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  • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
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  • G01N2333/47—Assays involving proteins of known structure or function as defined in the subgroups
  • G01N2333/4701—Details
  • G01N2333/4716—Complement proteins, e.g. anaphylatoxin, C3a, C5a
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  • G01—MEASURING; TESTING
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  • G01N2333/81—Protease inhibitors
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  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
  • G01N2333/81—Protease inhibitors
  • G01N2333/8107—Endopeptidase (E.C. 3.4.21-99) inhibitors
  • G01N2333/811—Serine protease (E.C. 3.4.21) inhibitors
  • G01N2333/8121—Serpins
• • G—PHYSICS
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  • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
  • G01N2333/90—Enzymes; Proenzymes
  • G01N2333/91—Transferases (2.)
  • G01N2333/912—Transferases (2.) transferring phosphorus containing groups, e.g. kinases (2.7)
• • G—PHYSICS
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  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/28—Neurological disorders
  • G01N2800/2814—Dementia; Cognitive disorders
  • G01N2800/2821—Alzheimer

Definitions

• AD Alzheimer's disease
• AD is a chronic neurodegenerative disease currently identified by progressive cognitive impairment and loss of memory leading to severe dementia.
• AD is typically a disease of the elderly, most prevalent in persons over the age of 65. It is the leading cause of dementia in the elderly and with an increasingly higher life expectancy, the prevalence in the population is only set to increase.
• AD is not typically life threatening, however as the disease progresses to severe dementia, patients are unable to care for themselves and usually require full time professional care.
• There is currently no known cure for AD but there are treatments that can slow the progression of the disease. Therefore a method that can identify patients with AD and potentially monitor their response to treatment would be an invaluable assay (tool for clinicians).
• AD Alzheimer's disease
• CT/M I mental assessment
• ApoE4 variant genetic risk factors
• Deficiencies of these methods can include a lack of specificity, they can be open to errors in interpretation, and may be highly invasive; generally a true diagnosis can only be made post mortem.
• biomarker methods to assist the positive diagnosis for AD.
• AD Alzheimer's disease
• pathological investigations of patients revealed the presence of neurofibrillary tangles (caused by accumulation of Tau protein) and Beta-amyloid plaques.
• neurofibrillary tangles caused by accumulation of Tau protein
• Beta-amyloid plaques There is also widespread neuronal and synaptic loss, which is thought to underlie the reduced cognitive and mnemonic function.
• the formation of plaques has been shown to cause neurodegeneration, however the causes of plaque formation are unknown. Diagnostic tests that identify specific isoforms of these proteins have been the main focus in diagnostic assay development. However, the presence of these proteins may indicate that the disease has progressed past a therapeutically viable stage and therefore earlier risk markers may be more beneficial.
• EP2293075 A2 and WO 201 1/143597 A1 identified several markers expressed in blood platelets using 2-D gel electrophoresis, which were differentially expressed between AD and control patients. These included variants of proteins which may correspond to a genetic susceptibility to AD.
• EP2507638 protein biomarkers were combined in an algorithm along with genotyping to improve the diagnostic model. In this algorithm patients whom were ApoE4 positive were more likely to have AD, as were patients whom were ApoE4 negative, but expressed two copies of the wild-type glutathione S-transferase 1 Omega (wtGSTO) gene.
• wtGSTO was defined as any GSTO gene which did not contain the rs4825 mutation (which encodes an Aspartic acid instead of an Alanine at residue 140 [A140D]).
• This invention highlights the effectiveness of combining blood-based biomarkers and genotyping to assist in the diagnosis of disease.
• WO 201 1/143597 A1 identified multiple biomarkers that are differentially expressed between serum of AD and control patients using multiplexed assays.
• greater accuracy of diagnosis is observed when using multiple combinations of biomarkers combined with clinical measurements and demographic variables using Random forests to develop a classification algorithm.
• these methods have not found clinical utility and there is an urgent need for a method that can be used routinely to aid the diagnosis of AD.
• the present invention relates to methods and compositions for the diagnosis of Alzheimer's disease.
• the present invention identifies and describes proteins that are differentially expressed in the Alzheimer's disease state relative to their expression in the normal state. According to the first aspect of the invention, there is provided a method of diagnosing Alzheimer's disease in a subject, comprising detecting two or more differentially expressed proteins chosen from Table 1 in a sample taken from the subject, whereby one of these is Afamin.
• a method comprising detecting levels of Afamin and any of Alpha- 1 antichymotrypsin, Alpha-2-macroglobulin, Apolipoprotein B100, complement C3 , Serine threonine kinase TBK-1 , vitamin D binding protein, alpha-1 -B glycoprotein, hemopexin, serum albumin, ceruloplasmin, alpha-2-antiplasmin, apolipoprotein A1 , complement factor H, IgG, IgG fc binding protein, hornerin, fibrinogen or complement C5 in a sample taken from a subject.
• the sample is serum or plasma.
• the relative levels of the differentially expressed proteins are used in conjunction with the ApoE or GST01 genotype or phenotype of a subject to increase the ability to differentiate between patients at risk of developing or having AD and those who are not at risk or do not have AD.
• a method of detecting differentially expressed proteins chosen from Table 1 in a sample taken from a subject is provided wherein a specific probe for the protein is attached to the surface of a device. The respective levels of these proteins in a sample are calculated based on their ability to compete with biotinylated tracer substance.
• the tracer substance is modified plasma, where proteins contained have been conjugated to biotin.
• a method for predicting the likelihood that a subject can be defined as suffering from or at risk of developing Alzheimer's disease through developing a categorical prediction model using statistical modelling or machine learning methods.
• Such methods may include, but are not limited to; perceptron neural networks, support vector machines, logistic regression, decision trees and random forests.
• Figures 1 -9 boxplots comparing relative levels of Afamin (BSI0268), Afamin (BSI0223), Afamin (BSI0220), Alpha-1 -antichymotrypsin (BSI0221 ), Complement C5 (BSI0782), Not known (BSI0183), Not known (BSI0279), Complement C3 (BSI0243) and Alpha-1 B-glycoprotein (BSI0182) of control and AD patients.
• Figure 1 ROC curve for use of Alpha-1 antichymotrypsin (BSI0221 ) to discriminate between Control and AD Patients.
• BSI0221 Alpha-1 antichymotrypsin
• ROC curve for use of a model comprising Afamin (BSI0268) and Alpha-1 antichymotrypsin (BSI0221 ) to discriminate between Control and AD Patients.
• FIG 14 ROC curve for use of a model comprising Afamin (BSI0268)/Alpha-1 antichymotrypsin (BSI0221 ) ratio and Complement C3 (BSI0217) to discriminate between Control and AD Patients.
• Figure 15 ROC curve for use of a model comprising Afamin (BSI0268)/Alpha-1 antichymotrypsin (BSI0221 ) ratio and Alpha-2 macroglobulin (BSI0195) to discriminate between Control and AD Patients.
• ROC curve for use of a model comprising Afamin (BSI0268)/Alpha-1 antichymotrypsin (BSI0221 ) ratio and Serine threonine protein kinase TBK1
• FIG 17 ROC curve for use of a model comprising Afamin (BSI0268)/Alpha-1 antichymotrypsin (BSI0221 ) ratio and Complement C5 (BSI0792) to discriminate between Control and AD Patients.
• Figure 18 ROC curve for use of a model comprising Afamin (BSI0268)/Alpha-1 antichymotrypsin (BSI0221 ) ratio and ApoE4 status to discriminate between Control and AD Patients.
• FIG 19 ROC curve for ability of ApoE4 status to discriminate between Control and AD Patients.
• Figure 20 ROC curve for use of a model comprising Afamin (BSI0268)/Alpha-1 antichymotrypsin (BSI0221 ) ratio, Complement C5 (BSI0792) and ApoE4 status to discriminate between Control and AD Patients.
• Figure 21 decision tree for use of Afamin/ Alpha-1 antichymotrypsin ratio and ApoE status to distinguish between Control and AD patients.
• the present invention describes a biomarker-based method to aid in the diagnosis of Alzheimer's disease (AD). Specifically the measurement of relative levels or concentration of biomarkers within a fluid sample taken from a patient suspected of having or at risk of developing Alzheimer's disease are measured.
• AD Alzheimer's disease
• the utility for diagnosing AD has been used as way of an example.
• the invention may also be used for monitoring the progression of AD and diagnosing and monitoring other forms of dementia and cognitive disorders, these include but are not limited to; Parkinson's dementia, Lewy body dementia, Vascular dementia, mild cognitive impairment, frontotemporal dementia.
• biomarker' in the context of the current invention, refers to a molecule present in a biological sample of a patient, the levels of which in said biological fluid may be indicative of Alzheimer's disease. Such molecules may include peptides/proteins or nucleic acids and derivatives thereof; the term 'relative levels', in the context of the current invention refers to the light intensity or absorbance reading (However the invention is not restricted to measurement using these techniques, the skilled person will be aware of other methods for measuring biological molecules that do not utilise measuring the properties of visible light to determine a measurement) from a biological assay that results from comparing the levels of the biomarker in a given biological sample to a reference material with a known concentration (this concentration may be zero) of the biomarker or level by which the biomarker within a biological sample directly competes with a reference material known to contain said biomarker to bind to a specific probe for said biomarker, the latter method generates a level inversely related to the concentration of the biomarker; the term
• the term 'genetic prevalence' in the context of the current invention can imply that the patients genome contains specific genotypes for certain proteins which are known in the art to be altered in patients who develop AD, such proteins include, but are not limited to, Apolipoprotein E (ApoE) and Glutathione S-Transferase Omega 1 (GSTO), this may be determined through genotyping or identifying the disease relevant form of the expressed protein in a biological fluid from the patient. More specifically, the number of alleles encoding ApoE4 and wild-type GSTO (wtGSTO) variants shall be determined.
• Apolipoprotein E Apolipoprotein E
• GSTO Glutathione S-Transferase Omega 1
• wtGSTO in the context of the current invention, refers to any variant of GSTO that does not contain the rs4825 mutation in the genomic sequence, or an alanine to aspartic acid substitution at residue 140 of the protein sequence.
• the invention describes various biomarkers for use in diagnosing AD either alone or in combination with other diagnostic methods or as complementary biomarkers.
• a complementary biomarker in the current context implies a biomarker that can be used in conjunction with other biomarkers for AD.
• a first aspect of the invention describes a method for diagnosing AD in a patient suspected of having, at risk of developing or of having AD which comprises taking an in vitro sample from the patient, determining the relative level or concentration of Afamin and one or more biomarkers chosen from Table 1 and establishing the significance of the relative level(s) or concentration(s) of Afamin and one or more biomarkers.
• the significance of the relative level or concentration is gauged by comparing said relative level or concentration to a control value for the specific biomarker.
• the control value is derived from determining the relative level or concentration of said biomarker in a biological sample taken from an individual(s) who does not have AD, as determined by clinical assessment.
• Afamin the relative level or concentration in a patient with AD is reduced compared with a control value.
• a preferred embodiment of the invention utilises a method employing a combination of Afamin and at least one other biomarker chosen from Alpha-1 antichymotrypsin, Alpha-2-macroglobulin, Apolipoprotein B100, complement C3, Serine threonine kinase TANK Binding Kinase-1 (TBK1 ), vitamin D binding protein, alpha-1 -B glycoprotein, hemopexin, serum albumin, ceruloplasmin, alpha-2-antiplasmin, apolipoprotein A1 , complement factor H, IgG, IgG fc binding protein, hornerin, fibrinogen or complement C5.
• a further preferred embodiment the invention uses a method whereby the relative level or concentration of Afamin is divided by the relative level or concentration of Alpha-1 antichymotrypsin to produce a ratio of Afamin/Alpha-1 antichymotrypsin.
• the term 'ratio' in the context of the current embodiment of the invention relates to dividing the value of one biomarker by the other, this value should be the same for both biomarkers and can be represented as a weight or moles of biomarker in a given volume (concentration) or by a light intensity or absorbance level generated by means of an assay (relative level).
• a further embodiment of the invention utilises the value of the ratio of Afamin/Alpha-1 antichymotrypsin in combination with relative levels or concentration of one or more biomarkers chosen from Alpha-1 antichymotrypsin, Alpha-2- macroglobulin, Apolipoprotein B100, complement C3 , Serine threonine kinase TBK-1 , vitamin D binding protein, alpha-1-B glycoprotein, hemopexin, serum albumin, ceruloplasmin, alpha-2-antiplasmin, apolipoprotein A1 , complement factor H, IgG, IgG fc binding protein, hornerin, fibrinogen or complement C5.
• biomarkers chosen from Alpha-1 antichymotrypsin, Alpha-2- macroglobulin, Apolipoprotein B100, complement C3 , Serine threonine kinase TBK-1 , vitamin D binding protein, alpha-1-B glycoprotein, hemopexin, serum albumin, ceruloplasmin, al
• a preferred combination of the current invention is the ratio of Afamin/Alpha-1 antichymotrypsin in combination with the relative level or concentration of Complement C3.
• Another preferred combination of the invention is the ratio of Afamin/Alpha-1 antichymotrypsin in combination with the relative level or concentration of serine threonine kinase TBK-1 .
• a further aspect of the invention is directed to the use of one or more of Afamin, Alpha-1 antichymotrypsin, Alpha-2-macroglobulin, Apolipoprotein B100, complement C3 , Serine threonine kinase TBK-1 , vitamin D binding protein, alpha-1 -B glycoprotein, hemopexin, serum albumin, ceruloplasmin, alpha-2-antiplasmin, apolipoprotein A1 , complement factor H, IgG, IgG fc binding protein, hornerin, fibrinogen or complement C5 as complementary biomarkers of AD.
• AD diagnosis in conjunction with other clinical evidence such as mental state assessment (MMSE), neurological imaging, Beta-amyloid peptides, phosphorylated Tau, ApoE genotype, and wild- type GSTO 1 genotype (wtGSTO).
• MMSE mental state assessment
• Beta-amyloid peptides Beta-amyloid peptides
• phosphorylated Tau ApoE genotype
• wtGSTO wild- type GSTO 1 genotype
• this clinical evidence may be added to the predictive model, based on the output measurement.
• ApoE status of a patient may be determined through genotyping, by identifying the disease relevant form of protein that is expressed at the genetic level (DNA and/or RNA), or by detecting the presence of the specific expressed form of the protein from a fluid sample taken from the patient.
• this output is expressed as either a dichotomised value, whereby the patient is either positive for the ApoE4 gene or protein or not; or as an ordinal output for the number of ApoE4 alleles present in the patients genomic DNA (0-2), which can be calculated using relative levels of the gene or protein within a sample taken from the patient.
• Biomarker relative levels or concentrations can be determined by contacting the sample with probes, preferably immobilised on a substrate, specific for each of the biomarkers included in the combination of biomarkers. Interactions between biomarker and its respective probe can be monitored and quantified using various techniques that are well-known in the art.
• An example of a suitable technique is an enzyme-linked immunosorbent assay (ELISA). Performing an ELISA involves at least one antibody with specificity for a particular antigen.
• the sample with an unknown amount of antigen is immobilized on a solid support (usually a polystyrene microtiter plate) either non-specifically (via adsorption to the surface) or specifically (via capture by another antibody specific to the same antigen, in a "sandwich” ELISA).
• a solid support usually a polystyrene microtiter plate
• the detection antibody is added, forming a complex with the antigen.
• the detection antibody can be covalently linked to an enzyme, or can itself be detected by a secondary antibody that is linked to an enzyme through bioconjugation.
• the plate is typically washed to remove any proteins or antibodies that are not specifically bound.
• the plate is developed by adding an enzymatic substrate to produce a visible signal, which indicates the quantity of antigen in the sample.
• the 'sample' as referred to herein is serum or plasma, however it may be any sample from a patient from which biomarker levels or concentrations can be determined. These include but are not limited to whole blood, urine, saliva, cerebrospinal fluid and platelets.
• the substrate comprises at least two, preferably three or four probes, each probe specific to an individual biomarker.
• the term 'specific' means that the probe binds only to one of the biomarkers of the invention, with negligible binding to other biomarkers of the invention or to other analytes in the biological sample being analysed. This ensures that the integrity of the diagnostic assay and its result using the biomarkers of the invention is not compromised by additional binding events.
• the substrate can be any surface able to support one or more probes, but is preferably a biochip.
• a "Biochip” is a general term for a reaction platform for hosting chemical, biochemical, proteomic or molecular tests, as may be required for medical diagnosis, drug detection, etc.
• a Biochip comprises an inert substrate, such as silicon, glass or ceramic (often of the order of about 1 cm 2 or less in surface area), on which one or a plurality of reaction sites is provided.
• the sites generally carry one or more ligands, for example, one or more antibodies, selected for the test (or "assay") to be performed, adsorbed to the surface of the chip for activation upon combination with a sample applied to the chip (e.g.
• biochips carry a very large number (hundreds or thousands) of such tests sites, typically arranged in a grid or array, making it possible to carry out numerous assays simultaneously, and using the same single specimen.
• a preferred probe of the invention is a polyclonal or monoclonal antibody
• other probes such as aptamers, molecular imprinted polymers, phages, short chain antibody fragments and other antibody-based probes may be used.
• the invention also allows for nucleic acid sequence probes.
• a solid state device is used in the methods of the present invention, preferably the Biochip Array Technology system (BAT) (available from Randox Laboratories Limited). More preferably, the Evidence Evolution and Evidence Investigator apparatus (available from Randox Laboratories) may be used to determine the levels of biomarkers in the sample.
• BAT Biochip Array Technology system
• the Evidence Evolution and Evidence Investigator apparatus available from Randox Laboratories may be used to determine the levels of biomarkers in the sample.
• ROC receiver operating characteristics
• a suitable mathematical or machine learning classification model such as logistic regression equation
• the logistic regression equation might include other variables such as age and gender of the patient.
• the ROC curve can be used to assess the accuracy of the logistic regression model.
• the logistic regression equation can be used independently or in an algorithm to aid clinical decision making. Although a logistic regression equation is a common mathematical/statistical procedure used in such cases and is preferred in the context of the present invention, other mathematical/statistical, decision trees or machine learning procedures can also be used.
• a logistic regression equation applicable to the present invention (at a classification cut-off value of 0.5) for the biomarker combination for indication of AD versus non-AD (control) in a patient suspected of having or being at risk of developing AD is calculated as follows;
• a decision tree may be grown where a decision branch is grown from each node (sub-population) to divide the population into classification groups.
• Figure 19 represents an example of a tree that was grown using the data described in this invention, which could correctly classify all AD patients with a relatively small error.
• Plasma normalisation was conducted as per US 2009/0136966. Briefly human plasma was normalised by removing high abundance proteins utilising the propriety method. Firstly, high abundance proteins were removed using Multiple Affinity Removal System (MARS) technology. The resultant plasma was then loaded on to a Multi-lmmunoAffinity Normalisation (MIAN) column, where normalisation stringency was adjusted by altering the flow rate. The flow-throw and wash samples were combined to give a differentially normalised sample. Some of this normalised plasma was then ubiquitously biotinylated to provide a tracer substance, known as QuantiplasmaTM. 2. Antibody generation
• Monoclonal antibodies were produced as per US 2009/0136966. Normalised plasma was used as an immunogen to generate polyclonal antibodies. B-cells were then isolated and monoclonal hybridomas were generated. Initial selection of hybridomas was done using an ELISA. Plates were coated with mouse Ig gamma-Fc specific GAM, and then incubated with the mAb hybridoma supernatant, following a wash step this complex was then incubated with the QuantiplasmaTM and finally an enzyme-substrate reaction was induced to detect the binding of the biotinylated plasma (QuantiplasmaTM) to the mAb. This selection identified more than 1000 mAb.
• Serum samples were obtained from 19 clinically confirmed Alzheimer's disease (AD) patients and 19 age/gender-matched control participants with normal cognitive function. These samples were frozen shortly after collection and stored at (-80°C) until analysis was performed. Additional clinical information was gathered for these subjects, this included basic personal and family medical history. Further to this, ApoE and GSTO genotype were determined through methods known in the art. For each patient, genomic DNA was isolated and the presence of DNA that encodes each of the 3 isoforms of ApoE (E2, E3, E4) or GSTO (wild-type, mutant A140 [rs4825]) were determined utilising polymerase chain reaction (PCR) techniques. Further analysis allowed the allelic frequency of each of the isoforms to be determined through methods known in the art.
• PCR polymerase chain reaction
• a panel of 69 mAb antibodies (Table 1 ) were selected out of a catalogue of >1000 generated as per Section 2. Antibodies were then evaluated by competitive immunoassay. They were first immobilised on a biochip platform (9mm x 9mm), which was the substrate for the immunoreactions.
• the semi-automated bench top analyser Evidence Investigator was used (EV3602, Randox Laboratories Ltd., Crumlin, UK, patents-EP98307706, EP98307732, EP0902394, EP122731 1 , EP1434995 and EP1354623).
• the assay principle is based on competition for binding sites of the monoclonal antibody between free antigen (the patient sample) and labelled tracer plasma (QuantiplasmaTM).
• Sample and reagents are added to the biochip and incubated under controlled conditions. Following addition of substrate, a light signal is generated which is then detected using digital imaging technology.
• the system incorporates dedicated software to automatically process, report and archive the data generated.
• the level of a specific protein in the patient sample is determined by comparing the difference between the light signal (RLU) at the position of the respective antibody on a biochip containing sample and the tracer (test) and a biochip containing just the tracer (blank). A ratio between test and control samples is determined as;
• BSI0221 generated a model with an AUC of 0.906 (Fig 10-13), a significant improvement on the predictive power of the individual measurements.
• Complement C3 (BSI0217), Alpha-2 macroglobulin (BSI0195), Serine threonine protein kinase TBK1 (BSI01 12) or Complement C5 (BSI0782)
• BSI0217 The addition of Complement C3 (BSI0217), Alpha-2 macroglobulin (BSI0195), Serine threonine protein kinase TBK1 (BSI01 12) or Complement C5 (BSI0782) to the model improved the model, AUC of 0.889, 0.906, 0.892 and 0.920 respectively (Fig 14-17). Further, improvements were identified when considering the ApoE genotype of the patients. A categorical variable, whereby each patient was identified as having either no Apoe4 alleles (0) or having one or more (1 ), was added to the analysis. This was further refined by identifying the number of ApoE4 alleles each patient had (0, 1 or 2).
• Alpha-1 -antichymotrypsin .613 .468 .722 .668 .388 .777 .01734 (BSI0221 )
• Alpha-1 B-glycoprotein .504 .265 .737 .475 .238 .681 .05396 (BSI0182)
• Alpha-1 -antichymotrypsin .605 .342 .724 .652 .51 1 .744 .05584 (BSI0144)
• Serum albumin (BSI0097) .725 .643 .756 .701 .583 .772 .08766
• Apolipoprotein A1 .41 1 .275 .522 .382 .250 .478 .11827 (BSI0239)
• Alpha-2-macroglobulin .544 .168 .670 .444 .101 .660 .15249 (BSI0197)
• Alpha-1 B-glycoprotein .259 .109 .348 .227 .092 .335 .15679 (BSI01 16)
• Serine threonine protein .576 -.207 .691 .464 -.275 .660 .17460 kinase TBK1 (BSI01 12)
• Alpha-1 -antichymotrypsin .478 -.195 .868 .365 -.691 .736 .18406 (BSI0186)
• Alpha-1 B-glycoprotein .431 .239 .549 .453 .255 .569 .21469 (BSI0196)
• Alpha-2-macroglobulin .940 .796 .974 .924 .423 .982 .37323 (BSI0195)
• Apolipoprotein A1 .794 .328 .917 .761 .110 .937 .37323 (BSI0179)
• IgG (BSI0248) .638 .400 .781 .556 .253 .699 .44774
• Alpha-2-macroglobulin .577 .338 .660 .544 .309 .703 .96507 (BSI0200)
• Alpha-1 B-glycoprotein (BSI0182) .683
• Serum albumin (BSI0097) .662
• Apolipoprotein A1 (BSI0239) .648
• Alpha-1 B-glycoprotein (BSI0196) .618
• Alpha-1-B glycoprotein (BSI0198) .600
• D-vitamin binding protein (BSI0257) .594
• Apolipoprotein A1 (BSI0179) .584
• IgG (BSI0248) .572
• Alpha-2-macroglobulin (BSI0173) .546 D vitamin binding protein (BSI0185) .543
• D-vitamin binding protein (BSI0051 ) .512
• IgG Fc Binding protein (BSI0032) .507

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