NOVEL BIOMARKER PANEL FOR MAJOR DEPRESSIVE DISEASE

FIELD OF THE INVENTION

The invention relates to biomarkers and methods of diagnosing or monitoring major depressive disorder, or a predisposition thereto.

BACKGROUND OF THE INVENTION

Major depressive disorder (MDD) is a prevalent disabling and costly psychiatric disorder, with a lifetime prevalence of up to 20%. Within the primary care setting, recognition and diagnosis of patients with depression is sub-optimal. Approximately two thirds of patients present with somatic symptoms such as lack of energy and general aches and pains. Therefore, depression is often not recognized initially. In most cases, the depressive symptoms are overlooked and are seen as being part of a non-psychiatric condition. In other cases, the physical symptoms are dealt with as having higher priority over assessing depressive symptoms. The limited time general practitioners have for assessment of each patient is also a limiting factor for recognition and diagnosis of depression in patients who present with chiefly somatic complaints or patients who have difficulties in verbalizing their psychiatric complaints. These patients are substantially harder to treat as most are only recognized at subsequent consultations, often several years after their initial visit. This lag in diagnosis, leads to delays in appropriate pharmacological intervention and increases the subsequent risk of developing chronic, recurrent or even treatment resistance depression in the longer term.

The consequences of unrecognized depression in patients with and without somatic health problems include higher health care expenditure, higher rates of disability and use of medical services, along with markedly lower work productivity and lower treatment adherence. There are currently no objective means to recognize depression patients who present with physical or ambiguous psychological symptoms to the general practitioner. A blood test aiding in the diagnosis of depression would promote a paradigm shift towards an earlier initiation of the most suitable treatment approaches. The healthcare system would benefit by cutting costs associated with the large group of patients who remain undiagnosed and are consequently at risk of developing chronic depression (Tylee, A. and P. Gandhi (2005) Prim. Care Companion J. Clin. Psychiatry 7 {A), p. 167-176). Therefore, there is a need to develop an objective test which enables screening for depression among patients presenting with somatic symptoms or presenting with non-specific depression symptoms. Such a screening test would improve recognition and management of depression within the primary care arena and may also be beneficial to the secondary care system .

SUMMARY OF THE INVENTION

According to a first aspect of the invention, there is provided the use of Interleukin-1 receptor antagonist (IL-lra), Ferritin (FRTN), EN-RAGE and Tenascin-C (TNC) as a specific panel of analyte biomarkers for the diagnosis of major depressive disorder, or predisposition thereto.

According to a further aspect of the invention, there is provided the use of Interleukin-1 receptor antagonist (IL-lra), Ferritin (FRTN), EN-RAGE, Tenascin-C (TNC), Macrophage Migration Inhibitory Factor (MIF), Angiotensin Converting Enzyme (ACE), Epidermal Growth Factor (EGF), Testosterone Total, Growth Hormone (GH), Interleukin-13 (IL-13), von Willebrand Factor (vWF), Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha), Interleukin-16 (IL-16), Thyroxine-Binding Globulin (TBG), T-Cell-Specific Protein RANTES (RANTES), Superoxide Dismutase 1 soluble (SOD-1), Apolipoprotein A-I (Apo A-I), Macrophage Inflammatory Protein-1 beta (MIP-1 beta), Myeloperoxidase (MPO), Cancer Antigen 19-9 (CA-19-9), Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2), Interleukin-7 (IL-7), B Lymphocyte Chemoattractant (BLC), Haptoglobin, Chemokine CC-4 (HCC-4), Cortisol , Hepatocyte Growth Factor (HGF), Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78), Growth- Regulated alpha protein (GRO-alpha), Thrombospondin-1, Apolipoprotein H (Apo H), Beta-2-Microglobulin (B2M), Cystatin-C and Serum Amyloid P-Component (SAP) as a specific panel of analyte biomarkers for the diagnosis of major depressive disorder, or predisposition thereto. According to a further aspect of the invention, there is provided a method of diagnosing major depressive disorder or predisposition in an individual thereto, comprising :

(a) quantifying the amounts of the analyte biomarkers as defined herein in a biological sample obtained from an individual;

(b) comparing the amounts of the analyte biomarkers in the biological sample with the amounts present in a normal control biological sample from a normal subject, such that a difference in the level of the analyte biomarkers in the biological sample is indicative of major depressive disorder, or predisposition thereto.

According to a further aspect of the invention, there is provided a method of monitoring efficacy of a therapy in a subject having, suspected of having, or of being predisposed to major depressive disorder, comprising detecting and/or quantifying, in a sample from said subject, the analyte biomarkers as defined herein.

A further aspect of the invention provides ligands, such as naturally occurring or chemically synthesised compounds, capable of specific binding to the peptide biomarker. A ligand according to the invention may comprise a peptide, an antibody or a fragment thereof, or an aptamer or oligonucleotide, capable of specific binding to the peptide biomarker. The antibody can be a monoclonal antibody or a fragment thereof capable of specific binding to the peptide biomarker. A ligand according to the invention may be labelled with a detectable marker, such as a luminescent, fluorescent or radioactive marker; alternatively or additionally a ligand according to the invention may be labelled with an affinity tag, e.g. a biotin, avidin, streptavidin or His {e.g. hexa-His) tag.

A biosensor according to the invention may comprise the peptide biomarker or a structural/shape mimic thereof capable of specific binding to an antibody against the peptide biomarker. Also provided is an array comprising a ligand or mimic as described herein. Also provided by the invention is the use of one or more ligands as described herein, which may be naturally occurring or chemically synthesised, and is suitably a peptide, antibody or fragment thereof, aptamer or oligonucleotide, or the use of a biosensor of the invention, or an array of the invention, or a kit of the invention to detect and/or quantify the peptide. In these uses, the detection and/or quantification can be performed on a biological sample such as from the group consisting of whole blood, blood serum, plasma, CSF, urine, saliva, or other bodily fluid, breath, e.g. as condensed breath, or an extract or purification therefrom, or dilution thereof.

Diagnostic or monitoring kits are provided for performing methods of the invention. Such kits will suitably comprise a ligand according to the invention, for detection and/or quantification of the peptide biomarker, and/or a biosensor, and/or an array as described herein, optionally together with instructions for use of the kit.

According to a further aspect of the invention, there is provided the use of a kit comprising a biosensor capable of detecting and/or quantifying the analyte biomarkers as defined herein for monitoring or diagnosing major depressive disorder.

Biomarkers for major depressive disorder or other psychotic disorder are essential targets for discovery of novel targets and drug molecules that retard or halt progression of the disorder. As the level of the peptide biomarker is indicative of disorder and of drug response, the biomarker is useful for identification of novel therapeutic compounds in in vitro and/or in vivo assays. Biomarkers of the invention can be employed in methods for screening for compounds that modulate the activity of the peptide. Thus, in a further aspect of the invention, there is provided the use of a ligand, as described, which can be a peptide, antibody or fragment thereof or aptamer or oligonucleotide according to the invention; or the use of a biosensor according to the invention, or an array according to the invention; or a kit according to the invention, to identify a substance capable of promoting and/or of suppressing the generation of the biomarker.

Also there is provided a method of identifying a substance capable of promoting or suppressing the generation of the peptide in a subject, comprising administering a test substance to a subject animal and detecting and/or quantifying the level of the peptide biomarker present in a test sample from the subject. In general, when a doctor or other medical practitioner is apprised that a patient is suffering from major depressive disorder, the practitioner will treat the individual to alleviate the causes or symptoms of the disorder. Thus, according to a further aspect of the invention, there is provided a method for treating major depressive disorder. Methods of treatment may comprise treating a patient with anxiolytic or antidepressant drugs and/or non-drug therapies. Treatment may be based upon a diagnosis or suspicion of major depressive disorder derived from the methods, analyte biomarkers and specific panels of analyte biomarkers as described herein. The results of any analyses according to the invention will often be communicated to physicians and/or patients (or other interested parties such as researchers) in a transmittable form that can be communicated or transmitted to any of the above parties. Therefore, according to a further aspect of the invention, there is provided systems for diagnosing and treating major depressive disorder. These systems may comprise sample analyzers, computers and software as described herein.

BRIEF DESCRIPTION OF THE FIGURES

FIGURE 1: ROC curves illustrating test performance achieved in discriminating MDD patients from healthy controls using the top 4 analytes (IL-lra, FRTN, EN-RAGE and TNC) identified in MDD cohorts 1-4 and DN MDD cohorts 1-2. For MDD cohort 4 and DN MDD2 the test performance achieved was excellent, for MDD cohorts 1 and 3, and DN MDD1 the test performance achieved was good and for MDD cohort 2 test performance was fair. According to standard classification, ROC-AUC indicates test performance and accuracy: ROC-AUC: 0.90-1 = excellent; 0.80-0.90 = good; 0.70-0.80 = fair; 0.60-0.70 = poor. FIGURE 2: ROC curves illustrating test performance achieved in discriminating MDD patients from healthy controls using the top 4 analytes (IL-lra, FRTN, EN-RAGE and TNC) identified in the medicated and un-medicated NESDA MDD cohorts. In the un-medicated cohort the test performance achieved was fair. According to standard classification, ROC-AUC indicates test performance and accuracy: ROC-AUC: 0.90-1 = excellent; 0.80- 0.90 = good; 0.70-0.80 = fair; 0.60-0.70 = poor.

FIGURE 3: ROC curves illustrating test performance achieved in discriminating MDD patients from healthy controls using the top most reproducible serum analytes identified in MDD cohorts 1-4 and Drug Na ^' ive (DN) MDD cohorts 1-2. The top 6 analytes: IL-lra, FRTN, EN-RAGE, TNC, MIF and ACE; The top 7 analytes: IL-lra, FRTN, EN-RAGE, TNC, MIF, ACE and EGF. The top 24 analytes: IL-lra, FRTN, EN-RAGE, TNC, MIF, ACE, Testosterone, EGF, GH, IL-13, vWF, MIP-1 alpha, IL-16, TBG, RANTES, SOD-1, Apo A-I, MIP-1 beta, MPO, CA-19-9, IGFBP-2, IL-7, BLC and Haptoglobin. According to standard classification, ROC-AUC indicates test performance and accuracy: ROC-AUC: 0.90-1 = excellent; 0.80-0.90 = good; 0.70-0.80 = fair; 0.60-0.70 = poor. For MDD cohorts 2-4, DN MDD cohorts 1-2, the test performance achieved was excellent (ROC-AUC>0.90). For MDD cohort 1, the test performance achieved was good (ROC-AUC=0.89).

FIGURE 4: ROC curves illustrating assay performance achieved in discriminating MDD patients from healthy controls using the top most reproducible serum analytes identified in the medicated or un- medicated NESDA MDD cohorts. The top 4 analytes for these particular cohorts: IL-lra, FRTN, MIF, EN-RAGE; The top 7 analytes for these particular cohorts: IL-lra, FRTN, MIF, EN-RAGE, ACE, TNC and EGF. The top 19 analytes for these particular cohorts: IL-lra, FRTN, MIF, EN-RAGE, ACE, TNC, EGF, Testosterone, GH, vWF, IL-16, TBG, RANTES, SOD-1, Apo A-I, MIP-1 beta, MPO, IGFBP-2, and Haptoglobin. For both the medicated (A) and un-medicated (B) NESDA MDD cohorts, the test performance achieved using the top 7 or 19 analytes was on average fair. DETAILED DESCRIPTION OF THE INVENTION

The data described herein shows the development of a reproducible blood-based biomarker test, resulting from analysis of eight independent MDD cohorts of recurrent previously-medicated patients as well as drug naive first onset MDD patients. The latter group of patients is particularly important for the identification of MDD diagnostic biomarkers to aid early diagnosis of depression within primary care facilities since the majority of patients with somatic or ambiguous symptoms remain undiagnosed for months or years.

The invention described herein identifies a panel of biomarkers for diagnosing major depressive disorder (MDD), in particular recurrent and/or drug naive first onset major depressive disorder (MDD) patients.

Biomarkers

The term "biomarker" means a distinctive biological or biologically derived indicator of a process, event, or condition. Peptide biomarkers can be used in methods of diagnosis, e.g. clinical screening, and prognosis assessment and in monitoring the results of therapy, identifying patients most likely to respond to a particular therapeutic treatment, drug screening and development. Biomarkers and uses thereof are valuable for identification of new drug treatments and for discovery of new targets for drug treatment.

Data is provided herein which demonstrates that the specific panel of analyte biomarkers described herein, contains statistically significant biomarkers for the diagnosis of major depressive disorder.

Therefore, according to a first aspect of the invention, there is provided the use of Interleukin-1 receptor antagonist (IL-lra), Ferritin (FRTN), EN-RAGE and Tenascin-C (TNC) as a specific panel of analyte biomarkers for the diagnosis of major depressive disorder (MDD), or predisposition thereto. As shown in Tables 21-22 and Figures 1 and 2, this combination of 4 analytes can be used to discriminate between MDD patients and healthy controls. In particular, this combination was shown to provide a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4, and DN MDD cohorts 1 and 2.

These findings highlight the potential of this panel of markers to be used as a blood based test in the clinic to improve and/or aid diagnosis of MDD, especially, but not limited to the primary care setting. Such a test would be an important breakthrough in the field of psychiatry. It would help to identify a need for antidepressant treatment for each patient at an early stage and thus improve patient well-being and prognosis. In one embodiment, the panel additionally comprises one or more analyte biomarkers selected from : Angiotensin Converting Enzyme (ACE), Macrophage Migration Inhibitory Factor (MIF) and Epidermal Growth Factor (EGF). Thus, according to a further aspect of the invention, there is provided the use of Interleukin-1 receptor antagonist (IL-lra), Ferritin (FRTN), Macrophage Migration Inhibitory Factor (MIF), EN-RAGE, Angiotensin Converting Enzyme (ACE), Tenascin-C (TNC) and Epidermal Growth Factor (EGF) as a specific panel of analyte biomarkers for the diagnosis of major depressive disorder (MDD), or predisposition thereto. As shown in Figures 3 and 4, this combination is successful at discriminating MDD patients from healthy controls in the tested cohorts. For example, these seven reproducible markers (EN-RAGE, IL-lra, MIF, FRTN, TNC, EGF and ACE) were used to discriminate MDD patients from healthy individuals in four smaller MDD cohorts (average AUC=0.94, sensitivity = 89%, specificity = 87%).

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, EN-RAGE and ACE as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, EN-RAGE and TNC as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb2" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2. In particular, this combination was found to be one of the best performing combinations, as shown in Table 22.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, ACE and TNC as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb3" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2. In a further aspect of the invention, there is provided the use of IL-lra, FRTN, EN-RAGE, ACE and TNC as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb4" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2. In particular, this combination was found to be one of the best performing combinations, as shown in Table 22.

In a further aspect of the invention, there is provided the use of IL-lra, MIF, ENRAGE, ACE and TNC as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb5" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1 and 4 and DN MDD cohorts 1 and 2. In a further aspect of the invention, there is provided the use of FRTN, MIF, ENRAGE, ACE and TNC as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb6" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, EN-RAGE and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb7" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, ACE and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb8" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, EN-RAGE, ACE and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb9" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2. In a further aspect of the invention, there is provided the use of IL-lra, MIF, ENRAGE, ACE and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comblO" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of FRTN, MIF, EN- RAGE, ACE and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl l" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl2" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2. In particular, this combination was found to be one of the best performing combinations, as shown in Table 22.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, EN-RAGE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl3" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2. In particular, this combination was found to be one of the best performing combinations, as shown in Table 22.

In a further aspect of the invention, there is provided the use of IL-lra, MIF, ENRAGE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl4" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1 and 4 and DN MDD cohorts 1 and 2. In a further aspect of the invention, there is provided the use of FRTN, MIF, ENRAGE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl5" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, ACE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl6" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2. In particular, this combination was found to be one of the best performing combinations, as shown in Table 22.

In a further aspect of the invention, there is provided the use of IL-lra, MIF, ACE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl7" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of FRTN, MIF, ACE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl8" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2. In a further aspect of the invention, there is provided the use of IL-lra, ENRAGE, ACE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "combl9" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of FRTN, EN-RAGE, ACE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb20" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1, 3 and 4 and DN MDD cohorts 1 and 2.

In a further aspect of the invention, there is provided the use of MIF, EN-RAGE, ACE, TNC and EGF as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Tables 13-22, this combination (referred to as "comb21" in the Results section herein) provides a good to excellent performance in discriminating MDD patients from controls in MDD cohorts 1 and 4 and DN MDD cohorts 1 and 2.

In an alternative aspect of the invention, there is provided the use of IL-lra, FRTN, MIF and EN-RAGE as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Figure 4, this combination can be used to discriminate MDD patients from healthy controls in the tested cohorts.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, EN-RAGE, ACE and TNC as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Figures 3 and 4, this combination is successful at discriminating MDD patients from healthy controls in the tested cohorts.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, EN-RAGE, ACE, TNC, Testosterone, EGF, GH, IL-13, vWF, MIP-1 alpha, IL- 16, TBG, RANTES, SOD-1, Apo A-I, MIP-1 beta, MPO, CA-19-9, IGFBP-2, IL-7, BLC and Haptoglobin as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Figure 3, this combination is successful at discriminating MDD patients from healthy controls in MDD cohorts 1-4 and DN MDD cohorts 1-2. For example, these twenty four most reproducible markers resulted in an average assay sensitivity and specificity of 91% and 82%, respectively, with a ROC-AUC of 0.93.

In a further aspect of the invention, there is provided the use of IL-lra, FRTN, MIF, EN-RAGE, ACE, TNC, EGF, Testosterone, GH, vWF, IL-16, TBG, RANTES, SOD-1, Apo A-I, MIP-1 beta, MPO, IGFBP-2, and Haptoglobin as a specific panel of analyte biomarkers for the diagnosis of MDD, or predisposition thereto. As shown in Figure 4, this combination is successful at discriminating MDD patients from healthy controls in the medicated or un-medicated NESDA MDD cohorts.

In one embodiment, the panel additionally comprises one or more analyte biomarkers selected from : Testosterone Total, Growth Hormone (GH), Interleukin-13 (IL-13), von Willebrand Factor (vWF), Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha), Interleukin-16 (IL-16), Thyroxine-Binding Globulin (TBG), T-Cell-Specific Protein RANTES (RANTES), Superoxide Dismutase 1 soluble (SOD-1), Apolipoprotein A-I (Apo A-I), Macrophage Inflammatory Protein-1 beta (MIP-1 beta), Myeloperoxidase (MPO), Cancer Antigen 19-9 (CA- 19-9), Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2), Interleukin-7 (IL- 7), B Lymphocyte Chemoattractant (BLC), Haptoglobin, Chemokine CC-4 (HCC- 4), Cortisol, Hepatocyte Growth Factor (HGF), Epithelial-Derived Neutrophil- Activating Protein 78 (ENA-78), Growth -Regulated alpha protein (GRO-alpha), Thrombospondin-1, Apolipoprotein H (Apo H), Beta-2-Microglobulin (B2M), Cystatin-C and Serum Amyloid P-Component (SAP).

According to a further aspect of the invention, there is provided the use of Interleukin-1 receptor antagonist (IL-lra), Ferritin (FRTN), Macrophage Migration Inhibitory Factor (MIF), EN-RAGE, Angiotensin Converting Enzyme (ACE), Tenascin-C (TNC), Epidermal Growth Factor (EGF), Testosterone Total, Growth Hormone (GH), Interleukin-13 (IL-13), von Willebrand Factor (vWF), Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha), Interleukin-16 (IL-16), Thyroxine-Binding Globulin (TBG), T-Cell-Specific Protein RANTES (RANTES), Superoxide Dismutase 1 soluble (SOD-1), Apolipoprotein A-I (Apo A-I), Macrophage Inflammatory Protein-1 beta (MIP-1 beta), Myeloperoxidase (MPO), Cancer Antigen 19-9 (CA-19-9), Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2), Interleukin-7 (IL-7), B Lymphocyte Chemoattractant (BLC), Haptoglobin, Chemokine CC-4 (HCC-4), Cortisol, Hepatocyte Growth Factor (HGF), Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78), Growth- Regulated alpha protein (GRO-alpha), Thrombospondin-1, Apolipoprotein H (Apo H), Beta-2-Microglobulin (B2M), Cystatin-C and Serum Amyloid P-Component (SAP) as a specific panel of analyte biomarkers for the diagnosis of major depressive disorder, or predisposition thereto.

In one embodiment, the use additionally comprises one or more analyte biomarkers selected from : Apolipoprotein C-III (Apo C-III), FASLG Receptor (FAS), Prolactin (PRL), Leptin, Immunoglobulin A (IgA), Macrophage-Derived Chemokine (MDC), Resistin, Interleukin-3 (IL-3), Interleukin-15 (IL-15), Progesterone, Interleukin-5 (IL-5), Interleukin-8 (IL-8), Follicle-Stimulating Hormone (FSH), Creatine Kinase-MB (CK-MB), Vascular Endothelial Growth Factor (VEGF), Interferon gamma (IFN-gamma), Tumor necrosis factor receptor 2 (TNFR2), Thyroid-Stimulating Hormone (TSH), Serotransferrin (Transferrin), Tamm-Horsfall Urinary Glycoprotein (THP), Myoglobin, CD40 Ligand (CD40-L), Pancreatic Polypeptide (PPP), Chromogranin-A (CgA), Osteopontin, Plasminogen Activator Inhibitor 1 (PAI-1), Fetuin-A, Complement C3 (C3), Alpha-l-Antitrypsin (AAT) and Alpha-l-Microglobulin (AlMicro).

Major Depressive Disorder

References herein to "major depressive disorder" (MDD) also include patients with "depression" and "dysthymia". The term "major depressive disorder" (which is also known as clinical depression, major depression, unipolar depression, or unipolar disorder) was selected by the American Psychiatric Association for this symptom cluster under mood disorders in the 1980 version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) classification, and has become widely used since.

The general term "depression" is often used to describe MDD, but as it is also used to describe a depressed mood, more precise terminology is preferred in clinical and research use. Major depressive disorder is a disabling condition which adversely affects a person's family, work or school life, sleeping and eating habits, and general health. In the United States, approximately 3.4% of people with major depression commit suicide, and up to 60% of all people who commit suicide have depression or another mood disorder.

Most patients are treated in the community with antidepressant medication and some with psychotherapy or counseling. Hospitalization may be necessary in cases with associated self-neglect or a significant risk of harm to self or others. A minority are treated with electroconvulsive therapy (ECT), under a short- acting general anaesthetic.

The course of the disorder varies widely, from one episode lasting months to a lifelong disorder with recurrent major depressive episodes. Depressed individuals have shorter life expectancies than those without depression, in part because of greater susceptibility to medical illnesses. Current and former patients may be stigmatized.

The diagnosis of major depressive disorder is based on the patient's self- reported experiences, behaviour reported by relatives or friends, and a mental status exam . There is no laboratory test for major depression, although physicians generally request tests for physical conditions that may cause similar symptoms. The most common time of onset is between the ages of 30 and 40 years, with a later peak between 50 and 60 years. Major depression is reported about twice as frequently in women as in men, although men are at higher risk for suicide.

In one embodiment, the patient is diagnosed with major depressive disorder (MDD). In a further embodiment, the patient is a recurrent major depressive disorder patient. In an alternative further embodiment, the patient is a drug naive major depressive disorder patient {e.g. a first onset drug patient). In an alternative further embodiment, the patient is a chronic major depressive disorder patient. In an alternative further embodiment, the patient is an un- medicated major depressive disorder patient. It will be understood that the term "drug naive" patients includes patients which have not previously been diagnosed or medicated for major depressive disorder. It will also be understood that the term "un-medicated" refers to patients which have not been taking medication for major depressive disorder for at least 1 year, for example for at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 years, in particular for at least 3 years.

In one embodiment, the patient is a severe, moderate or mild major depressive disorder patient. In a further embodiment, the patient is a severe or moderate major depressive patient, in particular a severe major disorder patient.

References herein to "other psychotic disorder" relate to any appropriate psychotic disorder according to DSM-IV Diagnostic and Statistical Manual of Mental Disorders, 4th edition, American Psychiatric Assoc., Washington, D.C., 2000. In one particular embodiment, the other psychotic disorder is a psychotic disorder related to major depressive disorder.

Methods of diagnosis or monitoring

According to a further aspect of the invention, there is provided a method of diagnosing major depressive disorder or predisposition in an individual thereto, comprising :

(a) quantifying the amounts of the analyte biomarkers as defined herein in a biological sample obtained from an individual;

(b) comparing the amounts of the analyte biomarkers in the biological sample with the amounts present in a normal control biological sample from a normal subject, such that a difference in the level of the analyte biomarkers in the biological sample is indicative of major depressive disorder, or predisposition thereto.

It should be noted that references to biomarker amounts or levels also include references to a biomarker range. It will be appreciated that references herein to "difference in the level" refer to either a higher or lower level of the biomarker(s) in the test biological sample compared with the reference sample(s). In one embodiment, the higher or lower level is a < 1 fold difference relative to the reference sample, such as a fold difference of 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.01 or any ranges therebetween. In one embodiment, the lower level is between a 0.1 and 0.85 fold difference relative to the reference sample, such as between a 0.2 and 0.7 fold difference relative to the reference sample. In a further embodiment, the lower level is between a 0.25 and 0.75 fold difference relative to the reference sample.

In one embodiment, the higher or lower level is a > 1 fold difference relative to the reference sample, such as a fold difference of 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10, 10.5, 11, 11.5, 12, 12.5, 15 or 20 or any ranges therebetween. In one embodiment, the higher level is between a 1 and 15 fold difference relative to the reference sample, such as between a 1.5 and 12 fold difference relative to the reference sample. In a further embodiment, the higher level is between a 1 and 7 fold difference relative to the reference sample.

According to a further aspect of the invention, there is provided a method of monitoring efficacy of a therapy in a subject having, suspected of having, or of being predisposed to major depressive disorder, comprising detecting and/or quantifying, in a sample from said subject, the analyte biomarkers as defined herein.

Monitoring methods of the invention can be used to monitor onset, progression, stabilisation, amelioration and/or remission.

In methods of diagnosing or monitoring according to the invention, detecting and/or quantifying the peptide biomarker in a biological sample from a test subject may be performed on two or more occasions. Comparisons may be made between the level of biomarker in samples taken on two or more occasions. Assessment of any change in the level of the peptide biomarker in samples taken on two or more occasions may be performed. Modulation of the peptide biomarker level is useful as an indicator of the state of major depressive disorder or other psychotic disorder or predisposition thereto. An increase in the level of the biomarker, over time is indicative of onset or progression, i.e. worsening of this disorder, whereas a decrease in the level of the peptide biomarker indicates amelioration or remission of the disorder, or vice versa.

A method of diagnosis of or monitoring according to the invention may comprise quantifying the peptide biomarker in a test biological sample from a test subject and comparing the level of the peptide present in said test sample with one or more controls.

The control used in a method of the invention can be one or more control(s) selected from the group consisting of: the level of biomarker peptide found in a normal control sample from a normal subject, a normal biomarker peptide level; a normal biomarker peptide range, the level in a sample from a subject with major depressive disorder or other psychotic disorder, or a diagnosed predisposition thereto; major depressive disorder or other psychotic disorder biomarker peptide level, or major depressive disorder or other psychotic disorder biomarker peptide range.

Also provided is a method of monitoring efficacy of a therapy for major depressive disorder in a subject having such a disorder, suspected of having such a disorder, or of being predisposed thereto, comprising detecting and/or quantifying the peptide present in a biological sample from said subject. In monitoring methods, test samples may be taken on two or more occasions. The method may further comprise comparing the level of the biomarker present in the test sample with one or more reference(s) and/or with one or more previous test sample(s) taken earlier from the same test subject, e.g. prior to commencement of therapy, and/or from the same test subject at an earlier stage of therapy. The method may comprise detecting a change in the level of the biomarker in test samples taken on different occasions. In one embodiment, the method comprises comparing the amount of biomarker(s) in said test biological sample with the amount present in one or more samples taken from said patient prior to commencement of treatment, and/or one or more samples taken from said patient during treatment.

For biomarkers which are increased in patients with major depressive disorder, a higher level of the peptide biomarker in the test sample relative to the level in the normal control is indicative of the presence of major depressive disorder or other psychotic disorder, or predisposition thereto; an equivalent or lower level of the peptide in the test sample relative to the normal control is indicative of absence of major depressive disorder and/or absence of a predisposition thereto.

For biomarkers which are decreased in patients with major depressive disorder, a lower level of the peptide biomarker in the test sample relative to the level in the normal control is indicative of the presence of major depressive disorder or other psychotic disorder, or predisposition thereto; an equivalent or lower level of the peptide in the test sample relative to the normal control is indicative of absence of major depressive disorder and/or absence of a predisposition thereto. The term "diagnosis" as used herein encompasses identification, confirmation, and/or characterisation of major depressive disorder or other psychotic disorder, or predisposition thereto. By "predisposition" it is meant that a subject does not currently present with the disorder, but is liable to be affected by the disorder in time. Methods of monitoring and of diagnosis according to the invention are useful to confirm the existence of a disorder, or predisposition thereto; to monitor development of the disorder by assessing onset and progression, or to assess amelioration or regression of the disorder. Methods of monitoring and of diagnosis are also useful in methods for assessment of clinical screening, prognosis, choice of therapy, evaluation of therapeutic benefit, i.e. for drug screening and drug development.

Efficient diagnosis and monitoring methods provide very powerful "patient solutions" with the potential for improved prognosis, by establishing the correct diagnosis, allowing rapid identification of the most appropriate treatment (thus lessening unnecessary exposure to harmful drug side effects), reducing "downtime" and relapse rates.

Methods for monitoring efficacy of a therapy can be used to monitor the therapeutic effectiveness of existing therapies and new therapies in human subjects and in non-human animals {e.g. in animal models). These monitoring methods can be incorporated into screens for new drug substances and combinations of substances.

Suitably, the time elapsed between taking samples from a subject undergoing diagnosis or monitoring will be 3 days, 5 days, a week, two weeks, a month, 2 months, 3 months, 6 or 12 months. Samples may be taken prior to and/or during and/or following therapy for major depressive disorder, such as an antidepressant therapy. Samples can be taken at intervals over the remaining life, or a part thereof, of a subject.

The term "detecting" as used herein means confirming the presence of the peptide biomarker present in the sample. Quantifying the amount of the biomarker present in a sample may include determining the concentration of the peptide biomarker present in the sample. Detecting and/or quantifying may be performed directly on the sample, or indirectly on an extract therefrom, or on a dilution thereof.

In alternative aspects of the invention, the presence of the peptide biomarker is assessed by detecting and/or quantifying antibody or fragments thereof capable of specific binding to the biomarker that are generated by the subject's body in response to the peptide and thus are present in a biological sample from a subject having major depressive disorder or a predisposition thereto. Detecting and/or quantifying can be performed by any method suitable to identify the presence and/or amount of a specific protein in a biological sample from a patient or a purification or extract of a biological sample or a dilution thereof. In methods of the invention, quantifying may be performed by measuring the concentration of the peptide biomarker in the sample or samples. Biological samples that may be tested in a method of the invention include whole blood, blood serum, plasma, cerebrospinal fluid (CSF), urine, saliva, or other bodily fluid (stool, tear fluid, synovial fluid, sputum), breath, e.g. as condensed breath, or an extract or purification therefrom, or dilution thereof. Biological samples also include tissue homogenates, tissue sections and biopsy specimens from a live subject, or taken post-mortem. The samples can be prepared, for example where appropriate diluted or concentrated, and stored in the usual manner. In one embodiment, the biological sample is whole blood, blood serum or plasma, such as blood serum .

Detection and/or quantification of peptide biomarkers may be performed by detection of the peptide biomarker or of a fragment thereof, e.g. a fragment with C-terminal truncation, or with N-terminal truncation. Fragments are suitably greater than 4 amino acids in length, for example 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 amino acids in length.

In one embodiment, the biomarker defined herein may be replaced by a molecule, or a measurable fragment of the molecule, found upstream or downstream of the biomarker in a biological pathway.

Methods of detection

As used herein, the term "biosensor" means anything capable of detecting the presence of the biomarker. Examples of biosensors are described herein.

Biosensors according to the invention may comprise a ligand or ligands, as described herein, capable of specific binding to the peptide biomarker. Such biosensors are useful in detecting and/or quantifying a peptide of the invention.

The biomarker may be directly detected, e.g. by SELDI or MALDI-TOF. Alternatively, the biomarker may be detected directly or indirectly via interaction with a ligand or ligands such as an antibody or a biomarker-binding fragment thereof, or other peptide, or ligand, e.g. aptamer, or oligonucleotide, capable of specifically binding the biomarker. The ligand may possess a detectable label, such as a luminescent, fluorescent or radioactive label, and/or an affinity tag.

For example, detecting and/or quantifying can be performed by one or more method(s) selected from the group consisting of: SELDI (-TOF), MALDI (-TOF), a 1-D gel-based analysis, a 2-D gel-based analysis, Mass spec (MS), reverse phase (RP) LC, size permeation (gel filtration), ion exchange, affinity, HPLC, UPLC and other LC or LC MS-based techniques. Appropriate LC MS techniques include ICAT® (Applied Biosystems, CA, USA), or iTRAQ® (Applied Biosystems, CA, USA). Liquid chromatography {e.g. high pressure liquid chromatography (HPLC) or low pressure liquid chromatography (LPLC)), thin-layer chromatography, NMR (nuclear magnetic resonance) spectroscopy could also be used.

Methods according to the invention may comprise analysing a sample of blood serum by SELDI-TOF or MALDI-TOF to detect the presence or level of the peptide biomarker. These methods are also suitable for clinical screening, prognosis, monitoring the results of therapy, identifying patients most likely to respond to a particular therapeutic treatment, for drug screening and development, and identification of new targets for drug treatment.

Detecting and/or quantifying the peptide biomarkers may be performed using an immunological method, involving an antibody, or a fragment thereof capable of specific binding to the peptide biomarker. Suitable immunological methods include sandwich immunoassays, such as sandwich ELISA, in which the detection of the peptide biomarkers is performed using two antibodies which recognize different epitopes on a peptide biomarker; radioimmunoassays (RIA), direct, indirect or competitive enzyme linked immunosorbent assays (ELISA), enzyme immunoassays (EIA), Fluorescence immunoassays (FIA), western blotting, immunoprecipitation and any particle-based immunoassay {e.g. using gold, silver, or latex particles, magnetic particles, or Q-dots). Immunological methods may be performed, for example, in microtitre plate or strip format. Immunological methods in accordance with the invention may be based, for example, on any of the following methods.

Immunoprecipitation is the simplest immunoassay method; this measures the quantity of precipitate, which forms after the reagent antibody has incubated with the sample and reacted with the target antigen present therein to form an insoluble aggregate. Immunoprecipitation reactions may be qualitative or quantitative. In particle immunoassays, several antibodies are linked to the particle, and the particle is able to bind many antigen molecules simultaneously. This greatly accelerates the speed of the visible reaction. This allows rapid and sensitive detection of the biomarker. In immunonephelometry, the interaction of an antibody and target antigen on the biomarker results in the formation of immune complexes that are too small to precipitate. However, these complexes will scatter incident light and this can be measured using a nephelometer. The antigen, i.e. biomarker, concentration can be determined within minutes of the reaction.

Radioimmunoassay (RIA) methods employ radioactive isotopes such as I ¹²⁵ to label either the antigen or antibody. The isotope used emits gamma rays, which are usually measured following removal of unbound (free) radiolabel. The major advantages of RIA, compared with other immunoassays, are higher sensitivity, easy signal detection, and well-established, rapid assays. The major disadvantages are the health and safety risks posed by the use of radiation and the time and expense associated with maintaining a licensed radiation safety and disposal program. For this reason, RIA has been largely replaced in routine clinical laboratory practice by enzyme immunoassays.

Enzyme (EIA) immunoassays were developed as an alternative to radioimmunoassays (RIA). These methods use an enzyme to label either the antibody or target antigen. The sensitivity of EIA approaches that of RIA, without the danger posed by radioactive isotopes. One of the most widely used EIA methods for detection is the enzyme-linked immunosorbent assay (ELISA). ELISA methods may use two antibodies one of which is specific for the target antigen and the other of which is coupled to an enzyme, addition of the substrate for the enzyme results in production of a chemiluminescent or fluorescent signal.

Fluorescent immunoassay (FIA) refers to immunoassays which utilize a fluorescent label or an enzyme label which acts on the substrate to form a fluorescent product. Fluorescent measurements are inherently more sensitive than colorimetric (spectrophotometric) measurements. Therefore, FIA methods have greater analytical sensitivity than EIA methods, which employ absorbance (optical density) measurement.

Chemiluminescent immunoassays utilize a chemiluminescent label, which produces light when excited by chemical energy; the emissions are measured using a light detector.

Immunological methods according to the invention can thus be performed using well-known methods. Any direct {e.g., using a sensor chip) or indirect procedure may be used in the detection of the peptide biomarker of the invention.

The Biotin-Avidin or Biotin-Streptavidin systems are generic labelling systems that can be adapted for use in immunological methods of the invention. One binding partner (hapten, antigen, ligand, aptamer, antibody, enzyme etc) is labelled with biotin and the other partner (surface, e.g. well, bead, sensor etc) is labelled with avidin or streptavidin. This is conventional technology for immunoassays, gene probe assays and (bio)sensors, but is an indirect immobilisation route rather than a direct one. For example a biotinylated ligand {e.g. antibody or aptamer) specific for a peptide biomarker of the invention may be immobilised on an avidin or streptavidin surface, the immobilised ligand may then be exposed to a sample containing or suspected of containing the peptide biomarker in order to detect and/or quantify a peptide biomarker of the invention. Detection and/or quantification of the immobilised antigen may then be performed by an immunological method as described herein.

The term "antibody" as used herein includes, but is not limited to: polyclonal, monoclonal, bispecific, humanised or chimeric antibodies, single chain antibodies, Fab fragments and F(ab') ₂ fragments, fragments produced by a Fab expression library, anti-idiotypic (anti-Id) antibodies and epitope-binding fragments of any of the above. The term "antibody" as used herein also refers to immunoglobulin molecules and immunologically-active portions of immunoglobulin molecules, i.e., molecules that contain an antigen binding site that specifically binds an antigen. The immunoglobulin molecules of the invention can be of any class {e.g., IgG, IgE, IgM, IgD and IgA) or subclass of immunoglobulin molecule. The identification of key biomarkers specific to a disease is central to integration of diagnostic procedures and therapeutic regimes. Using predictive biomarkers, appropriate diagnostic tools such as biosensors can be developed, accordingly, in methods and uses of the invention, detecting and quantifying can be performed using a biosensor, microanalytical system, microengineered system, microseparation system, immunochromatography system or other suitable analytical devices. The biosensor may incorporate an immunological method for detection of the biomarker, electrical, thermal, magnetic, optical {e.g. hologram) or acoustic technologies. Using such biosensors, it is possible to detect the target biomarker at the anticipated concentrations found in biological samples.

Thus, according to a further aspect of the invention there is provided an apparatus for monitoring major depressive disorder (MDD), which comprises a biosensor, microanalytical, microengineered, microseparation and/or immunochromatography system configured to detect and/or quantify the biomarker defined herein.

The biomarker of the invention can be detected using a biosensor incorporating technologies based on "smart" holograms, or high frequency acoustic systems, such systems are particularly amenable to "bar code" or array configurations. In smart hologram sensors (Smart Holograms Ltd, Cambridge, UK), a holographic image is stored in a thin polymer film that is sensitised to react specifically with the biomarker. On exposure, the biomarker reacts with the polymer leading to an alteration in the image displayed by the hologram. The test result read-out can be a change in the optical brightness, image, colour and/or position of the image. For qualitative and semi-quantitative applications, a sensor hologram can be read by eye, thus removing the need for detection equipment. A simple colour sensor can be used to read the signal when quantitative measurements are required. Opacity or colour of the sample does not interfere with operation of the sensor. The format of the sensor allows multiplexing for simultaneous detection of several substances. Reversible and irreversible sensors can be designed to meet different requirements, and continuous monitoring of a particular biomarker of interest is feasible.

Suitably, biosensors for detection of the biomarker of the invention combine biomolecular recognition with appropriate means to convert detection of the presence, or quantitation, of the biomarker in the sample into a signal. Biosensors can be adapted for "alternate site" diagnostic testing, e.g. in the ward, outpatients' department, surgery, home, field and workplace.

Biosensors to detect the biomarker of the invention include acoustic, plasmon resonance, holographic and microengineered sensors. Imprinted recognition elements, thin film transistor technology, magnetic acoustic resonator devices and other novel acousto-electrical systems may be employed in biosensors for detection of the biomarker of the invention.

Methods involving detection and/or quantification of the peptide biomarker of the invention can be performed on bench-top instruments, or can be incorporated onto disposable, diagnostic or monitoring platforms that can be used in a non-laboratory environment, e.g. in the physician's office or at the patient's bedside. Suitable biosensors for performing methods of the invention include "credit" cards with optical or acoustic readers. Biosensors can be configured to allow the data collected to be electronically transmitted to the physician for interpretation and thus can form the basis for e-neuromedicine.

Any suitable animal may be used as a subject non-human animal, for example a non-human primate, horse, cow, pig, goat, sheep, dog, cat, fish, rodent, e.g. guinea pig, rat or mouse; insect {e.g. Drosophila), amphibian {e.g. Xenopus) or C. elegans.

There is provided a method of identifying a substance capable of promoting or suppressing the generation of the peptide biomarker in a subject, comprising exposing a test cell to a test substance and monitoring the level of the peptide biomarker within said test cell, or secreted by said test cell .

The test cell could be prokaryotic, however a eukaryotic cell will suitably be employed in cell-based testing methods. Suitably, the eukaryotic cell is a yeast cell, insect cell, Drosophila cell, amphibian cell {e.g. from Xenopus), C. elegans cell or is a cell of human, non-human primate, equine, bovine, porcine, caprine, ovine, canine, feline, piscine, rodent or murine origin. The test substance can be a known chemical or pharmaceutical substance, such as, but not limited to, an anti-depressive disorder therapeutic; or the test substance can be novel synthetic or natural chemical entity, or a combination of two or more of the aforesaid substances. In methods for identifying substances of potential therapeutic use, non-human animals or cells can be used that are capable of expressing the peptide.

Screening methods also encompass a method of identifying a ligand capable of binding to the peptide biomarker according to the invention, comprising incubating a test substance in the presence of the peptide biomarker in conditions appropriate for binding, and detecting and/or quantifying binding of the peptide to said test substance. High-throughput screening technologies based on the biomarker, uses and methods of the invention, e.g. configured in an array format, are suitable to monitor biomarker signatures for the identification of potentially useful therapeutic compounds, e.g. ligands such as natural compounds, synthetic chemical compounds {e.g. from combinatorial libraries), peptides, monoclonal or polyclonal antibodies or fragments thereof, which may be capable of binding the biomarker.

Methods of the invention can be performed in array format, e.g. on a chip, or as a multiwell array. Methods can be adapted into platforms for single tests, or multiple identical or multiple non-identical tests, and can be performed in high throughput format. Methods of the invention may comprise performing one or more additional, different tests to confirm or exclude diagnosis, and/or to further characterise a condition.

The invention further provides a substance, e.g. a ligand, identified or identifiable by an identification or screening method or use of the invention. Such substances may be capable of inhibiting, directly or indirectly, the activity of the peptide biomarker, or of suppressing generation of the peptide biomarker. The term "substances" includes substances that do not directly bind the peptide biomarker and directly modulate a function, but instead indirectly modulate a function of the peptide biomarker. Ligands are also included in the term substances; ligands of the invention {e.g. a natural or synthetic chemical compound, peptide, aptamer, oligonucleotide, antibody or antibody fragment) are capable of binding, suitably specific binding, to the peptide.

The invention further provides a substance according to the invention for use in the treatment of major depressive disorder, or predisposition thereto. In one embodiment, the method additionally comprises administering an antidepressant to a patient who is diagnosed with or predicted to have MDD.

Thus, according to a further aspect of the invention there is provided a method of treating an MDD patient, such as a recurrent and/or drug naive first onset MDD patient, which comprises the step of administering an antidepressant to a patient identified as having differing levels of the analyte biomarkers as defined herein when compared to the levels of said analyte biomarkers from a normal subject.

According to a further aspect of the invention there is provided a method of treating an MDD patient, such as a recurrent and/or drug naive first onset MDD patient, which comprises the following steps:

(a) quantifying the amounts of the analyte biomarkers as defined herein in a biological sample obtained from an individual;

(b) comparing the amounts of the analyte biomarkers in the biological sample with the amounts present in a normal control biological sample from a normal subject, such that a difference in the level of the analyte biomarkers in the biological sample is indicative of MDD, or predisposition thereto; and

(c) administering an antidepressant to a patient diagnosed in step (b) as a patient with MDD.

Also provided is the use of a substance according to the invention in the treatment of major depressive disorder, or predisposition thereto.

Also provided is the use of a substance according to the invention as a medicament.

Diagnostic kits

A further aspect of the invention provides a kit for diagnosing and/or monitoring major depressive disorder comprising reagents and/or a biosensor capable of detecting and/or quantifying the biomarkers described herein. Suitably a kit according to the invention may contain one or more components selected from the group: a ligand specific for the peptide biomarker or a structural/shape mimic of the peptide biomarker, one or more controls, one or more reagents and one or more consumables; optionally together with instructions for use of the kit in accordance with any of the methods defined herein. In one embodiment, the kit additionally comprises a questionnaire for use in diagnosing a patient with MDD. The questionnaire may be used to support the results obtained from use of the kit and/or to help determine the severity of major depressive disorder {i.e. severe, moderate or mild). In a further embodiment, the questionnaire is the Hamilton Rating scale for depression (HAM-D, 17, 21 or 29 items) questionnaire. Other examples of suitable questionnaires which may be used, include: the Montgomery-Asberg Depression Rating Scale (MADRS), the Beck Depression Inventory (BDI), the Zung Self- Rating Depression Scale, the Wechsler Depression Rating Scale, the Raskin Depression Rating Scale, the Inventory of Depressive Symptomatology (IDS) or the Quick Inventory of Depressive Symptomatology (QIDS).

Diagnostic kits for the diagnosis and monitoring of major depressive disorder or other psychotic disorder are described herein. In one embodiment, the kits additionally contain a biosensor capable of detecting and/or quantifying a peptide biomarker.

The identification of biomarkers for major depressive disorder or other psychotic disorder permits integration of diagnostic procedures and therapeutic regimes. Currently there are significant delays in determining effective treatment and hitherto it has not been possible to perform rapid assessment of drug response. Traditionally, many anti-depressive or anti-psychotic therapies have required treatment trials lasting weeks to months for a given therapeutic approach. Detection of a peptide biomarker of the invention can be used to screen subjects prior to their participation in clinical trials. The biomarkers provide the means to indicate therapeutic response, failure to respond, unfavourable side-effect profile, degree of medication compliance and achievement of adequate serum drug levels. The biomarkers may be used to provide warning of adverse drug response. Biomarkers are useful in development of personalized brain therapies, as assessment of response can be used to fine-tune dosage, minimise the number of prescribed medications, reduce the delay in attaining effective therapy and avoid adverse drug reactions. Thus by monitoring a biomarker of the invention, patient care can be tailored precisely to match the needs determined by the disorder and the pharmacogenomic profile of the patient, the biomarker can thus be used to titrate the optimal dose, predict a positive therapeutic response and identify those patients at high risk of severe side effects. Biomarker-based tests provide a first line assessment of 'new' patients, and provide objective measures for accurate and rapid diagnosis, in a time frame and with precision, not achievable using the current subjective measures.

Furthermore, diagnostic biomarker tests are useful to identify family members or patients at high risk of developing major depressive disorder or other psychotic disorder. This permits initiation of appropriate therapy, or preventive measures, e.g. managing risk factors. These approaches are recognised to improve outcome and may prevent overt onset of the disorder. Biomarker monitoring methods, biosensors and kits are also vital as patient monitoring tools, to enable the physician to determine whether relapse is due to worsening of the disorder, poor patient compliance or substance abuse. If pharmacological treatment is assessed to be inadequate, then therapy can be reinstated or increased; a change in therapy can be given if appropriate. As the biomarker is sensitive to the state of the disorder, it provides an indication of the impact of drug therapy or of substance abuse.

Reference Standards for Treatment

In many embodiments, the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers in a sample are compared to a reference standard ("reference standard" or "reference level") in order to direct treatment decisions. The reference standard used for any embodiment disclosed herein may comprise average, mean, or median levels of the one or more analyte biomarkers or the levels of the specific panel of analyte biomarkers in a control population. The reference standard may additionally comprise cutoff values or any other statistical attribute of the control population, such as a standard deviation from the mean levels of the one or more analyte biomarkers or the levels of the specific panel of analyte biomarkers. In some embodiments, comparing the level of the one or more analyte biomarkers is performed using a cutoff value. In related embodiments, if the level of the one or more analyte biomarkers is greater than the cutoff value, the individual may be diagnosed as having, or being at risk of developing major depressive disorder. In other distinct embodiments, if the level of the one or more analyte biomarkers is less than the cutoff value, the individual may be diagnosed as having, or being at risk of developing major depressive disorder. Cutoff values may be determined by statistical analysis of the control population to determine which levels represent a high likelihood that an individual does or does not belong to the control population. In some embodiments, comparing the level of the one or more analyte biomarkers is performed using other statistical methods. In related embodiments, comparing comprises logistic or linear regression. In other embodiments, comparing comprises computing an odds ratio.

In some embodiments, the control population may comprise healthy individuals or individuals with major depressive disorder.

In some embodiments, individuals with levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers greater than the reference levels would be more likely to have major depressive disorder. Therefore, an individual presenting with levels of the one or more analyte biomarkers or levels of the specific panel of analyte biomarkers greater than the reference standard would be a candidate for treatment with antidepressant or anxiolytic therapy, or with more aggressive therapy. On the other hand, an individual presenting with levels of the one or more analyte biomarkers or levels of the specific panel of analyte biomarkers less than or equal to the reference standard would be less likely to have major depressive disorder and therefore be a candidate for no antidepressant or anxiolytic therapy, delayed antidepressant or anxiolytic therapy or less aggressive antidepressant or anxiolytic therapy.

In other embodiments, individuals with levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers less than the reference levels would be more likely to have major depressive disorder. Therefore, an individual presenting with levels of the one or more analyte biomarkers or levels of the specific panel of analyte biomarkers less than the reference standard would be a candidate for treatment with antidepressant or anxiolytic therapy, or with more aggressive therapy. On the other hand, an individual presenting with levels of the one or more analyte biomarkers or levels of the specific panel of analyte biomarkers greater than or equal to the reference standard would be less likely to have major depressive disorder and therefore be a candidate for no antidepressant or anxiolytic therapy, delayed antidepressant or anxiolytic therapy or less aggressive antidepressant or anxiolytic therapy.

Reference Therapy for Treatment

In some embodiments, a patient is treated more or less aggressively than a reference therapy. A reference therapy is any therapy that is the standard of care for major depressive disorder. The standard of care can vary temporally and geographically, and a skilled person can easily determine the appropriate standard of care by consulting the relevant medical literature.

In some embodiments, based on a determination that levels of a panel of biomarkers is a) greater than, b) less than, c) equal to, d) greater than or equal to, or e) less than or equal to a reference standard, treatment will be either 1) more aggressive, or 2) less aggressive than a standard therapy.

In some embodiments, a more aggressive therapy than the standard therapy comprises beginning treatment earlier than in the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises administering additional treatments than in the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises treating on an accelerated schedule compared to the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises administering additional treatments not called for in the standard therapy.

In some embodiments, a less aggressive therapy than the standard therapy comprises delaying treatment relative to the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering less treatment than in the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering treatment on a decelerated schedule compared to the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering no treatment.

Treatment of Depression

Health practitioners treat depression by taking actions to ameliorate the causes or symptoms of the disorder in a patient. Treatment may comprise drug-based or non-drug-based therapies.

Drug-based therapies may include: selecting and administering one or more antidepressant drugs to the patient, adjusting the dosage of an antidepressant drug, adjusting the dosing schedule of an antidepressant drug, and adjusting the length of the therapy with an antidepressant drug. Antidepressant drugs are selected by practitioners based on the nature of the symptoms and the patient's response to any previous treatments. The dosage of an antidepressant drug can be adjusted as well by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. The dosing schedule can also be adjusted by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. Also, the length of the therapy can be adjusted by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug. Additionally, the practitioner can select between a single drug therapy, a dual drug therapy, or a triple drug therapy. In some embodiments, a practitioner may optionally treat the patient with a combination of one or more antidepressant drugs and one or more non-drug-based therapies.

In one embodiment, the practitioner begins antidepressant therapy based on a comparison between a reference level and the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers in a sample from a patient. In one embodiment, therapy comprises the selection and administration of an antidepressant drug to the patient by the practitioner. In another embodiment, therapy comprises the selection and administration of two antidepressant drugs to the patient by the practitioner as part of dual therapy. In another embodiment, therapy comprises the selection and administration of three antidepressant drugs to the patient by the practitioner as part of triple therapy.

Antidepressant drugs are commonly used by medical practitioners, and a skilled person may identify the appropriate antidepressant drug to administer based on the medical literature. In some embodiments, treatment comprises administering to an individual a selective serotonin reuptake inhibitor ("SSRI"). In some embodiments, the SSRI is citalopram . In some embodiments, the SSRI is escitalopram . In some embodiments, the SSRI is fluoxetine. In some embodiments, the SSRI is paroxetine. In some embodiments, the SSRI is sertraline.

In other embodiments, treatment comprises administering to an individual a serotonin-norepinephrine reuptake inhibitors ("SNRI"). In some embodiments, the SNRI is venlafaxine. In other embodiments, the SNRI is duloxetine.

In other embodiments, treatment comprises administering to an individual a norepinephrine and dopamine reuptake inhibitor ("NDRI"). In one embodiment, the NDRI is bupropion. In other embodiments, treatment comprises administering to an individual a tetracyclic antidepressant ("tetracyclic"). In some embodiments, the tetracyclic is amoxapine. In some embodiments, the tetracyclic is maprotiline. In some embodiments, the tetracyclic is mazindol. In some embodiments, the tetracyclic is mirtazapine.

In other embodiments, treatment comprises administering to an individual a tricyclic antidepressant ("tricyclic"). In some embodiments, the tricyclic is amitriptyline. In some embodiments, the tricyclic is imipramine. In some embodiments, the tricyclic is nortriptyline. In other embodiments, treatment comprises administering to an individual a monoamine oxidase inhibitor ("MAOI"). In some embodiments, the MAOI is selegiline. In some embodiments, the MAOI is isocarboxazid. In some embodiments, the MAOI is phenelzine. In some embodiments, the MAOI is tranylcypromine.

In addition to or in lieu of drug-based therapies, in some embodiments a practitioner may also treat an individual with non-drug-based antidepressant therapies. In some embodiments, the non-drug based therapy comprises cognitive-behavioral therapy. In some embodiments, the non-drug based therapy comprises psychotherapy. In a related embodiment, the non-drug based therapy comprises psychodynamic therapy. In some embodiments, the non-drug based therapy comprises electroconvulsive therapy. In some embodiments, the non-drug based therapy comprises hospitalization and residential treatment programs. In some embodiments, the non-drug based therapy comprises vagus nerve stimulation. In some embodiments, the non-drug based therapy comprises transcranial magnetic stimulation. In some embodiments, the non-drug based therapy comprises regular, vigorous exercise.

In one embodiment, the practitioner adjusts the antidepressant therapy based on a comparison between a reference level and the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers in a sample from a patient. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different combination of drugs. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage. In one embodiment, the practitioner adjusts the therapy by adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting drug dosage. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage and dose schedule. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting dose schedule and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, and adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting dose schedule, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage, adjusting dose schedule, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, adjusting dose schedule, and adjusting length of therapy.

In some embodiments, treatment comprises a less aggressive therapy than a reference therapy. In one embodiment a less aggressive therapy comprises not administering drugs and taking a "watchful waiting" approach. In one embodiment a less aggressive therapy comprises delaying treatment. In one embodiment a less aggressive therapy comprises selecting and administering less potent drugs. In one embodiment a less aggressive therapy comprises decreasing dosage of antidepressant drugs. In one embodiment a less aggressive therapy comprises decreasing the frequency treatment. In one embodiment a less aggressive therapy comprises shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and decreasing drug dosage. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and decelerating dose schedule. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and shortening length of therapy. In one embodiment, less aggressive therapy comprises decreasing drug dosage and decelerating dose schedule. In one embodiment, less aggressive therapy comprises decreasing drug dosage and shortening length of therapy. In one embodiment, less aggressive therapy comprises decelerating dose schedule and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, and decelerating dose schedule. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decelerating dose schedule, and shortening length of therapy. In one embodiment, less aggressive therapy comprises decreasing drug dosage, decelerating dose schedule, and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, decelerating dose schedule, and shortening length of therapy. In some embodiments, a less aggressive therapy comprises administering only non-drug-based therapies.

In another aspect of the present application, treatment comprises a more aggressive therapy than a reference therapy. In one embodiment a more aggressive therapy comprises earlier administration of antidepressant drugs. In one embodiment a more aggressive therapy comprises increased dosage of antidepressant drugs. In one embodiment a more aggressive therapy comprises increased length of therapy. In one embodiment a more aggressive therapy comprises increased frequency of the dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and increasing drug dosage. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and accelerating dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and increasing length of therapy. In one embodiment, more aggressive therapy comprises increasing drug dosage and accelerating dose schedule. In one embodiment, more aggressive therapy comprises increasing drug dosage and increasing length of therapy. In one embodiment, more aggressive therapy comprises accelerating dose schedule and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, and accelerating dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, accelerating dose schedule, and increasing length of therapy. In one embodiment, more aggressive therapy comprises increasing drug dosage, accelerating dose schedule, and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, accelerating dose schedule, and increasing length of therapy. In some embodiments, a more aggressive therapy comprises administering a combination of drug-based and non-drug-based therapies.

Systems for Diagnosing and Treating Depression

The results of any analyses according to the invention will often be communicated to physicians and/or patients (or other interested parties such as researchers) in a transmittable form that can be communicated or transmitted to any of the above parties. Such a form can vary and can be tangible or intangible. The results can be embodied in descriptive statements, diagrams, photographs, charts, images or any other visual forms. The statements and visual forms can be recorded on a tangible medium such as papers, computer readable media such as hard disks, compact disks, etc., or on an intangible medium, e.g., an electronic medium in the form of email or website on internet or intranet. In addition, results can also be recorded in a sound form and transmitted through any suitable medium, e.g., analog or digital cable lines, fiber optic cables, etc., via telephone, facsimile, wireless mobile phone, internet phone and the like.

Thus, the information and data on a test result can be produced anywhere in the world and transmitted to a different location. As an illustrative example, when an assay is conducted outside the United States, the information and data on a test result may be generated, cast in a transmittable form as described above, and then imported into the United States. Accordingly, the present invention also encompasses a method for producing a transmittable form of information on levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers for at least one patient sample. The method comprises the steps of (1) determining levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers for at least one patient sample according to methods of the present invention; and (2) embodying the result of the determining step in a transmittable form. The transmittable form is the product of such a method.

Techniques for analyzing levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers for at least one patient sample will often be implemented using hardware, software or a combination thereof in one or more computer systems or other processing systems capable of effectuating such analysis.

Thus, the present invention further provides a system for determining whether an individual suffers from major depressive disorder, comprising : (1) a sample analyzer for determining the levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers for at least one patient sample, wherein the sample analyzer contains the patient sample; (2) a first computer program for (a) receiving data regarding the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers; and optionally (3) a second computer program for comparing the test value to one or more reference standards each associated with a predetermined degree of risk of major depressive disorder. The sample analyzer can be any instruments useful in determining the levels of biomarkers in a sample, as described herein.

The computer-based analysis function can be implemented in any suitable language and/or browsers. For example, it may be implemented with C language and preferably using object-oriented high-level programming languages such as Visual Basic, SmallTalk, C++, and the like. The application can be written to suit environments such as the Microsoft Windows™ environment including Windows™ 98, Windows™ 2000, Windows™ NT, and the like. In addition, the application can also be written for the MacIntoshTM, SUNTM, UNIX or LINUX environment. In addition, the functional steps can also be implemented using a universal or platform-independent programming language. Examples of such multi-platform programming languages include, but are not limited to, hypertext markup language (HTML), JAVA™, JavaScript™, Flash programming language, common gateway interface/structured query language (CGI/SQL), practical extraction report language (PERL), AppleScript™ and other system script languages, programming language/structured query language (PL/SQL), and the like. Java™- or JavaScript™-enabled browsers such as HotJava™, Microsoft™ Explorer™, or Netscape™ can be used. When active content web pages are used, they may include Java™ applets or ActiveX™ controls or other active content technologies.

The analysis function can also be embodied in computer program products and used in the systems described above or other computer- or internet-based systems. Accordingly, another aspect of the present invention relates to a computer program product comprising a computer-usable medium having computer-readable program codes or instructions embodied thereon for enabling a processor to carry out disease risk analysis. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions or steps described above. These computer program instructions may also be stored in a computer-readable memory or medium that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory or medium produce an article of manufacture including instructions which implement the analysis. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions or steps described above. Thus one aspect of the present invention provides a system for determining whether a patient has major depressive disorder. Generally speaking, the system comprises (1) computer program for receiving, storing, and/or retrieving data regarding levels of biomarkers in a patient's sample and optionally clinical parameter data {e.g., disease-related symptoms); (2) computer program for querying this patient data; (3) computer program for concluding whether an individual suffers from major depressive disorder based on this patient data; and optionally (4) computer program for outputting/displaying this conclusion. In some embodiments this computer program for outputting the conclusion may comprise a computer program for informing a health care professional of the conclusion.

The practice of the present invention may also employ conventional biology methods, software and systems. Computer software products of the invention typically include computer readable media having computer-executable Instructions for performing the logic steps of the method of the invention. Suitable computer readable medium include floppy disk, CD-ROM/DVD/DVD- ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes and etc. Basic computational biology methods are described in, for example, Setubal et al., INTRODUCTION TO COMPUTATIONAL BIOLOGY METHODS (PWS Publishing Company, Boston, 1997); Salzberg et al. (Ed.), COMPUTATIONAL METHODS IN MOLECULAR BIOLOGY, (Elsevier, Amsterdam, 1998); Rashidi & Buehler, BIOINFORMATICS BASICS : APPLICATION IN BIOLOGICAL SCIENCE AND MEDICINE (CRC Press, London, 2000); and Ouelette & Bzevanis, Attorney Docket No. 3330-01-lP Page 38 of 64 BIOINFORMATICS : A PRACTICAL GUIDE FOR ANALYSIS OF GENE AN D PROTEINS (Wiley & Sons, Inc., 2nd ed., 2001); see also, U.S. Pat. No. 6,420,108.

The following studies illustrate the invention.

METHODS

Clinical samples

A total of 8 major depressive disorder (MDD) cohorts were analyzed, 6 of which were independent and 2 were a subset of larger cohorts. These 2 cohorts were independent from each other and selected to include only drug naive (DN) first onset MDD patients and healthy controls. MDD cohort 1 was from University of Cologne (Germany), MDD cohort 2 from University of Muenster (Germany), MDD cohorts 3 and 4 were from the University of Magdeburg (Germany), the DN MDD cohorts 1 and 2 were a subset of MDD cohorts 3 and 4 and, NESDA MDD cohorts were from the Netherlands Study of Depression and Anxiety Severity (NESDA), an on-going multi-site naturalistic longitudinal cohort study. The NESDA patients had a current MDD diagnosis based on disease occurrence within 6 months. The study protocols were approved by the institutional ethical committee associated with each of the clinical centres. All diagnoses and clinical tests were performed by trained psychiatrists to minimize variability. Informed written consent was given by all participants and all studies were conducted according to the Declaration of Helsinki. All diagnoses were carried out using the Diagnostic and Statistical Manual DSM-IV-TR for a unipolar major depressive episode. Severity of depressive symptoms was assessed at baseline using the Hamilton Rating scale for depression (HAM-D, 17 or 21 items) questionnaire. The exclusion criteria typically included most or all of the following : chronic illnesses {e.g. diabetes, cardiovascular disorders, hypertension and autoimmune diseases), somatic medication affecting mood, mental retardation, alcohol or substance abuse or dependence within 3 months of enrolment. Clinicians had access to all detailed clinical files including medical histories by proxy and referral letters from general practitioners. See Table 1 for the demographic details of the MDD patients from each cohort. Regarding antidepressant medication status at baseline, all patients from MDD cohorts 3 and 4 were either off medication for at least 6 weeks prior to blood withdrawal or drug naive; all patients from DN MDD cohorts 1 and 2 were drug naive; all patients from the un-medicated NESDA MDD cohort were not taking medication for the past 3 years; all patients from medicated NESDA MDD cohort were taking antidepressant medication at baseline and, finally, medication information was not available for most of the patients from MDD cohorts 1 and 2. TABLE 1: Demographics Overview

BMI : body mass index; HAM-D : Hamilton Rating scale for depression; results are presented as mean ± standard deviation

Multiplexed Immunoassay

Approximately 150 analytes were measured in sera from patients in MDD cohorts 1, 2, 3 and 4 using the HumanMAP® multiplexed antigen immunoassays ("HumanMAP") in a CLIA-certified laboratory at Myriad-RBM (Austin, TX, USA). For the DN MDD cohorts 1 and 2, a newer version of the HumanMAP was used which measures greater than 200 analytes. For the NESDA MDD cohorts, the latest version of the HumanMAP was used which measures greater than 250 analytes. All samples were randomized and blinded to analysts using code numbers until all biochemical assays were complete to avoid any sequential bias due to the diagnosis, age and gender. Assays were calibrated using duplicate standard curves, raw intensity measurements converted to absolute protein concentrations using proprietary software, and instrument performance was verified using quality control samples. The protocol for the study participants, clinical samples and test methods was carried out in compliance with the Standards for Reporting of Diagnostic Accuracy (STARD) initiative. Data analyses were performed using the statistical software package R (http://www.r- project.org).

Serum collection and preparation

Blood samples were collected from all fasting subjects by venepuncture into 7.5 mL S-Monovette serum tubes (Sarstedt; Numbrecht, Germany). The blood tubes were incubated at room temperature for 2 hours to allow blood coagulation, followed by centrifugation at 4000 g for 5 minutes to deposit the clotted material. The supernatants or sera were transferred to Low Binding Eppendorf tubes (Eppendorf, Histon, Cambridge) and stored at -80 °C until analysis.

Data analysis

Data pre-processing and imputation were performed as reported previously (Schwarz E. et a/. (2012) Transl. Psychiatry 2, p. e82; Schwarz E. et a/. (2013) Schizophr. Bull.). Briefly, data was pre-processed by excluding the results of all analyte assays which contained missing values (measurements outside the limits of quantitation) in more than 30% of samples. Pre-processed data was subjected to imputation as described previously. The analyte data were log transformed to stabilize variance. Shapiro-Wilk analysis showed that over 60% of the analytes were not normally distributed. Data quality was assessed using Principal Components analysis to detect subject outliers. Logistic regression analysis was used to identify biomarkers which can distinguish major depressive disorder compared to controls. Bayesian information criterion (BIC) was applied for stepwise (forward and backward) selection of covariates which included age, gender, BMI and smoking status. Regression diagnostics were performed for all the relevant models to ensure that residual normality, data linearity, independence and homoscedasticity were met. Diagnostic accuracy of the combined set of biomarkers was determined by plotting the receiver operating characteristic (ROC) curves and estimating the area under the ROC curve (ROC- AUC). The area under an ROC curve captures the overall diagnostic accuracy of the test. The optimal test sensitivity and specificity was determined by applying the Youden's index (J). This index is calculated by J = Sensitivity + Specificity - 1. Maximizing this index allows to find, from the ROC curve, an optimal cut-off point independently from the prevalence.

RESULTS

The study described herein aimed to identify a panel of candidate biomarkers in serum which can discriminate MDD patients recruited from both primary and specialized mental health care from healthy controls. This was determined by stepwise logistic regression and the most frequently selected covariates included gender and smoking. Application of this method led to identification of 34 serum molecules in at least 3 cohorts and the directions of change (increased or decreased levels) were mostly consistent (see Table 2 for a brief summary of the MDD diagnostic markers identified in common and Tables 3 - 10 for the full results from each cohort). The biomarkers identified were more similar between MDD cohorts 1, 3, 4 and DN MDD cohorts 1 and 2, compared to those identified in MDD cohort 2 and the NESDA MDD cohorts. This may be explained by disease heterogeneity among MDD patients. Furthermore, the NESDA MDD patients were classified as current MDD patients based on disease occurrence within 6 months, while patients from the remaining cohorts were experiencing a current major depressive episode at the time of blood withdrawal . These differences may also explain the variations observed in the results obtained. Table 11 provides an overview of the overall results in more detail and highlights the most reproducible markers identified across cohorts.

Of the 34 significant discriminative molecules, 2 were identified in 6 out of the 8 cohorts [IL-lra and FRTN], 6 were found in 5 cohorts [MIF, EN-RAGE, ACE, TNC, EGF and Testosterone] and 10 occurred in 4 cohorts. The directions of change were largely consistent across all cohorts in which significant changes of each molecule occurred. The majority of the most reproducible markers were identified in cohorts comprising DN first onset and mixed DN first onset/recurrent previously-medicated patient cohorts (MDD cohorts 3 and 4). These results highlight the potential usefulness of these markers as a diagnostic panel for MDD, especially in patients who remain unrecognized and untreated within the primary care setting.

The diagnostic accuracy of this panel of serum protein assays for discriminating patients from controls was determined by plotting receiver operating characteristic (ROC) curves and estimating the area under the curve (AUC). According to standard classification, ROC-AUC analyses indicate test performance and accuracy as follows: ROC-AUC: 0.90-1.00 = excellent; 0.80- 0.90 = good; 0.70-0.80 = fair; 0.60-0.70 = poor. For each cohort, the combined number of molecules used for testing was different as this is dependent on sample size which differed across the cohorts (see Figures 1 to 4 which show ROC curves plotted for each cohort).

For the 4 smaller cohorts (MDD cohorts 1 and 4, DN MDD cohorts 1 and 2), the top 5-7 most reproducible markers (selected from IL-lra, FRTN, MIF, EN-RAGE, ACE, TNC and EGF) could be tested together as a panel to assess the diagnostic accuracy for discriminating MDD patients from controls. On average, the resulting test performance was excellent (ROC-AUC= 0.94) and the sensitivity and specificity achieved was 89% and 87%, respectively. For the 4 larger cohorts (MDD cohorts 2 and 3, mediated and un-medicated NESDA MDD cohorts), the top 19-24 most reproducible markers could be tested together as a panel (Figures 3 and 4). The average test performance achieved using a panel of 24 molecules (IL-lra, FRTN, MIF, EN-RAGE, ACE, TNC, Testosterone, EGF, GH, IL-13, vWF, MIP-1 alpha, IL-16, TBG, RANTES, SOD-1, Apo A-I, MIP-1 beta, MPO, CA-19-9, IGFBP-2, IL-7, BLC and Haptoglobin) was excellent (ROC-AUC=0.93; sensitivity = 91%; specificity = 82%) for MDD cohorts 2 and 3. However, testing of the NESDA cohorts using the top 19 markers (IL-lra, FRTN, MIF, EN-RAGE, ACE, TNC, Testosterone, EGF, GH, vWF, IL-16, TBG, RANTES, SOD-1, Apo A-I, MIP-1 beta, MPO, IGFBP-2, and Haptoglobin) resulted in lower diagnostic performance (ROC-AUC=0.76, fair performance). Note that 5 analytes from the panel of 24 above (IL-13, MIP-1 alpha, CA-19-9, IL-7 and BLC) were not tested as part of the top 19 markers used for the NESDA MDD cohorts as they were either not measured or failed QC (labelled as NA in tables). Testing the top 7 analytes (IL-lra, FRTN, MIF, ENRAGE, ACE, TNC and EGF) using the NESDA MDD cohorts also resulted in fair performance (0.7< ROC-AUC<0.8). However, testing the top 4 analytes (IL-lra, FRTN, MIF, EN-RAGE) resulted in poor performance (0.6<ROC-AUC<0.7) in these particular cohorts.

Next, the study attempted to identify the best performing panels including 5 analytes. These panels were identified by testing combinations where one or two of the top 5 analytes is dropped and substituted by one or both of the next two (6th and 7th top analytes). In total, diagnostic performances of 21 combinations were tested for each cohort (see Table 12 for details of the combinations tested).

The test performances achieved for all of the 21 combinations of 5 analytes were either good or excellent for 4 of the 8 cohorts (MDD cohorts 1 and 4 and, DN MDD cohorts 1 and 2) (Table 13, 16, 17 and 18). However, all combinations tested poorly for the NESDA MDD cohort comprising patients who were all medicated (Table 19). This finding suggests that there is a potential influence of medication on performance of the various analyte panels. Although, most of these combinations also performed poorly in the un-medicated NESDA MDD cohort, analyte panel combinations 2, 4, 12, 13 and 16 resulted in fair performance (ROC-AUC>0.7) (Table 20). In addition, while most of the combinations tested resulted in a good performance in the MDD cohort 3, most resulted in only fair performance for MDD cohort 2. In summary, analyte panel combinations 2 [IL-lra, FRTN, MIF, EN-RAGE, TNC], 4 [IL-lra, FRTN, EN-RAGE, ACE, TNC], 12 [IL-lra + FRTN + MIF + TNC + EGF], 13 [IL-lra + FRTN + ENRAGE + TNC + EGF] and 16 [IL-lra + FRTN + ACE + TNC + EGF] gave the best performance apart from only poor performance in cohort 2. These findings suggest that these 5 combinations of 5 analytes represent candidate diagnostic tests for diagnosing MDD in unrecognized patients that present to the primary care setting. All other combinations gave poor performance in at least 2 cohorts.

Finally, the study tested the top 4 analytes (IL-lra, FRTN, EN-RAGE and TNC) across all cohorts. The test performances achieved for the 4 analytes were either good or excellent for 5 of the 8 cohorts (MDD cohorts 1, 3 and 4 and, DN MDD cohorts 1 and 2) (Table 21).

The results are summarized in the following Tables:

TABLE 2: Table summarizing the MDD diagnostic markers identified in at least 2 cohorts

The top 34 most robust markers (>3 cohorts) are highlighted in bold. A star (*) indicates a cohort where an analyte wa found to be altered in patients relative to controls. An up arrow (†) and down arrow (|) indicates a cohort where an analyt was found to be increased and decreased in patients relative to controls, respectively.

Sig. : p≤0.05; Trend: 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP used.

TABLE 3: MDD diagnostic markers identified in MDD cohort 1

MDD1 (21CT + 10 MDD)

Analyte

Estimate p-value

Myeloperoxidase (MPO) 4.83 0.008

Vascular Cell Adhesion Molecule-1 (VCAM-1) 19.68 0.011

Thyroxine-Binding Globulin (TBG) 19.75 0.012

AXL Receptor Tyrosine Kinase (AXL) -12.28 0.013

Interleukin-13 (IL-13) -10.24 0.016 von Willebrand Factor (vWF) 7.09 0.021

Interleukin-1 receptor antagonist (IL-lra) 2.8 0.029

Alpha-2-Macroglobulin (A2Macro) -44.5 0.030

Apolipoprotein C-III (Apo C-III) 7.51 0.031

Macrophage Inflammatory Protein-1 beta (MIP-1 beta) -7.82 0.031

Epidermal Growth Factor (EGF) 7.31 0.032

Growth Hormone (GH) 1.34 0.032

Tenascin-C (TNC) 7.91 0.036

Hepatocyte Growth Factor (HGF) 8.03 0.037

FASLG Receptor (FAS) -5.7 0.039

Apolipoprotein H (Apo H) 11.29 0.040

Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78) 3.66 0.040

Adiponectin 4.47 0.042

CD40 Ligand (CD40-L) 9.41 0.044

Macrophage Migration Inhibitory Factor (MIF) -4.87 0.047

T-Cell-Specific Protein RANTES (RANTES) 5.22 0.047

Angiotensin-Converting Enzyme (ACE) -7.17 0.057

EN-RAGE 2.3 0.065

Haptoglobin 2.51 0.054

Interleukin-16 (IL-16) 9.48 0.061

Interleukin-7 (IL-7) -5.56 0.067

Thyroid-Stimulating Hormone (TSH) -5.59 0.058

Plasminogen Activator Inhibitor 1 (PAI-1) 6.72 0.081

Resistin 5.35 0.094

Interleukin-15 (IL-15) -2.25 0.094

Glutathione S-Transferase alpha (GST-alpha) -3.02 0.094

Growth-Regulated alpha protein (GRO-alpha) 4.56 0.095

Stem Cell Factor (SCF) 3.99 0.107

Progesterone 5.23 0.110

Apolipoprotein A-I (Apo A-I) 4.96 0.114

Superoxide Dismutase 1, soluble (SOD-1) -2.64 0.114

Luteinizing Hormone (LH) -2.12 0.141

Complement C3 (C3) 5.4 0.148

Leptin 1.27 0.162

TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- 3.65 0.183 R3)

Amphiregulin (AR) 2.79 0.197

Monocyte Chemotactic Protein 1 (MCP-1) -3.76 0.243 CD 40 antigen (CD40) 8.57 0.251

Interleukin-5 (IL-5) -1.61 0.266

Factor VII -3.59 0.281

Follicle-Stimulating Hormone (FSH) -1.1 0.291

Cancer Antigen 19-9 (CA-19-9) -1.45 0.292

Intercellular Adhesion Molecule 1 (ICAM-1) 3.3 0.301

Myoglobin 3.12 0.306

Cortisol 1.74 0.319

Vascular Endothelial Growth Factor (VEGF) 3.38 0.332

Interleukin-18 (IL-18) 2.78 0.348

Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha) 2.67 0.383

Beta-2-Microglobulin (B2M) 5.64 0.388

Interferon gamma (IFN-gamma) 1.2 0.399

Interleukin-8 (IL-8) -1.65 0.433

Serum Amyloid P-Component (SAP) 1.62 0.471

Pancreatic Polypeptide (PPP) 0.93 0.485

Chromogranin-A (CgA) 0.6 0.518

C-Reactive Protein (CRP) -0.44 0.535

Immunoglobulin A (IgA) -1.53 0.559

Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2) 1.28 0.578

B Lymphocyte Chemoattractant (BLC) 0.69 0.599

Creatine Kinase-MB (CK-MB) -0.64 0.626

Testosterone, Total 1.1 0.653

Macrophage-Derived Chemokine (MDC) 1.94 0.674

Prostatic Acid Phosphatase -0.84 0.713

Interleukin-1 alpha (IL-1 alpha) 0.86 0.717

Chemokine CC-4 (HCC-4) -0.73 0.727

Interleukin-3 (IL-3) -0.58 0.771

Prolactin (PRL) -0.45 0.778

Tumor necrosis factor receptor 2 (TNFR2) 0.89 0.836

T Lymphocyte-Secreted Protein 1-309 (1-309) 0.12 0.846

Interleukin-10 (IL-10) -0.28 0.874

Angiotensinogen 0.06 0.914

Ferritin (FRTN) -0.08 0.933

Sex Hormone-Binding Globulin (SHBG) -0.09 0.959

Alpha-l-Antitrypsin (AAT) 0.19 0.959

Matrix Metalloproteinase-3 (MMP-3) -0.07 0.974

Thrombospondin-1 NA NA

Serotransferrin (Transferrin) NA NA

Interferon gamma Induced Protein 10 (IP-10) NA NA

Tamm-Horsfall Urinary Glycoprotein (THP) NA NA

Cystatin-C NA NA

Trefoil Factor 3 (TFF3) NA NA

Apolipoprotein D (Apo D) NA NA

Matrix Metalloproteinase-2 (MMP-2) NA NA

Epidermal Growth Factor Receptor (EGFR) NA NA

Lectin-Like Oxidized LDL Receptor 1 (LOX-1) NA NA Neutrophil Gelatinase-Associated Lipocalin (NGAL) NA NA

Alpha-l-Antichymotrypsin (AACT) NA NA

Agouti-Related Protein (AGRP) NA NA

Osteopontin NA NA

Connective Tissue Growth Factor (CTGF) NA NA

Alpha-l-Microglobulin (AlMicro) NA NA

Monocyte Chemotactic Protein 4 (MCP-4) NA NA

Apolipoprotein A-II (Apo A-II) NA NA

E-Selectin NA NA

Clusterin (CLU) NA NA

Fetuin-A NA NA

Key: Signal : p≤0.05; Trend : 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP used

TABLE 4: MDD diagnostic markers identified in MDD cohort 2

MDD2 (33CT + 99 MDD)

Analyte

Estimate p-value

Ferritin (FRTN) 2.15 2.98E-05

Growth Hormone (GH) -1.1 1.22E-04

Matrix Metalloproteinase-2 (MMP-2) 1.9 7.15E-04

Prostatic Acid Phosphatase 4.14 7.39E-04

Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha) 4.41 1.27E-03

Prolactin (PRL) 2.21 0.002

Interleukin-18 (IL-18) 5.7 0.002

Leptin 1.77 0.002

Matrix Metalloproteinase-3 (MMP-3) -3.29 0.004

Macrophage Inflammatory Protein-1 beta (MIP-1 beta) 3.59 0.004

Intercellular Adhesion Molecule 1 (ICAM-1) 5.48 0.005

Apolipoprotein A-I (Apo A-I) -3.38 0.006

Macrophage-Derived Chemokine (MDC) 5.14 0.009

Sex Hormone-Binding Globulin (SHBG) -1.5 0.011

Serum Amyloid P-Component (SAP) 3.35 0.014

C-Reactive Protein (CRP) 0.94 0.016

Cancer Antigen 19-9 (CA-19-9) 1.03 0.018

T-Cell-Specific Protein RANTES (RANTES) -2.04 0.019

Amphiregulin (AR) -1.82 0.023

Glutathione S-Transferase alpha (GST-alpha) 1.01 0.024

Interleukin-10 (IL-10) 1.9 0.024

Hepatocyte Growth Factor (HGF) 1.78 0.028

Growth-Regulated alpha protein (GRO-alpha) -2.22 0.029

Immunoglobulin A (IgA) -2.23 0.039

T Lymphocyte-Secreted Protein 1-309 (1-309) 0.86 0.045

FASLG Receptor (FAS) 1.23 0.057

Haptoglobin 1.11 0.061 Interleukin-13 (IL-13) 1.65 0.066

Tumor necrosis factor receptor 2 (TNFR2) 2.83 0.070

Vascular Endothelial Growth Factor (VEGF) 2.43 0.062

Myeloperoxidase (MPO) 1.4 0.073

Monocyte Chemotactic Protein 1 (MCP-1) 2.49 0.074

Factor VII 1.9 0.084

Interleukin-15 (IL-15) 0.74 0.086

B Lymphocyte Chemoattractant (BLC) -0.68 0.098

Adiponectin -1.45 0.106

Thrombospondin-1 -2.63 0.115

TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- 1.78 0.126 R3)

Beta-2-Microglobulin (B2M) 2.8 0.158

Complement C3 (C3) 2.56 0.183

CD40 Ligand (CD40-L) 0.59 0.187

Interleukin-7 (IL-7) 0.9 0.224

Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2) -1.19 0.230

Creatine Kinase-MB (CK-MB) -0.95 0.231

Angiotensin-Converting Enzyme (ACE) -1.52 0.239

Stem Cell Factor (SCF) 1.34 0.240

EN-RAGE 0.7 0.264

AXL Receptor Tyrosine Kinase (AXL) -1.72 0.264

Interleukin-3 (IL-3) 1.03 0.287

Epidermal Growth Factor (EGF) 0.68 0.295

Apolipoprotein C-III (Apo C-III) -1.21 0.295

CD 40 antigen (CD40) 1.6 0.315

Luteinizing Hormone (LH) 0.76 0.318

Plasminogen Activator Inhibitor 1 (PAI-1) 1.42 0.345

Resistin 1.35 0.389

Pancreatic Polypeptide (PPP) -0.44 0.389

Alpha-2-Macroglobulin (A2Macro) -1.28 0.398

Apolipoprotein H (Apo H) 1.32 0.407

Macrophage Migration Inhibitory Factor (MIF) -0.53 0.424

Chromogranin-A (CgA) -0.24 0.443

Follicle-Stimulating Hormone (FSH) 0.33 0.448

Alpha-l-Antitrypsin (AAT) 1.47 0.452

Interleukin-5 (IL-5) 0.41 0.484

Angiotensinogen 0.21 0.488

Testosterone, Total 0.51 0.534

Cortisol 0.28 0.597 von Willebrand Factor (vWF) -0.45 0.603

Interleukin-1 receptor antagonist (IL-lra) 0.24 0.617

Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78) -0.36 0.660

Chemokine CC-4 (HCC-4) -0.35 0.683

Interleukin-8 (IL-8) -0.31 0.746 Thyroxine-Binding Globulin (TBG) -0.47 0.783

Interleukin-1 alpha (IL-1 alpha) -0.17 0.802

Superoxide Dismutase 1, soluble (SOD-1) -0.2 0.818

Myoglobin 0.17 0.852

Interleukin-16 (IL-16) 0.26 0.854

Progesterone 0.14 0.905

Vascular Cell Adhesion Molecule-1 (VCAM-1) -0.14 0.933

Tenascin-C (TNC) -0.04 0.974

Thyroid-Stimulating Hormone (TSH) -0.01 0.989

Interferon gamma (IFN-gamma) NA NA

Serotransferrin (Transferrin) NA NA

Interferon gamma Induced Protein 10 (IP-10) NA NA

Tamm-Horsfall Urinary Glycoprotein (THP) NA NA

Cystatin-C NA NA

Trefoil Factor 3 (TFF3) NA NA

Apolipoprotein D (Apo D) NA NA

Epidermal Growth Factor Receptor (EGFR) NA NA

Lectin-Like Oxidized LDL Receptor 1 (LOX-1) NA NA

Neutrophil Gelatinase-Associated Lipocalin (NGAL) NA NA

Alpha-l-Antichymotrypsin (AACT) NA NA

Agouti-Related Protein (AGRP) NA NA

Osteopontin NA NA

Connective Tissue Growth Factor (CTGF) NA NA

Alpha-l-Microglobulin (AlMicro) NA NA

Monocyte Chemotactic Protein 4 (MCP-4) NA NA

Apolipoprotein A-II (Apo A-II) NA NA

E-Selectin NA NA

Clusterin (CLU) NA NA

Fetuin-A NA NA

Key: Signal : p≤0.05; Trend : 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP used

TABLE 5: MDD diagnostic markers identified in MDD cohort 3

MDD3 ( 125 CT + 56

Analyte MDD)

Estimate p-value

Ferritin (FRTN) 2.29 3.44E-07

EN-RAGE 2.55 2.90E-06

Interleukin-1 receptor antagonist (IL-lra) 4.45 7.98E-06

Pancreatic Polypeptide (PPP) 2.01 8.05E-06

Macrophage Migration Inhibitory Factor (MIF) 3.36 1.89E-05

Interleukin-16 (IL-16) 5.75 4.04E-05

Cortisol 2.26 5.53E-05

Creatine Kinase-MB (CK-MB) -2.51 1.12E-04 Myoglobin -3.8 1.85E-04

Leptin -1.77 2.69E-04

Angiotensinogen 0.79 4.14E-04

Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2) 2.42 9.50E-04

Interferon gamma (IFN-gamma) 1.82 0.002

Interleukin-7 (IL-7) -1.24 0.002

Thyroid-Stimulating Hormone (TSH) -1.74 0.002

Thrombospondin-1 1.55 0.003

Interleukin-5 (IL-5) -1.33 0.004

CD 40 antigen (CD40) 5.7 0.004

Cancer Antigen 19-9 (CA-19-9) 1.18 0.006

Interleukin-3 (IL-3) -1.59 0.006

Myeloperoxidase (MPO) 1.96 0.006

Growth Hormone (GH) -0.54 0.008

Progesterone 1.51 0.008

Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha) 4.1 0.010

Testosterone, Total 2.21 0.011

Beta-2-Microglobulin (B2M) -4.53 0.011

TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- 2.28 0.013

R3)

Resistin 3.08 0.014

Follicle-Stimulating Hormone (FSH) 0.82 0.017

Interleukin-13 (IL-13) -1.96 0.019

B Lymphocyte Chemoattractant (BLC) 0.76 0.024

Prolactin (PRL) -1.2 0.026

Angiotensin-Converting Enzyme (ACE) -2.62 0.028

Tumor necrosis factor receptor 2 (TNFR2) -2.93 0.029

Vascular Endothelial Growth Factor (VEGF) 2.35 0.033

Interleukin-8 (IL-8) 1.59 0.037

Haptoglobin 1.23 0.038

Interleukin-15 (IL-15) -0.7 0.043

Chemokine CC-4 (HCC-4) 1.73 0.046

Superoxide Dismutase 1, soluble (SOD-1) 1.48 0.047

Thyroxine-Binding Globulin (TBG) -2.5 0.056 von Willebrand Factor (vWF) 1.43 0.058

Monocyte Chemotactic Protein 1 (MCP-1) -2.08 0.072

Tenascin-C (TNC) 1.65 0.081

Apolipoprotein C-III (Apo C-III) -1.64 0.127

T-Cell-Specific Protein RANTES (RANTES) -1.04 0.136

Growth-Regulated alpha protein (GRO-alpha) -1.41 0.148

Intercellular Adhesion Molecule 1 (ICAM-1) 1.73 0.167

Epidermal Growth Factor (EGF) 1.23 0.193

Vascular Cell Adhesion Molecule-1 (VCAM-1) -1.98 0.196

Alpha-l-Antitrypsin (AAT) -1.58 0.223

Serum Amyloid P-Component (SAP) -1.32 0.224

Immunoglobulin A (IgA) 1.02 0.238

Plasminogen Activator Inhibitor 1 (PAI-1) -1.64 0.253 Apolipoprotein H (Apo H) 1.78 0.254

Hepatocyte Growth Factor (HGF) -1.48 0.255

Prostatic Acid Phosphatase -0.99 0.262

AXL Receptor Tyrosine Kinase (AXL) -1.06 0.278

Complement C3 (C3) -1.85 0.281

Adiponectin 0.72 0.331

Alpha-2-Macroglobulin (A2Macro) 1.3 0.334

Stem Cell Factor (SCF) -0.88 0.389

Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78) -0.53 0.393

Luteinizing Hormone (LH) 0.31 0.411

C-Reactive Protein (CRP) -0.2 0.413

T Lymphocyte-Secreted Protein 1-309 (1-309) 0.18 0.549

Sex Hormone-Binding Globulin (SHBG) 0.24 0.596

Macrophage-Derived Chemokine (MDC) -0.68 0.623

Glutathione S-Transferase alpha (GST-alpha) 0.15 0.643

Interleukin-10 (IL-10) 0.31 0.694

Factor VII -0.38 0.701

Macrophage Inflammatory Protein-1 beta (MIP-1 beta) 0.35 0.704

FASLG Receptor (FAS) -0.39 0.754

CD40 Ligand (CD40-L) 0.11 0.804

Interleukin-18 (IL-18) 0.21 0.865

Chromogranin-A (CgA) 0.04 0.883

Matrix Metalloproteinase-3 (MMP-3) -0.08 0.896

Apolipoprotein A-I (Apo A-I) 0.11 0.911

Interleukin-1 alpha (IL-1 alpha) -0.05 0.940

Serotransferrin (Transferrin) NA NA

Interferon gamma Induced Protein 10 (IP-10) NA NA

Tamm-Horsfall Urinary Glycoprotein (THP) NA NA

Cystatin-C NA NA

Trefoil Factor 3 (TFF3) NA NA

Apolipoprotein D (Apo D) NA NA

Matrix Metalloproteinase-2 (MMP-2) NA NA

Amphiregulin (AR) NA NA

Epidermal Growth Factor Receptor (EGFR) NA NA

Lectin-Like Oxidized LDL Receptor 1 (LOX-1) NA NA

Neutrophil Gelatinase-Associated Lipocalin (NGAL) NA NA

Alpha-l-Antichymotrypsin (AACT) NA NA

Agouti-Related Protein (AGRP) NA NA

Osteopontin NA NA

Connective Tissue Growth Factor (CTGF) NA NA

Alpha-l-Microglobulin (AlMicro) NA NA

Monocyte Chemotactic Protein 4 (MCP-4) NA NA

Apolipoprotein A-II (Apo A-II) NA NA

E-Selectin NA NA

Clusterin (CLU) NA NA

Fetuin-A NA NA Key: Signal : p≤0.05; Trend : 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP used

TABLE 6: MDD diagnostic markers identified in MDD cohort 4

MDD4 (32CT + 22 MDD)

Analyte

Estimate p-value

Interleukin-1 receptor antagonist (IL-lra) 8.86 0.003

Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha) 14.18 0.009

EN-RAGE 4.61 0.016

Macrophage Migration Inhibitory Factor (MIF) 8.11 0.022

Testosterone, Total 47.18 0.023

Epidermal Growth Factor (EGF) 10.65 0.024

Superoxide Dismutase 1, soluble (SOD-1) 8.34 0.028

Resistin 13.58 0.029

Angiotensin-Converting Enzyme (ACE) -14.15 0.031

Ferritin (FRTN) 2.43 0.034

Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2) 6.2 0.035

Tenascin-C (TNC) 9.34 0.049

Creatine Kinase-MB (CK-MB) -4.72 0.058

Apolipoprotein A-I (Apo A-I) -10.93 0.061

Macrophage Inflammatory Protein-1 beta (MIP-1 beta) 7.61 0.072

Leptin -2.42 0.074

Apolipoprotein C-III (Apo C-III) -6.62 0.084

Interleukin-16 (IL-16) 6.27 0.088

Luteinizing Hormone (LH) 5.79 0.093

Angiotensinogen 1.12 0.112

Chemokine CC-4 (HCC-4) -3.46 0.115

T-Cell-Specific Protein RANTES (RANTES) -3.18 0.150

Interleukin-1 alpha (IL-1 alpha) 2.05 0.166

Interleukin-10 (IL-10) 3.18 0.167

Growth-Regulated alpha protein (GRO-alpha) -4.39 0.173

Factor VII -4.02 0.177

Myeloperoxidase (MPO) 2.93 0.195

Matrix Metalloproteinase-3 (MMP-3) 3.64 0.198

Adiponectin -3.62 0.210

Cortisol 1.79 0.261

Thrombospondin-1 -5.02 0.262

Follicle-Stimulating Hormone (FSH) 1.99 0.264

Alpha-l-Antitrypsin (AAT) 4.85 0.274

Sex Hormone-Binding Globulin (SHBG) -1.93 0.281

Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78) -2.19 0.289

C-Reactive Protein (CRP) 0.89 0.310

FASLG Receptor (FAS) -2.83 0.311 Immunoglobulin A (IgA) 1.88 0.315

Prolactin (PRL) 1.6 0.321

Interleukin-18 (IL-18) 2.94 0.355

Pancreatic Polypeptide (PPP) -1.23 0.365

Monocyte Chemotactic Protein 1 (MCP-1) -1.89 0.365

Haptoglobin 1.34 0.368

B Lymphocyte Chemoattractant (BLC) -0.99 0.371

Stem Cell Factor (SCF) -1.93 0.408 von Willebrand Factor (vWF) 1.46 0.480

Beta-2-Microglobulin (B2M) 3.49 0.485

Vascular Cell Adhesion Molecule-1 (VCAM-1) -2.75 0.504

CD 40 antigen (CD40) 3.85 0.507

Myoglobin -1.42 0.541

Serum Amyloid P-Component (SAP) 1.38 0.576

TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- 1.2 0.579 R3)

Prostatic Acid Phosphatase 1.7 0.589

Growth Hormone (GH) -0.29 0.618

Progesterone 1.38 0.660

Complement C3 (C3) -1.84 0.682

Chromogranin-A (CgA) 0.34 0.711

Apolipoprotein H (Apo H) 1.78 0.740

Macrophage-Derived Chemokine (MDC) 1.3 0.742

Interleukin-13 (IL-13) 0.61 0.753

Interleukin-7 (IL-7) 0.36 0.754

Alpha-2-Macroglobulin (A2Macro) -1.36 0.772

CD40 Ligand (CD40-L) 0.48 0.773

Tumor necrosis factor receptor 2 (TNFR2) 0.96 0.788

Glutathione S-Transferase alpha (GST-alpha) -0.31 0.807

Interleukin-8 (IL-8) -0.43 0.825

Intercellular Adhesion Molecule 1 (ICAM-1) 0.9 0.835

AXL Receptor Tyrosine Kinase (AXL) -0.36 0.843

Vascular Endothelial Growth Factor (VEGF) -0.58 0.856

Plasminogen Activator Inhibitor 1 (PAI-1) -0.67 0.856

Hepatocyte Growth Factor (HGF) -0.41 0.859

Cancer Antigen 19-9 (CA-19-9) -0.13 0.893

Matrix Metalloproteinase-2 (MMP-2) -0.17 0.901

T Lymphocyte-Secreted Protein 1-309 (1-309) 0.09 0.913

Thyroxine-Binding Globulin (TBG) 0.31 0.924

Thyroid-Stimulating Hormone (TSH) 0.12 0.953

Interleukin-3 (IL-3) 0.01 0.994

Interleukin-15 (IL-15) NA NA

Interleukin-5 (IL-5) NA NA

Interferon gamma (IFN-gamma) NA NA

Serotransferrin (Transferrin) NA NA Interferon gamma Induced Protein 10 (IP-10) NA NA

Tamm-Horsfall Urinary Glycoprotein (THP) NA NA

Cystatin-C NA NA

Trefoil Factor 3 (TFF3) NA NA

Apolipoprotein D (Apo D) NA NA

Amphiregulin (AR) NA NA

Epidermal Growth Factor Receptor (EGFR) NA NA

Lectin-Like Oxidized LDL Receptor 1 (LOX-1) NA NA

Neutrophil Gelatinase-Associated Lipocalin (NGAL) NA NA

Alpha-l-Antichymotrypsin (AACT) NA NA

Agouti-Related Protein (AGRP) NA NA

Osteopontin NA NA

Connective Tissue Growth Factor (CTGF) NA NA

Alpha-l-Microglobulin (AlMicro) NA NA

Monocyte Chemotactic Protein 4 (MCP-4) NA NA

Apolipoprotein A-II (Apo A-II) NA NA

E-Selectin NA NA

Clusterin (CLU) NA NA

Fetuin-A NA NA

Key: Signal : p≤0.05; Trend : 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP used

TABLE 7: MDD diagnostic markers identified in DN MDD cohort 1

DN MDD cohort 1

Analyte (42 CT + 23 MDD)

Estimate p-value

Macrophage Migration Inhibitory Factor (MIF) 6.06 3.95E-04

EN-RAGE 5.46 1.40E-04

Interleukin-16 (IL-16) 10.58 1.00E-03

Interleukin-3 (IL-3) -3.74 1.00E-03

Ferritin (FRTN) 1.92 0.002

Interleukin-1 receptor antagonist (IL-lra) 5.5 0.003

Testosterone, Total 6.3 0.004

Myeloperoxidase (MPO) 4.13 0.004

Interleukin-15 (IL-15) -1.81 0.005

Epidermal Growth Factor Receptor (EGFR) -12.86 0.006

Lectin-Like Oxidized LDL Receptor 1 (LOX-1) 3.56 0.009

Neutrophil Gelatinase-Associated Lipocalin (NGAL) 5.73 0.009

Progesterone 3.37 0.010

Interleukin-7 (IL-7) -2.59 0.012

Serotransferrin (Transferrin) -8.54 0.014

Tenascin-C (TNC) 4.02 0.015

Cortisol 2.15 0.015

Alpha-l-Antichymotrypsin (AACT) 2.41 0.015 Interleukin-5 (IL-5) -1.77 0.017

Interferon gamma Induced Protein 10 (IP-10) -4.02 0.018

Interleukin-13 (IL-13) -4.8 0.022

Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78) -2.45 0.025

Agouti-Related Protein (AGRP) 3.68 0.025

Growth-Regulated alpha protein (GRO-alpha) -4.02 0.028

Stem Cell Factor (SCF) -3.81 0.028

Apolipoprotein A-I (Apo A-I) -4.09 0.031

Angiotensin-Converting Enzyme (ACE) -5.29 0.032

Apolipoprotein C-III (Apo C-III) -4.55 0.032

Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2) 2.74 0.033

Tamm-Horsfall Urinary Glycoprotein (THP) -3.28 0.033

B Lymphocyte Chemoattractant (BLC) 1.61 0.038

Chromogranin-A (CgA) -1.15 0.040

Cancer Antigen 19-9 (CA-19-9) 1.41 0.042

Cystatin-C 9.7 0.043

Thyroxine-Binding Globulin (TBG) -4.46 0.044 von Willebrand Factor (vWF) 2.42 0.046

Monocyte Chemotactic Protein 1 (MCP-1) -4.35 0.046

Thrombospondin-1 2.03 0.047

Interleukin-8 (IL-8) 2.89 0.048

Macrophage-Derived Chemokine (MDC) 4.16 0.057

T-Cell-Specific Protein RANTES (RANTES) -2.12 0.058

Trefoil Factor 3 (TFF3) -2 0.059

Interferon gamma (IFN-gamma) 1.77 0.061

Apolipoprotein D (Apo D) 4.8 0.071

Apolipoprotein H (Apo H) 6.04 0.074

CD 40 antigen (CD40) 6.53 0.079

Prolactin (PRL) -1.63 0.081

FASLG Receptor (FAS) -4.34 0.082

Intercellular Adhesion Molecule 1 (ICAM-1) 4.18 0.084

Osteopontin 2.01 0.089

Creatine Kinase-MB (CK-MB) -1.43 0.090

TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- 2.45 0.095 R3)

Hepatocyte Growth Factor (HGF) -4.15 0.098

Growth Hormone (GH) -0.5 0.103

Beta-2-Microglobulin (B2M) -4.51 0.119

Resistin 2.55 0.126

Leptin -0.96 0.130

Interleukin-18 (IL-18) 3.02 0.130

Monocyte Chemotactic Protein 4 (MCP-4) 2.65 0.142

Angiotensinogen 0.52 0.145

Immunoglobulin A (IgA) 2.35 0.178

Clusterin (CLU) -4.96 0.179

Superoxide Dismutase 1, soluble (SOD-1) 1.58 0.193

Vascular Endothelial Growth Factor (VEGF) 2.34 0.196 Factor VII -1.85 0.212

Interleukin-1 alpha (IL-1 alpha) -1.53 0.218

Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha) 2.8 0.225

Chemokine CC-4 (HCC-4) -1.62 0.229

Pancreatic Polypeptide (PPP) 0.77 0.238

Haptoglobin 1.25 0.244

Apolipoprotein A-II (Apo A-II) -2.37 0.245

Myoglobin -1.45 0.254

Adiponectin -1.3 0.288

Serum Amyloid P-Component (SAP) -2.01 0.288

Follicle-Stimulating Hormone (FSH) 0.58 0.306

CD40 Ligand (CD40-L) 0.73 0.334

Alpha-2-Macroglobulin (A2Macro) 2.06 0.405

AXL Receptor Tyrosine Kinase (AXL) -1.79 0.407

Matrix Metalloproteinase-3 (MMP-3) 0.9 0.417

T Lymphocyte-Secreted Protein 1-309 (1-309) 0.37 0.433

Interleukin-10 (IL-10) 0.86 0.487

Alpha-l-Microglobulin (AlMicro) -2.05 0.511

Macrophage Inflammatory Protein-1 beta (MIP-1 beta) 0.78 0.603

Sex Hormone-Binding Globulin (SHBG) -0.36 0.621

Complement C3 (C3) -1.44 0.641

Plasminogen Activator Inhibitor 1 (PAI-1) -1.01 0.648

C-Reactive Protein (CRP) -0.18 0.665

Luteinizing Hormone (LH) 0.2 0.694

Vascular Cell Adhesion Molecule-1 (VCAM-1) -0.78 0.754

E-Selectin 0.24 0.879

Alpha-l-Antitrypsin (AAT) -0.23 0.904

Glutathione S-Transferase alpha (GST-alpha) 0.02 0.973

Fetuin-A -0.04 0.986

Epidermal Growth Factor (EGF) -0.01 0.994

Tumor necrosis factor receptor 2 (TNFR2) NA NA

Thyroid-Stimulating Hormone (TSH) NA NA

Prostatic Acid Phosphatase NA NA

Matrix Metalloproteinase-2 (MMP-2) NA NA

Amphiregulin (AR) NA NA

Connective Tissue Growth Factor (CTGF) NA NA

Key: Signal : p≤0.05; Trend : 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP used

TABLE 8: MDD diagnostic markers identified in DN MDD cohort 2

DN MDD cohort 2

Analyte (21 CT + 15 MDD)

Estimate p-value

Osteopontin 14.67 0.002

EN-RAGE 6.77 0.004 Interleukin-1 receptor antagonist (IL-lra) 8.12 0.006

Ferritin (FRTN) 2.50 0.006

Testosterone, Total 33.62 0.007

Tenascin-C (TNC) 9.55 0.011

Serotransferrin (Transferrin) -13.39 0.011

Epidermal Growth Factor (EGF) 10.47 0.012

Macrophage Migration Inhibitory Factor (MIF) 5.44 0.013

Macrophage Inflammatory Protein-1 alpha (MIP-1 alpha) 13.70 0.016

Chemokine CC-4 (HCC-4) -8.08 0.019

Growth Hormone (GH) -1.35 0.021

Follicle-Stimulating Hormone (FSH) 5.01 0.022 von Willebrand Factor (vWF) 4.19 0.025

Macrophage Inflammatory Protein-1 beta (MIP-1 beta) 7.86 0.028

Connective Tissue Growth Factor (CTGF) 2.45 0.029

Luteinizing Hormone (LH) 4.56 0.030

Immunoglobulin A (IgA) 3.91 0.038

Superoxide Dismutase 1, soluble (SOD-1) 6.14 0.043

Factor VII -7.03 0.044

Interleukin-1 alpha (IL-1 alpha) 2.55 0.045

Angiotensin-Converting Enzyme (ACE) -7.29 0.046

Tamm-Horsfall Urinary Glycoprotein (THP) 5.85 0.047

Trefoil Factor 3 (TFF3) -4.28 0.053

Interleukin-16 (IL-16) 7.09 0.062

B Lymphocyte Chemoattractant (BLC) -1.94 0.067

Beta-2-Microglobulin (B2M) 8.60 0.072

FASLG Receptor (FAS) -4.20 0.080

Cystatin-C 11.52 0.083

Creatine Kinase-MB (CK-MB) -2.57 0.088

Apolipoprotein A-I (Apo A-I) -7.05 0.089

Fetuin-A -6.84 0.089

Monocyte Chemotactic Protein 1 (MCP-1) -3.73 0.094

Apolipoprotein C-III (Apo C-III) -5.44 0.098

Growth-Regulated alpha protein (GRO-alpha) -5.13 0.100

Sex Hormone-Binding Globulin (SHBG) -2.54 0.104

Resistin 7.72 0.106

Cortisol 1.85 0.106

E-Selectin -3.73 0.121

Alpha-l-Antichymotrypsin (AACT) 5.31 0.127

Matrix Metalloproteinase-3 (MMP-3) -3.00 0.140

Vascular Cell Adhesion Molecule-1 (VCAM-1) 6.38 0.164

Epidermal Growth Factor Receptor (EGFR) -7.49 0.166

Interleukin-8 (IL-8) -2.76 0.167

Apolipoprotein A-II (Apo A-II) -5.59 0.167

Angiotensinogen 0.81 0.169

Interleukin-10 (IL-10) 3.00 0.170

Alpha-l-Antitrypsin (AAT) 4.25 0.170

C-Reactive Protein (CRP) 1.03 0.172 Plasminogen Activator Inhibitor 1 (PAI-1) 4.21 0.182

Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78) -2.77 0.189

Interleukin-7 (IL-7) 1.12 0.222

Myoglobin -2.66 0.222

Adiponectin -3.11 0.225

Neutrophil Gelatinase-Associated Lipocalin (NGAL) 2.67 0.271

Haptoglobin 1.51 0.275

Stem Cell Factor (SCF) -2.72 0.276

Serum Amyloid P-Component (SAP) 2.45 0.282

Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2) 2.09 0.304

Complement C3 (C3) 3.49 0.331

Cancer Antigen 19-9 (CA-19-9) 0.99 0.339

Intercellular Adhesion Molecule 1 (ICAM-1) -3.33 0.388

Clusterin (CLU) 4.57 0.388

Apolipoprotein H (Apo H) 3.96 0.405

Prolactin (PRL) 0.99 0.405

TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- 1.58 0.408 R3)

CD 40 antigen (CD40) 3.85 0.420

Leptin -0.93 0.422

T-Cell-Specific Protein RANTES (RANTES) -1.22 0.451

Interleukin-3 (IL-3) -1.51 0.464

Apolipoprotein D (Apo D) 2.42 0.490

Matrix Metalloproteinase-2 (MMP-2) 0.78 0.525

Thrombospondin-1 1.89 0.548

Vascular Endothelial Growth Factor (VEGF) 1.55 0.580

Lectin-Like Oxidized LDL Receptor 1 (LOX-1) -0.87 0.585

Alpha-l-Microglobulin (AlMicro) 1.78 0.593

Macrophage-Derived Chemokine (MDC) 1.49 0.657

Myeloperoxidase (MPO) 0.84 0.670

CD40 Ligand (CD40-L) 0.47 0.683

Chromogranin-A (CgA) -0.30 0.685

AXL Receptor Tyrosine Kinase (AXL) -0.69 0.697

Progesterone -0.84 0.738

Interleukin-13 (IL-13) -0.36 0.769

Interleukin-18 (IL-18) 0.75 0.805

Monocyte Chemotactic Protein 4 (MCP-4) 0.72 0.809

Alpha-2-Macroglobulin (A2Macro) -0.91 0.823

T Lymphocyte-Secreted Protein 1-309 (1-309) 0.12 0.862

Thyroxine-Binding Globulin (TBG) -0.41 0.880

Glutathione S-Transferase alpha (GST-alpha) -0.15 0.901

Pancreatic Polypeptide (PPP) 0.14 0.904

Interferon gamma Induced Protein 10 (IP-10) 0.27 0.910

Hepatocyte Growth Factor (HGF) 0.03 0.989

Interleukin-15 (IL-15) NA NA

Interleukin-5 (IL-5) NA NA

Interferon gamma (IFN-gamma) NA NA Tumor necrosis factor receptor 2 (TNFR2) NA NA

Thyroid-Stimulating Hormone (TSH) NA NA

Prostatic Acid Phosphatase NA NA

Amphiregulin (AR) NA NA

Agouti-Related Protein (AGRP) NA NA

Key: Signal : p≤0.05; Trend : 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP used

TABLE 9: MDD diagnostic markers identified in the medicated NESDA MDD cohort

NESDA MDD cohort, medicated

Analyte

(222 CT + 77 MDD)

Estimates p-value

Cystatin-C 7.74 9.38E-05

Fatty Acid-Binding Protein, adipocyte (FABP, adipocyte) 2.68 1.69E-04

Alpha-l-Microglobulin (AlMicro) 5.80 2.69E-04

Endostatin 6.35 3.21E-04

Beta-2-Microglobulin (B2M) 3.82 0.001

Hepsin 5.92 0.001

Eotaxin-1 2.10 0.001

Matrix Metalloproteinase-10 (MMP-10) 2.29 0.002

Apolipoprotein A-I (Apo A-I) 4.82 0.002

Pepsinogen I (PGI) 2.09 0.002

Complement C3 (C3) 4.45 0.003

Hepatocyte Growth Factor (HGF) 2.01 0.003

Cortisol 2.17 0.005

Epidermal Growth Factor (EGF) -1.43 0.005

Cathepsin D 3.50 0.005

Alpha-l-Antitrypsin (AAT) 3.91 0.005

Cancer Antigen 15-3 (CA-15-3) -1.82 0.005

Chromogranin-A (CgA) 1.58 0.006

Prostasin 2.58 0.007

Apolipoprotein H (Apo H) 3.08 0.008

Superoxide Dismutase 1, soluble (SOD-1) -1.35 0.010

Receptor for advanced glycosylation end products (RAGE) 1.53 0.011

Fibulin-lC (Fib-lC) 3.02 0.012

Interleukin-6 receptor subunit beta (IL-6R beta) 5.56 0.012

Ferritin (FRTN) -0.76 0.013

Thyroxine-Binding Globulin (TBG) 2.98 0.018

Plasminogen Activator Inhibitor 1 (PAI-1) -2.76 0.019

Myoglobin -1.89 0.022

Fetuin-A 3.15 0.023

Testosterone, Total -1.19 0.024

Transthyretin (TTR) -3.26 0.026 Macrophage-Stimulating Protein (MSP) 1.70 0.028

Clusterin (CLU) 4.92 0.028

B cell-activating factor (BAFF) 3.00 0.030

Pancreatic Polypeptide (PPP) 1.15 0.032

Lactoylglutathione lyase (LGL) -0.97 0.036

Osteopontin 1.30 0.038

Apolipoprotein C-I (Apo C-I) 2.98 0.038

Neuron-Specific Enolase (NSE) -1.39 0.042

Vitamin K-Dependent Protein S (VKDPS) 2.90 0.048

E-Selectin -1.34 0.056

Apolipoprotein A-II (Apo A-II) 2.48 0.059

Serum Amyloid P-Component (SAP) 1.90 0.069

Tyrosine kinase with Ig and EGF homology domains 2 (TIE- -2.24 0.072 2)

Leptin 0.57 0.073

Creatine Kinase-MB (CK-MB) -1.00 0.077

Apolipoprotein D (Apo D) 2.00 0.078

YKL-40 1.06 0.088

Insulin-like Growth Factor Binding Protein 6 (IGFBP6) 2.54 0.090

Interleukin-2 receptor alpha (IL-2 receptor alpha) 1.73 0.090

Complement Factor H - Related Protein 1 (CFHR1) 1.44 0.093

Monokine Induced by Gamma Interferon (MIG) 0.94 0.093

Apolipoprotein A-IV (Apo A-IV) 1.55 0.095

Insulin-like Growth Factor Binding Protein 4 (IGFBP4) 2.36 0.097

Apolipoprotein C-III (Apo C-III) 1.54 0.098

Lpa -0.46 0.099

Urokinase-type plasminogen activator receptor (uPAR) 1.16 0.104

Interferon gamma Induced Protein 10 (IP-10) -1.47 0.105

Interleukin-18 (IL-18) -1.17 0.109

Urokinase-type Plasminogen Activator (uPA) 1.44 0.115

Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2) 1.10 0.116

Follicle-Stimulating Hormone (FSH) 0.52 0.126

Haptoglobin 0.58 0.127

Latency-Associated Peptide of Transforming Growth Factor -1.33 0.135 beta 1 (LAP TGF-bl)

Vascular Endothelial Growth Factor (VEGF) -1.12 0.148

FASLG Receptor (FAS) 0.95 0.149

Stromal cell-derived factor-1 (SDF-1) 2.38 0.149

Intercellular Adhesion Molecule 1 (ICAM-1) 1.83 0.151

Galectin-3 1.82 0.172

N-terminal prohormone of brain natriuretic peptide (NT -0.44 0.175 proBNP)

Angiotensinogen -0.24 0.195

Insulin-like Growth Factor-Binding Protein 1 (IGFBP-1) 0.37 0.198

Receptor tyrosine-protein kinase erbB-3 (ErbB3) -0.71 0.204

Macrophage inflammatory protein 3 beta (MIP-3 beta) 1.05 0.213

Interleukin-16 (IL-16) -1.04 0.214 Prolactin (PRL) 0.78 0.226 von Willebrand Factor (vWF) 0.88 0.234

Osteoprotegerin (OPG) 1.31 0.236

Factor VII 1.38 0.237

Adiponectin 0.75 0.243

Vascular Cell Adhesion Molecule-1 (VCAM-1) 1.67 0.247

Immunoglobulin M (IgM) 0.75 0.249

Tumor Necrosis Factor Receptor I (TNF RI) 0.96 0.273

CD 40 antigen (CD40) -1.41 0.278

Tenascin-C (TNC) 0.93 0.283

Thyroid-Stimulating Hormone (TSH) -0.50 0.294

6Ckine 1.23 0.295

Tamm-Horsfall Urinary Glycoprotein (THP) 0.88 0.301

Brain-Derived Neurotrophic Factor (BDNF) -0.74 0.313

Interleukin-12 Subunit p40 (IL-12p40) 0.94 0.327

Gelsolin -1.08 0.338

Tumor necrosis factor receptor 2 (TNFR2) 1.13 0.344

Matrix Metalloproteinase-7 (MMP-7) 1.21 0.346

Macrophage-Derived Chemokine (MDC) 0.89 0.357

Vascular Endothelial Growth Factor C (VEGF-C) 1.31 0.363

Kallikrein 5 -0.79 0.364

Human Epidermal Growth Factor Receptor 2 (HER-2) -0.89 0.381

Macrophage Colony-Stimulating Factor 1 (M-CSF) -0.60 0.399

Platelet-Derived Growth Factor BB (PDGF-BB) -0.61 0.415

Immunoglobulin A (IgA) 0.50 0.434

Macrophage Inflammatory Protein-1 beta (MIP-1 beta) 0.59 0.437

Aldose Reductase -0.79 0.449

Progesterone 0.60 0.456

Vascular endothelial growth factor receptor 3 (VEGFR-3) -0.49 0.466

Myeloid Progenitor Inhibitory Factor 1 (MPIF-1) -0.66 0.472

Growth Hormone (GH) 0.17 0.472

Matrix Metalloproteinase-3 (MMP-3) -0.44 0.500

TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- 0.51 0.506 R3)

Neuronal Cell Adhesion Molecule (Nr-CAM) -0.43 0.511

Insulin-like Growth Factor-Binding Protein 3 (IGFBP-3) -1.25 0.513

Sex Hormone-Binding Globulin (SHBG) 0.31 0.518

Eotaxin-2 0.31 0.538

Tetranectin 0.89 0.538

Alpha-2-Macroglobulin (A2Macro) 0.66 0.560

Vitronectin 0.72 0.572

Thrombomodulin (TM) 0.80 0.573

Interleukin-1 receptor antagonist (IL-lra) -0.58 0.573

Matrix Metalloproteinase-1 (MMP-1) -0.27 0.580

Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) -0.69 0.603

Apolipoprotein E (Apo E) -0.36 0.611

Chemokine CC-4 (HCC-4) 0.30 0.619 Neuropilin-1 0.74 0.622

Sortilin -0.55 0.624

Tissue Inhibitor of Metalloproteinases 1 (TIMP-1) -0.71 0.630

Hepatocyte Growth Factor receptor (HGF receptor) 0.57 0.650

Monocyte Chemotactic Protein 2 (MCP-2) -0.36 0.650

C-Reactive Protein (CRP) 0.12 0.654

Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78) 0.25 0.660

Stem Cell Factor (SCF) 0.51 0.670

CD40 Ligand (CD40-L) 0.16 0.689

Mesothelin (MSLN) -0.29 0.691

Insulin-like Growth Factor Binding Protein 5 (IGFBP5) -0.53 0.703

CD5 Antigen-like (CD5L) 0.32 0.712

Neutrophil Gelatinase-Associated Lipocalin (NGAL) -0.28 0.722

Monocyte Chemotactic Protein 4 (MCP-4) -0.27 0.722

Tissue type Plasminogen activator (tPA) -0.28 0.724

EN-RAGE -0.13 0.726

Glucose-6-phosphate Isomerase (G6PI) -0.22 0.736

Macrophage Migration Inhibitory Factor (MIF) -0.16 0.736

Angiotensin-Converting Enzyme (ACE) -0.33 0.738

Interleukin-8 (IL-8) 0.22 0.748

Trefoil Factor 3 (TFF3) -0.12 0.749

Pulmonary and Activation-Regulated Chemokine (PARC) 0.19 0.769

Interleukin-6 receptor (IL-6r) 0.28 0.771

Matrix Metalloproteinase-9, total (MMP-9, total) -0.20 0.785

Growth-Regulated alpha protein (GRO-alpha) -0.18 0.804

Alpha-l-Antichymotrypsin (AACT) 0.29 0.806

Myeloperoxidase (MPO) -0.10 0.815

Collagen IV -0.14 0.834

Apolipoprotein B (Apo B) -0.19 0.839

Phosphoserine Aminotransferase (PSAT) -0.12 0.840

Serotransferrin (Transferrin) 0.32 0.844

Interleukin-23 (IL-23) -0.20 0.852

Carcinoembryonic Antigen (CEA) 0.09 0.857

C-Peptide 0.12 0.868

Epidermal Growth Factor Receptor (EGFR) -0.32 0.873

AXL Receptor Tyrosine Kinase (AXL) 0.13 0.885

Monocyte Chemotactic Protein 1 (MCP-1) 0.10 0.888

Resistin -0.06 0.943

Angiopoietin-2 (ANG-2) -0.04 0.951

T-Cell-Specific Protein RANTES (RANTES) -0.03 0.956

Vitamin D-Binding Protein (VDBP) -0.04 0.957

Angiogenin 0.06 0.958

Endoglin -0.03 0.984

Thrombospondin-1 0.00 0.999

Key: Signal : p≤0.05; Trend : 0.05< p<0.07; NA: analyte failed QC or not included in the DiscMAP used TABLE 10: MDD diagnostic markers identified in the un-medicated

NESDA MDD cohort

NESDA MDD cohort, medicated

Analyte

(222 CT + 134 MDD)

Estimates p-value

Cystatin-C 9.42 5.05E-08

Beta-2-Microglobulin (B2M) 3.97 5.89E-05

Epidermal Growth Factor (EGF) -1.81 8.30E-05

Apolipoprotein H (Apo H) 3.56 3.10E-04

Thrombospondin-1 -2.74 3.16E-04

Endostatin 5.18 4.02E-04

N-terminal prohormone of brain natriuretic peptide (NT -1.01 4.18E-04 proBNP)

Complement C3 (C3) 4.32 0.001

Prostasin 2.98 0.001

Monocyte Chemotactic Protein 4 (MCP-4) -2.18 0.001

Alpha-l-Microglobulin (AlMicro) 4.38 0.001

CD40 Ligand (CD40-L) -1.13 0.001

Alpha-l-Antitrypsin (AAT) 3.51 0.002

Cortisol 1.96 0.003

Insulin-like Growth Factor Binding Protein 4 (IGFBP4) 3.60 0.005

Kallikrein 5 -1.87 0.008

Fatty Acid-Binding Protein, adipocyte (FABP, adipocyte) 1.47 0.008

Fibulin-lC (Fib-lC) 2.78 0.009

Brain-Derived Neurotrophic Factor (BDNF) -1.61 0.011

T-Cell-Specific Protein RANTES (RANTES) -1.03 0.011

Monocyte Chemotactic Protein 2 (MCP-2) -1.82 0.011

Tenascin-C (TNC) 2.10 0.011

Hepsin 3.70 0.015

Plasminogen Activator Inhibitor 1 (PAI-1) -2.38 0.019

Hepatocyte Growth Factor receptor (HGF receptor) -2.18 0.023

Fetuin-A 2.48 0.025

Epithelial-Derived Neutrophil-Activating Protein 78 (ENA-78) -1.14 0.029

Serum Amyloid P-Component (SAP) 1.90 0.034

Interleukin-1 receptor antagonist (IL-lra) -1.70 0.039

Growth-Regulated alpha protein (GRO-alpha) -1.35 0.042

Apolipoprotein A-IV (Apo A-IV) 1.59 0.043

Chemokine CC-4 (HCC-4) 1.15 0.050

Apolipoprotein D (Apo D) 1.89 0.051

Angiogenin 2.04 0.052

Macrophage-Stimulating Protein (MSP) 1.21 0.058

Vitamin K-Dependent Protein S (VKDPS) 2.21 0.061

Neuropilin-1 2.33 0.061

Interleukin-2 receptor alpha (IL-2 receptor alpha) 1.59 0.068 C-Peptide 1.24 0.072 von Willebrand Factor (vWF) 1.16 0.074

Latency-Associated Peptide of Transforming Growth Factor -1.39 0.092 beta 1 (LAP TGF-bl)

Interleukin-23 (IL-23) -1.39 0.094

Apolipoprotein A-I (Apo A-I) 2.07 0.097

Thyroxine-Binding Globulin (TBG) 1.64 0.099

Urokinase-type plasminogen activator receptor (uPAR) 1.06 0.104

Apolipoprotein A-II (Apo A-II) 1.78 0.113

Macrophage Migration Inhibitory Factor (MIF) 0.61 0.129

Macrophage inflammatory protein 3 beta (MIP-3 beta) 1.17 0.131

AXL Receptor Tyrosine Kinase (AXL) 1.13 0.132

Collagen IV -0.88 0.133

Complement Factor H - Related Protein 1 (CFHR1) 1.07 0.134

Sex Hormone-Binding Globulin (SHBG) -0.62 0.137

Angiopoietin-2 (ANG-2) -1.01 0.138

Clusterin (CLU) 2.63 0.145

Vascular Endothelial Growth Factor (VEGF) -0.85 0.170

Insulin-like Growth Factor Binding Protein 5 (IGFBP5) 1.64 0.171

Cathepsin D 1.44 0.172

Interleukin-6 receptor subunit beta (IL-6R beta) 2.20 0.179

Alpha-l-Antichymotrypsin (AACT) 1.34 0.179

Vascular endothelial growth factor receptor 3 (VEGFR-3) -0.77 0.180

Apolipoprotein B (Apo B) -1.10 0.182

Sortilin -1.30 0.185

Macrophage Inflammatory Protein-1 beta (MIP-1 beta) -0.75 0.191

Tetranectin 1.58 0.194

FASLG Receptor (FAS) 0.69 0.196

C-Reactive Protein (CRP) 0.28 0.196

Monocyte Chemotactic Protein 1 (MCP-1) -0.78 0.203

Tumor Necrosis Factor Receptor I (TNF RI) 0.86 0.204

Hepatocyte Growth Factor (HGF) 0.77 0.207

Leptin 0.33 0.213

Urokinase-type Plasminogen Activator (uPA) 0.97 0.229

6Ckine 1.16 0.236

Myeloid Progenitor Inhibitory Factor 1 (MPIF-1) -0.96 0.243

Immunoglobulin M (IgM) 0.65 0.244

Serotransferrin (Transferrin) 1.66 0.249

Insulin-like Growth Factor Binding Protein 6 (IGFBP6) 1.33 0.256

Tamm-Horsfall Urinary Glycoprotein (THP) -0.78 0.265

Ferritin (FRTN) -0.30 0.267

Myoglobin -0.71 0.268

Platelet-Derived Growth Factor BB (PDGF-BB) -0.69 0.281

Aldose Reductase -0.94 0.286

Pancreatic Polypeptide (PPP) 0.45 0.296

Pepsinogen I (PGI) 0.66 0.318

Matrix Metalloproteinase-10 (MMP-10) 0.65 0.320 Monokine Induced by Gamma Interferon (MIG) 0.43 0.346

Intercellular Adhesion Molecule 1 (ICAM-1) 0.97 0.347

Apolipoprotein E (Apo E) -0.49 0.354

Matrix Metalloproteinase-1 (MMP-1) -0.38 0.357

Gelsolin -0.90 0.364

Vascular Cell Adhesion Molecule-1 (VCAM-1) 1.03 0.371

Carcinoembryonic Antigen (CEA) -0.37 0.380

Glucose-6-phosphate Isomerase (G6PI) 0.51 0.386

Macrophage Colony-Stimulating Factor 1 (M-CSF) -0.49 0.427

Thyroid-Stimulating Hormone (TSH) -0.36 0.427

Lpa 0.17 0.445

Epidermal Growth Factor Receptor (EGFR) -1.31 0.451

Interferon gamma Induced Protein 10 (IP-10) -0.48 0.462

Interleukin-18 (IL-18) 0.45 0.467

CD 40 antigen (CD40) -0.86 0.473

Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2) -0.42 0.478

Alpha-2-Macroglobulin (A2Macro) 0.65 0.479

Neuron-Specific Enolase (NSE) -0.36 0.497

Mesothelin (MSLN) -0.41 0.498

Growth Hormone (GH) -0.12 0.510

Apolipoprotein C-III (Apo C-III) 0.53 0.519

Endoglin -0.77 0.524

Tumor necrosis factor receptor 2 (TNFR2) 0.64 0.531

E-Selectin -0.36 0.555

Tissue type Plasminogen activator (tPA) 0.38 0.556

Eotaxin-2 0.25 0.558

Human Epidermal Growth Factor Receptor 2 (HER-2) -0.52 0.558

Creatine Kinase-MB (CK-MB) -0.27 0.567

Factor VII -0.50 0.572

Trefoil Factor 3 (TFF3) -0.17 0.575

Phosphoserine Aminotransferase (PSAT) 0.27 0.577

Immunoglobulin A (IgA) 0.30 0.582

Receptor tyrosine-protein kinase erbB-3 (ErbB3) -0.26 0.590

Progesterone 0.35 0.597

Vitronectin -0.59 0.610

TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL- -0.33 0.622 R3)

Matrix Metalloproteinase-3 (MMP-3) 0.24 0.640

B cell-activating factor (BAFF) 0.53 0.656

Vitamin D-Binding Protein (VDBP) 0.28 0.657

Galectin-3 0.47 0.664

Haptoglobin 0.13 0.668

Receptor for advanced glycosylation end products (RAGE) 0.20 0.680

Thrombomodulin (TM) 0.50 0.681

Insulin-like Growth Factor-Binding Protein 3 (IGFBP-3) -0.68 0.691

Interleukin-6 receptor (IL-6r) -0.33 0.696

Angiotensinogen -0.05 0.707 Resistin -0.26 0.708

Adiponectin 0.20 0.709

Insulin-like Growth Factor-Binding Protein 1 (IGFBP-1) -0.09 0.711

Vascular Endothelial Growth Factor C (VEGF-C) -0.46 0.718

Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) 0.45 0.719

Prolactin (PRL) 0.20 0.725

Pulmonary and Activation-Regulated Chemokine (PARC) -0.18 0.730

Follicle-Stimulating Hormone (FSH) 0.09 0.760

Angiotensin-Converting Enzyme (ACE) -0.26 0.767

CD5 Antigen-like (CD5L) 0.22 0.769

Matrix Metalloproteinase-7 (MMP-7) 0.32 0.772

Interleukin-8 (IL-8) 0.16 0.777

Testosterone, Total -0.11 0.791

Stem Cell Factor (SCF) -0.24 0.802

Macrophage-Derived Chemokine (MDC) -0.20 0.811

Cancer Antigen 15-3 (CA-15-3) -0.13 0.819

Lactoylglutathione lyase (LGL) 0.09 0.819

Stromal cell-derived factor-1 (SDF-1) -0.26 0.827

Osteopontin 0.10 0.836

Eotaxin-1 0.10 0.839

YKL-40 -0.11 0.861

Superoxide Dismutase 1, soluble (SOD-1) -0.07 0.884

Matrix Metalloproteinase-9, total (MMP-9, total) -0.08 0.900

Interleukin-16 (IL-16) 0.07 0.918

Apolipoprotein C-I (Apo C-I) 0.11 0.925

Neutrophil Gelatinase-Associated Lipocalin (NGAL) -0.06 0.930

Tissue Inhibitor of Metalloproteinases 1 (TIMP-1) 0.11 0.935

Chromogranin-A (CgA) -0.04 0.938

Interleukin-12 Subunit p40 (IL-12p40) 0.06 0.942

Transthyretin (TTR) -0.10 0.943

Osteoprotegerin (OPG) -0.05 0.959

Myeloperoxidase (MPO) 0.02 0.963

Neuronal Cell Adhesion Molecule (Nr-CAM) -0.02 0.979

Tyrosine kinase with Ig and EGF homology domains 2 (TIE- -0.03 0.982 2)

EN-RAGE 0.01 0.982

Key: Signal : p≤0.05; Trend : 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP used TABLE 11: Overall summary of the results showing the cohorts where the MDD diagnostic markers were identified

The top 34 most robust markers (>3 cohorts) are highlighted in bold, below. A star (*) indicates a cohort where an analyte was found to be altered in patients relative to controls. An up arrow (†) and down arrow Q) indicates a cohort where an analyte was found to be increased and decreased in patients relative to controls, respectively.

Key: Sig. : p≤0.05; Trend : 0.05<p<0.07; NA: analyte failed QC or not included in the DiscMAP use

TABLE 12: Tables showing the 21 combinations of 5 analytes tested across each cohort

a) analyte combinations where one of the top 5 analytes is dropped and substituted by the 6th top analyte (TNC)

b) analyte combinations where one of the top 5 analytes is dropped and substituted by the 7th top analyte (EGF)

c) analyte combinations where two of the top 5 analytes is dropped and substituted by both the 6th and the 7th top analytes.

The results for each of the combinations in each cohort are as follows:

TABLE 13: Performances achieved in discriminating MDD patients from healthy controls in the MDD cohort 1 for each combination of 5 analytes along with the corresponding true and false positive rates, sensitivity, specificity and ROC-AUC

Panel of 5 analytes true false sensitivity specificity

AUC

tested positives positives (%) (%)

TABLE 14: Performances achieved in discriminating MDD patients from healthy controls in the MDD cohort 2 for each combination of 5 analytes along with the corresponding true and false positive rates, sensitivity, specificity and ROC-AUC

Panel of 5 analytes true false positives sensitivity specificity

AUC

tested positives ( 1-specificity) (%) (%)

TABLE 15: Performances achieved in discriminating MDD patients from healthy controls in the MDD cohort 3 for each combination of 5 analytes along with the corresponding true and false positive rates, sensitivity, specificity and ROC-AUC

false

Panel of 5 analytes true sensitivity specificity

positives AUC tested positives

( 1-specificity) (%) (%)

IL-lra, FRTN, MIF, 0.77 0.20 77 80 0.83

TABLE 16: Performances achieved in discriminating MDD patients from healthy controls in the MDD cohort 4 for each combination of 5 analytes along with the corresponding true and false positive rates, sensitivity, specificity and ROC-AUC

false

Panel of 5 analytes true sensitivity specificity

positives AUC tested positives

( 1-specificity) (%) (%)

IL-lra, FRTN, MIF,

0.95 0.06 95 94 0.97 EN-RAGE, ACE

TABLE 17: Performances achieved in discriminating MDD patients from healthy controls in the DN MDD cohort 1 for each combination of 5 analytes along with the corresponding true and false positive rates, sensitivity, specificity and ROC-AUC

Panel of 5 analytes true false positives sensitivity specificity

AUC

tested positives ( 1-specificity) (%) (%)

IL-lra, FRTN, MIF,

1.00 0.21 100 79 0.91 EN-RAGE, ACE

IL-lra, FRTN, MIF, 1.00 0.24 100 76 0.90

TABLE 18: Performances achieved in discriminating MDD patients from healthy controls in the DN MDD cohort 2 for each combination of 5 analytes along with the corresponding true and false positive rates, sensitivity, specificity and ROC-AUC

Panel of 5 analytes true false positives sensitivity specificity

AUC

tested positives ( 1-specificity) (%) (%)

IL-lra, FRTN, MIF,

0.87 0.00 87 100 0.98 EN-RAGE, ACE

TABLE 19: Performances achieved in discriminating MDD patients from healthy controls in the medicated NESDA MDD cohort for each combination of 5 analytes along with the corresponding true and false positive rates, sensitivity, specificity and ROC-AUC

TABLE 20: Performances achieved in discriminating MDD patients from healthy controls in the un-medicated NESDA MDD cohort for each combination of 5 analytes along with the corresponding true and false positive rates, sensitivity, specificity and ROC-AUC

TABLE 21: Performances achieved in discriminating MDD patients from healthy controls across all cohorts using 4 analytes (IL-lra, FRTN, EN-RAGE, TNC) with the corresponding sensitivity, specificity and ROC- AUC

Panel of 4 analytes tested:

sensitivity (%) specificity (%) AUC ILlra, FRTN, ENRAGE, TNC

MDD cohort 1 90 71 0.84

MDD cohort 2 59 82 0.75

MDD cohort 3 84 72 0.82

MDD cohort 4 86 90 0.91

DN MDD cohort 1 100 80 0.89 DN MDD cohort 2 87 100 0.95

Medicated NESDA MDD cohort 64 67 0.67

Un-medicated NESDA MDD cohort 72 62 0.70

TABLE 22: Summary of the ROC-AUCs produced for each of the analyte combinations tested across each cohort

Performance achieved using 4 analytes (IL- lra, FRTN, EN-RAGE, TNC) is shown in the first row. This combination had a goo to excellent performance in discriminating M DD patients from controls in MDD cohorts 1, 3 and 4, and DN MDD cohorts 1 an 2. All of the 21 combinations of 5 analytes tested had a good to excellent performance in discriminating M DD patients fro controls in M DD cohorts 1 and 4, DN M DD cohorts 1 and 2. However, all of these combinations performed poorly in th medicated N ESDA M DD cohort. This finding suggests a potential influence of medication that patients were taking at the tim of blood donation . Out of the 21 combinations of 5 analytes tested, analyte combinations 2, 4, 12, 13 and 16 were found t be the best performing combinations (highlighted in bold) . If any other combinations were used, these would perform poorl in at least 2 cohorts.

AUC classification and performance: 0.90 - 1 = excellent; 0.80 - 0.90 = good; 0.70 - 0.80 = fair; 0.60 - 0.70 = poor