Identification of Metabolic Phenotypes and Systemic Biochemical Reaction Networks Associated with Human Blood Pressure

Lead Research Organisation: Imperial College London
Department Name: School of Public Health

Abstract

High blood pressure is an important component of metabolic syndrome and one of the most common modifiable risk factors for human cardiovascular disease and non-communicable disease. High blood pressure remains to be the leading cause for global disease burden and is estimated to attribute to 10.7 million deaths globally.
Different clinical and population-wide strategies have been in use to reduce high blood pressure such as to slow the rise of obesity, to increase physical activity, to reduce tobacco use and to change dietary patterns including the reduction of salt intake.
However, better knowledge of the effects of high blood pressure on human metabolism is needed to provide insight into the molecular mechanisms by which blood pressure is controlled. The human metabolic phenotype reflects the health status of an individual and is affected by genetic, environmental, dietary and gut-microbial variation.
I propose to use a metabolic phenotyping approach to discover biochemical compounds (metabolites) in urine related to blood pressure and associated dietary patterns, and to investigate how these potential clinical metabolic targets fit in the biochemical processes involved with the onset of high blood pressure and homeostatic regulation of blood pressure.

Technical Summary

I aim to evaluate the relationship between the urinary metabolic profile and blood pressure, including the linkage with adiposity, salt intake and associated dietary patterns and map these in a holistic systems framework.
Objectives:
A) To uncover the metabolic signature of blood pressure by means of a Metabolome-Wide Association Study with control for confounding variables using 1H Nuclear Magnetic Resonance (NMR) spectroscopy and Direct Injection Mass Spectrometry (DIMS) data.
B) To identify novel associations between the intake of dietary nutrients and excretion of urinary metabolites to pinpoint dietary patterns associated with cardiometabolic risk.
C) To investigate the interdependence between cation (calcium, magnesium, potassium and sodium) and metabolite excretions with respect to renal function and (re-)absorption, and homeostatic regulation.
D) To map the pathways perturbed by high blood pressure, sodium consumption and dietary patterns, and assess the overlap with the metabolic and systemic signature of adiposity and other risk factors to reveal the integrated systems network of blood pressure.
Methodology: I propose to use the Metabolome-Wide Association Study paradigm on 24-hour urine samples measured with 1H NMR spectroscopy and DIMS, and enhance this by coupling multivariate statistical methods with the analysis of integrated systems metabolic reaction networks.
Outcomes: This work will improve the understanding of the molecular mechanisms underlying high blood pressure and define new potential starting points for the development of appropriate and comprehensive preventive and treatment strategies to address the increasing problem of blood pressure worldwide.

Publications

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Abbott KA (2022) Evidence-Based Tools for Dietary Assessments in Nutrition Epidemiology Studies for Dementia Prevention. in The journal of prevention of Alzheimer's disease

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Lahiri S (2019) The gut microbiota influences skeletal muscle mass and function in mice. in Science translational medicine

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Posma J (2021) URINARY METABOLIC PHENOTYPE OF BLOOD PRESSURE in Journal of Hypertension

 
Title Developing annotated corpora for training and evaluating biomedical text mining algorithms 
Description Poster presented at the 5th annual UK Healthcare Text Analytics Conference: HealTAC 2022. 
Type Of Art Film/Video/Animation 
Year Produced 2022 
URL https://figshare.com/articles/poster/Developing_annotated_corpora_for_training_and_evaluating_biomed...
 
Title Developing annotated corpora for training and evaluating biomedical text mining algorithms 
Description Poster presented at the 5th annual UK Healthcare Text Analytics Conference: HealTAC 2022. 
Type Of Art Film/Video/Animation 
Year Produced 2022 
URL https://figshare.com/articles/poster/Developing_annotated_corpora_for_training_and_evaluating_biomed...
 
Description COMBATTING DIET RELATED NON-COMMUNICABLE DISEASE THROUGH ENHANCED SURVEILLANCE
Amount € 7,330,589 (EUR)
Funding ID 101084642 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 01/2023 
End 12/2026
 
Description COMBATTING DIET RELATED NON-COMMUNICABLE DISEASE THROUGH ENHANCED SURVEILLANCE (CoDiet) (Horizon Europe Guarantee)
Amount £3,566,168 (GBP)
Funding ID 10060437 
Organisation United Kingdom Research and Innovation 
Sector Public
Country United Kingdom
Start 01/2023 
End 12/2026
 
Description Multi-modal deep learning and domain knowledge integration to aid multidisciplinary teams in diagnosing in idiopathic pulmonary fibrosis
Amount £7,890,679 (GBP)
Funding ID 2602987 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 09/2021 
End 04/2025
 
Description Programme Grant
Amount £3,363,711 (GBP)
Funding ID MR/V012452/1 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 08/2021 
End 08/2026
 
Description Scalable extraction of human genetic and phenotypic data from peer-reviewed literature
Amount € 158,875 (EUR)
Funding ID 2022-Humangenphen 
Organisation ELIXIR 
Sector Charity/Non Profit
Country United Kingdom
Start 01/2022 
End 12/2023
 
Title Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer - pre-processed data 
Description A multicentre, prospective observational study was conducted of colorectal cancer (CRC) patients undergoing primary surgical resection in the United Kingdom and Czech Republic. Analysis was performed using metataxonomics (microbiome) and ultra-performance liquid chromatography mass spectrometry (UPLC-MS, metabolomics). Both datasets were pre-processed as described in the methods section of the main article. The data here were used as the input to the data analysis workflows available from Github. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/7326673
 
Description Imperial-Leicester-Nottingham biomedical literature NLP 
Organisation University of Leicester
Country United Kingdom 
Sector Academic/University 
PI Contribution I have recruited over 10 master students at Imperial College to work on projects in collaboration with Tim Beck from the University of Leicester (not at University of Nottingham). The focus was on developing natural language processing (NLP) algorithms to read the biomedical research literature of metabolome-wide association studies (MWAS - directly related to this/my fellowship) and to genome-WAS (GWAS - directly related to Tim's fellowship). We have published two articles of this work (Auto-CORPus, TABoLiSTM), with one article under review after revision (eNzymER) and two more in submission. All students (10 at Imperial and 2 at UoL) are co-supervised remotely by the other. This work has benefitted my fellowship work as it allows a quicker way to scan the literature of reported findings relevant to my ongoing work.
Collaborator Contribution Tim Beck (UoL) has recruited master students at Leicester as well as brought an an existing PhD student to work on this collaborative project. Tim has contributed extensive knowledge of ontologies and team-based coding.
Impact Beck T, Shorter T, Hu S, Li Z, Sun S, Popovici C, McQuibban NAR, Makraduli F, Yeung C, Rowlands T, Posma JM, 2022, Auto-CORPus: a natural language processing tool for standardising and reusing biomedical literature, Frontiers in Digital Health, Vol: 4 Yeung C, Beck T, Posma JM, 2022, MetaboListem and TABoLiSTM: two deep learning algorithms for metabolite named entity recognition, Metabolites, Vol: 12, Pages: 1-23 Meiqi Wang, Avish Vijayaraghavan, Tim Beck, Joram M. Posma, 2024, Vocabulary Matters: An Annotation Pipeline and Two Deep Learning Algorithms for Enzyme Named Entity Recognition (bioRxiv preprint) Open-source codes: github.com/omicsNLP Our findings have been added to the Information Artefact Ontology in the December 2020 release of the IAO: https://github.com/information-artifact-ontology/IAO/releases/tag/v2020-12-09
Start Year 2019
 
Title TABoLiSTM (BERT-embedding) model 
Description TABoLiSTM weights and model files to run the code at https://github.com/omicsNLP/MetaboliteNER 
Type Of Technology Software 
Year Produced 2022 
Open Source License? Yes  
URL https://zenodo.org/record/6340001
 
Company Name Melico 
Description Melico operates a nutrition analysis service which analyses metabolites and suggests tailored interventions to improve dietary habits. 
Year Established 2017 
Impact Still in start-up phase and attracting investors. I am a minor shareholder in the company since 2022. I contributed know-how and data/models to the company through Imperial College Innovations.
Website https://www.melicosciences.com
 
Description Interview on Next Generation of Leaders - HDR UK Fellowships 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact I was interviewed by a freelancer writer who was writing on the achievements of HDR UK Fellows.
I suggested to involve my collaborator and URKI Innovation Fellow Tim Beck from the University of Leicester in the interview.
The writer has written this up with statistics on the fellows' achievements and posted it on the HDR UK newsfeed.
Year(s) Of Engagement Activity 2021
URL https://www.hdruk.ac.uk/careers-in-health-data-science/science-community-and-partners/fellowships/
 
Description Interview with New Scientist magazine 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Our research in personalised precision nutrition has been included the New Scientist Magazine.
Year(s) Of Engagement Activity 2020
URL https://institutions.newscientist.com/article/mg24732990-600-why-there-is-no-such-thing-as-a-healthy...
 
Description Participated in TV show to use research 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Public/other audiences
Results and Impact We took part in a TV program where using our developed technology to measure the diet of 10 members of the public and give them personalized advice.
ITV came to the university to record us in the lab, and we went to the shoot to talk about our work and for an interview with the presenters.
The impact that our work has had on the majority of participants has been overwhelmingly positive, in addition we have discovered additional research lines based on the outcome.
Year(s) Of Engagement Activity 2019,2020
URL https://www.channel4.com/programmes/how-to-beat/on-demand/70184-004
 
Description School Engagement Activity - St Andrews University (HDR-UK Summer School): Man vs Machine Learning 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Schools
Results and Impact 45 secondary school children (15-16 years old, from two schools) took part in an engagement activity to learn about pattern recognition and machine learning on Friday the 23rd of August at the University of St Andrews, Scotland. They were first introduced to heart physiology and anatomy by local GP and researcher Dr David Fraile Navarro. With this background knowledge they then were shown the activity by Dr Joram Posma where groups of 2-3 students each would compare electrocardiograms (ECGs) from six healthy patients with those from patients that have had a myocardial infarction (also six). Each group was then given 8 minutes to classify as many as they could from a set of 26 ECGs to either healthy or myocardial infarction by trying to spot differences between healthy and myocardial infarction ECGs. The aim of the exercise was to get the students to apply human pattern recognition system to these ECGs. After the 8 minutes were up the results were collected and compared against the true classification and a simple machine learning algorithm that classified the same unknown ECGs with the same training data. We then explained the difference between false positives and false negatives and what these mean in terms of classification of healthy and ill individuals and its importance for treatment options. The activity was rounded off by comparing the aggregated results of each group against the machine learning algorithm and discussing the current and future use of machine learning algorithms in routine healthcare. With the limited knowledge the students had of ECGs they still managed to correctly classify 61.5-88.5% of ECGs, compared to 92.3% of the machine learning algorithm, with 7.7-33.3% of false positives and 15.4-42.9% false negatives (compared to 0% false positives and 14.3% of false negatives of the machine learning algorithm). This highlighted both the benefit of machine learning as well as the improvements new health data scientists can make to improve the accuracy and limit false negatives.
During lunchtime the students were asking how I used machine learning in my day-to-day research which allowed me to explain how I use different methods to identify compounds in urine that relate to differences in blood pressure. The feedback received from teachers and students was very positive, teachers reported increased interest in (health) data science and students considered data science as a career choice (some previously only considered medicine or biology as means to 'help save lives').
Year(s) Of Engagement Activity 2019
URL https://www.hdruk.ac.uk/news/how-to-become-a-health-data-scientist-a-recipe-for-success/