Periodic Reporting for period 1 - BrainGutAnalytics (Multiomics based analysis of brain-gut axis: A search for gastrointestinal disease phenotypes)
Reporting period: 2020-10-01 to 2022-09-30
Irritable Bowel Syndrome (IBS) is a well-known gastrointestinal disorder, which affects about 10% of global human population. IBS alters bowel habits of affected patients by means of diarrhoea, constipation, or a mix of both of these conditions. This is often accompanied by unexplained abdominal pain, fatigue and aggravated food sensitivities, which together significantly deteriorate the quality of life of a patient with IBS.
Traditionally, IBS is thought to be a functional gastrointestinal disorder as its origin was associated with the dysfunction of gut related variables e.g. intestinal motility, transit time and motor complexes. However, the recent scientific literature instead describes IBS as a consequence of dysfunction of Brain-Gut-Microbiota (BGM) axis. The BGM axis is a relatively new but rapidly emerging theory in the field of medicine and gastrointestinal health, which refers to the presence of an intricate and bi-directional relationship between human gut and brain. This nexus is thought to be modulated by the diverse colonies of gut microbiome through neuronal signalling, immune system and systemic circulation channels. A disruption in BGM axis is hypothesised as an underlying cause of various previously unexplained psychological as well as gastrointestinal disorders, such as IBS.
As the exact aetiology of IBS is unknown, a clear differential diagnosis test that can identify IBS in patients does not yet exists. Instead, IBS is often termed as a ‘diagnosis of exclusion’ as various other overlapping gastrointestinal disorders first need to be ruled out before IBS can be determined. The additional diagnostic testing significantly increases physical and emotional suffering for the patients as a clear diagnosis for IBS is a starting point to receive an effective treatment. This also causes additional burden and financial strain on national health system as the clinicians and gastroenterologists who work with IBS cases on a day to day basis are obliged to spend more time and resources for diagnosing IBS compared to other gastrointestinal disorders.
This Marie Skłodowska Curie (MSC) Individual Fellowship action entitled "Multiomics based analysis of brain-gut axis: A search for gastrointestinal disease phenotypes” and abbreviated as BrainGutAnalytics, aims to study brain-gut axis and associated irritable bowel syndrome by employing advanced data science, machine learning and statistical analysis techniques. The keys aim of this research is to search for novel, exclusive and generalisable digital phenotypes of IBS from multiomics datasets, which can be used as reliable biomarkers for IBS. The secondary aim of this work is to translate these biomarkers into a novel test for IBS diagnosis, which can be applied in regular clinical practice.
Traditionally, IBS is thought to be a functional gastrointestinal disorder as its origin was associated with the dysfunction of gut related variables e.g. intestinal motility, transit time and motor complexes. However, the recent scientific literature instead describes IBS as a consequence of dysfunction of Brain-Gut-Microbiota (BGM) axis. The BGM axis is a relatively new but rapidly emerging theory in the field of medicine and gastrointestinal health, which refers to the presence of an intricate and bi-directional relationship between human gut and brain. This nexus is thought to be modulated by the diverse colonies of gut microbiome through neuronal signalling, immune system and systemic circulation channels. A disruption in BGM axis is hypothesised as an underlying cause of various previously unexplained psychological as well as gastrointestinal disorders, such as IBS.
As the exact aetiology of IBS is unknown, a clear differential diagnosis test that can identify IBS in patients does not yet exists. Instead, IBS is often termed as a ‘diagnosis of exclusion’ as various other overlapping gastrointestinal disorders first need to be ruled out before IBS can be determined. The additional diagnostic testing significantly increases physical and emotional suffering for the patients as a clear diagnosis for IBS is a starting point to receive an effective treatment. This also causes additional burden and financial strain on national health system as the clinicians and gastroenterologists who work with IBS cases on a day to day basis are obliged to spend more time and resources for diagnosing IBS compared to other gastrointestinal disorders.
This Marie Skłodowska Curie (MSC) Individual Fellowship action entitled "Multiomics based analysis of brain-gut axis: A search for gastrointestinal disease phenotypes” and abbreviated as BrainGutAnalytics, aims to study brain-gut axis and associated irritable bowel syndrome by employing advanced data science, machine learning and statistical analysis techniques. The keys aim of this research is to search for novel, exclusive and generalisable digital phenotypes of IBS from multiomics datasets, which can be used as reliable biomarkers for IBS. The secondary aim of this work is to translate these biomarkers into a novel test for IBS diagnosis, which can be applied in regular clinical practice.
The work in BrainGutAnalytics project was performed in three work packages. The first work package dealt with collection, preparation, processing and integration of multiomics data collected from various sites across human body in parent Bergen Brain-Gut study. This data included microbiomics (fecal and saliva microbiome), metabolomics (short chain fatty acids), functional connectivity maps of brain regions (functional MRI), patient questionnaires (IBS specific clinical questionnaires, psychological and cognitive questionnaires, sleep and fatigue questionnaires etc.) and physiology measurements (heart and respiratory rates, before and after lactulose intake). After necessary cleaning and pre-processing, all data was algorithmically readout from their source data bases and was integrated into a uniform database for downstream processing and analysis.
In the second work package, a variety of data science based analysis techniques e.g. exploratory data analysis, machine learning based modelling and statistical methods were applied with methodological variations (e.g. feature selection techniques, ensembles, etc.) on the collective multiomics data frame, developed in the first phase. The aim of these experiments was to search for any significant associations or differences in complex multiomics data, which could be used to identify any potential set of variables (or features) for effective classification between patients with IBS and healthy control subjects. For this purpose, we looked into abundance and diversity of microbiota species within faeces and saliva samples, metabolites collected from fecal samples, functional connectivity maps of various brain regions, heart and respiration rate before and after lactulose intake by the participants and a number of patient questionnaires on both individual basis as well as in various possible combinations with each other.
Unexpectedly, our extensive search in the given multiomics data did not yield any individual or combination of variables that could be exclusively associated with either patients with IBS or healthy control subjects and eventually account for IBS phenotypes. However, our experimentation revealed a new set of features that mainly comprised of anxiety, fatigue and gender related variables, which resulted in classification accuracy of about 93% on a hold out test dataset. This finding shows prevalence of a complex multivariate combination of psychological symptoms in patients with IBS and provides an opportunity to use the same feature set for psychological evaluation of IBS cases to potentially complement and improve existing IBS diagnosis practice in clinical setting.
The third work package in BrainGutAnalytics concerned the development of a clinical diagnosis system for IBS evaluation based on the psychological feature set identified and validated in the second work package. For this purpose, a free and open access online web application named www.checkibs.com was developed, which uses the machine learning model (developed in previous step) to provide a new decision support system to clinicians in making an effective diagnosis of IBS.
Throughout the project duration, the Fellow working in BrainGutAnalytics project participated in various conferences, seminars, scientific events and in-person meetings to disseminate information on his scientific research, the project and the Marie Curie Fellow program. He also setup two websites dedicated to the project to disseminate information on his research activities. His work on development of the web application based on psychological evaluation of IBS was awarded with National Scholar Award in United European Gastroenterology Week 2022, which one of the biggest scientific conference in the field of gastrointestinal health.
In the second work package, a variety of data science based analysis techniques e.g. exploratory data analysis, machine learning based modelling and statistical methods were applied with methodological variations (e.g. feature selection techniques, ensembles, etc.) on the collective multiomics data frame, developed in the first phase. The aim of these experiments was to search for any significant associations or differences in complex multiomics data, which could be used to identify any potential set of variables (or features) for effective classification between patients with IBS and healthy control subjects. For this purpose, we looked into abundance and diversity of microbiota species within faeces and saliva samples, metabolites collected from fecal samples, functional connectivity maps of various brain regions, heart and respiration rate before and after lactulose intake by the participants and a number of patient questionnaires on both individual basis as well as in various possible combinations with each other.
Unexpectedly, our extensive search in the given multiomics data did not yield any individual or combination of variables that could be exclusively associated with either patients with IBS or healthy control subjects and eventually account for IBS phenotypes. However, our experimentation revealed a new set of features that mainly comprised of anxiety, fatigue and gender related variables, which resulted in classification accuracy of about 93% on a hold out test dataset. This finding shows prevalence of a complex multivariate combination of psychological symptoms in patients with IBS and provides an opportunity to use the same feature set for psychological evaluation of IBS cases to potentially complement and improve existing IBS diagnosis practice in clinical setting.
The third work package in BrainGutAnalytics concerned the development of a clinical diagnosis system for IBS evaluation based on the psychological feature set identified and validated in the second work package. For this purpose, a free and open access online web application named www.checkibs.com was developed, which uses the machine learning model (developed in previous step) to provide a new decision support system to clinicians in making an effective diagnosis of IBS.
Throughout the project duration, the Fellow working in BrainGutAnalytics project participated in various conferences, seminars, scientific events and in-person meetings to disseminate information on his scientific research, the project and the Marie Curie Fellow program. He also setup two websites dedicated to the project to disseminate information on his research activities. His work on development of the web application based on psychological evaluation of IBS was awarded with National Scholar Award in United European Gastroenterology Week 2022, which one of the biggest scientific conference in the field of gastrointestinal health.
The key innovation of this project is development of a new machine learning based approach for psychological evaluation of IBS and its subsequent open access availability through an online web application. The psychological evaluation of patients with IBS is likely to improve the existing diagnostic criteria in clinical evaluation of patients with IBS.
Our experiments have also shown that available multiomics brain-gut data failed to effectively distinguish (or classify) between IBS and non-IBS cases. This finding challenges the emerging paradigm of Brain-Gut-Microbiome axis, which is increasingly viewed as underlying cause of various gastrointestinal and psychological disorders. This finding emphasises the need of lodging broader scientific and empirical investigations into brain-gut axis.
Our experiments have also shown that available multiomics brain-gut data failed to effectively distinguish (or classify) between IBS and non-IBS cases. This finding challenges the emerging paradigm of Brain-Gut-Microbiome axis, which is increasingly viewed as underlying cause of various gastrointestinal and psychological disorders. This finding emphasises the need of lodging broader scientific and empirical investigations into brain-gut axis.