Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
  • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/30—Psychoses; Psychiatry

Definitions

• the presently disclosed subject matter relates to methods for identifying a risk of developing a psychotic disorder in a subject. Also provided are methods for identifying whether or not a subject is developing a psychotic disorder.
• Psychotic disorders including schizophrenia, schizoaffective disorder, schizophreniform disorder, brief psychotic disorder, psychotic disorder not otherwise specified, mania with psychotic features, delusional disorder, and major depression with psychotic features, are a family of brain disorders characterized by the presence of psychotic symptoms that may include hallucinations, delusions, and disorganized thought process and/or behaviors. The symptoms significantly interfere with social and vocational function.
• the majority of psychotic disorders emerge during adolescence and early adulthood. About 1% of the general population develops a psychotic disorder, typically in late adolescence or early adulthood.
• blood tests are routinely used to aid diagnosis, to provide prognosis, and to plan and refine treatment of many disorders such as, but not limited to diabetes (see e.g., Abbasi et al, 2012; Lee & Colagiuri, 2013) and cardiovascular diseases (see e.g., Ahluwalia et al, 2013; Bleakley et al, 2013; Goh et al, 2013).
• diabetes see e.g., Abbasi et al, 2012; Lee & Colagiuri, 2013
• cardiovascular diseases see e.g., Ahluwalia et al, 2013; Bleakley et al, 2013; Goh et al, 2013.
• biomarker tests are most useful in persons already determined to be at elevated risk for a disease, typically based on clinical criteria (e.g., obesity as a clinical risk factor for diabetes and cardiovascular disease).
• any biomarker test with less than perfect specificity should ideally be employed in a relatively high risk population.
• Persons meeting clinical criteria for a high risk of psychosis have about a 30-35% risk of fully developed psychosis within two years (Fusar-Poli et al, 2012), and thus a biomarker test with reasonable sensitivity and specificity (-0.80) can achieve PPV of about 0.63; equivalently, about two-thirds of persons identified by such a test as at risk would truly be on a trajectory to develop psychosis.
• a diagnostic test would have a negative predictive ability of -0.90, greatly improving a clinician's confidence in predicting who is likely not to develop psychosis.
• peripheral blood biomarkers There are numerous studies that evaluate peripheral blood biomarkers in schizophrenia, and at least one study that evaluated peripheral blood biomarkers in persons prior to the onset of psychosis. Most studies have examined a small number of biomarkers (Miller et al, 2011) comparing persons with schizophrenia, on or off medications, to unaffected persons.
• the presently disclosed subject matter provides a panel of analyte assays (in some embodiments, blood analyte assays) that when used in combination create an index that increases the clinical certainty of psychosis risk in subjects at a substantially higher risk than that of the general population for the development of a psychotic disorder.
• the panel of analytes includes in some embodiments a summary measure of the levels of malondialdehyde-modified low density lipoprotein (CAS Registry No. 542-78-9), thyroid stimulating hormone (SWISS-PROT Accession No. P01222), Interleukin-IB (SWISS-PROT Accession No. P01584), matrix metalloproteinase-7 (SWISS-PROT Accession No.
• the levels of additional analytes in a sample isolated from a subject can be added to this core set of analytes to further increase the sensitivity and specificity of the assay.
• the additional analytes include but are not limited to one or more of growth hormone, KIT ligand, interleukin-8, apolipoprotein D, mucin- 16, Factor 7, chemokine (c-c motif) ligand 2, resistin, Cortisol, chemokine (c-c motif) ligand 8, alpha-2-macroglobulin, transthyretin, uromodulin, beta-2 transferrin, prostaglandin D synthase (beta trace-protein), adrenocorticotropin releasing hormone, and insulin-like growth factor.
• Subjects meeting high risk criteria for psychosis are frequently prescribed medications, such as but not limited to antipsychotics and antidepressants, which can alter the expression of analytes in the blood and/or serum of subjects.
• antipsychotics and antidepressants which can alter the expression of analytes in the blood and/or serum of subjects.
• one or more of N-(alpha)- acetyltransferase 15, ferritin, and alpha 1-antiichymotrypsin can in some embodiments be added to the panel to increase sensitivity and specificity.
• chromogranin A and/or endothelin 1 can be added to the panel of core analytes disclosed herein.
• Analytes can also be expressed differently in males than in females.
• one or more of interleukin-15, chemokine (c-c motif) ligand 11, and apolipoprotein A2 can in some embodiments be added to the core panel.
• one or more of calbindin 1, transforming growth factor beta 3, macrophage migration inhibitory factor, pappalysin-1, and testosterone can in some embodiments be added to the core panel.
• the presently disclosed subject matter relates to use of an analyte biomarker panel (in some embodiments, a blood analyte biomarker panel) at two time points, where a change in level of the analyte summary measure and/or of individual analytes can increase the clinical certainty of psychosis risk.
• the presently disclosed subject matter provides using the analyte biomarker panel (in some embodiments, a blood analyte biomarker panel) disclosed herein in combination with other indicators of psychosis risk, including but not limited to premorbid function; symptom severity; social and vocational function; volume or change in volume of brain gray matter, white matter, and/or ventricular volumes; measures of blood brain barrier permeability; electroencephalogram (EEG) measures or change in measures of brain function; brain imaging measures or change in measures of brain function; and/or salivary Cortisol and/or salivary DHEA levels to increase the clinical certainty of psychosis risk in persons meeting high risk criteria for psychosis.
• EEG electroencephalogram
• the presently disclosed subject matter provides in some embodiments, methods for identifying a risk of developing a psychotic disorder in a subject.
• the presently disclosed methods comprise isolating and/or providing a biological sample comprising serum, plasma, urine, and/or saliva from a subject who meets clinical criteria for elevated risk of psychosis; quantifying a level of expression in the biological sample of a plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in the biological sample, wherein the gene products or other endogenous or exogenous substances, or the mixture thereof, are selected from the group consisting of malodialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-1B, matrix metalloproteinase 7, and immunoglobulin E, combining the quantified levels of expression from step (b) to create a summary measure of expression for the subject; and comparing the summary measure of expression for the subject to one or more standards.
• the one or more standards are selected from the group consisting of summary measures of expression in subjects with elevated risk for psychosis who did not develop psychosis; summary measures of expression in subjects with elevated risk for psychosis who did develop psychosis; and summary measures of expression in unaffected subjects; wherein the comprising step identifies a risk of developing a psychotic disorder in the subject.
• the summary measure of expression for the subject is compared to both summary measures of expression in subjects with elevated risk for psychosis who did not develop psychosis and summary measures of expression in subjects with elevated risk for psychosis who did develop psychosis in order to determine whether the summary measures of expression for the subject in question more closely approximates (a) the summary measures of expression in subjects with elevated risk for psychosis who did not develop psychosis of (b) the summary measures of expression in subjects with elevated risk for psychosis who did develop psychosis in order to classify the subject in question as being more likely to develop psychosis or not develop psychosis.
• the presently disclosed subject matter also provides in some embodiments, methods for identifying whether or not a subject is developing a psychotic disorder.
• the presently disclosed methods comprise isolating and/or providing a biological sample comprising serum, plasma, urine, and/or saliva from a subject who meets one or more clinical criteria for elevated risk of psychosis; quantifying a level of expression in the biological sample of a plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in the biological sample, wherein the gene products or other endogenous or exogenous substances are malodialdehyde-modified low density lipoprotein, thyroid stimulating hormone, mterleukin-lB, matrix metalloproteinase 7, and immunoglobulin E; combining the quantified levels of expression to create a summary measure of expression for the subject; and comparing the summary measure of expression for the subject to one or more standards; repeating the isolating, quantifying, combining, and comparing steps at a second, later time point; and
• the isolating, quantifying, combining, and comparing steps are performed at least two different time points, and the results of the comparing step from the at least two different time points is indicative of the subject developing a psychotic disorder.
• the one or more standards are selected from the group consisting of summary measures of expression in subjects with elevated risk for psychosis who did not develop psychosis; summary measures of expression in subjects with elevated risk for psychosis who did develop psychosis; summary measures of expression in unaffected subjects; or any combination thereof.
• at least one of the one or more standards comprises a summary measure that is normalized with respect to healthy subjects of similar age, sex, and/or ancestry as the subject.
• the plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in the biological sample further comprises one or more of transthyretin (either total or low molecular weight), uromodulin, growth hormone, KIT ligand, interleukin-8, apolipoproptein D, chemokine (c-c motif) ligand 8, factor 7, Cortisol, resistin, alpha-2- macroglobulin, mucin- 16, chemokine (c-c motif) ligand 2.
• the plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in serum, plasma, urine and/or saliva further comprises one or more of beta 2 transferrin, prostaglandin D synthase (beta trace protein), adrenocorticotropin releasing hormone, and insulin-like growth factor.
• the subject is not being treated with an antidepressant, and the plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in the biological sample further comprises chromogranin A, endothelin 1, or both.
• the subject is not being treated with an antipsychotic, and the plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in the biological sample further comprises one or more of N-(alpha)-acetyltransferase 15, ferritin, and alpha 1- anti-chymotrypsin.
• the subject is a female and the plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found the biological sample further comprises one or more of calbindin 1, transforming growth factor beta, and cytokine (c-c motif) ligand 18.
• the subject is a male and the plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in the biological sample further comprises one or more of interleukin 15 and chemokine (c-c motif) ligand 11.
• the subject has or is at risk for developing Attenuated Psychosis Syndrome (APS; see the Diagnostic and Statistical Manual of Mental Disorders, Fifth ed. (DSM - 5)).
• the presently disclosed methods further comprise administering to the subject an antipsychotic medication if the subject is determined to be at risk of developing a psychotic disorder or is determined to be developing a psychotic disorder.
• the quantifying step comprises employing an plurality of antibodies, at least one of which binds to each of the gene products or other endogenous or exogenous substances with sufficient specificity to allow for quantification of the gene products or other endogenous or exogenous substances in the biological sample.
• the summary measure of expression for the subject is calculated by determining a z-score for each analyte assayed, wherein the z-score is based on the average and standard deviation of the unaffected comparison subjects, and summing the individual calculated z-scores.
• the subject is a female and the plurality of gene products or other endogenous or exogenous substances for which level of expressions are quantified comprise (1) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-1B, matrix metalloproteinase 7, and immunoglobulin E; or (2) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-1B, matrix metalloproteinase 7, and immunoglobulin E, and one or more of transthyretin (either total or low molecular weight), uromodulin, growth hormone, KIT ligand, IL-8, apolipoproptein D, chemokine (c-c motif) ligand 8, Factor 7, Cortisol, resistin, a2- macroglobulin, mucin- 16, and chemokine (c-c motif) ligand 2; or (3) malondialdehyde- modified low density lipoprotein, thyroid stimulating hormone, Interleukin-1B, matrix
• the subject is a male and the plurality of gene products or other endogenous or exogenous substances for which level of expressions are quantified comprise (1) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-IB, matrix metalloproteinase 7, and immunoglobulin E; or (2) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-IB, matrix metalloproteinase 7, and immunoglobulin E, and one or more of transthyretin (either total or low molecular weight), uromodulin, growth hormone, KIT ligand, IL-8, apolipoproptein D, chemokine (c-c motif) ligand 8, Factor 7, Cortisol, resistin, o2- macroglobulin, mucin- 16, and chemokine (c-c motif) ligand 2; or (3 )malondialdehy de- modified low density lipo
• the subject is not being treated with an antidepressant and the plurality of gene products or other endogenous or exogenous substances for which level of expressions are quantified comprise (1) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin- IB, matrix metalloproteinase 7, and immunoglobulin E; or (2) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-IB, matrix metalloproteinase 7, and immunoglobulin E, and one or more of transthyretin (either total or low molecular weight), uromodulin, growth hormone, KIT ligand, IL-8, apolipoproptein D, chemokine (c-c motif) ligand 8, Factor 7, Cortisol, resistin, 2-macroglobulin, mucin- 16, and chemokine (c-c motif) ligand 2; or (3) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone
• the subject is not being treated with an antipsychotic and the plurality of gene products or other endogenous or exogenous substances for which level of expressions are quantified comprise (1) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-I B, matrix metalloproteinase 7, and immunoglobulin E; or (2) malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-I B, matrix metalloproteinase 7, and immunoglobulin E, and one or more of n-ansthyretin (either total or low molecular weight), uromodulin, growth hormone, KIT ligand, IL-8, apolipoproptein D, chemokine (c-c motif) ligand 8, Factor 7, Cortisol, resistin, a2-macroglobulin, mucin- 16, and chemokine (c-c motif) ligand 2; or (3) malondialdeh
• the presently disclosed subject matter provides methods for identifying a risk of developing a psychotic disorder in a subject and also for identifying whether or not a subject is developing a psychotic disorder.
• Figures 1A-1C provide a comparison of positive prediction for a diagnostic test in groups with low (1%; Figure 1A), medium (30%; Figure IB), and high (50%; Figure 1C) risk of disease.
• a "true positive” or “true negative” result means that the test was correct.
• the test sensitivity true positive/all actually positive
• specificity true negative/all actually negative
• the positive predictive value PV; true positive/all who test positive
• negative predictive value NPV; true negative/all who test negative
• Other test metrics such as sensitivity, specificity, and accuracy, did not depend on the disease rate in the population of interest.
• Figure 2 is a flow diagram of an exemplary greedy algorithm for determining a plurality of analytes that could be employed for identifying a risk of developing a psychotic disorder in a subject.
• Figure 3 is a graph of the results of 5x5x5-fold cross-validation of a greedy algorithm with random 80%-20% partitions of each group (subjects who met psychosis risk syndrome criteria and later developed a psychotic disorder (CHR-P), subjects who met psychosis risk syndrome criteria but later did not develop a psychotic disorder (CHR- NP), and unaffected comparison subjects who did not meet psychosis risk syndrome criteria (UC)).
• CHR-P psychotic disorder
• CHR- NP psychotic disorder
• UC psychosis risk syndrome criteria
• Figures 4A and 4B are histograms of frequencies of values of the area under the resulting receiver operating curve (AUC) for pemiutations of random data.
• the types of the data were randomly permuted and were allocated into bins of 35, 40, and 32 samples (same as UC, CHR-NP, CHR-P).
• classifiers from the pseudo data were built using the same greedy algorithm and five-fold cross validation process, retaining the sum of the five most frequently selected analytes in every trial. Both true data and 100 trials of pseudo data were thereby used in 101 classifiers with sums of the five most frequently chosen analytes.
• Figure 4A relates to AUCs for UC versus CHR-P of the classifiers built with just the five most selected analytes of 101 classifier constructions with 125 iterations of the greedy algorithm each, one with actual data and 100 with data with randomly permuted sample labels. In all trials, analytes could be chosen up to 125 times in five-by-five-by-five cross validations.
• the construction of the true classifier (applied to true data) outperformed the 100 pseudo classifiers (applied to respective pseudo data).
• Figure 4B relates to the same exercise calculating 101 AUCs of the same 101 classifiers applied to CHR-NP versus CHR-P.
• the true classifier distinguished true data by choosing as first five analytes a combination with higher AUC values.
• the observations in this Figure like those in Figure 5, indicated that: (1) the raw assay data must have had information distinguishing UC from CHR-P and CHR-NP from CHR-P; (2) the normalization method used did not obliterate the information; and (3) the classifier construction that was employed actually revealed the information.
• Figures 5A and 5B are plots of the frequencies of first five analytes chosen with true labels and randomly permuted labels of the 107 subjects into subsets of 35, 40, and 32. The most frequently chosen analyte with randomly permuted labels was chosen less frequently than the most frequently chosen analyte with true labels, as were the second most frequently chosen, and so on.
• frequencies of the five most selected analytes of two classifier constructions with 125 iterations of the greedy algorithm each, one with actual data (black; plots 1, 3, 5, 7, and 9 from the left) and one with data with randomly permuted sample labels (gray; plots 2, 4, 6, 8, and 10 from the left).
• FIG. 5B is a comparison of the true classifier average total frequency with those of 100 pseudo classifiers.
• a pseudo classifier started with a random permutation of the 107 sample labels and type memberships and was otherwise constructed exactly like a true classifier. The pseudo classifiers were applied to the respective sets of pseudo data.
• the true classifier distinguished true data by choosing as the first five analytes a combination with higher frequencies.
• Figures 6A and 6B are receiver operating curves for the classifier constructed with all 107 samples from the 18 most frequently selected analytes in 125 five-by-five -by-five cross validations.
• Figure 6A pertains to the UC versus CHR-P classification and
• Figure 6B is the same classifier applied to the CHR-NP versus CHR-P classification.
• the middle (darker) curve of each group of three curves represents the data generated and the first and third (lighter) curves represent the 95% confidence interval (CI).
• Figure 7 is a schematic diagram of the Immune-Hypothalamus-Pituitary (IHP) interactions in blood analytes included in the predictive index.
• the asterisk indicates analytes that were included in the index.
• Figure 8 is a graph illustrating reproducibility of assays for the 18 most frequently selected analytes.
• a technical replicate of the sample from subject 283 was assayed twice, each time in duplicate, to generate the normalized z-scores.
• 111 reported values were not minima.
• Over the full 141 analytes the correlation was 0.84; for the 18 analytes identified in Figure 8, the correlation was 0.96 including three of the analytes for which both samples were at their minima; these 18 analytes are shown in Figure 8. Data were generated in duplicate for subject 283 to insure quality.
• the darker line in the Figure denotes the normalized values from one of the assays of subject 283 and the lighter line denotes the normalized values from the other assay of subject 283.
• the 18 analytes listed in Figure 8 are, from left to right, mucin-16, intei eukin-8, malondialdehyde-modified low density lipoprotein, matrix metalloproteinase-7, uromodulin, immunoglobulin E, growth hormone, chemokine (c-c motif) ligand 8, Factor 7, thyroid stimulating hormone, KIT ligand, Cortisol, interleukin- IB, resistin, apolipoprotein D, alpha-2-macroglobulin, chemokine (c-c motif) ligand 2, and transthyretin.
• the presently disclosed subject matter differs from previous studies in several respects.
• different patient populations were employed, including persons at elevated risk for psychosis, some of whom developed a psychotic disorder within two years, while others who did not.
• very different data analytic methods that are methodologically robust, avoid overfitting, and thus produce a reliable indicator of disease risk are disclosed herein.
• the level of no single analyte provided meaningful sensitivity or specificity to increase psychosis risk prediction. Rather, a summary measure of a specific group of analytes that has high sensitivity and specificity for psychosis risk prediction is disclosed herein.
• analyte refers to one or more analytes, unless the context clearly indicates otherwise.
• the phrase “A, B, C, and/or D” includes A, B, C, and D individually, but also includes any and all combinations and subcombinations of A, B, C, and D.
• the presently disclosed and claimed subject matter can include the use of either of the other two terms.
• the presently disclosed subject matter relates in some embodiments to comparing a summary measure of expression of a plurality of analytes in one subject to a standard, which in some embodiments can comprise a summary measure that is normalized with respect to healthy subjects of similar age, sex, and/or ancestry as the subject.
• the presently disclosed subject matter thus also encompasses methods wherein the standard consists essentially of a summary measure that is normalized with respect to healthy subjects of similar age, sex, and/or ancestry as the subject, as well as methods that in some embodiments employs a standard that consists of a summary measure that is normalized with respect to healthy subjects of similar age, sex, and/or ancestry as the subject.
• the methods of the presently disclosed subject matter comprise the steps that are disclosed herein and/or recited in any given claim, in some embodiments the methods of the presently disclosed subject matter consist essentially of the steps that are disclosed herein and/or recited in any given claim, and in some embodiments the methods of the presently disclosed subject matter consist of the steps that are disclosed herein and/or recited in any given claim.
• the presently disclosed methods comprise in some embodiments (a) isolating and/or providing a biological sample comprising serum, plasma, urine, and/or saliva from a subject who meets clinical criteria for elevated risk of psychosis; (b) quantifying a level of expression in the biological sample of a plurality of gene products or other endogenous or exogenous substances, or a nuxture thereof, normally found in the biological sample, wherein the gene products or other endogenous or exogenous substances, or the mixture thereof, are selected from the group consisting of malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleuldn-IB, matrix metalloproteinase 7, and immunoglobulin E; (c) combining the quantified levels of expression from step (b) to create a summary measure of expression; and (d) comparing the summary measure
• the phrase "clinical criteria for elevated risk of psychosis” refers to the 19 psychological criteria outlined in the "Scale of Prodromal Symptoms” (SOPS; see Miller et at, 2003). These 19 psychological criteria include Positive Symptoms (Unusual Thought Content/Delusional Ideas, Suspiciousness/Persecutory Ideas, Grandiosity, Perceptual Abnormalities/Hallucinations, and Disorganized Communication), Negative Symptoms (Social Anhedonia, Avolition, Expression of Emotion, Experience of Emotions and Self, Ideational Richness, and Occupational Functioning), Disorganization Symptoms (Odd Behavior and Appearance, Bizarre thinking, Trouble With Focus and Attention, and Personal Hygiene), and General Symptoms (Sleep Disturbance, Dysphoric Mood, Motor Disturbances, and Impaired Tolerance to Normal Stress). Each symptoms is given a rating of severity (0, 1, 2, 3, 4, 5, 6) for criteria such as "paranoia
• a plurality of gene products or other endogenous or exogenous substances, or a mixture thereof refers generally to a plurality of analytes the presence of which can be assayed in a biological sample isolated from a subject such as but not limited to blood.
• analyte refers to any measurable substance that is present in a biological sample from a subject, an analysis of the concentration and/or expression of which in the biological sample can be employed in the methods disclosed herein to identifying subjects at risk for developing a psychotic disorder and/or whether or not a subject is developing a psychotic disorder.
• An analyte can be a protein, peptide, lipid, and/or carbohydrate, and/or any modified variant thereof, alone or in any combination, which is present in a biological sample isolated from a subject including, but not limited to a human subject.
• endogenous or exogenous substances refer to anything that might be present in and therefore assayable with respect to expression level, concentration, etc. in a biological sample that can be isolated from a subject.
• Endogenous substances are those that are produced by or otherwise originate from within the subject itself.
• an endogenous substance can be a gene product encoded by the subject's genome, or a modified variant thereof.
• the modified variant is modified by a biological process that occurs within the subject, such as but not limited to malondialdehyde-modified low density lipoprotein, which is a lipid peroxidation marker of oxidative stress associated with chronic stress and inflammation.
• a biological process that occurs within the subject, such as but not limited to malondialdehyde-modified low density lipoprotein, which is a lipid peroxidation marker of oxidative stress associated with chronic stress and inflammation.
• exogenous substances include any substance that is found within a biological sample but that has been introduced into the subject, either intentionally (including but not limited to ingestion, administration, etc.) or unintentionally (e.g., by infection of a pathogen).
• Food and therapeutic agents are considered “exogenous agents", as are viruses, bacteria, etc.
• biological sample refers to any material that can be isolated from a subject and that is expected to comprise an analyte.
• exemplary, non-limiting biological samples include a body fluid (for example blood, cerebral spinal fluid, saliva, urine, etc., or any fraction thereof such as, but not limited to plasma), a cell (for example white blood cells, red blood cells, cultured human cells, etc.), and a tissue (for example skin, fat, olfactory epithelium, bone marrow, etc.).
• the quantity of an analyte can vary, depending on the biological sample from or in which it is measured, the time of day that it is measured, any recent use of a drug and/or ingestion of food, physical exercise, and/or any other factors that might impact a level of an analyte that might be present in an individual.
• a biological sample comprises human blood, and in some embodiments the blood specimen is collected mid-day.
• the blood is collected in tubes that contain compounds to protect the stability of analytes present within the blood, particularly those to be assayed in the presently disclosed methods.
• blood isolated from a subject is processed to obtain the biological sample to be assayed, which is then tested for levels of expression of desired analytes.
• the subject is not being treated with an antidepressant when a biological sample is isolated from the subject.
• the phrase "not being treated with an antidepressant” refers to the subject not receiving any antidepressant therapeutic agent for a time period that is sufficiently long that any therapeutic effect and/or any effect on the expression of any analyte employed in the practice of the presently disclosed subject matter would be expected to have been eliminated in the subject.
• Exemplary time periods for clearance of antidepressants can be in some embodiments 24 hours, in some embodiments 48, hours, in some embodiments 72 hours, in some embodiments one week, in some embodiments two weeks, in some embodiments one month, in some embodiments two months, in some embodiments three months, in some embodiments six months, etc.
• the plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in the biological sample can in some embodiments further comprise chromogranin A, endothelin 1 , or both.
• the subject is not being treated with an antipsychotic.
• the phrase "not being treated with an antipsychotic” refers to the subject not receiving any antipsychotic therapeutic agent for a time period that is sufficiently long that any therapeutic effect and/or any effect on the expression of any analyte employed in the practice of the presently disclosed subject matter would be expected to have been eliminated in the subject.
• Exemplary time periods for clearance of antipsychotics can also bein some embodiments 24 hours, in some embodiments 48, hours, in some embodiments 72 hours, in some embodiments one week, in some embodiments two weeks, in some embodiments one month, in some embodiments two months, in some embodiments three months, in some embodiments six months, etc.
• the plurality of gene products or other endogenous or exogenous substances, or a mixture thereof, normally found in the biological sample can in some embodiments further comprise one or more of N-(alpha)-acetyltransferase 15, ferritin, and alpha 1-anti-chymotrypsin.
• the phrase "level of expression” relates to an amount of an analyte that is present in a biological sample at a time at which it is assayed. Levels of expression can be referred to in whatever measure is desirable. Exemplary measures for levels of expression include absolute measures such as concentration ⁇ e.g. , nanograms/microliter or any other such mass per unit volume or mass per unit mass determination), relative measures, or any other measure that provides a reasonably repeatable articulation of an amount of an analyte in a biological sample. Those skilled in the art recognize the variety of methods available to measure the expression level of an analyte from a bodily fluid, tissue extract, cell extract, etc.
• a level of expression can relate to an abundance of a transcription or translation product of that gene, which can be determined using standard molecular biological techniques including, but not hmited to quantitative reverse transcription polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), mass spectrometry, planar microarrays (protein chips), and bead-based microarrays (suspension arrays; see Jun et ah, 2012). These methods and others can be used alone or in combination with any suitable method of measuring the levels of the specified analytes.
• qRT-PCR quantitative reverse transcription polymerase chain reaction
• ELISA enzyme-linked immunosorbent assay
• mass spectrometry mass spectrometry
• planar microarrays protein chips
• bead-based microarrays suspension arrays
• the choice of method for measuring the expression levels of an analyte or combination of analytes can be made by evaluating any relevant property of the test, including but not limited to the inherent variability of the test result, the test dynamic range, the amount of sample required, the complexity of the test protocol, and/or any other practical consideration that might be relevant to a particular quantifying methodology.
• biomarker tests reflected in the steps of the presently disclosed methods can be most useful when performed on persons that have aheady been determined to be at elevated risk for psychosis, typically based on clinical criteria. This is related to the fact that the proportion of patients who test positive on a given biomarker test who actually get the disease (positive predictive value; PPV) can vary dramatically depending on the actual rate of disease in the tested population. If the rate of disease is low, for example if only 1% of tested persons are actually at risk for the disease, than even a test with high sensitivity and specificity (-0.8) can have a significant fraction of false positives as true positives (see Figure 1).
• At least a 10-fold elevation in risk as compared to the population at least is considered a "high risk”.
• Examples include persons meeting the Criteria for Prodromal States (COPS), ultra high- risk criteria, basic symptom criteria, schizotypal personality disorder criteria or having a relative with a psychotic disorder (Golembo-Smith et ah, 2012; Schultze-Lutter et ⁇ , 2013; see also Miller et al, 2003; Lencz et al, 2004; Fusar-Poli et al, 2012).
• Other clinical or biological assessments can further indicate even greater elevations in psychosis risk in persons meeting clinical or other criteria for elevated psychosis risk.
• Examples include, but are not limited to, neurocognitive impairments (Seidman et al, 2010), social cognitive impairments (Healey et al, 2013), social and vocational functional impairments (Cornblatt et al, 2012), salivary Cortisol levels (Walker et al, 2013), change in gray or white matter volume (Witthaus et al, 2008; Chan et al, 2009; Takahashi et al, 2009), electroencephalogram (Shin et al, 2009; Belger et al, 2012; Nagai et al, 2013; Perez et al, 2013) and so on. Therefore, in some embodiments a blood test can be combined with other measures of psychosis risk vulnerability to increase psychosis prediction.
• a person using a biomarker test should ideally be aware of the actual disease risk in the person tested in order to correctly interpret test results related to psychosis risk. For example, if the test is used in a general population, different cut-off points with high specificity (> 0.99) at the sacrifice of sensitivity can be desirable.
• the presently disclosed methods are thus in some embodiments used in persons who meet criteria for at minimum a -10-fold increase in psychosis risk compared to the general population risk of -1% as assessed using criteria other than the presently disclosed methods.
• a biomarker test with good sensitivity and specificity can achieve PPV of about 0.63; equivalently, about two-thirds of persons identified by such a test as at risk would truly be on a trajectory to develop psychosis.
• a diagnostic test would have a negative predictive ability of -0.90, greatly improving a clinician's confidence in predicting who is likely not to develop psychosis.
• a treatment methodology can be implemented wherein the subject undergoes some therapeutic treatment to address the psychotic disorder.
• Treatment strategies for addressing psychotic disorders are known to those of skill and include, but are not limited to administration of antipsychotic medication (such as, but not limited to chlorpromazine, flupenthixol, fluphenazine, haloperidol, loxapine, perphenazine, pimozide, thioridazine, thiothixene, trifluoperazine and zuclopenthixol); and psychosocial intervention including, but not limited to patient case management, supportive psychotherapy, group therapy, individual Cognitive Behavior therapy (CBT), and/or vocational counseling.
• a subject who is identified to at risk for developing a psychotic disorder and/or is presently developing a psychotic disorder using the methods disclosed herein is placed on antipsychotic medications and/or given one or more types of supportive psychosocial
• a subject can present with clinical high risk symptoms.
• a blood specimen can be collected and assayed for the presently disclosed markers. Initially the five "core" analytes, malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, interleukin-lb, matrix metalloproteinase 7, and immunoglobulin E, are assayed.
• transthyretin either total or low molecular weight
• uromodulin growth hormone
• KIT ligand IL-8
• apolipoproptein D chemokine (C-C motif) ligand 8
• chemokine (C-C motif) ligand 8 Factor 7, Cortisol
• resistin a2-macroglobulin
• mucin- 16 chemokine (C-C motif) ligand 2
• Other characteristics of the subject can also be employed to add further analytes to the assay, such as whether the subject is female (one or more of calbindin 1, transforming growth factor beta, and cytokine (c-c motif) ligand 18 can be added), male (one or more of interleukin 15 and chemokine (c-c motif) ligand 11 can be added), the subject is not being treated with an antidepressant (chromogranin A and/or endothelin 1 can be added), and/or the subject is not being treated with an antipsychotic (one or more of N-(alpha)-acetyltransferase 15, ferritin, and alpha 1-anti-chymotrypsin can be added).
• an antidepressant chromogranin A and/or endothelin 1 can be added
• an antipsychotic one or more of N-(alpha)-acetyltransferase 15, ferritin, and alpha 1-anti-chymotrypsin can be added.
• the result of the assays will yield a new vector of values (in some embodiments, a normalized component-by-component as disclosed herein).
• the classifier function disclosed herein is then applied to the new vector.
• a clinician can then choose a combination of specificity and sensitivity (i.e., a compromise or trade-off is chosen based on relevant considerations for that subject) on the ROC curve, hence a threshold (break point) for the classifier function. For example, the clinician might regard false negatives as more costly per subject than false positives, hence a choice is made to reduce false negatives. From this, a decision with respect to treatment is chosen.
• the new vector can compared to some or all of the historical vectors that can be generated are from persons who did progress to schizophrenia.
• the comparison could be among mean calculations of Pearson correlations or Spearman correlations and/or other known method of comparing n-dimensional vectors.
• the new vector can also be compared to all the historical vectors that are from persons who did not progress to schizophrenia.
• the presently disclosed subject matter can compare all of the comparisons to determine whether the new vector is more similar to the PROGRESSED patients or the NOT PROGRESSED patients.
• the new vector can have Pearson correlations with 1000 PROGRESSED vectors with an average of 0.1 and a standard deviation of 0.2.
• the new vector can also have Pearson correlations with 1000 NOT PROGRESSED vectors with an average of 0.5 and a standard deviation of 0.2. This would provide strong evidence that the new vector represents a subject who is unlikely to progress to schizophrenia or another psychotic disorder. A clinician could then decide not to embark at present on a regime of treatments that would themselves inevitably carry risks of adverse side-effects.
• comparisons of new vectors with two or more sets of historical vectors can be accomplished using techniques that are well known to those skilled in the art.
• the presently disclosed subject matter also provides assay kits comprising reagents, in some embodiments antibodies, for detecting the presence of and quantifying levels of expression in biological samples of pluralities of gene products or other endogenous or exogenous substances, or mixtures thereof, normally found in the biological samples, wherein the gene products or other endogenous or exogenous substances, or the mixture thereof, are selected from the group consisting of malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-1B, matrix metalloproteinase 7, and immunoglobulin E.
• the reagents can be, in some embodiments, antibodies that binds to malondialdehyde-modified low density lipoprotein, thyroid stimulating hormone, Interleukin-1B, matrix metalloproteinase 7, and immunoglobulin E.
• the kits further comprise reagents that bind to one or more of transthyretin (either total or low molecular weight), uromodulin, growth hormone, KIT ligand, IL-8, apolipoproptein D, chemokine (c-c motif) ligand 8, Factor 7, Cortisol, resistin, a2-macroglobulin, mucin- 16, chemokine (c-c motif) ligand 2, of beta 2 transferrin, prostaglandin D synthase (beta trace protein), adrenocorticotropin releasing hormone, insulin-like growth factor, chromogranin A, endothelin 1, N-(alpha)- acetyltransferase 15, ferr
• the reagents for detecting the presence of and quantifying levels of expression in biological samples of pluralities of gene products or other endogenous or exogenous substances hsted herein above are detectably labeled.
• the kits further comprise compounds that permit detection of the detectably labeled reagents.
• one or more of the reagents provided in the kit is labeled with a different detectable label, allowing for simultaneous detection and quantification of multiple gene products or other endogenous or exogenous substances in biological samples.
• the reagents for detecting the presence of and quantifying levels of expression in biological samples of pluralities of gene products or other endogenous or exogenous substances are antibodies, optionally monoclonal antibodies.
• the antibodies are affixed to a solid support.
• the reagents for detecting the presence of and quantifying levels of expression in biological samples of pluralities of gene products or other endogenous or exogenous substances are oligonucleotide probes that are specific for one or more of the gene products or other endogenous or exogenous substances.
• the nucleotide sequences of the oligonucleotide probes are designed to bind to cDNAs derived from the gene products but not bind to genomic DNA (i.e., have sequences that flank introns that are present in the subject's genome such that the oligonucleotides include sequences from different exons of the gene products.
• Example provides further illustrative embodiments.
• those of skill will appreciate that the following Example is intended to be exemplary only and that numerous changes, modifications, and alterations can be employed without departing from the scope of the presently disclosed subject matter.
• NAPLS2 North American Prodrome Longitudinal Study
• CHR- P psychotic disorder
• CHR-NP psychosis risk syndrome criteria
• UC psychosis risk syndrome criteria
• SOPS Schedule of Prodromal Symptoms
• the SOPS was composed of four symptom domains that were classified as positive (e.g., unusual thought content, suspiciousness, grandiose ideation, perceptual abnormalities, disorganized communication); negative (e.g., social anhedonia, avolition, expression of emotion, experience of emotions and self, ideational richness, occupational functioning); disorganized (e.g., odd behavior or appearance, playful thinking, trouble with focus and attention, impairment in personal hygiene); and general (sleep disturbance, dysphoric mood, motor disturbances, impaired tolerance to normal stress).
• positive e.g., unusual thought content, suspiciousness, grandiose ideation, perceptual abnormalities, disorganized communication
• negative e.g., social anhedonia, avolition, expression of emotion, experience of emotions and self, ideational richness, occupational functioning
• disorganized e.g., odd behavior or appearance, playful thinking, trouble with focus and attention, impairment in personal hygiene
• general sep disturbance, dysphoric mood, motor disturbances, impaired tolerance to normal
• Attenuated positive symptom There were three criteria for clinical high risk: attenuated positive symptom; genetic risk and deterioration; and brief intermittent psychotic symptom.
• attenuated positive symptom criteria subjects had a rating of "3", "4", or "5" on at least one of the positive symptom items and at least one symptom that began or worsened in the past year and occurred at least once per week in the past month.
• genotyping GAF; Coffey et al, 1996) scale in the past year and schizotypal personality disorder or a first-degree relative with a psychotic disorder.
• SCID-I/NP The Structured Clinical Interview for Axis I DSM-TV Disorders (SCID-I/NP; First et al, 2002) was administered during the initial evaluation and during subsequent annual follow-up assessments.
• the SCID-I/P was utilized to maintain consistency in the diagnostic procedure across participants and over time as they entered young adulthood through the longitudinal course of the study.
• Plasma Collection Blood samples were collected in Becton Dickenson PI 00 blood collection tubes that contain EDTA as anticoagulant, proprietary protein stabilizers, and a mechanical separator. Most samples were processed within 2 hours, and the plasma stored at -80°C until analysis.
• Plasma Assay Plasma samples were sent on dry ice to Myriad Rules Based Medicine, a biomarker testing laboratory that has maintained Clinical Laboratory Improvement Amendments (CLIA)-accreditation by COLA (Columbia, Maryland, United States of America) since 2006. Samples were analyzed with the Human DISCOVERYMAP® assay (Myriad RBM, Austin, Texas, United States of America), a LUMINEX® bead-based multiplex immunoassay (Luminex Corporation, Austin, Texas, United States of America) that included 185 analytes involved in hormonal responses, inflammation, growth, oxidative stress, and metabolism. The value for an individual analyte was based on a standard curve, and samples were run in duplicate.
• CLIA Clinical Laboratory Improvement Amendments
• the least detectable dose was the concentration interpolated by the average plus 3 standard deviations of 20 readings of diluent blanks.
• the lower limit of quantification was the lowest concentration of an analyte in a sample that could be reliably detected, as defined by the coefficient of variation of replicate standard samples ⁇ 30% (90% of analytes had a coefficient of variation of standard samples ⁇ 15%).
• Technicians ran protein assays without knowledge of clinical status of the subjects and used standard protocols.
• Analyte values that were not quantifiable were converted to the LLOQ. Exclusion of Analytes.
• the original data set contained 185 analytes. Twenty-three analytes that were not detected in at least 20% of the subjects were excluded. Most of the included analytes (80%) were detected in at least 90% of the subjects.
• the type of medication was as follows: 25% of CHR-NP subjects and 13% of CHR-P subjects were on an antipsychotic; 30% of CHR-NP subjects and 25% of CHR-P were on an antidepressant; 8% of CHR-NP subjects and 6% of CHR-P subjects were on a stimulant; 5% of CHR-NP subjects and 3% of CHR-P subjects were on a mood stabilizer; and 5% of CHR-NP subjects and 6% of CHR-P subjects were on a benzodiazepine.
• No subjects were taking a non-steroidal anti-inflammatory drug (NSAID) or an antibiotic at the time of the blood draw.
• NSAID non-steroidal anti-inflammatory drug
• Among the UC subjects one was prescribed an antidepressant after enrollment but before the blood draw.
• the remaimng 39 analytes had six to 83 samples with minimum values.
• Four of the 39 are described herein to be especially informative: namely, malondialdehyde-modified low- density lipoprotein (64 minima), interleukin- 1 beta (63 minima), immunoglobulin E (11 minima), and interleukin-8 (13 minima).
• Greedy algorithms are capable of selecting collectively informative markers from large candidate sets (Liu et al. , 2005). They linearly build marker selections and avoid brute force examination of all possible subsets of markers. A program that first selected the very best single analyte for distinguishing the three types was developed. Then a second analyte was added that best improved performance, if possible. Additional analytes were selected and added until no further selection of any analyte improved performance. The selections were made over numerous subsets of the subjects, and selected sets of analytes were intersected to find analytes that consistently contributed to performance (see Figure 2).
• index also referred to herein as a “summary measure of expression”, defined as the sum of the z-scores of all selected analytes.
• the index or summary measure takes into account the five "core” analytes (i.e., malondialdehy de-modified low density lipoprotein, thyroid stimulating hormone, interleukin-lB, matrix metalloproteinase 7, and immunoglobulin E), and in some embodiments the index or summary measure takes into account the 18 analytes listed in Figure 8.
• analytes beyond the five core analytes are included in the analysis (for example, if the subject is not being treated with an antidepressant, one or both of the analytes chromogranin A and/or endothelin 1 can be added; if the subject is not being treated with an antipsychotic, one or more of the analytes N-(alpha)- acetyltransferase 15, ferritin, and alpha 1-anti-chymotrypsin can be added; if the subject is a female, one or more of the analytes calbindin 1 , transforming growth factor beta, and cytokine (c-c motif) ligand 18 can be added; and if the subject is a male, one or both of the analytes interleukin 15 and/or chemokine (c-c motif) ligand 11 can be added).
• classifier analyses on the results of the greedy algorithm were executed using five-by-five-by-five-fold cross validation (Kohavi, 1995; Tropsha, 2010) with repeated random sub-sampling; it was implemented in the Microsoft EXCEL® brand spreadsheet program with macros (nested loops designed to implement the greedy algorithm depicted in Figure 2 and add-ins (Ablebits, a a project of Add-in Express Ltd., Homel, illness; CAMO Software Inc., Woodbridge, New Jersey, United States of America; MEDCALC® Software bvba, Ostend, Belgium; and MathWave Technologies, Dnepropetrovsk, Ukraine).
• the groups of 32 CHR-P, 40 CHR-NP, and 35 UC subjects were randomly assigned to 1 of 5 equal subgroups.
• 4 of the CHR-P, 4 of the CHR-NP, and 4 of the UC subgroups were used to train a classifier that was tested on the complementary subgroups.
• the performance of the 125 preliminary classifiers for the 125 tests was noted. The entire process was repeated 20 times: that is, 20 initial selections of the random 20% subgroups, for a total of 2500 executions of the greedy algorithm.
• a review of all the preliminary classifiers led to a final, integrated classifier because in the 125 tests several analytes were repeatedly selected while other analytes were never selected.
• AUC area under the resulting receiver operating curve
• the AUC was, after the Student t-test /?-value, a second, reasonable performance metric, and two were used to avoid the risk of some sort of idiosyncratic effect of choosing one.
• a receiver operating curve is a plot of sensitivity (i.e., test correctly predicts positive/all true positives) versus the false positive rate (i.e., 1 — specificity; test correctly predicts negative/all true negatives).
• Various threshold settings yield the points along the curve.
• An AUC of 0.5 indicates that the classification is equal to chance, and an AUC of 1 indicates perfect classification.
• CHR clinical high risk
• CHR-NP clinical high risk subjects who did progress to psychosis
• CHR-P clinical high risk subjects who did progress to psychosis
• UC unaffected comparison
• All of the CHR subjects met attenuated positive symptom diagnostic criteria.
• the psychosis diagnoses of the CHR-P included 13 with psychosis, not otherwise specified, 14 with schizophrenia, two with major depression with psychotic features, and one each with schizoaffective, delusional, or bipolar disorder.
• Marijuana use 6 9% 28% 31%
• Interleukin-15 P40933 1.25 6.75 Pleiotropic, involved in both innate and adaptive immune systems. (Perera et ah, 2012)
• Macrophage P14174 0.91 9.67 A pro-inflammatory cytokine migration inhibitory produced by the activated T factor lymphocytes, macrophages, and in the anterior pituitary from arenocorticotropic hormone and thyroid stimulating hormone cells. (Nishino et ah, 1995) Previously described as altered in schizophrenia. (Schwarz et ah, 2013)
• Metalloproteinase P01033 1.11 11.35 In addition to inhibiting inhibitor 1 metalloproteinase, has cell growth-promoting activities.
• Chemokine (C-C P78556 1.25 11.35 Pro-inflammatory chemokine, motif) ligand 20 increased expression associated with autoimmune disease. (Li, Qi et al, 2013)
• Eotaxin' 2 P51671 1.29 11.35 A chemokine implicated in allergic response, increased expression associated with aging (Villeda et al, 2011) and with use of marijuana. (Femandez-Egea, Scoriels et al, 2013) Serum elevations associated with chronic schizophrenia. (Teixeira et al, 2008)
• Matrix P14780 1.17 11.35 A matrix metalloproteinase metalloproteinase 9 and thus involved in proteolysis of extracellular matrix. Associated with inflammation. (Yabluchanskiy et al, 2013) Elevated activity reported in patients with chronic schizophrenia. (Chang e? al, 2011)
• Plasma Analytes and Psychosis Risk Prediction An analyte could be chosen up to 125 times with each of the 20 runs (2500 total executions) in the cross-validation procedure. As expected, somewhat different combinations of analytes were chosen every time, but certain analytes were very frequently chosen. The average and quartiles of frequencies are shown in Figure 3. The most confidence in the informativeness of analytes should likely be placed with those most frequently chosen. It was observed, for example, that malondialdehyde-modified low-density lipoprotein was selected in almost all of the 2500 executions of the greedy algorithm. However, after the eighteenth most popular analyte (alpha-2-macroglobulin), the frequency fell by 30%, suggesting a cutoff point and hence a selection of 18 analytes.
• the types of the data were randomly permuted and were allocated into bins of 35, 40, and 32 samples (same as UC, CHR-NP, CHR-P). Then, classifiers were built from the pseudo data using the same greedy algorithm and five-fold cross validation process, retaining the sum of the five most frequently selected analytes in every trial. Both true data and 100 trials of pseudo data were thereby used in 101 classifiers with sums of the five most frequently chosen analytes. It terms of AUCs, the true data classifiers had higher performance than all (CHR-NP verus CHR-P) or all but one (UC versus CHR-P) classifier built with random data (and applied to the same random data), suggesting that the true data produced results that are unlikely by chance.
• Immunoglobulin E P01854 Found only in human, receptors are expressed on mast cells, monocytes, macrophages, and other white blood cells. Classically known to mediate allergic responses. Activation of perivascular (blood brain barrier) mast cells in the hypothalamus by IgE results in HPA axis activation. (Theoharides & Konstantinidou, 2007; Lindsberg et al, 2010)
• Uromodulin P07911 Made by the kidney tubules and excreted with urine, as well as by the choroid plexus .(Schuller et al, 1984; Zalc et al, 1984) Uromodulin induces innate immune response(Ratliff, 2005; Weichhart et al, 2005) and stimulates monocytes to release proinflammatory cytokines. (Su et al, 1997)
• Transthyretin P02766 Circulating transthyretin tetramer is produced by liver, and monomer produced by choroid plexus. (Redzic & Segal, 2004) Functions to transport thyroid hormone and retinol. Characteristically decreased in acute phase immune response (a negative acute phase reactant), elevation in our sample may indicate increased blood brain barrier/blood CSF barrier permeability; this hypothesis can be tested by looking at transthyretin monomer in blood, which is extremely low in persons with intact BBB and elevated with BBB disruption. (Marchi et al, 2003)
• KIT ligand 2 P21583 Circulating KIT ligand is produced by fibroblasts, endothelial cells, and leptin receptor expressing perivascular stromal cells (Ding et al, 2012; Lennartsson & Ronnstrand, 2012). In the adult KIT ligand is a pleotropic cytokine, and is important for stem cell development, especially hematopoietic stem cells. KIT ligand signaling is important for mast cell responses, including degranulation and cytokine production. KIT Ligand is also associated with dendritic cell activation, promoting release of IL-6. Administration of KIT ligand induces hypothalamic release of adrenocorticotropin (Kovacs et al, 1996). Elevations previously reported in persons with schizophrenia (Schwarz, Guest et al, 2012).
• Interleukin-8 4 P10145 Produced by numerous cells including macrophages and epithelial cells, involved in innate immune response, is an acute phase reactant. Regulates hypothalamic-pituitary response to stress (Rostene et al, 2011). Elevations previously reported in persons with schizophrenia (Miller et al, 2011).
• Apolipoprotein D P05090 A lipid-binding molecule involved in transport of hydrophobic molecules (HDL, progesterone, arachadonic acid). Apolipoprotein D is up- regulated with oxidative stress (Ganfornina et al, 2008) . Levels increased in plasma of recent onset schizophrenia (Mahadik et al, 2002), and decreased in the serum of persons with chronic schizophrenia. (Thomas et al, 2001)
• Mucin- 16 J Q8WXI7 A marker for ovarian and other cancers, and cardiovascular disease, elevated with inflammatory processes (Hamdy, 2011).
• Chemokine (C-C P13500 Released by endothelial cells, astrocytes, and motif) ligand 2 microglia. recruits monocytes, dendritic cells, and
• T-lymphocytes to site of inflammation activates mast cells (Castellani et al, 2010). Elevated via sympathetic system activation in response to social stress (Hanke et al, 2012). Receptors located in several hypothalamic nuclei, including the paraventricular nuclei, a region that integrates neuroendocrine, autonomic, and behavioral reactions to stress (Banisadr et al, 2002; Banisadr et al, 2005; Rostene et al, 2011).
• the adipokine resistin is an insulin-antagonizing factor that also plays a regulatory role in inflammation, immunity, food intake, and gonadal function.
• the adipokine resistin is an insulin-antagonizing factor that also plays a regulatory role in inflammation, immunity, food intake, and gonadal function.
• Rodriguez-Pacheco et al, 2009 In humans, secreted by immune and epithelial cells, increases IL1 beta production, proinflammatory (Miralbell et al, 2012), associated with inflammation but not BMI in obese adolescents (Maggio et al, 2012). Resistin regulates growth hormone (Rodriguez-Pacheco et al, 2009; Rodriguez-Pacheco et al, 2013) and thyroid stimulating hormone (Cinar and Gurlek, 2013) secretion in hypothalamic-pituitary axis
• Chemokine (C-C P80075 Produced by monocytes, endothelial cells, microglia motif) ligand 8 (also fibroblasts, epithelial cells), Induced by IL1 beta (among others), modulates mast cells, chemotaxic for monocytes, lymphocytes. Regulates BBB permeability.
• Alpha-2- P01023 Protease inhibitor including inhibition of matrix magroglobulin 4 metalloproteinases, released with blood brain barrier failure by perivascular astrocytes. (Cucullo et al, 2003) Previously reported to be elevated in schizophrenia.
• Elevation of apolipoprotein D is also associated with oxidative stress (Ganfornina et al, 2008).
• most of the chosen analytes are either involved in the inflammatory response or elevated with inflammation.
• several analytes are related to hormones of the hypothalamic-pituitary axes.
• a biomarker assay in some embodiments, a blood biomarker assay
• Clinical criteria alone identified persons with a positive prediction of about 30% in two years.
• the receiver operating characteristic (ROC) for the 18-analyte index shown in Figure 6 indicated that if a sensitivity of 0.6 is accepted, the specificity will be 0.1.
• CHR+ clinically high risk
• CHR-P 70% of persons identified by the test as positive
• 82% true negatives This cut-off score for the index can be useful for interventions where the risk or cost of treatment is moderately high; of course other cutoff scores with other levels of sensitivity and specificity could also have clinical utility.
• the assay included several quality assurance steps, including evaluation of each antibody in a single-plex assay and batch testing to ensure reproducibility over different lots.
• the intra-assay coefficient of variance based on native proteins spiked at the low, medium, and high end of the test dynamic range, was less than 0.15 for -90% of the 181 analytes included in the assay.
• the relevant patient population will frequently be treated with various medications, especially antidepressants and antipsychotics, and these medications could influence the levels of certain analytes.
• various medications especially antidepressants and antipsychotics, and these medications could influence the levels of certain analytes.
• prescription medication use is likely to be common in CHR patients, analytes that showed a possible (trend level) relation to medication use were eliminated.
• the results presented herein were confirmed in the subjects not treated with medications, increasing confidence that prescribed medications were not an important driver of differences between groups.
• hypothalamic Goldstein et ah, 2007
• pituitary Nadholm et ah, 2013
• activation of the hypothalamic-pituitary-adrenal axis, as evidenced by elevated salivary Cortisol has also been reported in persons at clinical high risk (CHR) who developed psychosis as compared clinical high risk who did not develop psychosis and unaffected subjects (see Walder et al, 2010; Walker et al, 2010; Walker et al, 2013).
• Neoplasia 3(6):509-520 Maliner-Stratton et al. (2001) Neoplasia 3(6):509-520.

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