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.
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.
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
Yeung CS
(2022)
MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.
in Metabolites
Wu Y
(2021)
Odd Chain Fatty Acids Are Not Robust Biomarkers for Dietary Intake of Fiber.
in Molecular nutrition & food research
Wilson T
(2019)
Spot and Cumulative Urine Samples Are Suitable Replacements for 24-Hour Urine Collections for Objective Measures of Dietary Exposure in Adults Using Metabolite Biomarkers.
in The Journal of nutrition
Wang M
(2024)
Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition
in Journal of Proteome Research
Rodriguez-Martinez A
(2019)
pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra.
in Bioinformatics (Oxford, England)
Posma JM
(2018)
Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data.
in Journal of proteome research
Posma JM
(2020)
Nutriome-metabolome relationships provide insights into dietary intake and metabolism.
in Nature food
Posma JM
(2021)
Author Correction: Nutriome-metabolome relationships provide insights into dietary intake and metabolism.
in Nature food
Posma J
(2023)
PS-P02-8: HOST GENOMIC INFLUENCE ON THE GUT MICROBIAL METABOLITE-BLOOD PRESSURE RELATIONSHIP
in Journal of Hypertension
Posma J
(2021)
URINARY METABOLIC PHENOTYPE OF BLOOD PRESSURE
in Journal of Hypertension
Penney NC
(2022)
Multi-omic phenotyping reveals host-microbe responses to bariatric surgery, glycaemic control and obesity.
in Communications medicine
Penney N
(2020)
Investigating the Role of Diet and Exercise in Gut Microbe-Host Cometabolism.
in mSystems
Ocvirk S
(2020)
A prospective cohort analysis of gut microbial co-metabolism in Alaska Native and rural African people at high and low risk of colorectal cancer.
in The American journal of clinical nutrition
Mujagic Z
(2022)
Integrated fecal microbiome-metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome.
in Gut microbes
Mayneris-Perxachs J
(2021)
Gut Microbial and Metabolic Profiling Reveal the Lingering Effects of Infantile Iron Deficiency Unless Treated with Iron.
in Molecular nutrition & food research
Lahiri S
(2019)
The gut microbiota influences skeletal muscle mass and function in mice.
in Science translational medicine
Garcia-Perez I
(2020)
RETRACTED ARTICLE: Dietary metabotype modelling predicts individual responses to dietary interventions
in Nature Food
Garcia-Perez I
(2020)
Identifying unknown metabolites using NMR-based metabolic profiling techniques.
in Nature protocols
Eriksen R
(2020)
Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study.
in EBioMedicine
Chan Q
(2022)
Blood pressure interactions with the DASH dietary pattern, sodium, and potassium: The International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP).
in The American journal of clinical nutrition
Brignardello J
(2022)
Characterization of diet-dependent temporal changes in circulating short-chain fatty acid concentrations: A randomized crossover dietary trial.
in The American journal of clinical nutrition
Boubnovski MM
(2022)
Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs.
in Clinical radiology
Beck T
(2022)
Auto-CORPus: A Natural Language Processing Tool for Standardizing and Reusing Biomedical Literature.
in Frontiers in digital health
Alexander JL
(2023)
Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer.
in Microbiome
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
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/ |