. The biological sample may be, for example, a fasting blood sample, a fasting plasma sample, a fasting serum sample, or a fraction obtainable therefrom. However, the biological sample need not necessarily be a fasting sample. The blood sample may also be a venous blood sample.

[0101] The dried blood sample may be a dried whole blood sample, a dried plasma sample, a dried serum sample, or a dried sample derived therefrom.

[0102] Some of the biomarkers may be determined from bodily fluids other than blood or fractions obtainable from blood. The biological sample may additionally or alternatively include or be a sample or one or more other bodily or biological fluids, such as an amniotic fluid sample, a urine sample, a saliva sample, a bile sample, a tear sample, and / or a spinal fluid sample.

[0103] The method may include obtaining a biological sample from a subject before determining the quantitative value of at least one biomarker. Collecting blood or tissue samples from a subject or patient is part of normal clinical practice. The collected blood or tissue sample can be prepared and serum or plasma separated using techniques well known to those skilled in the art. Methods for separating one or more fractions from a biological sample, such as a blood or tissue sample, are also available to those skilled in the art. The term "fraction," in the context of this specification, may also refer to a portion or component of a biological sample separated according to one or more physical properties, such as solubility, hydrophilicity or hydrophobicity, density, or molecular size.

[0104] The dried blood sample can in principle be any dried blood sample.

[0105] The dried blood sample may be a dried blood spot. The dried blood sample may be provided as a dried blood spot on filter paper or other substrate or medium capable of absorbing blood, for example, for NMR analysis. Examples of other substrates or media may include or be, for example, various fibers and / or resins. The above process involves providing a sample, for example, applying (placing, absorbing) blood, which may be obtained by either venipuncture or finger stick, onto paper, such as filter paper, or other substrate or medium capable of absorbing blood, and allowing the blood to dry on the paper or other substrate or medium to which the blood is applied. During the drying process, the blood may be passively separated into its components, for example, blood cells and plasma or serum, on the paper or other medium to which the blood is applied. Pieces of the paper or other medium onto which the blood or its components have dried may be punched or otherwise cut from the paper or medium. Alternatively or additionally, the dried blood sample may be provided as a dried blood sample in a HemaSpot HF device or other suitable device. Various other means for collecting and / or storing the dried blood sample may also be contemplated.

[0106] The biological sample may be obtainable by further sample preparation from a dried blood sample.

[0107] A biological sample may be obtained from a dried blood sample or a dried sample obtainable from blood by extracting at least a portion of the blood or one or more components thereof into a solvent. Subsequent sample preparation may include, for example, extracting blood or components thereof from the dried blood sample, for example, from a cut or punched piece of paper or other medium. The blood or components thereof may be extracted, for example, with an aqueous buffer or other suitable solvent, thus providing a liquid sample for NMR analysis or other suitable analytical methods. 1 For H NMR, deuterated solvents may be added.

[0108] In the present context, the term "control sample" may refer to a sample obtained from a subject known to be free of a disease or condition or known not to be at risk of having or developing a disease or condition. This control sample may be matched. In one embodiment, the control sample may be a biological sample derived from a healthy individual or a generalized population of healthy individuals. The term "control value" may be understood as a value obtainable from a control sample and / or a quantitative value derivable therefrom. For example, it may be possible to calculate a threshold value from the control sample and / or control value, above or below which the risk of developing a disease or condition increases. In other words, a value higher or lower than said threshold value (depending on the biomarker, risk score, hazard ratio, and / or predicted absolute risk) may indicate that the subject is at increased risk of developing the disease or condition.

[0109] An increase or decrease in the quantitative value of the at least one biomarker or biomarkers as compared to a control sample or control value may indicate that the subject has or is at increased risk of developing a disease or condition. Whether an increase or decrease indicates that the subject is at increased risk of developing a disease or condition may depend on the biomarker.

[0110] A 1.2-fold, 1.5-fold, or even, for example, a 2-fold or 3-fold increase or decrease in the quantitative value of at least one biomarker (or of an individual biomarker of a plurality of biomarkers) compared to a control sample or control value may indicate that the subject is at increased risk for developing the disease or condition.

[0111] In one embodiment, an increase in the quantification value of glycoprotein acetyl as compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0112] In one embodiment, an increase in the glycoprotein acetyl quantification value relative to the albumin quantification value in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0113] In one embodiment, a decrease in the quantified value of omega-6 fatty acids compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0114] In one embodiment, a decrease in the quantified value of omega-6 fatty acids relative to the quantified value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0115] In one embodiment, an increase in the quantitative value of a monounsaturated fatty acid compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0116] In one embodiment, an increase in the quantitative value of monounsaturated fatty acids relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0117] In one embodiment, a decrease in the quantitative value of saturated fatty acids compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0118] In one embodiment, an increase in the quantitative value of saturated fatty acids relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0119] In one embodiment, a decrease in the quantitative value of omega-3 fatty acids compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0120] In one embodiment, a decrease in the quantitative value of omega-3 fatty acids relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0121] In one embodiment, a decrease in the quantitative value of apolipoprotein A1 (ApoA1) compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0122] In one embodiment, a decrease in the quantitative value of apolipoprotein A1 (ApoA1) relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0123] In one embodiment, a decrease in the quantitative value of docosahexaenoic acid (DHA) compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0124] In one embodiment, a decrease in the quantitative value of docosahexaenoic acid (DHA) relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0125] In one embodiment, a decrease in the quantified value of leucine as compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0126] In one embodiment, a decrease in the quantified value of leucine relative to the quantified value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0127] In one embodiment, a decrease in the quantified value of acetic acid compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0128] In one embodiment, a decrease in the quantification of acetic acid relative to the quantification of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0129] In one embodiment, a decrease in the quantified value of alanine as compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0130] In one embodiment, an increase in the quantification of alanine relative to the quantification of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0131] In one embodiment, a decrease in the quantitative value of apolipoprotein B (ApoB) compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0132] In one embodiment, a decrease in the quantitative value of apolipoprotein B (ApoB) relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0133] In one embodiment, a decrease in the quantified value of glutamine as compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0134] In one embodiment, an increase in the quantitative value of glutamine relative to the quantitative value of albumin in a biological sample as compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0135] In one embodiment, a decrease in the quantified value of isoleucine as compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0136] In one embodiment, an increase in the quantification value of isoleucine relative to the quantification value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0137] In one embodiment, a decrease in the quantified value of linoleic acid (LA) compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0138] In one embodiment, a decrease in the quantified value of linoleic acid relative to the quantified value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0139] In one embodiment, an increase in the quantitative value of phenylalanine compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0140] In one embodiment, an increase in the quantification value of phenylalanine relative to the quantification value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0141] In one embodiment, an increase in the quantitative value of tyrosine as compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0142] In one embodiment, an increase in the quantification of tyrosine relative to the quantification of albumin in a biological sample as compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0143] In one embodiment, a decrease in the quantitative value of fatty acid unsaturation compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0144] In one embodiment, an increase in the quantitative value of fatty acid unsaturation relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0145] In one embodiment, a decrease in the quantified value of valine compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0146] In one embodiment, an increase in the valine quantification relative to the albumin quantification in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0147] In one embodiment, a decrease in the quantitation of histidine as compared to a control sample or control value may indicate that the subject is at increased risk for developing an infectious disease or a complication thereof, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0148] In one embodiment, a decrease in the quantified value of histidine relative to the quantified value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0149] In one embodiment, a decrease in the quantitative value of HDL cholesterol (HDL-C) compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0150] In one embodiment, a decrease in the quantitative value of HDL cholesterol (HDL-C) relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0151] In one embodiment, an increase in the quantitative value of glucose compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0152] In one embodiment, an increase in the quantitative value of glucose relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0153] In one embodiment, an increase in the quantified value of acetoacetate compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0154] In one embodiment, an increase in the quantification of acetoacetate relative to the quantification of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0155] In one embodiment, an increase in the quantified value of 3-hydroxybutyrate compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0156] In one embodiment, an increase in the quantification of 3-hydroxybutyrate relative to the quantification of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0157] In one embodiment, a decrease in the quantitative value of LDL cholesterol (LDL-C) compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0158] In one embodiment, a decrease in the quantitative value of LDL cholesterol (LDL-C) relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0159] In one embodiment, an increase in the quantitative value of lactate compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0160] In one embodiment, an increase in the quantification of lactate relative to the quantification of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0161] In one embodiment, an increase in the quantitative value of triglycerides in LDL (LDL-TG) compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0162] In one embodiment, an increase in the quantitative value of LDL triglycerides (LDL-TG) relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0163] In one embodiment, an increase in the quantitative value of pyruvate compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0164] In one embodiment, an increase in the quantification of pyruvate relative to the quantification of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory infections, and / or sepsis.

[0165] In one embodiment, a decrease in the quantitative value of cholesterol in VLDL (VLDL-C) compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis.

[0166] In one embodiment, a decrease in the quantitative value of VLDL cholesterol (VLDL-C) relative to the quantitative value of albumin in a biological sample compared to a control sample or control value may indicate that the subject is at increased risk of developing an infectious disease or its complications, such as pneumonia, other lower respiratory tract infections, and / or sepsis. [Example]

[0167] Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings.

[0168] The following description discloses some embodiments in sufficient detail to enable those skilled in the art to utilize the embodiments based on the present disclosure. Not every step or feature of the embodiments is discussed in detail, as many of the steps or features will be apparent to those skilled in the art based on this specification.

[0169] Example 1 Biomarker indices quantified by nuclear magnetic resonance (NMR) were investigated for their ability to predict the following infectious diseases and their complications, even years after blood sampling: pneumonia and severe pneumonia, other lower respiratory diseases, and sepsis. In addition, the association of biomarkers with type 2 diabetes was examined, and the effect of scaling individual biomarker concentrations to albumin was investigated. All analyses were conducted using the UK Biobank, with over 100,000 study participants for whom blood biomarker data from NMR was available.

[0170] Study population Details of the UK Biobank design are reported by Sudlow et al., 2015, PLoS Med. 2015;12(3):e1001779. Briefly, the UK Biobank recruited 502,639 participants aged 37–73 years at 22 assessment centers across the UK. All participants provided written informed consent, and ethical approval was obtained from the North West Multi-Center Research Ethics Committee. Blood samples were collected at baseline between 2007 and 2010. No selection criteria were applied to sampling.

[0171] NMR metabolomics A random subset of baseline plasma samples from 118,466 individuals from the entire UK Biobank population was measured using the Nightingale high-throughput NMR metabolomics platform, which provides simultaneous quantification of routine lipids, lipoprotein subclasses profiled by lipid concentration within 14 subclasses, fatty acid composition, and various low-molecular-weight metabolites, including amino acids, ketone bodies, and gluconeogenesis-related metabolites, at molar concentrations. Technical details and epidemiological applications have been reviewed (Soininen et al., 2015, Circ Cardiovasc Genet;2015;8:192-206; Wuertz et al., 2017, Am J Epidemiol 2017;186:1084-1096).

[0172] Quantitative metabolomic biomarker data from UK Biobank samples were curated and approved in mid-May 2020. Values ​​outside the four interquartile ranges from the median were considered outliers and were excluded.

[0173] Analysis of biomarker relationships with disease risk Associations between blood biomarkers and disease risk were performed using UK Biobank data. The analysis focused on the relationship of biomarker concentrations to disease occurrence after blood sample collection to determine how each individual biomarker could predict future disease risk. We investigated how a multi-biomarker score, in the form of weighted biomarkers, could predict future disease risk significantly more powerfully than each individual biomarker. Information on disease events occurring after blood sampling for all study participants was recorded from UK Hospital Episode Statistics data, death registries, primary care records, and self-reports. All analyses were based on the occurrence of the first diagnosis; therefore, individuals with a recorded diagnosis of a given disease before blood sampling were excluded from statistical analysis. The following diagnoses were used to define disease endpoints: For type 2 diabetes diagnoses, ICD-10 E11 and surgical procedures were used, as described by Rao et al., Circ Genom Precis Med. 2018;11(7):e002162. Self-reported diabetes was not included as an endpoint. Pneumonia-related diagnoses were based on any occurrence of ICD-10 diagnoses J12 to J18. Sepsis was defined as a diagnosis of either A40 (streptococcal sepsis) or other sepsis (A41) using diagnoses recorded in primary care, hospital registries, or death registries. Other lower respiratory tract infections were defined as acute bronchitis, acute bronchiolitis, or unspecified acute lower respiratory tract infections using the respective ICD-10 diagnosis codes J20, J21, or J22. For analyses of severe pneumonia, individuals with a recorded pneumonia diagnosis in primary care settings and as self-reports were excluded from the analysis. In other words, severe pneumonia was defined as having a diagnosis of J12 to J18 in hospital registries or death registries. Approximately 110,000 individuals were available for analysis of biomarkers for these diseases. Register-based follow-up was from blood sampling from 2007–2010 to 2016–2017 according to UK Biobank assessment centres (approximately 872,000 person-years).For the analysis of severe pneumonia, further analyses were performed to demonstrate that the results were consistent for men and women, across different age groups, and for people with and without chronic disease states at the time of blood sampling. Specifically, the analysis of biomarker scores versus severe pneumonia was repeated when individuals with chronic respiratory and cardiometabolic diseases (cardiovascular disease, diabetes, lung cancer, chronic obstructive pulmonary disease, liver disease, and renal failure) were excluded from the analysis. The analysis of biomarker scores was also performed against the short-term risk of severe pneumonia, defined as a pneumonia diagnosis recorded from a hospital or death registry within the first two years after blood sample collection.

[0174] Cox proportional hazards regression models adjusted for age, sex, and assessment center were used to examine biomarker associations. Biomarkers were examined for the magnitude of association without and with scaling to albumin concentration. Results were plotted as the magnitude per standard deviation of each biomarker index to allow for direct comparison of the magnitude of associations. The relationship between three specific multi-biomarker scores (calculated based on multiple biomarkers) and the risk of severe pneumonia was further explored in analyses of different subsets of the study population (including or not including individuals with chronic respiratory or cardiometabolic disease at the time of blood sampling, different age groups of the study population, sex specific, and short-term versus long-term risk of severe pneumonia). Additionally, the same three multi-biomarker scores were plotted in the form of a slope percentile plot, showing the proportion of individuals who developed severe pneumonia during follow-up when individuals were binned into percentiles of biomarker levels. We also examined these three multi-biomarker scores across quintiles of the multi-biomarker score using Kaplan-Meier plots of the cumulative risk of pneumonia during follow-up, further stratifying for extreme values ​​of two biomarkers.

[0175] Summary of results Baseline characteristics of the study population for biomarker analysis versus future disease risk are shown in Table 1. The number of disease cases for severe pneumonia is illustrated. Among 108,449 study participants with complete data available for calculating biomarker scores, 2531 pneumonia events were recorded in hospital or death registries after baseline blood sampling (median follow-up time 8.1 years). For the other diseases considered here, the number of individuals who developed a given disease after blood samples were collected is listed in the figures illustrating the results, with clinical characteristics similar to those in Table 1. For the multi-biomarker scores shown in Figures 3a and 3b, only half of the study population was used in the plots. This is because the other half was used to derive weights for the biomarker combinations in the multi-biomarker scores.

[0176] [Table 1]

[0177] Biomarker associations for future diabetes and pneumonia when concentrations are scaled to albumin concentration Figure 1a shows the hazard ratios of 29 blood biomarkers for the future development of type 2 diabetes. The results are based on a statistical analysis of over 110,000 individuals from the UK Biobank, of whom over 4,000 developed type 2 diabetes during a median follow-up of 8.1 years. The results are shown for absolute biomarker concentrations (gray circles) and when biomarker concentrations are scaled relative to albumin concentrations measured from the same blood sample (black triangles). The analysis was adjusted for age, sex, and UK Biobank assessment center in a Cox proportional hazards regression model. Results for both unscaled and albumin-scaled biomarkers are shown for a one-standard-deviation increase in the given biomarker index.

[0178] All 29 biomarkers were statistically significantly associated with future development of type 2 diabetes, both when analyzed as absolute concentrations and when concentrations were scaled to albumin. All associations were in the same direction with respect to type 2 diabetes risk, regardless of whether concentrations were scaled to albumin.

[0179] Figure 1b shows the hazard ratios of 29 blood biomarkers for the future development of severe pneumonia. The results are based on a statistical analysis of over 100,000 individuals from the UK Biobank, of whom over 2,400 developed severe pneumonia (defined as diagnoses J12–J18 in the National Hospital Registry or Death Register) during a median follow-up of 8.1 years. The results are shown for absolute biomarker concentrations (gray circles) and when biomarker concentrations are scaled relative to albumin concentrations measured from the same blood sample (black triangles). The analysis was adjusted for age, sex, and UK Biobank assessment center in a Cox proportional hazards regression model. Results for both unscaled and albumin-scaled biomarkers are shown for a one-standard-deviation increase in the given biomarker index.

[0180] All 29 biomarkers were associated with future development of severe pneumonia when analyzed as absolute concentrations or when concentrations were scaled relative to albumin. 23 of the 29 biomarkers were statistically significantly associated with future development of severe pneumonia when analyzed as absolute concentrations. 23 biomarkers were also statistically significantly associated with future development of severe pneumonia when concentrations were scaled relative to albumin.

[0181] Overall, the above analysis illustrates that a wide range of blood biomarkers measured by NMR spectroscopy are indicative of future risk of type 2 diabetes and severe pneumonia in a general population setting. The association of biomarkers with type 2 diabetes is similar when concentrations are scaled relative to albumin concentrations. The association of biomarkers with severe pneumonia changes to some extent when concentrations are scaled relative to albumin, but many biomarkers remain strongly associated with risk of severe pneumonia after albumin scaling or become even more strongly associated with severe pneumonia after albumin scaling.

[0182] Abbreviations used in the figures: VLDL: very low density lipoprotein LDL: low-density lipoprotein HDL: high-density lipoprotein C: Cholesterol TG: Triglyceride LA: Linoleic acid DHA: Docosahexaenoic acid SFA: Saturated fatty acids MUFA: Monounsaturated fatty acids SD: standard deviation

[0183] Example 2 The study population and statistical methods for Example 2 are as in Example 1.

[0184] Summary of findings on biomarkers for infectious diseases and their complications Figure 2a shows the hazard ratios of 29 blood biomarkers for future development of pneumonia (severe or non-severe) when biomarker concentrations are analyzed as absolute values. The association of a multi-biomarker score, termed the "NGH infection biomarker score," which includes a weighted sum of individual biomarkers, is also shown. The results are based on a statistical analysis of over 100,000 individuals from the UK Biobank, of whom over 2,650 developed pneumonia (defined as a diagnosis J12–J18 in primary care records, national hospital registries, death records, or self-reports) during a median follow-up of 8.1 years. Closed circles indicate a P value for association of P<0.001 (corresponding to multiple testing correction), whereas open circles indicate a P value for association of P>0.001. The analysis was adjusted for age, sex, and UK Biobank assessment center in a Cox proportional hazards regression model. Results are presented for a one-standard-deviation increase in a given biomarker index. Twenty-two of the 29 biomarkers were statistically significantly associated with future development of pneumonia based on P < 0.001. The strongest association with risk of future development of pneumonia was observed for a multi-biomarker score called the "NGH infection biomarker score."

[0185] Figure 2b shows the relationship between baseline biomarker levels and future development of other lower respiratory diseases (acute bronchitis, acute bronchiolitis, and unspecified acute lower respiratory infections) when biomarker concentrations are analyzed as absolute concentrations. The hazard ratios for 29 blood biomarkers for future development of sepsis are also shown when biomarker concentrations are analyzed as absolute concentrations. The association of a multi-biomarker score, called the "NGH Infection Biomarker Score," which includes a weighted sum of individual biomarkers, is also shown. The results are based on a statistical analysis of over 100,000 individuals from the UK Biobank, of which over 5,900 developed sepsis (defined as a diagnosis of J20 (acute bronchitis), J21 (acute bronchiolitis), or J22 (other acute lower respiratory infection) in primary care records, self-reported cases, national hospital registries, or death records) during a median follow-up of 8.1 years. Filled circles represent a P value for association of P<0.001, and open circles represent a P value for association of P>0.001. Analyses were adjusted for age, sex, and UK Biobank assessment center in a Cox proportional hazards regression model. Results are presented for a 1 standard deviation increase in a given biomarker index. Twenty-one of the 29 biomarkers were statistically significantly associated with future development of sepsis based on P<0.001. The strongest association with risk of future development of sepsis was observed for a multi-biomarker score called the "NGH infection biomarker score."

[0186] Figure 2c shows the hazard ratios of 29 blood biomarkers for the future development of sepsis when biomarker concentrations are analyzed as absolute values. The association of a multi-biomarker score, called the "NGH infection biomarker score," which includes a weighted sum of individual biomarkers, is also shown. The results are based on a statistical analysis of over 100,000 individuals from the UK Biobank, of whom over 1,250 developed sepsis (defined as a diagnosis of A40 (streptococcal sepsis) or A41 (other sepsis) in primary care records, self-reports, national hospital registries, or death records) during a median follow-up of 8.1 years. Closed circles indicate a P value for association of P<0.001, and open circles indicate a P value for association of P>0.001. The analysis was adjusted for age, sex, and UK Biobank assessment center in a Cox proportional hazards regression model. Results are presented for a one-standard-deviation increase in a given biomarker index. Fifteen of the 29 biomarkers were statistically significantly associated with future development of sepsis based on P<0.001. The strongest association with risk of future development of sepsis was observed for a multi-biomarker score called the "NGH infection biomarker score."

[0187] Figure 3a shows the relationship between baseline biomarker levels and future development of severe pneumonia for three multi-biomarker scores. Specifically, the multi-biomarkers were: 1) a weighted combination of up to 29 biomarkers in absolute concentrations (referred to as NGH infection biomarker score; no biomarker scaling), 2) a weighted combination of up to 29 biomarkers in concentrations scaled to albumin (referred to as NGH infection biomarker score; albumin-scaled biomarkers), and 3) glycoprotein acetyltransferase (GlycA / Alb) scaled to albumin concentration. The weighted combination of biomarkers used in the multi-biomarker scores was derived using statistical regression analysis and machine learning based on 50% of the study population (derivation set; approximately 53,000 individuals). The predictive performance of the three multi-biomarker scores was then evaluated in the other 50% of the study population (validation set; 53,477 individuals) to avoid overfitting the results. Associations of the three multi-biomarker scores are shown for all individuals in the validation portion of the study population and for the subset of individuals without chronic respiratory or cardiometabolic disease at baseline. Results are also shown for different age groups (separated by the study participants' age tertiles at the time they provided their blood sample) and for men and women. The relationship between baseline biomarker levels for the three different multi-biomarker scores and severe pneumonia occurring within two years of blood sample collection is also shown.

[0188] These results show the following: The three multi-biomarker scores were more strongly associated with future disease than any single biomarker (see Figure 1b for single biomarker results). A multi-biomarker score based on glycoproteins relative to albumin and biomarkers scaled to albumin concentration strongly predicts future development of severe pneumonia. Results were similar regardless of the age of study participants. Results are similar for men and women. Results are stronger for predicting the short-term risk of severe pneumonia (disease occurring in the first 2 years of blood sampling) compared with the long-term risk of severe pneumonia (disease occurring up to 8 years after blood sampling, on average).

[0189] Figure 3b shows the risk gradient for future severe pneumonia by percentile of the three multi-biomarker scores (top half of the figure). Each dot corresponds to approximately 500 individuals. We also calculated the cumulative risk for severe pneumonia after blood sample collection at various quantiles of the three multi-biomarker scores using Kaplan-Meier plots (bottom half of the figure). These plots are shown for the validation set portion of the study population, i.e., the 50% (n = 53,477 individuals) that were not included in the derivation of the multi-biomarker score.

[0190] These results show the following: The association between the three multi-biomarker scores and the risk of future severe pneumonia development was particularly high for individuals with very high levels of the multi-biomarker scores, i.e., a nonlinear effect was observed. Increased risk for individuals with very high levels of the multiple biomarker score is present within the first few years after the time of blood sampling.

[0191] It is obvious to those skilled in the art that with the advancement of technology, the basic idea may be implemented in various ways. Therefore, the embodiments are not limited to the above examples. Instead, the embodiments may vary within the scope of the claims.

[0192] The above-described embodiments may be used in any combination with each other. Some of the embodiments may be combined together to form further embodiments. A method, product, or use disclosed herein may include at least one of the above-described embodiments herein. It will be understood that the benefits and advantages described above may relate to one embodiment or to several embodiments. The embodiments are not limited to those that solve any or all of the stated problems or have any or all of the stated benefits and advantages. It will be further understood that a reference to "an" item refers to one or more of those items. The term "comprising" is used herein to mean including the features or acts that follow the phrase without excluding the presence of one or more additional features or acts.

Claims

1. 1. A method for determining whether a subject has or is at risk for developing a disease or condition, comprising: determining a quantitative value of at least one biomarker in a biological sample obtained from said subject relative to a quantitative value of albumin in said biological sample; comparing the quantitative value of said at least one biomarker to a control sample or control value; Including, an increase or decrease in the quantitative value of the at least one biomarker as compared to the control sample or control value indicates that the subject has or is at increased risk for developing the disease or condition; the biological sample is a dried blood sample, a dried sample obtainable from blood, or a sample obtainable from a dried blood sample; the quantitative value of the at least one biomarker and the quantitative value of the albumin are measured using nuclear magnetic spectroscopy; The method, wherein said at least one biomarker comprises or is glycoprotein acetyl.

2. 2. The method of claim 1, wherein the quantitative value of the at least one biomarker relative to the quantitative value of albumin in the biological sample is determined by calculating a ratio of an initial quantitative value of the at least one biomarker in the biological sample to the quantitative value of albumin.

3. The method of claim 1 or claim 2, wherein the dried blood sample is a dried blood spot.

4. 4. The method of any one of claims 1 to 3, wherein the dried blood sample or the dried sample obtainable from blood is obtainable from the subject's fingertip, from capillary blood sampling from the subject's upper arm, and / or by venipuncture of the subject.

5. 5. The method of any one of claims 1 to 4, wherein the biological sample is obtainable from the dried blood sample or from a dried sample obtainable from the blood by extracting at least a portion of the blood or one or more components thereof from the dried blood sample or from a dried sample obtainable from the blood into a solvent.

6. 6. The method of claim 1, comprising determining quantitative values ​​of a plurality of biomarkers in the biological sample.

7. In a biological sample obtained from the subject, the following is determined for the quantitative value of albumin in the biological sample: -glycoprotein acetyl, - omega-6 fatty acids, - monounsaturated fatty acids, ・Saturated fatty acids, - omega-3 fatty acids, apolipoprotein A1 (ApoA1), Docosahexaenoic acid (DHA), leucine, acetic acid, Alanine, apolipoprotein B (ApoB), ·glutamine, - Isoleucine, Linoleic acid, phenylalanine, - tyrosine, - Unsaturation degree of fatty acids, - Balin, - histidine, - Cholesterol in HDL (HDL-C), ·glucose, acetoacetic acid, 3-hydroxybutyric acid, - Cholesterol in LDL (LDL-C), Lactic acid, - Triglycerides in LDL (LDL-TG), pyruvate, or Cholesterol in VLDL (VLDL-C) 7. The method of claim 1, further comprising determining a quantitative value of at least one biomarker selected from the group consisting of:

8. In the biological sample obtained from the subject, the following is determined for the quantitative value of albumin in the biological sample: - omega-6 fatty acids, - monounsaturated fatty acids, saturated fatty acids, or Omega-3 fatty acids and / or a quantitative value of at least one biomarker selected from the group consisting of The following for the quantitative value of albumin in the biological sample: apolipoprotein A1 (ApoA1), Docosahexaenoic acid (DHA), leucine, acetic acid, Alanine, apolipoprotein B (ApoB), ·glutamine, - Isoleucine, Linoleic acid, phenylalanine, - tyrosine, - Unsaturation degree of fatty acids, - Balin, Histidine and / or a quantitative value of at least one biomarker selected from the group consisting of The following for the quantitative value of albumin in the biological sample: - Cholesterol in HDL (HDL-C), ·glucose, acetoacetic acid, 3-hydroxybutyric acid, - Cholesterol in LDL (LDL-C), Lactic acid, - Triglycerides in LDL (LDL-TG), pyruvate, or Cholesterol in VLDL (VLDL-C) Quantitative value of at least one biomarker among 8. The method of claim 1, further comprising determining:

9. 9. The method according to any one of claims 1 to 8, wherein the disease is an infectious disease or a complication thereof.

10. 10. The method according to any one of claims 1 to 9, wherein the dried blood sample is a dried whole blood sample, a dried plasma sample, a dried serum sample, or a dried sample derived therefrom.

11. 11. The method of any one of claims 1 to 10, further comprising determining whether the subject has the disease or condition or is at risk of developing the disease or condition using a risk score, hazard ratio, and / or predicted absolute risk calculated based on the quantitative values ​​of the at least one biomarker or the plurality of biomarkers relative to the quantitative value of albumin.

12. The method of claim 9, wherein the disease is sepsis, pneumonia, severe pneumonia, or other lower respiratory tract infection, or diabetes.

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