WO2008144613A1 - Biomarqueurs destinés au diagnostic et à l'évaluation de troubles bipolaires - Google Patents

Biomarqueurs destinés au diagnostic et à l'évaluation de troubles bipolaires Download PDF

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Publication number
WO2008144613A1
WO2008144613A1 PCT/US2008/064055 US2008064055W WO2008144613A1 WO 2008144613 A1 WO2008144613 A1 WO 2008144613A1 US 2008064055 W US2008064055 W US 2008064055W WO 2008144613 A1 WO2008144613 A1 WO 2008144613A1
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bipolar disorder
subject
level
sample
biomarker
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PCT/US2008/064055
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English (en)
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Marlon Quinones
Jair Soares
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The University Of North Carolina At Chapel Hill
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    • 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
    • G01N33/6896—Neurological disorders, e.g. Alzheimer's disease
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • 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
    • G01N2800/304—Mood disorders, e.g. bipolar, depression
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • the invention relates to biomarkers useful for diagnosing and monitoring treatment for bipolar affective disorder.
  • Bipolar disorder occurs in about 1.5% of the population world-wide. The condition can involve either discrete periods of depression and mania or rapid cycling between these extremes. Bipolar disorder is thought to be a consequence of genetic and environmental factors, e.g., pre- or post-natal exposure to infectious organisms that potentially affect brain development and/or cause neurodegeneration. In addition, inflammatory/immune imbalances, e.g., autoimmune responses, and endocrine abnormalities may also be a part of the disease pathogenesis.
  • bipolar disorder significantly co-occurs with anxiety disorders at rates that are higher than those in the general population.
  • Clinical studies have also demonstrated high comorbidity between bipolar disorder and panic disorder, obsessive compulsive disorder (OCD), social phobia, and post-traumatic stress disorder.
  • OCD obsessive compulsive disorder
  • BD in children has unique clinical features and often presents with co-morbid conditions such as attention-deficit/hyperactivity disorder (ADHD).
  • ADHD attention-deficit/hyperactivity disorder
  • the methods comprise detecting a level of expression of one or more biomarker proteins associated with bipolar disorder status in a sample from a subject and comparing this value with a level of expression of the one or more biomarker proteins in a sample obtained from a subject free of bipolar disorder, wherein a greater or lesser expression level in the subject sample, compared to the sample from the subject free of bipolar disorder, is characteristic of bipolar disorder status.
  • the methods and compositions are useful for diagnosing, prognosing, and monitoring response to therapy for bipolar disorder.
  • the present invention also encompasses a pattern recognition system, such as an artificial neural network system, capable of classifying subjects according to bipolar disorder status.
  • a pattern recognition system such as an artificial neural network system, capable of classifying subjects according to bipolar disorder status.
  • This system is capable of processing a large number of subjects and subject variables such as biomarker and demographic factors.
  • These neural network(s) produce a diagnostic index comprised of one or several output values indicative of the presence (diagnosis) or future occurrence (prognosis) of bipolar disorder.
  • Figure 1 demonstrates increased sCD40 levels in pediatric bipolar disorder patients irrespective of mood state.
  • Figure 2 demonstrates increased sCD40 levels in euthymic pediatric bipolar disorder patients.
  • Figure 3 demonstrates that sCD40 plasma levels are not dependent on the mood state in pediatric bipolar disorder.
  • Figure 4 demonstrates an association between decreased verbal fluency/short- term memory and increased sCD40 plasma levels in pediatric bipolar disorder patients.
  • Figure 5 demonstrates an association between sCD40 plasma levels and reduced NAA in the DLPFC of pediatric bipolar disorder patients.
  • Figure 6 demonstrates a correlation between sCD40 plasma levels and reduced left anterior cingulated gray matter in pediatric bipolar disorder patients.
  • Figure 7 demonstrates an increase level of IL- 18 in adult bipolar disorder patients irrespective of mood state.
  • Figure 8 demonstrates an increase level of IL- 18 in euthymic adult bipolar disorder patients.
  • Figure 9 demonstrates a high correlation between artificial neural network modeling and validation using sCD40 biomarker.
  • Figure 10 demonstrates a high correlation between artificial neural network modeling and validation using IL- 18 biomarker.
  • Figure 11 demonstrates an association between IGF-I (top right) and RANTES (bottom right) with NAA in neural imaging studies of pediatric bipolar disorder patients.
  • Figure 12 shows that IGF-I levels were correlated (Pearson's) with subjects' age. There were significant negative relationships in (a) the healthy comparison group (HC) and (b) individuals with BD.
  • Figure 13 summarizes the predictive ability of the artificial neural network when using data from evaluation of neurochemicals and protein biomarkers along with neurocognitive assessments.
  • the healthy control subset is highlighted by the smaller circle surrounding the data points for these individuals
  • the bipolar subset is highlighted by the larger circle surrounding the data points for these individuals.
  • the dashed circle within larger circle in the top right panel encompasses children/adolescents in this dataset.
  • a biomarker is an organic biomolecule, particularly a polypeptide or protein, which is differentially present in a sample taken from a subject having bipolar disorder as compared to a comparable sample taken from a subject not having bipolar disorder, or a subject having a different mood disorder, such as major depressive disorder.
  • a biomarker is present differentially in samples taken from bipolar disorder and control subjects if it is present at an elevated level or a decreased level in samples of bipolar disorder subjects as compared to samples of control subjects.
  • the biomarkers of the invention can be used to assess bipolar disorder status in a subject.
  • Bipolar disorder status in this context subsumes, inter alia, the presence or absence of the disorder, the risk of developing the disorder, the stage of the disorder, or the effectiveness of treatment of the disorder. Based on this status, further procedures may be indicated, including additional diagnostic tests or therapeutic procedures or regimens as described elsewhere herein. More particularly, the biomarkers of the invention are capable of identifying bipolar disorder and successfully distinguishing it from other mood disorders, or from the absence of a mood disorder.
  • a method of diagnosis may be considered to be a method of prognosis.
  • the phrases "at risk of,” “predisposition towards,” and the like indicate a probability of being classified/diagnosed (or being able to be classified/diagnosed) with the predetermined condition which is greater (e.g., 1.5 x , 2 x , 5 x , 1O x , etc.) than for the corresponding control.
  • a time period e.g., within the next 5 years, 10 years, 20 years, etc.
  • a subject who is 2 x more likely to be diagnosed with the predetermined condition within the next 5 years, as compared to a suitable control is "at risk of that condition.
  • the degree of the bipolar disorder for example, the progress or phase of the disorder, or a recovery therefrom.
  • each of the different states in the progress of the disorder, or in the recovery from the disorder can be considered the bipolar disorder status.
  • the status may refer to how temporally advanced the condition is.
  • one or more of the biomarkers listed in Table 1 is used to assess bipolar disorder status.
  • Expression profiles using the biomarkers disclosed herein provide valuable molecular tools for rapidly diagnosing and monitoring the progression of bipolar disorder, and for evaluating therapeutic efficacy in treating bipolar disorder. Changes in the expression profile from a baseline profile can be used as an indication of such effects. Accordingly, the invention provides methods for screening a subject for bipolar disorder (diagnostic) or at risk of developing bipolar disorder (prognostic), methods for monitoring the progression of bipolar disorder in a subject, methods for the identification of agents that are useful in treating a subject having or at risk of having bipolar disorder, methods of treating a subject having or at risk of having bipolar disorder, and methods for monitoring the efficacy of certain therapeutic treatments for bipolar disorder.
  • a single biomarker is capable of identifying bipolar disorder with a predictive success of at least about 70%, at least about 71%, at least about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%, up to 100%, whereas, in other instances, a combination of biomarkers is used to obtain a predictive success of at least about 70%, at least about 71%, at least about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 7
  • a single biomarker is capable of identifying bipolar disorder with a sensitivity or specificity of at least about 70%, at least about 71%, at least about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%, up to 100%, whereas, in other instances, a combination or plurality of biomarkers is used to obtain a sensitivity or specificity of at least about 70%, at least about 71%, at least about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 8
  • the present invention also encompasses a pattern recognition system, such as an artificial neural network system, capable of classifying subjects according to bipolar disorder status.
  • a pattern recognition system such as an artificial neural network system, capable of classifying subjects according to bipolar disorder status.
  • This system is capable of processing a large number of subjects and subject variables such as biomarker and demographic factors.
  • These neural network(s) produce a diagnostic index comprised of one or several output values indicative of the presence (diagnosis) or future occurrence (prognosis) of bipolar disorder.
  • the methods described herein can be used for "pharmacometabonomics," in analogy to pharmacogenomics, e.g., predictive of response to therapy.
  • subjects could be divided into “responders” and “nonresponders” using the biomarker profile as evidence of "response,” and features of the biomarker profile could then be used to target future subjects who would likely respond to a particular therapeutic course.
  • the methods are also useful for evaluating clinical response to therapy, as well as for endpoints in clinical trials for efficacy of new therapies.
  • the extent to which sequential diagnostic expression profiles move towards normal can be used as one measure of the efficacy of the candidate therapy.
  • the biomarkers disclosed herein can also be used to aid in the design of animal models of bipolar disorder. Animal models of various diseases have been useful for the preclinical development of new therapies.
  • compositions and methods of the present invention are useful for assessing bipolar disorder status in a subject. These biomarkers can distinguish between subjects with bipolar disorder and subjects with major depressive disorder (MDD).
  • Major depressive disorder is also known as major depression, clinical depression, or unipolar depression.
  • unipolar refers to the presence of one pole, or one extreme of mood (depressed mood). This may be compared with bipolar depression which has the two poles of depressed mood and mania (i.e., euphoria, heightened emotion and activity).
  • the DSM- IV-TR (the Diagnostic and Statistical Manual of Mental Disorders, the book that describes mental health diagnoses) has subdivided the diagnosis of bipolar disorder into four basic categories, each defined by a particular pattern of severity of spontaneous depressions, manias, hypomanias or mixed episodes.
  • the term "Bipolar I Disorder” is applied to subjects who demonstrate full-strength manic and depressive episodes.
  • the term “Bipolar II Disorder” is applied to subjects who demonstrate full- strength depression, but only hypomanic presentations rather than full-strength manias.
  • the term “Cyclothymic Disorder” is used to describe subjects who demonstrate repeated mood swings which are never quite severe enough to qualify as major depressive or manic episodes.
  • NOS Not Otherwise Specified
  • Bipolar I disorder is a form of bipolar disorder that is characterized by a clinical history of both documented manic/mixed episodes and major depressive episodes, any of which might have been severe enough to have required hospitalization.
  • a manic episode lasts for at least one week.
  • the subject feels elated and will generally display grandiose, talkative, hyperactive, impulsive and distractible behavior. Poor judgment during the manic episode can lead to marked social or occupational problems.
  • a mixed episode the subject swings between mania and a major depression every day for less than a week.
  • a major depressive episode a subject has at least 2 weeks of depressed mood or loss of interest in life with problems sleeping, trouble concentrating, feelings of guilt, loss of energy, and/or thoughts about death.
  • Bipolar II Disorder is a type of bipolar disorder that is characterized by one or more major depressive episodes with at least one hypomanic episode in which hospitalization is not required. By definition, no actual manic episodes are present in Bipolar II.
  • Cyclothymia is characterized by at least a two-year period characterized by numerous fluctuating moods, none of which reach the severity necessary for a diagnosis of either full mania or major depression. Instead, hypomanic episodes may be experienced, in conjunction with sub-clinical severity depressive episodes.
  • Bipolar Disorder is a classification that is included in the DSM to enable mental health professionals to diagnose disorders with bipolar features that do not meet criteria for any of the defined bipolar disorder subtypes (as described above).
  • the methods disclosed herein are capable of specifically classifying subjects with bipolar disorder that are unable to be classified using existing evaluation methods.
  • the biomarkers are useful for identifying and characterizing new classes of bipolar disorders.
  • biomarker status is assessed through the evaluation of expression patterns, or profiles, of one or more bipolar disorder biomarkers (BDBs) in one or more subject samples.
  • BDBs bipolar disorder biomarkers
  • subject, or subject sample refers to an individual regardless of health and/or disease status.
  • a subject can be a subject, a study participant, a control subject, a screening subject, or any other class of individual from whom a sample is obtained and assessed in the context of the invention.
  • a subject can be diagnosed with bipolar disorder, can present with one or more symptoms of bipolar disorder, or a predisposing factor, such as a family (genetic) or medical history (medical) factor, for bipolar disorder, can be undergoing treatment or therapy for a mood disorder suspected of being bipolar disorder, or the like.
  • a subject can be healthy with respect to any of the aforementioned factors or criteria.
  • the term "healthy” as used herein is relative to bipolar disorder status, or disorder factor, or disorder criterion, as the term “healthy” cannot be defined to correspond to any absolute evaluation or status.
  • an individual defined as healthy with reference to any specified disease or disease criterion can in fact be diagnosed with any other one or more diseases, or exhibit any other one or more disease criterion, including one or more mood disorders other than bipolar disorder (e.g., major depressive disorder).
  • bipolar disorder e.g., major depressive disorder
  • an "expression profile" comprises one or more values corresponding to a measurement of the relative abundance, presence, or absence of a bipolar disease biomarker (BDB).
  • An expression profile can be derived from a biological sample collected from a subject at one or more time points prior to, during, or following diagnosis, treatment, or therapy for a mood disorder (or any combination thereof), can be derived from a biological sample collected from a subject at one or more time points during which there is no treatment or therapy for a mood disorder (e.g., to monitor progression of disease or to assess development of disease in a subject at risk for bipolar disorder), or can be collected from a healthy subject.
  • the expression profile derived from a subject is compared to a reference expression profile.
  • a “reference expression profile” can be a profile derived from the subject prior to diagnosis, treatment, or therapy; can be a profile produced from the subject sample at a particular time point (usually prior to, during, or following treatment or therapy, but can also include a particular time point prior to or following diagnosis of bipolar disorder or risk for bipolar disorder); or can be derived from a healthy individual or a pooled reference from healthy individuals.
  • a reference expression profile can also be the profile of a subject known to have a particular mood disorder (e.g., BD or MDD). The reference expression profile can be compared to a test expression profile.
  • test expression profile can be derived from the same subject as the reference expression profile except at a subsequent time point (e.g., one or more days, weeks or months following collection of the reference expression profile) or can be derived from a different subject.
  • any test expression profile of a subject can be compared to a previously collected profile from the same subject, to a profile collected from a subject known to have a mood disorder, or to a profile obtained from a healthy individual.
  • the expression profile from the test subject can be compared to a subject known to be free of bipolar disorder.
  • the level of one or more biomarkers is assessed.
  • a subject is determined to have bipolar disorder if the level of expression of one or more biomarkers listed in Table 1 is substantially increased or decreased relative to the subject known to be free of bipolar.
  • Table 1 indicates for each marker whether the level is increased or decreased in bipolar disorder relative to a healthy control (e.g., subject known to be free of bipolar disorder).
  • substantially increased or “substantially decreased” is intended at least about a 20%, at least about a 30%, at least about a 40%, at least about a 50%, at least about a 60%, at least about a 70%, at least about an 80%, at least about a 90%, at least about a 2-fold, at least about 3 -fold, at least about a 4-fold, at least about a 5-fold, or greater increase or decrease
  • the expression profile of the test subject can be compared to a subject known to have bipolar disorder.
  • the level of one or more biomarkers is substantially the same as the level of the biomarker(s) in the subject known to have bipolar disorder.
  • substantially the same or substantially similar is intended a variation of less than about 20%, less than about 15%, less than about 10%, less than about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, about 3%, about 2%, about 1% or less.
  • the biomarkers useful for assessing bipolar disorder status include markers of inflammation.
  • the biomarker is selected from one or more of tumor necrosis factor receptor II (TNF-RII), chemotactic cytokine for leukocytes 5 (CCL5), CCL22, interleukin-18, and soluble CD40 (sCD40), wherein these markers are present at a higher concentration in subjects with bipolar disorder than in subjects that do not have bipolar disorder.
  • TNF-RII tumor necrosis factor receptor II
  • CCL5 chemotactic cytokine for leukocytes 5
  • CCL22 CCL22
  • interleukin-18 interleukin-18
  • soluble CD40 soluble CD40
  • TNF family of cytokines is one of the major pro-inflammatory signals produced by the body in response to infection or tissue injury.
  • abnormal production of these cytokines for example, in the absence of infection or tissue injury, has been shown to be one of the underlying causes for some diseases such as arthritis and psoriasis.
  • TNF-RI and TNF-RII are mediated by two TNF transmembrane receptors, TNF-RI and TNF-RII.
  • TNF-RII, or TNFR(, is a 75 kDa glycoprotein that has been shown to transduce cytotoxic and proliferative signals as well as signals resulting in the secretion of GM-CSF.
  • IL- 18 plays a potential role in immunoregulation or in inflammation by augmenting the functional activity of Fas ligand on ThI cells (Conti et al. (1997) J. Biol. Chem. 272(4):2035-7). IL-18 is also expressed in the adrenal cortex and therefore might be a secreted neuro-immunomodulator, playing an important role in orchestrating the immune system following a stressful experience. Recently, it has been suggested that the interleukin IL- 18 is involved in the progression of pathogenicity in chronic inflammatory diseases, including endotoxin shock, hepatitis, and autoimmune-diabetes.
  • CCL5 also referred to as RANTES
  • CD8 + T cells CD8 + T cells, platelets, epithelial cells, and fibroblasts, typically in response to inflammatory mediators (Appay Rowland- Jones (2001) Trends Immunol 22: 83-87).
  • Its receptors, CCRl, 3, 4, and 5 may be found on a variety of cell types, including T cells, monocytes, dendritic cells, and mast cells (Schall (1999) Cytokine 3: 165-183), and upon binding its ligand, mediate the effects of this chemokine at nanomolar concentrations (Nieto et al. (1991) J Exp Med 186: 153-158).
  • CCL22 MDC or macrophage-derived chemokine
  • CCL 17 TARC or thymus and activation-regulated chemokine
  • CD40 The soluble form of CD40 (sCD40) is a natural antagonist of the CD40/CD154 interaction. This interaction is essential for B-cell growth and differentiation, and immunoglobulin class switching and somatic hypermutations. It is also required for antigen-presenting cell activation as it induces costimulatory molecules and cytokine synthesis.
  • the biomarkers useful for assessing bipolar disorder status include markers of neurotrophic factor depletion, such as insulin-like growth factor 1 (IGF-I).
  • IGFl is important for both the regulation of normal physiology, as well as a number of pathological states, including cancer.
  • the IGF axis has been shown to play roles in the promotion of cell proliferation and the inhibition of cell death (apoptosis).
  • IGF-I can also regulate cell growth and development, especially in nerve cells, as well as cellular DNA synthesis.
  • IGF- 1 in combination with brain-derived neurotrophic factor (BDNF) seem to play key roles in mediating neuronal plasticity in the hippocampus.
  • the hippocampus represents a useful region for studying neuroplasticity.
  • IGF-I insulin-like growth factor-I
  • BDNF brain-derived neurotrophic factor
  • peripheral infusion of IGF- 1 has been found to induce hippocampal neurogenesis (Aberg et al. (2000) JNeurosci 20: 2896-2903) and the peptide has been shown to directly act on hippocampal progenitor cells to stimulate proliferation (Aberg et al. (2003) MoI Cell Neurosci 24: 23-40).
  • the growth factor has also been shown in vitro to inhibit induction of apoptosis in hippocampal neurons (Nitta et al. (2004) MoI Cell Neurosci 24: 23 ⁇ 0; Zheng and Quirion (2004) JNeurochem 89: 844-852).
  • the biomarkers useful for assessing bipolar disorder status include markers of infection.
  • a simple infection by bacteria or viruses, a retroviral infection, or immune dysfunction can precipitate bipolar illnesses.
  • Several varieties of brain infection have been known to cause either manic or depressed states.
  • Bacterial infections such as syphilis and Lyme disease have neurological manifestations that can cause symptoms similar to bipolar disorder.
  • viral infections such as Epstein - Barr virus (EBV) and Herpes Simplex Virus (HSV) have been associated with bipolar disorder.
  • EBV capsid proteins e.g., EBV capsid protein
  • biomarkers described herein are also useful for identifying agents which are capable of interacting with and/or modulating the activity of the biomarker(s).
  • the biomarker protein is incubated with a test compound and the catalytic activity of the protein is determined.
  • This assay can be a cell culture assay or a cell free assay.
  • the binding affinity of the biomarker protein to a test compound can be determined by methods known in the art. Expression of one or more biomarkers listed in Table 1 can be detected by a probe which is detectably labeled, or which can be subsequently labeled. Generally, the probe is an antibody which recognizes the expressed protein.
  • the term antibody includes, but is not limited to, polyclonal antibodies, monoclonal antibodies, humanized or chimeric antibodies and biologically functional antibody fragments, which are those fragments sufficient for binding of the antibody fragment to the biomarker protein or a fragment of the protein.
  • Antibodies to each of the biomarker proteins are commercially available for human subjects as well as several other species.
  • antibodies specific to the biomarker proteins disclosed herein can be obtained via standard antibody production techniques.
  • various host animals may be immunized by injection with the polypeptide, or a portion thereof. Such host animals may include, but are not limited to, rabbits, mice and rats.
  • adjuvants may be used to increase the immunological response, depending on the host species, including, but not limited to, Freund's (complete and incomplete), mineral gels such as aluminum hydroxide, surface active substances such as lysolecithin, pluronic polyols, polyanions, peptides, oil emulsions, keyhole limpet hemocyanin, dinitrophenol, and potentially useful human adjuvants such as BCG (bacille Calmette-Guerin) and Corynebacterium parvum.
  • Polyclonal antibodies are heterogeneous populations of antibody molecules derived from the sera of animals immunized with an antigen or an antigenic functional derivative thereof.
  • host animals such as those described above, may be immunized by injection with the biomarker protein, or a portion thereof, supplemented with adjuvants as also described above.
  • Monoclonal antibodies which are homogeneous populations of antibodies to a particular antigen, may be obtained by any technique which provides for the production of antibody molecules by continuous cell lines in culture. These include, but are not limited to, the hybridoma technique of Kohler and Milstein (Nature, Vol. 256, pp. 495-497 (1975); and U.S. Pat. No. 4,376,110), the human B- cell hybridoma technique (Kosbor et al., Immunology Today, Vol. 4, p. 72 (1983); Cole et al., Proc. Natl. Acad. Sci. USA, Vol. 80, pp.
  • Such antibodies may be of any immunoglobulin class, including IgG, IgM, IgE, IgA, IgD, and any subclass thereof.
  • the hybridoma producing the mAb of this invention may be cultivated in vitro or in vivo. In addition, techniques developed for the production of "chimeric antibodies"
  • a chimeric antibody is a molecule in which different portions are derived from different animal species, such as those having a variable or hypervariable region derived from a murine mAb and a human immunoglobulin constant region.
  • single-chain antibodies can be adapted to produce single chain antibodies.
  • Single chain antibodies are formed by linking the heavy and light chain fragments of the Fv region via an amino acid bridge, resulting in a single-chain polypeptide.
  • techniques useful for the production of "humanized antibodies” can be adapted to produce antibodies to the proteins, fragments or derivatives thereof.
  • Antibody fragments which recognize specific epitopes may be generated by known techniques.
  • fragments include, but are not limited to, the F(ab') 2 fragments, which can be produced by pepsin digestion of the antibody molecule, and the Fab fragments, which can be generated by reducing the disulfide bridges of the F(ab') 2 fragments.
  • Fab expression libraries may be constructed (Huse et al., Science, Vol. 246, pp. 1275-1281 (1989)) to allow rapid and easy identification of monoclonal Fab fragments with the desired specificity. The extent to which the known proteins are expressed in the sample is then determined by immunoassay methods which utilize the antibodies described above.
  • Bipolar disorder status can be assessed using the biomarker proteins described herein in human as well as in non-human subjects (e.g., non-human animals, such as laboratory animals, e.g., mice, rats, guinea pigs, rabbits; domesticated livestock, e.g., cows, horses, goats, sheep, chicken, etc.; and companion animals, e.g., dogs, cats, etc.).
  • non-human animals such as laboratory animals, e.g., mice, rats, guinea pigs, rabbits
  • domesticated livestock e.g., cows, horses, goats, sheep, chicken, etc.
  • companion animals e.g., dogs, cats, etc.
  • controls are selected to represent the state of "normality.” As described herein, deviations from normality (e.g., higher than normal, lower than normal) in test data, test samples, test subjects, etc. are used in classification, diagnosis, etc. For example, in most cases, control subjects are the same species as the test subject and are chosen to be representative of the equivalent normal (e.g., healthy) subject.
  • a control population is a population of control subjects. If appropriate, control subjects may have characteristics in common (e.g., sex, ethnicity, age group, etc.) with the test subject. If appropriate, control subjects may have characteristics (e.g., age group, etc.) which differ from those of the test subject. For example, it may be desirable to choose healthy 20-year olds of the same sex and ethnicity as the study subject (e.g., "test subject") as control subjects.
  • control samples are taken from control subjects.
  • control samples are of the same sample type (e.g., plasma, serum, etc.), and are collected and handled under the same or similar conditions as the sample under study.
  • control data e.g., control values
  • control samples are usually obtained from control samples which are taken from control subjects.
  • control data are of the same type and are collected and handled (e.g., recorded, processed) under the same or similar conditions as the test data.
  • the methods of the invention are useful for assessing bipolar disorder status in any sample from which protein can be detected, including fluid and tissue samples.
  • fluid samples include, for example, blood plasma, blood serum, whole blood, urine, (gallbladder) bile, cerebrospinal fluid, milk, saliva, mucus, sweat, gastric juice, pancreatic juice, seminal fluid, prostatic fluid, seminal vesicle fluid, seminal plasma, amniotic fluid, fetal fluid, follicular fluid, synovial fluid, aqueous humour, ascite fluid, cystic fluid, blister fluid, and cell suspensions; and extracts thereof.
  • tissue samples include liver, kidney, prostate, brain, gut, blood, blood cells, skeletal muscle, heart muscle, lymphoid, bone, cartilage, and reproductive tissues.
  • bipolar disorder status is assessed using a blood sample or a blood-derived sample.
  • blood sample pertains to a sample of whole blood.
  • blood-derived sample pertains to an ex vivo sample derived from the blood of the subject under study.
  • blood and blood-derived samples include, but are not limited to, whole blood (WB), blood plasma (including, e.g., fresh frozen plasma (FFP)), blood serum, blood fractions, plasma fractions, serum fractions, blood fractions comprising red blood cells (RBC), platelets (PLT), leukocytes, etc., and cell lysates including fractions thereof (for example, cells, such as red blood cells, white blood cells, etc., may be harvested and lysed to obtain a cell lysate).
  • WB whole blood
  • blood plasma including, e.g., fresh frozen plasma (FFP)
  • RBC red blood cells
  • PHT platelets
  • leukocytes etc.
  • cell lysates including fractions thereof (for example, cells, such as red blood cells, white blood cells, etc., may be harvested and lysed to obtain a cell lysate).
  • a typical method for the preparation of serum suitable for analysis by the methods described herein is as follows: 10 mL of blood is drawn and immediately dispensed into a polypropylene tube and allowed to clot at room temperature. The clotted blood is then subjected to centrifugation (e.g., 4,500 x g for 5 minutes) and the serum supernatant removed to a clean tube. If necessary, the centrifugation step can be repeated to ensure the serum is efficiently separated from the clot. The serum supernatant may be analysed "fresh" or it may be stored frozen for later analysis.
  • a typical method for the preparation of plasma suitable for analysis by the methods described herein is as follows: High quality platelet-poor plasma is made by drawing the blood without the use of a tourniquet.
  • the first 2 mL of blood drawn is discarded and the remainder is rapidly mixed and aliquoted into Diatube H anticoagulant tubes (Becton Dickinson). After gentle mixing by inversion the anticoagulated blood is cooled on ice for 15 minutes then subjected to centrifugation to pellet the cells and platelets (approximately 1,200 x g for 15 minutes). The platelet poor plasma supernatant is carefully removed, drawing off the middle third of the supernatant and discarding the upper third (which may contain floating platelets) and the lower third which is close to the readily disturbed platelet layer on the top of the cell pellet. The plasma may then be aliquoted and stored frozen at -20° C. or colder, and then thawed when required for assay.
  • Diatube H anticoagulant tubes Becton Dickinson
  • Samples may be analysed immediately ("fresh"), or may be frozen and stored (e.g., at -80° C.) ("fresh frozen") for future analysis. If frozen, samples are completely thawed prior to analysis. In some embodiments, protease inhibitors may be added to the samples.
  • any suitable method for sample collection and storage can be used in the methods disclosed herein. Regardless of the specific protocol employed, the method used to prepare the blood fraction (e.g., tissue, serum or plasma) should be reproduced as carefully as possible from one subject to the next. It is important that the same or similar procedure be used for all subjects.
  • the blood fraction e.g., tissue, serum or plasma
  • bipolar disorder is assessed by detecting the level (or concentration) of one or more biomarker proteins in a sample derived from a test subject and comparing the level(s) to one or more protein biomarkers measured in a sample derived from a reference subject.
  • the level of each biomarker protein can be evaluated in a variety of different ways, each of which is well known to those of skill in the art.
  • the measurement may be either quantitative or qualitative, so long as the measurement is capable of indicating whether the level of the bipolar disorder biomarker in the sample is at, above, or below a reference value.
  • the level of the one or more protein biomarkers can be compared to a reference expression profile (e.g., a profile that has been predetermined to correspond to a particular level and/or pattern of expression or activity).
  • the reference expression profile can be derived from a healthy subject, from a subject known to have bipolar disorder, from a subject with a mood disorder that is not bipolar disorder, from a subject that is in the same or different age bracket (e.g., pediatric versus adult), from a responder to a particular therapy, to a non-responder to a particular therapy, or can be the expression profile derived from a sample taken from the test subject at a different timepoint.
  • bipolar disorder biomarkers are measured using an immunological detection method.
  • Immunological detection methods which can be used to measure the level of bipolar disorder biomarkers include, but are not limited to, competitive and non-competitive assay systems using techniques such as Western blots, radioimmunoassays, ELISA (enzyme linked immunosorbent assay), "sandwich” immunoassays, immunoprecipitation assays, precipitin reactions, gel diffusion precipitin reactions, immunodiffusion assays, agglutination assays, complement- fixation assays, immunoradiometric assays, fluorescent immunoassays, protein A immunoassays, and the like.
  • Immunoprecipitation protocols generally comprise obtaining supernatant from the assay media (optionally supplemented with protein phosphatase and/or protease inhibitors, e.g., ethylenediaminetetraacetic acid (EDTA), phenylmethanesulphonylfluoride (PMSF), aprotinin, sodium vanadate), adding a binding molecule of interest (i.e.
  • protein phosphatase and/or protease inhibitors e.g., ethylenediaminetetraacetic acid (EDTA), phenylmethanesulphonylfluoride (PMSF), aprotinin, sodium vanadate
  • EDTA ethylenediaminetetraacetic acid
  • PMSF phenylmethanesulphonylfluoride
  • aprotinin sodium vanadate
  • a molecule such as an antibody, that specifically binds to the bipolar disorder biomarker
  • incubating for a period of time e.g., 1 to 4 hours
  • adding protein A and/or protein G sepharose beads to the sample, incubating for about an hour or more at about 40° C
  • washing the beads in buffer and resuspending the beads in sodium dodecyl sulfate (SD S)/s ample buffer e.g., Western blot analysis.
  • Western blot analysis generally comprises preparing protein samples from the assay supernatant, electrophoretically separating the protein samples in a polyacrylamide gel (e.g., 8%-20% SDS-PAGE depending on the molecular weight of the antigen), transferring the protein sample from the polyacrylamide gel to a membrane such as nitrocellulose, PVDF or nylon, blocking the membrane in blocking solution (e.g., PBS with 3% BSA or non-fat milk), washing the membrane in washing buffer (e.g., PBS-T ween 20), blocking the membrane with the binding molecule of interest (e.g., an antibody specific for the bipolar disorder biomarker) diluted in blocking buffer, washing the membrane in washing buffer, blocking the membrane with an antibody (which recognizes the binding molecule, e.g., a secondary antibody) conjugated to an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase) or radioactive molecule (e.g., 32 P or 125 I)
  • ELISAs comprise preparing biomarker protein, coating the well of a 96-well microliter plate with the bipolar disorder biomarker or an assay supernatant comprising the bipolar disorder biomarker, adding a binding molecule capable of specifically binding to the biomarker of interest conjugated to a detectable compound such as an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase) to the well and incubating for a period of time, and detecting the presence of the biomarker protein.
  • a detectable compound such as an enzymatic substrate (e.g., horseradish peroxidase or alkaline phosphatase)
  • an enzymatic substrate e.g., horseradish peroxidase or alkaline phosphatase
  • the binding molecule may be coated to the well.
  • an antibody conjugated to a detectable compound may be added following the addition of the bipolar disorder biomarker or an assay supernatant comprising the bipolar disorder biomarker to the coated well.
  • ELISAs see, e.g., Ausubel et al, eds, 1994, Current Protocols in Molecular Biology, Vol.
  • ELISA kits may be obtained from commercial sources such as BioSource International (Montreal, Canada), BD Biosciences (San Jose, CA), R&D Systems (Minneapolis, MN), Millipore Corp. (Bedford, MA), and Pierce (Rockville, IL).
  • Affinity-based measurements that utilize a molecule that specifically binds to the biomarker protein being measured may also be used, as well as other technologies, such as spectroscopy-based technologies (e.g., matrix-assisted laser desorption ionization-time of flight, or MALDI-TOF, spectroscopy).
  • affinity reagent such as an antibody or aptamer
  • spectroscopy-based technologies e.g., matrix-assisted laser desorption ionization-time of flight, or MALDI-TOF, spectroscopy.
  • Affinity-based technologies include antibody-based assays (immunoassays as described above) and assays utilizing aptamers (nucleic acid molecules which specifically bind to other molecules), such as ELONA. Additionally, assays utilizing both antibodies and aptamers are also contemplated (e.g., a sandwich format assay utilizing an antibody for capture and an aptamer for detection). Generally, aptamers may be substituted for antibodies in nearly all formats of immunoassay, although aptamers allow additional assay formats (such as amplification of bound aptamers using nucleic acid amplification technology such as PCR (U.S. Pat. No. 4,683,202) or isothermal amplification with composite primers (U.S. Pat. Nos. 6,251,639 and 6,692,918).
  • PCR U.S. Pat. No. 4,683,202
  • isothermal amplification with composite primers U.S. Pat. Nos. 6,251,639 and 6,692,
  • Affinity-based assays may be in competition or direct reaction formats, utilize sandwich-type formats, and may further be heterogeneous (e.g., utilize solid supports) or homogenous (e.g., take place in a single phase) and/or utilize immunoprecipitation.
  • Most assays involve the use of labeled affinity reagent (e.g., antibody, polypeptide, or aptamer); the labels may be, for example, enzymatic, fluorescent, chemiluminescent, radioactive, or dye molecules.
  • Assays which amplify the signals from the probe are also known; examples of which are assays which utilize biotin and avidin, and enzyme-labeled and mediated immunoassays, such as ELISA and ELONA assays.
  • the assay utilizes two phases (typically aqueous liquid and solid).
  • a biomarker protein-specific affinity reagent is bound to a solid support to facilitate separation of the biomarker protein from the bulk of the sample.
  • the solid support or surface containing the antibody is typically washed prior to detection of bound polypeptides.
  • the affinity reagent in the assay for measurement of biomarker proteins may be provided on a support (e.g., solid or semi-solid); alternatively, the polypeptides in the sample can be immobilized on a support or surface.
  • supports examples include nitrocellulose (e.g., in membrane or microtiter well form), polyvinyl chloride (e.g., in sheets or microtiter wells), polystyrene latex (e.g., in beads or microtiter plates), polyvinylidine fluoride, diazotized paper, nylon membranes, activated beads, glass and Protein A beads. Both standard and competitive formats for these assays are known in the art.
  • Array-type heterogeneous assays are suitable for measuring levels of biomarker proteins when the methods of the invention are practiced utilizing multiple biomarker proteins.
  • Array-type assays used in the practice of the methods of the invention will commonly utilize a solid substrate with two or more capture reagents specific for different biomarker proteins bound to the substrate in a predetermined pattern (e.g., a grid).
  • the sample e.g., assay supernatant
  • the bound biomarker proteins are detected using a mixture of appropriate detection reagents that specifically bind the various biomarker proteins.
  • Binding of the detection reagent is commonly accomplished using a visual system, such as a fluorescent dye-based system. Because the capture reagents are arranged on the substrate in a predetermined pattern, array- type assays provide the advantage of detection of multiple biomarker proteins without the need for a multiplexed detection system.
  • the assay takes place in single phase (e.g., aqueous liquid phase).
  • the sample is incubated with an affinity reagent specific for the biomarker protein in solution.
  • an affinity reagent specific for the biomarker protein in solution.
  • it may be under conditions that will precipitate any affinity reagent/antibody complexes which are formed.
  • Both standard and competitive formats for these assays are known in the art.
  • biomarker protein/affinity reagent complex In a standard (direct reaction) format, the level of biomarker protein/affinity reagent complex is directly monitored. This may be accomplished by, for example, determining the amount of a labeled detection reagent that forms in bound to biomarker protein/affinity reagent complexes. In a competitive format, the amount of biomarker protein in the sample is deduced by monitoring the competitive effect on the binding of a known amount of labeled biomarker protein (or other competing ligand) in the complex. Amounts of binding or complex formation can be determined either qualitatively or quantitatively.
  • bipolar disorder status can be assessed by evaluating patterns of expression of the genes encoding the biomarker proteins.
  • expression patterns can be evaluated by Northern analysis, PCR, RT-PCR, Taq Man analysis, ribonuclease protection assays, FRET detection, monitoring one or more molecular beacons, hybridization to an oligonucleotide array, hybridization to a cDNA array, hybridization to a polynucleotide array, hybridization to a liquid microarray, hybridization to a microelectric array, cDNA sequencing, clone hybridization, cDNA fragment fingerprinting, and the like.
  • the particular method elected will be dependent on such factors as quantity of RNA recovered, artisan preference, available reagents and equipment, detectors, and the like.
  • the mode of detection of the signal will depend on the exact detection system utilized in the assay. For example, if a radiolabeled detection reagent is utilized, the signal will be measured using a technology capable of quantitating the signal from the sample or of comparing the signal from the sample with the signal from a reference sample, such as scintillation counting, autoradiography (typically combined with scanning densitometry), and the like. If a chemiluminescent detection system is used, then the signal will typically be detected using a luminometer. Methods for detecting signal from detection systems are well known in the art and need not be further described here.
  • the sample may be divided into a number of aliquots, with separate aliquots used to measure different biomarker protein (although division of the sample into multiple aliquots to allow multiple determinations of the levels of the biomarker protein in a particular sample are also contemplated).
  • the sample (or an aliquot therefrom) may be tested to determine the levels of multiple biomarker proteins in a single reaction using an assay capable of measuring the individual levels of different biomarker protein in a single assay, such as an array-type assay or assay utilizing multiplexed detection technology (e.g., an assay utilizing detection reagents labeled with different fluorescent dye markers).
  • the methods described herein may be implemented and/or the results recorded using any device capable of implementing the methods and/or recording the results.
  • devices that may be used include but are not limited to electronic computational devices, including computers of all types.
  • the computer program that may be used to configure the computer to carry out the steps of the methods may be contained in any computer readable medium capable of containing the computer program. Examples of computer readable medium that may be used include but are not limited to diskettes, CD-ROMs, DVDs, ROM, RAM, and other memory and computer storage devices.
  • the computer program that may be used to configure the computer to carry out the steps of the methods and/or record the results may also be provided over an electronic network, for example, over the internet, an intranet, or other network.
  • the process of comparing a measured value and a reference value can be carried out in any convenient manner appropriate to the type of measured value and reference value for the biomarker protein at issue.
  • “measuring” can be performed using quantitative or qualitative measurement techniques, and the mode of comparing a measured value and a reference value can vary depending on the measurement technology employed. For example, when a qualitative colorimetric assay is used to measure biomarker protein levels, the levels may be compared by visually comparing the intensity of the colored reaction product, or by comparing data from densitometric or spectrometric measurements of the colored reaction product (e.g., comparing numerical data or graphical data, such as bar charts, derived from the measuring device).
  • measured values used in the methods of the invention will most commonly be quantitative values (e.g., quantitative measurements of concentration, such as nanograms of biomarker protein per milliliter of sample, or absolute amount).
  • measured values are qualitative.
  • the comparison can be made by inspecting the numerical data, or by inspecting representations of the data (e.g., inspecting graphical representations such as bar or line graphs).
  • the process of comparing may be manual (such as visual inspection by the practitioner of the method) or it may be automated.
  • an assay device such as a luminometer for measuring chemiluminescent signals
  • a separate device e.g., a digital computer
  • Automated devices for comparison may include stored reference values for the biomarker protein(s) being measured, or they may compare the measured value(s) with reference values that are derived from contemporaneously measured reference samples (e.g., samples from control subjects).
  • the measured value that is compared with the reference value is a value that takes into account the replicate measurements.
  • the replicate measurements may be taken into account by using either the mean or median of the measured values as the "measured value.”
  • a “qualitative" difference in biomarker expression refers to a difference that is not assigned a relative value. That is, such a difference is designated by an "all or nothing" valuation.
  • Such an all or nothing valuation can be, for example, expression above or below a threshold of detection (an on/off pattern of expression) or can represent the "presence” or "absence” of expression.
  • a “quantitative" difference refers to a difference in expression that can be assigned a value on a graduated scale, (e.g., a 0-5 or 1-10 scale, a +-+++ scale, a grade 1 -grade 5 scale, or the like). It will be understood that the numbers selected for illustration are entirely arbitrary and in no way are meant to be interpreted to limit the invention. Any graduated scale (or any symbolic representation of a graduated scale) can be employed in the context of the present invention to describe quantitative differences in bipolar disorder biomarker levels.
  • the present invention provides a system comprising an apparatus and method for diagnosing, screening or monitoring bipolar disorders in subjects.
  • data obtained from analysis of biomarkers and optionally from demographic information is evaluated using one or more pattern recognition algorithms.
  • one or more of the proteins listed in Table 1 is used as the biomarker(s) in this invention. It is to be understood that other biomarkers and demographic data may be used in combination with the ones described herein. For example, the results of neural imaging tests or metabolic profiles may optionally be combined with other biomarkers or demographic data.
  • Such analysis methods may be used to form a predictive model, and then use that model to classify test data.
  • one convenient and particularly effective method of classification employs multivariate statistical analysis modeling, first to form a model (a "predictive mathematical model") using data ("modeling data") from samples of known class (e.g., from subjects known to have, or not have, a particular mood disorder, e.g., bipolar disorder), and second to classify an unknown sample (e.g., "test data”), as having, or not having, that mood disorder.
  • Pattern recognition (PR) methods have been used widely to characterize many different types of problems ranging, for example, over linguistics, fingerprinting, chemistry and psychology.
  • pattern recognition is the use of multivariate statistics, both parametric and non-parametric, to analyze spectroscopic data, and hence to classify samples and to predict the value of some dependent variable based on a range of observed measurements.
  • unsupervised One set of methods is termed “unsupervised” and these simply reduce data complexity in a rational way and also produce display plots which can be interpreted by the human eye.
  • the other approach is termed "supervised” whereby a training set of samples with known class or outcome is used to produce a mathematical model and is then evaluated with independent validation data sets.
  • Unsupervised PR methods are used to analyze data without reference to any other independent knowledge. Examples of unsupervised pattern recognition methods include principal component analysis (PCA), hierarchical cluster analysis (HCA), and non-linear mapping (NLM).
  • PCA principal component analysis
  • HCA hierarchical cluster analysis
  • NLM non-linear mapping
  • the methods allow the quantitative description of the multivariate boundaries that characterize and separate each class, for example, each class of mood disorders in terms of its biomarker expression profile. It is also possible to obtain confidence limits on any predictions, for example, a level of probability to be placed on the goodness of fit (see, for example, Kowalski et al., 1986). The robustness of the predictive models can also be checked using cross-validation, by leaving out selected samples from the analysis.
  • supervised pattern recognition methods include the following: soft independent modeling of class analysis (SIMCA) (see, for example, Wold, 1976); partial least squares analysis (PLS) (see, for example, Wold, 1966; Joreskog, 1982; Frank, 1984; Bro, R., 1997); linear discriminant analysis (LDA) (see, for example, Nillson, 1965); K-nearest neighbor analysis (KNN) (see, for example, Brown et al., 1996); artificial neural networks (ANN) (see, for example, Wasserman, 1989; Anker et al., 1992; Hare, 1994); probabilistic neural networks (PNNs) (see, for example, Parzen, 1962; Bishop, 1995; Speckt, 1990; Broomhead et al., 1988; Patterson, 1996); rule induction (RI) (see, for example, Quinlan, 1986); and, Bayesian methods (see, for example, Bretthorst, 1990a, 1990b, 1988).
  • SIMCA soft independent modeling of class analysis
  • PLS partial least squares
  • Multivariate projection methods such as principal component analysis (PCA) and partial least squares analysis (PLS), are so-called scaling sensitive methods.
  • PCA principal component analysis
  • PLS partial least squares analysis
  • Scaling and weighting may be used to place the data in the correct metric, based on knowledge and experience of the studied system, and therefore reveal patterns already inherently present in the data.
  • missing data for example gaps in column values
  • such missing data may replaced or "filled” with, for example, the mean value of a column ("mean fill”); a random value (“random fill”); or a value based on a principal component analysis ("principal component fill”).
  • mean fill a column
  • random fill a random value
  • principal component analysis a principal component analysis
  • each descriptor the average value of that descriptor for all samples is subtracted. In this way, the mean of a descriptor coincides with the origin, and all descriptors are "centered" at zero.
  • unit variance scaling data can be scaled to equal variance.
  • the value of each descriptor is scaled by 1/StDev, where StDev is the standard deviation for that descriptor for all samples.
  • Pareto scaling is, in some sense, intermediate between mean centering and unit variance scaling. In pareto scaling, the value of each descriptor is scaled by l/sqrt(StDev), where StDev is the standard deviation for that descriptor for all samples. In this way, each descriptor has a variance numerically equal to its initial standard deviation.
  • the pareto scaling may be performed, for example, on raw data or mean centered data.
  • Logarithmic scaling may be used to assist interpretation when data have a positive skew and/or when data spans a large range, e.g., several orders of magnitude. Usually, for each descriptor, the value is replaced by the logarithm of that value. In “equal range scaling,” each descriptor is divided by the range of that descriptor for all samples. In this way, all descriptors have the same range, that is, 1. However, this method is sensitive to presence of outlier points. In “autoscaling,” each data vector is mean centered and unit variance scaled. This technique is a very useful because each descriptor is then weighted equally, and large and small values are treated with equal emphasis. This can be important for analytes present at very low, but still detectable, levels.
  • a parameter e.g., a descriptor
  • variance weighting the variance weight of a single parameter (e.g., a descriptor) is calculated as the ratio of the inter-class variances to the sum of the intra-class variances.
  • a large value means that this variable is discriminating between the classes. For example, if the samples are known to fall into two classes (e.g., a training set), it is possible to examine the mean and variance of each descriptor.
  • Feature weighting is a more general description of variance weighting, where not only the mean and standard deviation of each descriptor is calculated, but other well known weighting factors, such as the Fisher weight, are used.
  • a trained neural network can be utilized to determine a diagnostic index corresponding to bipolar disorder status by analyzing a set of predetermined biomarkers or demographic data for bipolar disorder.
  • the levels of a plurality of biomarkers and/or other demographic data related to bipolar disorder are determined for a subject.
  • a neural network is trained by introducing a population of subjects in which the bipolar disorder status is known, along with the biomarker values or demographic data for those subjects and "teaching" the neural network to recognize the patterns in the biomarkers. After the neural network is trained, biomarker values from subjects with unknown bipolar disorder status are introduced to the trained neural network. The neural network then processes the information to produce an output value whereby the output values from the neural network are diagnostic values which indicate whether the subject has the disorder or may develop the disorder.
  • the neural network may also be able to identify unique patterns of data associated with a variety of types of mood disorders that may help to classify borderline cases that do not appear to fit into one of the defined mood disorder classifications (e.g., Bipolar I, Bipolar II, Cyclothymic disorder, Bipolar NOS, and major depressive disorder).
  • a variety of types of mood disorders e.g., Bipolar I, Bipolar II, Cyclothymic disorder, Bipolar NOS, and major depressive disorder.
  • the trained neural network provides an output value which indicates whether the subject has or is at risk for developing a mood disorder, particularly bipolar disorder.
  • the trained neural network may also be able to provide an output value that is associated with likely response of a subject diagnosed with or at risk for developing bipolar disorder to a particular course of therapy.
  • the biomarker values are analyzed by a computer-based, trained neural network to yield a single diagnostic value.
  • the most common neural network architecture for pattern classification problems is the feedforward network, which typically consists of an input layer, one or more hidden layers, and an output layer.
  • the elements that make up each layer of a neural network are referred to as neurons or nodes.
  • Inputs are fed forward from the input layer to the hidden layers and then to the output layer.
  • the number of neurons in each layer is determined before the network is trained.
  • the inputs to the neural network are predictor variables. These predictor variables can be quantitative or qualitative. Neural networks make no data distribution assumptions and can simultaneously use both quantitative and qualitative inputs.
  • biomarker values and demographic data values are scaled to provide relatively similar ranges of values between different biomarkers or demographic variables. In this manner, the variances due to the different numerical ranges inherent in the measurement of different variables are decreased.
  • Preprocessing of the input variables comprised of biomarkers and other demographic data is an important step in the training of the neural network. If the number of candidates is not too large, they may be all included in the initial attempt of neural network training. If one or several of the input biomarkers to the network are irrelevant to the classification decision making process, it will be reflected in the network connection weights of the trained neural networks. These values may then be removed from the biomarker set for a particular disease.
  • Other methods for evaluating the statistical significance of a biomarker selected for analysis by neural network and selecting biomarkers for training a neural network are well known in the art.
  • a training set is generated in which samples are collected and subdivided by classification groups according to the bipolar status of the subject.
  • the bipolar disorder status of each of the subjects in this training set is determined prior to inclusion in the training set.
  • the predictive patterns "learned" from this set are then used to classify subjects whose bipolar disorder status is unknown.
  • Bipolar disorder in the training subsets can be diagnosed according to the DSM-IV criteria, or by using the MDQ and the criteria used by Ghaemi and colleagues (Ghaemi et al. (2001) J Psychiatr Pract. 7:287-297), who proposed a heuristic definition of "bipolar spectrum disorder" that included all forms of bipolar illness and went beyond the type I or II definition.
  • Neuropsychological testing can be used to evaluate cognitive function, including general intelligence, attention, memory span, judgment, and motor, sensory and speech ability. Tests can also be used to assess emotional stability, quality of language production, distractibility and other qualities. These tests can document impairments that can be used to diagnose specific neurological illness or damage.
  • the MDQ is a screening instrument for bipolar I and II disorders that contains 13 yes-or-no questions asking about mood and behaviors that are typically associated with mania, and 2 additional questions asking about the co-occurrence of symptoms and about the severity of functional impairment caused by the symptoms.
  • a positive MDQ screen is defined as endorsement of at least 7 of the 13 yes or no questions, cooccurrence of 2 or more symptoms, and moderate to severe functional impairment.
  • An alternative MDQ scoring that does not require endorsement of the co-occurrence or impairment items of the MDQ can also be used.
  • a positive screen is simply defined as endorsement of any 7 of the total of 15 items.
  • a diagnosis of bipolar disorder can also be facilitated by collecting information about the patterns of depression, including the age of depression onset, age of first medical consultation for depression, the pattern of visits to medical professionals, comorbid anxiety (including panic attacks), family history of bipolar disorder, family history of mania or manic depression, family history of allergies, legal problems, recent depression diagnosis (within 5 years), or general questions relating to anxiety or feelings of despair.
  • Cognitive function IQ, performance, memory, attention, and executive functions
  • a correlation of results to mood status at the time of sample collection can also be evaluated by utilizing data obtained from responses in a mood change questionnaire (MCQ).
  • Samples from subjects can be taken during periods of depression, periods of elation, or during euthymic periods.
  • the term "euthymic” is used to describe a psychological state that is statistically or otherwise normal, neither elated nor depressed, or a subject in such a psychological state.
  • HAMD Hamilton Depression Rating Scale
  • ECDEU Assessment Manual U.S. Department of Health and Human Services, Public Health Service - Alcohol, Drug Abuse, and Mental Health Administration, Rev. 1976, pp. 180-192
  • HAMD Hamilton Depression Rating Scale
  • the biomarkers useful for assessing bipolar disorder status accurately classify subjects with bipolar disorders regardless of mood at the time of sample collection.
  • a variety of age-appropriate questionnaires can be utilized, including, for example, the Kiddie Schedule for Affective Disorders (K-SADS-E), Social Adjustment Inventory for Children and Adolescents (SAICA) and the Global Assessment of Functioning (GAF), Young Mania Rating Scale (Young et al. (1978) Br J Psychiatry 133:429-435), the Children's Depression Rating Scale (Poznanski et al.
  • CBCL Child Behavior Checklist
  • the pattern recognition methods described above can be used with a plurality of the biomarkers described in Table 1, optionally in combination with one or more demographic factors.
  • “Demographic factors” includes information concerning the subject's race, species, sex, ethnicity, environment, exposure to environmental toxins and radiation, stress level, behavioral patterns, previous occupations and current occupation. Demographic data may also be used to provide subject information that is useful in the diagnosis and prognosis of disease. Additionally, other relevant data can be used in combination with the biomarker proteins disclosed herein to assess bipolar disorder status, such as biological data, neural imaging data, and metabonomics data.
  • the subject data may include a variety of types of data which have some association with bipolar disorder.
  • the information may be biological. Such data may be derived from measurement of any biological parameter.
  • Such substances include, but are not limited to, endocrine substances such as hormones, exocrine substances such as enzymes, and neurotransmitters, electrolytes, proteins, carbohydrates, growth factors, cytokines, monokines, fatty acids, triglycerides, and cholesterol.
  • Biodata about a subject includes results from genetic and molecular biological analysis of the nuclear and cytoplasmic molecules associated with transcription and translation such as various forms of ribonucleic acid, deoxyribonucleic acid and other transcription factors, and the end product molecules resulting from the translation of such ribonucleic acid molecules.
  • radiographs are also included in the category of biological data, including but not limited to X-ray, magnetic resonance imaging, computerized assisted tomography, visualization of radiopaque materials introduced into the body, positron emission tomography, endoscopy, sonograms, echocardiograms, and improvements thereof.
  • Biological data also includes data concerning the age, height, growth rate, dental health, cardiovascular status, reproductive status (pre -pubertal, pubertal, post- pubertal, pre -menopausal, menopausal, post-menopausal, fertile, infertile), body fat percentage, and body fat distribution.
  • Biological data also includes the results of physical examinations, including but not limited to manual palpation, digital rectal examination, prostate palpation, testicular palpation, weight, body fat amount and distribution, auscultation, testing of reflexes, blood pressure measurements, heart and related cardiovascular sounds, vaginal and other gynecologic examinations, including cervical, uterine and ovarian palpation, evaluation of the uterine tubes, breast examinations, and radiograpic and infrared examination of the breasts. Additional biological data can be obtained in the form of a medical history of the subject.
  • Such data includes, but is not limited to the following: medical history of ancestors including grandparents and parents, siblings, and descendants, their medical problems, genetic histories, psychological profiles, psychiatric disease, age at death and cause of death; prior diseases and conditions; prior surgeries; prior angioplasties, vaccinations; habits such as exercise schedules, alcohol consumption, cigarette consumption and drug consumption; cardiac information including but not limited to blood pressure, pulse, electrocardiogram, echocardiogram, coronary arteriogram, treadmill stress tests, thallium stress tests and other cardiovascular imaging techniques. All of the aforementioned types of biological data are considered as "biomarkers" for the purposes of the present application.
  • data from neural imaging studies can be used in combination with one or more biomarkers in Table 1.
  • Brain imaging techniques allow visualization of the whole living brain or slices of the living brain without ever having to perform surgery.
  • Structural studies measure brain composition using technologies such as Computerized Axial Tomography (CAT) and Magnetic Resonance Imaging (MRI).
  • CAT Computerized Axial Tomography
  • MRI Magnetic Resonance Imaging
  • ventricle spaces spaces which carry cerebrospinal fluid through and around the brain
  • glial cell density and the level of white matter in the brain
  • Bipolar brains tend to contain an abnormal amount of small, white areas in the brain known as "white matter hyperintensities.” White matter is involved in transmitting information from one part of the brain to the other. Subjects who have these hyperintensities have an occurrence of bipolar disorder that is three times as likely as the general population.
  • PET Positron Emission Tomography
  • SPECT Single Photon Emission Computed Tomography
  • fMRI functional MRI
  • the level of neurotransmitters in a subject can also be used with one or more biomarkers disclosed herein to assess bipolar status. It is known that these chemicals are in some way unbalanced in the bipolar brain compared to normal brain. For example, GABA is observed to be lower in the blood and spinal fluid of bipolar subjects, while oxytocin-active neurons are increased in bipolar subjects, but the relevancy of these findings to overall brain functioning in bipolar and normal individuals is not yet understood.
  • neurotransmitter chemicals there are many different kinds of neurotransmitter chemicals (“neurochemicals”) in the brain.
  • the neurotransmitters that are implicated in bipolar disorder include dopamine, norepinephrine, serotonin, GABA (gamma- aminobutyrate), glutamate, and acetylcholine.
  • GABA gamma- aminobutyrate
  • glutamate glutamate
  • acetylcholine acetylcholine
  • neuropeptides including endorphins, somatostatin, vasopressin, and oxytocin
  • Magnetic resonance spectroscopy can be used to measure irregularities in neurochemical compounds in the brain.
  • MRS works in a similar way to MRI but can measure the amount of different chemicals in the brain.
  • the chemicals detected depend on the type of MRS scan. Most often the scan picks up a signal from either hydrogen or phosphorous.
  • a hydrogen scan also known as proton MRS
  • certain chemicals containing hydrogen can be detected, the largest signal comes from water and others include N-acetyl-L-aspartate (NAA).
  • NAA N-acetyl-L-aspartate
  • the level of NAA is used in combination with one or more BDBs listed in Table 1 to augment and complement the information provided by these biomarkers.
  • Phosphorous containing compounds which can be picked up on an MRS scan include ATP and phosphorous monoesters.
  • MRS scans produce a spectrum rather than an image and an area of the brain is chosen to be sampled by using a standard MRI image.
  • “metabonomic” data can be used to augment and complement the information provided by biomarker expression profile.
  • “Metabonomics” is conventionally defined as "the quantitative measurement of the multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification” (see, for example, Nicholson et al., 1999, Xenobiotica 29(11): 1181-9). This concept has arisen primarily from the application of iH NMR spectroscopy to study the metabolic composition of biofluids, cells, and tissues and from studies utilizing pattern recognition (PR), expert systems and other chemoinformatic tools to interpret and classify complex NMR-generated metabolic data sets.
  • PR pattern recognition
  • the biomarker expression profile can be combined with metabonomics data.
  • the levels of metabolites related to neurotransmitters can be measured (including, but not limited to, glutamate, 4-aminobutyrate (GABA), glutamine, norepinephrine, phenylalanine, tyrosine, serotonin, tryptophan, indoleacetic acid, indolepropionic acid, kynurenine, and betaine).
  • GABA 4-aminobutyrate
  • glutamine glutamine
  • norepinephrine phenylalanine
  • tyrosine phenylalanine
  • serotonin tryptophan
  • indoleacetic acid indolepropionic acid
  • kynurenine kynurenine
  • the additional criteria described herein can be used in combination with the biomarkers listed in Table 1 in any number of art-recognized ways to augment or complement the information provided by these biomarkers.
  • the additional criteria can be used to confirm a particular diagnosis rendered by the biomarkers listed in Table 1, or can be used along with these biomarkers to improve the diagnostic or prognostic accuracy.
  • the additional criteria can be used to develop an algorithm to characterize subjects according to the mood disorders described herein. Kits
  • kits useful for assessing bipolar disorder status comprise an array and reagents sufficient to facilitate hybridization of the protein derived from the sample to the capture probes and/or reagents sufficient for the detection of the hybridization, including reagents necessary for labeling the probe or the biomarker proteins (e.g., fluorescent dyes).
  • the kit may further comprise a computer readable medium.
  • the array comprises a substrate having a plurality of capture probes that can specifically bind the biomarker proteins of the invention.
  • the computer-readable medium has digitally-encoded expression profiles containing values representing the expression level of a biomarker detected by the array.
  • the expression profile is a reference expression profile associated with a particular mood disorder, e.g., bipolar disorder or control profile.
  • the array can be used to produce a test expression profile from a sample, and this test expression profile can then be compared to the reference profile or profiles contained in the computer readable medium to determine whether or not the test profile shares similarity with the reference profile.
  • the capture probes are immobilized on an array.
  • array is intended a solid support or substrate with peptide or nucleic acid probes attached to the support or substrate.
  • Arrays typically comprise a plurality of different capture probes that are coupled to a surface of a substrate in different, known locations.
  • the arrays of the invention comprise a substrate having a plurality of capture probes that can specifically bind a biomarker protein.
  • the number of capture probes on the substrate varies with the purpose for which the array is intended.
  • the arrays may be low-density arrays or high-density arrays and may contain 4 or more, 8 or more, 12 or more, 16 or more addresses.
  • arrays may be fabricated on a surface of virtually any shape or even a multiplicity of surfaces.
  • Arrays may be probes (e.g., antibodies) on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate, see U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193 and 5,800,992, each of which is hereby incorporated in its entirety for all purposes.
  • Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation on the device. See, for example, U.S. Pat. Nos. 5,856,174 and 5,922,591 herein incorporated by reference.
  • a variety of solid phase arrays can be employed to assess bipolar disorder status in the context of the invention.
  • Exemplary formats include membrane or filter arrays (e.g, nitrocellulose, nylon), pin arrays, and bead arrays (e.g., in a liquid "slurry").
  • probes that specifically interact with (e.g., hybridize to or bind to) the biomarker proteins of the invention are immobilized, for example by direct or indirect cross-linking, to the solid support.
  • any solid support capable of withstanding the reagents and conditions necessary for performing the particular expression assay can be utilized.
  • functionalized glass silicon, silicon dioxide, modified silicon, any of a variety of polymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, polycarbonate, or combinations thereof can all serve as the substrate for a solid phase array.
  • the molecular signatures/expression profiles are typically recorded in a database.
  • the database is a relational database accessible by a computational device, although other formats, e.g., manually accessible indexed files of expression profiles as photographs, analogue or digital imaging readouts, spreadsheets, etc. can be used.
  • the expression patterns initially recorded are analog or digital in nature and/or whether they represent quantitative or qualitative differences in expression
  • the expression patterns, expression profiles (collective expression patterns), and molecular signatures (correlated expression patterns) are stored digitally and accessed via a database.
  • the database is compiled and maintained at a central facility, with access being available locally and/or remotely.
  • the plasma proteome was surveyed for biomarkers relying on antibody binding to known proteins.
  • Antibodies recognizing different targets were linked to solid surfaces such as microbeads to create multiplex assays that allow the quantification of several analytes in the same sample and in one assay (see www.rulesbasedmedicine.com/Bibliography).
  • a series of methodologically validated and well standardize multiplex assays were used to survey 188 analytes in 204 plasma samples collected from extremely well clinically characterized individuals with BD or MDD and demographically matched healthy controls. Subjects included children/adolescents and adults.
  • the multiplex assays measured levels of inflammatory mediators, growth factors, hormones and other know plasma proteins.
  • the plasma was also surveyed for the presence of commonly analyzed auto- antibodies and measured titers of antibodies against a large number of infectious agents.
  • neuroimaging data was available.
  • Short echo-time 1 H MRS at 1.5 T, with an 8-cm 3 single voxel placed in the left dorsolateral prefrontal cortex (DLPFC) was used to measure absolute levels of N-acetyl aspartate (NAA), phosphocreatine plus creatine, choline -containing compounds and myoinositol and glutamate.
  • NAA N-acetyl aspartate
  • phosphocreatine plus creatine phosphocreatine plus creatine
  • choline -containing compounds choline -containing compounds
  • myoinositol and glutamate For statistical analysis of data collected in this exploratory study, the data was analyzed without adjustment for multiple comparisons.
  • Candidates BDBs were selected based on the magnitude of the statistical significance, the demonstration that differences in plasma levels were not solely determined by current mood state (i.e., lack of correlation between plasma levels and HAMD or YMRS scores), potential to discriminate between subjects with BD vs. MDD and adult vs. pediatric BP.
  • the candidate BDBs are listed in Table 1.
  • Plasma levels of some BDBs were correlated with the concentration of specific metabolites in the left DLPFC.
  • NAA is a non-specific marker of neuronal viability/function and these preliminary findings suggest neuronal abnormalities in the DLPFC of subjects with bipolar disorder.
  • plasma levels of RANTES and IGF- 1 were positively and negatively correlated with NAA levels in the left DLPFC, respectively. These correlations are of interest as proinflammatory factors such RANTES are likely to decrease neuronal viability, while neurotrophic factors such as IGF- 1 are likely to enhance it.
  • Plasma samples were prepared for analysis by extraction with aqueous acetonitrile. A series of 6 internal standards were added to the extracts for QC purposes. Samples were prepared in triplicate, randomized, and analyzed in a 96 well plate format. Up to sixteen quality control standards plus blanks were plated within the 96 well plates. Liquid chromatography-mass spectrometry (LC-MS) peaks from each sample were aligned by mass to charge (m/z) ratio and retention time (RT) across all samples for each matrix and quantified. Before analyzing the data we conduct (1) Normalization to internal standard and (2) Data quality: Assessment of technical variability.
  • LC-MS Liquid chromatography-mass spectrometry
  • LC-TOF-MS Liquid chromatography time of-flight mass spectrometry
  • METHODS i. Subjects Individuals were recruited from local advertisements on radio and in newspapers in the community of San Antonio, TX, USA. Trained researchers with inter-rater variability lower than 10% conducted assessments and applied scales. Adult subjects were eligible if they were 18 years old or older and met diagnostic criteria for BD Type-I, BD Type-II or MDD according to the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders — Fourth Edition (DSM-IV) criteria (SCID). Children and adolescents were eligible if they were 8-17 years old and met DSM-IV criteria for BD, according to K-SADS, administered by trained researchers (Olvera et al (2007) J Child Adolesc Psychopharmacol 17:461- 473).
  • Healthy comparison subjects were eligible if they did not have any DSM-IV axis-I disorder, any history of alcohol/substance abuse or dependence, and any history of psychiatric or neurological disorders in any of their first-degree relatives.
  • Young Mania Rating Scale (YMRS) and Hamilton Depression Rating Scale (HAMD) were administered.
  • Platelet-depleted plasma (hereafter referred simply as plasma) was used to avoid potential confounders associated with platelet-degranulation and the release of a myriad of mediators (Varo et al. (2006) Clin Sci (Lond) 111 :341-347). Personnel blinded to the study groups analyzed the samples. Methodologically validated suspension microbead-based, multiplex-assays were used to simultaneously survey cytokines and their receptors of interest (Rules Based Medicine, Austin TX). This type of multiplex assay is heavily utilized due to its high sensitivity relative to single factor ELISA and the sample savings associated with multiplexing (Sachdeva and Asthana (2007) Front Biosci 12:4682-4695). The multiplexed assays that were used have been validated previously using guidelines set forth by the Clinical and Laboratory Standards Institute (CLSI). Validation parameters include:
  • LDD Least Detectable Dose
  • NAA N-acetylaspartate
  • postmortem specimens were derived from the Stanley Array database that consists of mRNA samples extracted from the dorsolateral prefrontal cortex (DLPFC; Brodmann Area 46) of individuals with BD and HC. As described previously (Vawter et al. (2006) Hum Genet 119:558- 570), these specimens were collected, with informed consent from next of kin, by participating medical examiners between January 1995 and June 2002. The specimens were all collected, processed, and stored in a standardized manner.
  • DLPFC dorsolateral prefrontal cortex
  • Exclusion criteria for all specimens included the following: 1) significant structural brain pathology on postmortem examination by a qualified neuropathologist or by premortem imaging; 2) a history of significant focal neurological signs premortem; 3) a history of central nervous system disease that could be expected to alter gene expression in a persistent way; and, 4) documented IQ ⁇ 70, and 5) poor RNA quality (vide infra).
  • Additional exclusion criteria for unaffected controls included the following: 1) age less than 30 (thus, still in the period of maximum risk); and, 2) substance abuse within 1 year of death or evidence of significant alcohol-related changes in the liver.
  • Two senior psychiatrists made the diagnoses using DSM-IV, medical records and, when necessary, telephone interviews with family members. Diagnoses of HC were based on structured interviews by a senior psychiatrist with family member(s) to rule out Axis I diagnoses.
  • Codelink 2OK Oligonucleotide Microarrays platform was used for these analyses. In this platform, each probe is synthesized and purified prior to attachment to glass slides. The Codelink probes used were pre-tested and selected based on giving low background signal in tissues where the gene is not expressed. The Codelink software allows local background to determine noise levels for each spot.
  • the gene expression profile for each subject was individually measured with a Codelink UniSet Human 2OK I Bioarray (GE Amersham Biosciences, Chandler AZ). This array contains 20,289 oligonucleotide probes, which are 30-mers spotted on glass, representing 19,881 discovery genes.
  • the cRNA preparations and bioarray hybridizations were performed according to Codelink protocol (GE Amersham Biosciences). In brief, 2 ⁇ g of total RNA from each DLPFC sample was transformed into cDNA by reverse-transcription, cleaned on a column, and synthesized to biotinylated cRNA by in vitro transcription which made up to 50 ⁇ g of cRNA. The unfragmented cRNA was tested by running on an Agilent electropherogram to measure the size distribution. If a sample gave low amounts of cRNA by spectrophotometer or very small median cRNA length on gel analysis, the synthesis was repeated.
  • the microarray raw intensity for each gene after correction for background was exported from Codelink Expression v4.1 and transformed to Iog2 format.
  • the median of each array was calculated by eliminating genes with extremely low expression across all selected arrays. Afterwards all genes were normalized to the array median.
  • Quality control was conducted by determining the number of genes labeled in the Codelink Expression v4.1 software with a quality flag as Good (G), Contaminated (C), Irregular (I), Near Background (L), or Saturated (S) according to the manufacturer's preset parameters.
  • the Stanley Medical Research Institute database query The Stanley Online Genomics Database (which can be found on the world wide web at stanleygenomics.org) uses samples from the SMRI Brain Bank. These samples were processed and run on gene expression arrays by a variety of researchers in collaboration with the SMRI. These researchers have performed analyses on their respective studies using a range of analytic approaches. All of the genomic data have been aggregated in this online database, and consistent sets of analyses have been applied to each study. Additionally, a comprehensive set of cross-study analyses has been performed. A detailed description of the database is provided by Higgs (Higgs et al. (2006) BMC Genomics 7:70).
  • genomic data has been generated across 6 separate human array platforms (Affymetrix: hgul33a, hgul33plus, hgu95av2, Agilent, Codelink, and cDNA custom array) providing patterns/trends and analytical inferences that are not limited by platform dependencies.
  • MDD Major Depressive Disorder BD - Bipolar Disorder HC - Healthy controls ADHD - Attention-Deficit Hyperactivity Disorder N/A - Not Applicable na - Not assessed
  • IGF-I levels in bipolar are reduced irrespective of the mood state
  • DSM-IV criteria the levels of IGF-I were compared to those of HC.
  • Table 3 euthymic bipolar subjects were still found to have significantly lower levels of IGF-I than HC.
  • IGF-I levels Circulating IGF-I levels are lower in bipolar youth and adults compared to healthy controls. It is known that there is an age-associated decreased in the levels of IGF-I, which was reproduced in this study. Abnormalities in levels of IGF-I may be a specific biomarker of BD given that bipolar subjects had lower levels of this neurotrophin than HC; but subjects with MDD did not differ from HC. While not bound to any particular theory or mechanism, the finding that IGF-I levels also were lower in euthymic bipolar subjects compared with controls suggests that this difference may represent a trait of BD rather than a function of the current mood- state. IGF-I mRNA levels also were reduced in the DLPFC tissue of postmortem bipolar subjects compared to a control cohort. Notably, levels of IGF-I in bipolar subjects were significantly correlated with levels of the marker of neuronal viability, NAA in the PFC.
  • Healthy comparison subjects were eligible if they did not have any DSM-IV axis-I disorder, as assessed by trained psychiatrists, any history of alcohol/substance abuse or dependence, and any history of psychiatric or neurological disorders in any of their first-degree relatives.
  • GAF Global Assessment of Functioning
  • YMRS Young Mania Rating Scale
  • HAMD Hamilton Depression Rating Scale
  • results provided herein were derived from two cohorts of BD patients: one included adults and the other children/adolescents. Furthermore, to determine whether findings were specific to BD or rather common among different mood disorders, a cohort of adults with MDD was also included. For all three cohorts, demographically matched HC were included. The characteristics of the samples are summarized in Table 2 above.
  • TNF-RII levels in bipolar are elevated irrespective of the mood state
  • TNF-RII levels in bipolar are elevated irrespective of the mood state
  • two different analyses were conducted. First, only the euthymic BD subjects were selected, and the levels of TNF-RII were compared to those of the HC. As shown in Table 8, euthymic bipolars had significantly higher levels of TNF-RII than HC. Second, the correlation between levels of TNF-RII and mood scores on the YMRS and HAM-D was calculated.
  • TNF-RII levels were significantly higher in individuals with BD than MDD (Table 8). This was the case even when comparing only euthymic subjects in each group (Table 8).
  • TNF-RII levels are higher in bipolar youth and adults compared to healthy controls, but TNF ⁇ levels are comparable between these two groups. While not bound by any particular theory or mechanism, the findings of higher TNF-RII levels may have some specificity for BD, given that TNF-RII levels were elevated in BD patients but not in MDD patients. The finding that TNF-RII levels were higher in euthymic BD patients compared with controls suggests that this difference may represent a trait rather than a state marker of BD. Notably, levels of TNF-RII in BD subjects were significantly correlated with global functioning as measured by the GAF. Example 4. Data mining
  • Powerful computational and mathematical analytic tools can be useful in integrating multiple data streams (e.g., genetics, proteomics, metabonomics, neuroimaging, neurocognitive assessment and clinical information) for the generation of knowledge about BD pathogenesis and BDBs validation.
  • multiple data streams e.g., genetics, proteomics, metabonomics, neuroimaging, neurocognitive assessment and clinical information
  • An increasing recognition that diseases arise out of the dynamic dysregulation of several gene regulatory networks, proteins, and metabolic alterations, reflecting complex perturbations (genetic and environmental) of the "system” calls for a "systems biology” approach to investigate multiple components of malfunctioning regulatory networks (multiparameter analysis).
  • Such an analysis is likely to provide better insights into disease diagnosis, prognosis, and treatment.
  • This approach may be more powerful than relying on single biomarkers to capture the intricate derangements of a system and unambiguously identify disease.
  • KD knowledge discovery
  • the steps involved in KD include, data preparation, selection, cleaning, mining, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining.
  • Data mining in particular, is the application of specific algorithms for extracting patterns from data. Data mining was applied to the expanding data sets using integration/visualization and prediction tools. Visualization algorithms provided explicit information useful in the identification of key interactions and variables.
  • a neural network After a neural network has been trained, it can be deployed within an application and used to make decisions or perform actions when new data is presented.
  • the predictive ability of the ANN was used to generate Receiver Operating Characteristic (ROC) curves (a representative curve is shown in the lower left portion of Figure 13), establishing sensitivity (true positive rate) and specificity (true negative rate) of different biomarkers.
  • ROC Receiver Operating Characteristic
  • results from the multiplex assay described above were used, including 188 analytes measured in the plasma 204 subjects.
  • Accuracy for modeling training to recognize true healthy controls (specificity) and true bipolars (sensitivity)) in 80% of subjects is shown in Figure 13.
  • the other 20% of the sample was used for validation purposes (ability of the network to recognize healthy and bipolar after training).
  • the ANN used on average only 8-10 variables for discrimination.

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Abstract

La présente invention concerne des procédés et des compositions permettant d'évaluer un état de troubles bipolaires chez un sujet. Ces procédés et ces compositions sont utiles dans le diagnostic, le pronostic et la surveillance d'une réaction à une thérapie contre les troubles bipolaires. Les biomarqueurs utiles pour évaluer un état de troubles bipolaires incluent des marqueurs d'inflammation, tels que le récepteur II du facteur de nécrose tumorale, la cytokine chimiotactique des leucocytes 5 (CCL5), la CCL22, l'interleukine-18, et le CD40 soluble ; des marqueurs d'infection, tels que les protéines des capsides du virus d'Epstein-Barr ; et des marqueurs indiquant la déplétion du facteur neurotrophique. La présente invention concerne également un système de réseau neuronal artificiel capable de catégoriser des sujets selon l'état de leurs troubles bipolaires.
PCT/US2008/064055 2007-05-17 2008-05-19 Biomarqueurs destinés au diagnostic et à l'évaluation de troubles bipolaires WO2008144613A1 (fr)

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Cited By (5)

* Cited by examiner, † Cited by third party
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WO2010119295A1 (fr) * 2009-04-16 2010-10-21 Cambridge Enterprise Limited Biomarqueurs
US12089930B2 (en) 2018-03-05 2024-09-17 Marquette University Method and apparatus for non-invasive hemoglobin level prediction
CN111430027B (zh) * 2020-03-18 2023-04-28 浙江大学 基于肠道微生物的双相情感障碍生物标志物及其筛选应用
CN112048553A (zh) * 2020-09-28 2020-12-08 宜昌市优抚医院 血浆外周血分子标志物hsa-miR-574-5p的应用
CN116298233A (zh) * 2022-03-16 2023-06-23 昱言科技(北京)有限公司 用于诊断双相障碍的生物标记物

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