EP-4739791-A1 - A METHOD FOR DETERMINING BIOMARKER(S) PERTAINING TO MENTAL HEALTH DISORDER(S) AND BIOMARKER(S) DETERMINED THEREFROM

Abstract

The present disclosure provides a method for biomarker analysis. More particularly, provided herein is a method for identifying biomarker(s) associated with mental health disorder(s) such as but not limited to Major Depressive Disorder (MDD). The said method of the present disclosure is based on an integrative analysis between machine learning and bioinformatics techniques. The method allows efficient mining of publicly available datasets pertaining to mental health disorder patients to identify biomarkers that can aid in the diagnosis and prediction of the onset of such mental health disorders. Further envisaged herein are the biomarker(s) identified by the said method which allow early and efficient detection of mental health disorder(s) by non-invasive methods and application(s) of such biomarker(s).

Inventors

• HAMOUDI, Rifat
• BOUZID, Amal
• SHARAEV, Maksim
• BURNAEV, Evgeny
• BERNSTEIN, ALEXANDER
• MARIA, Zubrikhina

Assignees

• University of Sharjah

Dates

Publication Date

20260513

Application Date

20240708

Claims (20)

1. changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other modifications in the nature of the disclosure or the preferred embodiments will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation. [00116] All references, articles, publications, general disclosures etc. cited herein are incorporated by reference in their entireties for all purposes. However, mention of any reference, article, publication etc. cited herein is not, and should not be taken as, an acknowledgment or any form of suggestion that they constitute valid prior art or form part of the common general knowledge in any country in the world. WE CLAIM: A method for identifying biomarker(s) for detection of a mental health disorder, the method comprising: receiving a dataset comprising one or more input features, wherein the one or more input features includes at least one of biological parameters and clinical parameters; preprocessing the dataset to obtain a normalized and regularized dataset; subjecting the preprocessed dataset to an integrative analysis engine, wherein the integrative analysis engine comprises a biomarker identification model and a bioinformatics analysis model, wherein: the biomarker identification model is implemented to create an environment such that a learning agent, associated with the environment, is configured to: determine a similarity score for each of input features of the preprocessed dataset, wherein the biomarker identification model is trained by correlating training feature(s), derived from a repository dataset, with a corresponding predetermined similarity score, wherein the repository dataset includes a set of healthy reference parameters associated with one or more healthy controls, and a set of diseased reference parameters associated with one or more diseased subjects; and identify, based on the similarity score, a first set of biomarker(s) indicative of the mental health disorder(s); and the bioinformatics analysis model is configured to: compare each of the input features of the preprocessed dataset with the healthy reference parameters; and identify, based on the comparison, a second set of biomarker(s) indicative of the mental health disorder(s); and determining, based on an overlap between the first set of biomarker(s) and the second set of biomarker(s), the biomarker(s) for detection of the mental health disorder(s).
2. 2. The method as claimed in claim 1, wherein implementing the biomarker identification model comprises applying a machine learning (ML) model, based on the preprocessed dataset, to create an environment, wherein the learning agent is associated with the environment, the learning agent being configured to: determine patterns and relationships among the one or more input features that are indicative of the mental health disorder(s); computing a similarity score of the determined patterns and relationships based on related contribution to the detection of the mental health disorder(s); and identifying, based on the associated similarity scores, one or more target input features that are indicative of the mental health disorder(s).
3. 3. The method as claimed in claim 1, wherein the biomarker identification model comprises two or more machine learning (ML) models, to create the environment, wherein the learning agent is associated with the environment, the learning agent being configured to: select genes altered by the mental health disorder(s) based on individual outputs of the two or more ML models; perform hyperparameter optimization for the individual outputs of the two or more ML models; perform cross-validation on the individual outputs of the two or more ML models; and identifying, based on the cross-validation, the first set of biomarker(s) that are indicative of the mental health disorder.
4. 4. The method as claimed in claim 1, wherein the method comprises: implementing a training agent in the environment of the biomarker identification model, wherein the training agent is configured to train the learning agent using information of the set of healthy reference parameters associated with the one or more healthy controls, and the set of diseased reference parameters associated with the one or more diseased subjects from the repository dataset.
5. 5. The method as claimed in claim 1, wherein the similarity score of the biomarker(s) is determined by: identifying patterns and relationships among the one or more input features that are indicative of the mental health disorder.
6. 6. The method as claimed in claim 1, wherein the dataset comprising biological and clinical data, wherein the biological data includes genetic, proteomic, metabolic, and neuroimaging information, and the clinical data includes behavioral and psychological parameters.
7. 7. The method as claimed in claim 1, wherein preprocessing of the dataset comprises: performing data analysis on the dataset according to a data normalization rule to obtain normalized and regularized dataset, wherein the data normalization rule comprises a data classification rule and one or more data filtering conditions.
8. 8. The method as claimed in claim 1, wherein the mental health disorder(s) is selected from a group comprising major depressive disorder (MDD), anxiety, schizophrenia, attention-deficit hyperactivity disorder (ADHD) and bipolar disorder.
9. 9. The method as claimed in claim 1, wherein the dataset further comprises contextual features, wherein the contextual features are associated with one or more biological parameters and clinical parameters related to the mental health disorder(s).
10. 10. The method as claimed in claim 1, further comprising: generating a mapping profile of the determined biomarkers with corresponding brain regions and structural alterations thereof; and associating the mapping profile with cognitive functions and varied range of behaviors.
11. 11. The method as claimed in claim 1, wherein the mental health disorder is MDD and wherein determined biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRG1, 374 0EFM4, SERPING1, TCN1, and THBSlor any combination thereof.
12. 12. A method for determining level of one or more biomarker(s) selected from a group comprising CEACAM8, CEEC12B, DEFA4, HP, ECN2, NRG1, 374 OEFM4, SERPING1, TCN1, and THBS1, in a biological sample obtained from a subject.
13. 13. The method as claimed in claim 12, wherein the biological sample is a liquid biopsy sample.
14. 14. The method as claimed in claim 13, wherein the liquid biopsy sample is selected from a group comprising blood sample, saliva sample.
15. 15. The method as claimed in claim 12 wherein the level of biomarker(s) is determined by PCR.
16. 16. The method as claimed in claim 12 for use in one or more of: a) diagnosing MDD; b) diagnosing and treating MDD; c) determining genetic predisposition to MDD; and d) monitoring response to MDD therapy in patients diagnosed with MDD.
17. 17. A method for diagnosing mental health disorder(s), the said method comprising analyzing a biological sample from a subject to confirm differential expression level of one or more biomarker(s) compared to a healthy control, wherein the biomarker(s) is identified by the method as claimed in claim 1.
18. 18. The method as claimed in claim 17, wherein the mental health disorder is MDD; and wherein the one or more biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRGE 374 0EFM4, SERPING1, TCN1, and THBSE
19. 19. A method of diagnosing and treating mental health disorder(s) in a subject, comprising: - analyzing a biological sample from the subject to confirm differential expression level of one or more biomarker(s) identified by the method as claimed in claim 1, compared to a healthy control; - diagnosing the subject with the mental health disorder(s) once differential expression level of the one or more biomarker(s) is confirmed; and - administering therapy for the mental health disorder(s) to the diagnosed subject.
20. 20. The method as claimed in claim 19, wherein the mental health disorder is MDD; and wherein the one or more biomarker(s) is selected from a group comprising CEACAM8, CLEC12B, DEFA4, HP, ECN2, NRGE 374 OEFM4, SERPING1, TCN1, and THBSE

Description

A Method for Determining Biomarker(s) Pertaining to Mental Health Disorder(s) and Biomarker(s) Determined Therefrom TECHNICAL FIELD [001] The present disclosure provides a method for biomarker analysis. More particularly, the present disclosure provides a method for identifying biomarker(s) associated with mental health disorder(s) such as but not limited to Major Depressive Disorder (MDD). The identified biomarkers allows early and efficient detection of mental health disorder(s) by non-invasive methods. BACKGROUND [002] Major Depressive Disorder (MDD) is among the most prevalent, chronic complex psychiatric disorders nowadays. Depression affects over 280 million people globally according to the World Health Organization (WHO, 2023), and is the second leading cause of disability worldwide after cancer, while being predicted to be the leading cause by 2030. In the post- COVID-19 era, mental and behavioral disorders are reported to have become more severe, possibly due to the pandemic effects on healthcare and the economy worldwide. [003] MDD is a heterogeneous disorder, resulting from a complex interaction of social, psychological, environmental and genetic factors. Depression does not only affect the mental and psychological aspects of the individual’s health, but also affects physical health by disturbing the heart, kidney, nervous system, and immune system. It is characterized by the presence of depressed moods, functional impairments, a loss of interest in activities, fatigue, sleep disturbances, and psychomotor retardation or agitation. This negatively affects the patient’s productivity, self-perception, and self-esteem, resulting in an impaired quality of life which can lead to suicidal ideation and attempt. The WHO reported that more than 700,000 individuals worldwide die as a result of suicide each year, with depression being a leading cause (2023). Thus, MDD has grown into a major public health problem that needs urgent attention. [004] Despite extensive research, the pathophysiology of MDD is still poorly understood. Around 40% of MDD patients do not show an adequate response or remission to anti- depressant treatment and ultimately develop treatment resistance which further exacerbates the disease. Additionally, the diagnosis of MDD continues to be made on clinical assessment, which can be assisted by psychiatric questionnaire-based tools rather than laboratory -based tests which may be associated with several limitations and heterogeneity of the illness. Indeed, self-administered questionnaire -based approaches are not sufficient in evaluating efficiently the differences across patients’ sub-groups and identifying patients’ depressive stages. Adding to the fact that the absence of precise diagnostic biomarkers has resulted in a complex MDD diagnosis with other etiologically associated disorders such as bipolar disorder. [005] The use of functional neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) offer potential means for the assessment of the changes in brain activity associated with MDD as well as the prediction of response to treatment. However, these techniques are yet to be utilized in clinical practice due to multiple factors, including impracticality, expensive costs and limited specificity. Therefore, it is of great importance to screen for sensitive and specific biomarkers to improve MDD diagnostics and treatment. [006] Studies have shown that MDD affects the functional activity of the brain which can be identified by analyzing data on aberrations in the neuronal network. Moreover, the heterogeneity of depressive disorders may also be related to neuronal plasticity correlated with different depressive symptomatology. Besides, it is suggested that MDD results from systemic changes in the signaling and biochemical pathways involved in the regulation of moods, cognitive functions and disposition. Therefore, a comprehensive understanding of affected or associated biological pathways involved in MDD is of high importance to reveal the molecular mechanism of MDD and to identify accurate targets for MDD diagnosis. [007] To date, machine learning (ML) as an application of Al approaches is successfully applied in medical studies, by training computer models to process and understand complex patterns, for example within big-data, which facilitate classifications or predictions of new cases. Moreover, using OMICs strategies is amongst the most applied approaches at the forefront of personalized medicine in psychiatry with high efficiency, such as transcriptomics which showed increasing evidence of the potential to detect, and substantially improve the treatment of such complex cognitive disorders. Furthermore, there is a rising interest in using integrative OMICs and neuroimaging data to understand how an altered genetic profile in psychiatric disorders could influence brain structure and function. This can provide a deeper understandin