Molecules to Health Records
Lead Research Organisation:
Health Data Research UK
Department Name: UNLISTED
Abstract
Bringing together different types of information about patients and their diseases can lead to new ways to predict those at risk of getting them, prevent them from happening, detect them earlier to improve outcomes, and find new ways to treat them.
This programme will develop the new tools and science needed to bring together complex information from health records across the UK and beyond, together with genetic information and other detailed understandings of what changes are causing diseases to unlock these opportunities to improve the health and wellbeing of patients and the public.
This programme will develop the new tools and science needed to bring together complex information from health records across the UK and beyond, together with genetic information and other detailed understandings of what changes are causing diseases to unlock these opportunities to improve the health and wellbeing of patients and the public.
Technical Summary
This work is funded by the UKRI Medical Research Council, UKRI Engineering and Physical Sciences Research Council, UKRI Economic and Social Research Council, Department of Health and Social Care, National Institute for Health Research (England), Chief Scientist Office (Scottish Government), Health and Care Research Wales, Public Health Agency HSC (Northern Ireland), British Heart Foundation and Cancer Research UK
The programme’s vision is to create new informatics infrastructures and data science methods that help achieve a deep integration of biology, biomedicine and population health sciences to gain major new insights into the underlying causes and biology of diseases by bringing together on a grand scale multimodal information on genomics, other molecular traits, and high-resolution electronic health records (EHRs). The ‘Molecules to Health Records’ approach will help unlock the potential of major global investments in large-scale molecular bioresources of populations and patients.
Objectives
The priority areas of focus include:
1. Molecular underpinnings of multi-disease aetiology, aligned with the strategic aims of the Medical Research Council (MRC) and National Institute for Health Research (NIHR).
2. Therapeutic target prioritisation, aligned with the MRC’s strategic aim to enhance therapeutic target validation and promote collaboration with industry across both common and rare diseases.
3. Genomic causes of rare disorders, aligned with the UK Strategy for Rare Diseases and Genome UK.
4. Risk prediction and stratified prevention, aligned with MRC and the UK Prevention Research Partnership’s and Genome UK’s strategic aim to enhance prevention and early detection of common diseases.
Impact and legacy
This programme’s distinctive contribution is, therefore, to help develop approaches and infrastructuresthat realise novel insights from EHR-enabled bioresources, working across cohorts to maximise scale, synergy and diversity. This proposal aims to seize opportunities that HDR UK is uniquely positioned to deliver, and achieve a balance of risk and reward, scale and agility, infrastructure development and tangible research outputs. This programme will contribute to HDR UK’s key cross-cutting ambitions, including addressing inequalities (e.g., through creation of genomic medicine tools in diverse non-European populations), developing synergistic approaches to disease prevention (e.g., through multi-omic studies of multi-disease aetiology), and promoting resilience against future health shocks (e.g., through mobilisation and multipurpose use of global populations cohorts for communicable and non-communicable disease epidemiology).
The programme’s vision is to create new informatics infrastructures and data science methods that help achieve a deep integration of biology, biomedicine and population health sciences to gain major new insights into the underlying causes and biology of diseases by bringing together on a grand scale multimodal information on genomics, other molecular traits, and high-resolution electronic health records (EHRs). The ‘Molecules to Health Records’ approach will help unlock the potential of major global investments in large-scale molecular bioresources of populations and patients.
Objectives
The priority areas of focus include:
1. Molecular underpinnings of multi-disease aetiology, aligned with the strategic aims of the Medical Research Council (MRC) and National Institute for Health Research (NIHR).
2. Therapeutic target prioritisation, aligned with the MRC’s strategic aim to enhance therapeutic target validation and promote collaboration with industry across both common and rare diseases.
3. Genomic causes of rare disorders, aligned with the UK Strategy for Rare Diseases and Genome UK.
4. Risk prediction and stratified prevention, aligned with MRC and the UK Prevention Research Partnership’s and Genome UK’s strategic aim to enhance prevention and early detection of common diseases.
Impact and legacy
This programme’s distinctive contribution is, therefore, to help develop approaches and infrastructuresthat realise novel insights from EHR-enabled bioresources, working across cohorts to maximise scale, synergy and diversity. This proposal aims to seize opportunities that HDR UK is uniquely positioned to deliver, and achieve a balance of risk and reward, scale and agility, infrastructure development and tangible research outputs. This programme will contribute to HDR UK’s key cross-cutting ambitions, including addressing inequalities (e.g., through creation of genomic medicine tools in diverse non-European populations), developing synergistic approaches to disease prevention (e.g., through multi-omic studies of multi-disease aetiology), and promoting resilience against future health shocks (e.g., through mobilisation and multipurpose use of global populations cohorts for communicable and non-communicable disease epidemiology).
Organisations
Publications
Addey T
(2023)
Educational and social inequalities and cause-specific mortality in Mexico City: a prospective study.
in The Lancet. Public health
Akbari P
(2023)
A genome-wide association study of blood cell morphology identifies cellular proteins implicated in disease aetiology.
in Nature communications
Aung N
(2023)
Association of Longer Leukocyte Telomere Length With Cardiac Size, Function, and Heart Failure.
in JAMA cardiology
Barclay M
(2023)
Phenotypes and rates of cancer-relevant symptoms and tests in the year before cancer diagnosis in UK Biobank and CPRD Gold.
in PLOS digital health
Bassett E
(2023)
Inconsistency in UK Biobank Event Definitions From Different Data Sources and Its Impact on Bias and Generalizability: A Case Study of Venous Thromboembolism
in American Journal of Epidemiology
Bohrmann B
(2023)
Body Mass Index and Risk of Hospitalization or Death Due to Lower or Upper Respiratory Tract Infection.
in JAMA
Bouza L
(2023)
How to estimate carbon footprint when training deep learning models? A guide and review
in Environmental Research Communications
Burger PM
(2023)
Personalized lifetime prediction of survival and treatment benefit in patients with heart failure with reduced ejection fraction: The LIFE-HF model.
in European journal of heart failure
Carter JL
(2023)
Body composition and risk factors for cardiovascular disease in global multi-ethnic populations.
in International journal of obesity (2005)
Chen JY
(2023)
Genetically predicted androgenic profiles and adverse cardiac markers: a sex-specific Mendelian randomization study.
in ESC heart failure
Chung R
(2023)
Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment.
in Journal of the American Heart Association
Costanzo MC
(2023)
Cardiovascular Disease Knowledge Portal: A Community Resource for Cardiovascular Disease Research.
in Circulation. Genomic and precision medicine
Fachrul M
(2023)
Direct inference and control of genetic population structure from RNA sequencing data
in Communications Biology
Fritz J
(2024)
Body mass index, triglyceride-glucose index, and prostate cancer death: a mediation analysis in eight European cohorts.
in British journal of cancer
Hageman SHJ
(2023)
Improving 10-year cardiovascular risk prediction in apparently healthy people: flexible addition of risk modifiers on top of SCORE2.
in European journal of preventive cardiology
Hageman SHJ
(2023)
The relevance of competing risk adjustment in cardiovascular risk prediction models for clinical practice.
in European journal of preventive cardiology
Ismail S
(2023)
Risk Factors of Secondary Cardiovascular Events in a Multi-Ethnic Asian Population with Acute Myocardial Infarction: A Retrospective Cohort Study from Malaysia
in Journal of Cardiovascular Development and Disease
Jiang X
(2023)
Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.
in Nature genetics
Jin D
(2023)
Lipoprotein Characteristics and Incident Coronary Heart Disease: Prospective Cohort of Nearly 90 000 Individuals in UK Biobank.
in Journal of the American Heart Association
Jindal S
(2023)
Risk factors for a serious adverse outcome in neonates: a retrospective cohort study of vaginal births
in BJOG: An International Journal of Obstetrics & Gynaecology
Kaptoge S
(2023)
Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation
in The Lancet Diabetes & Endocrinology
Koponen K
(2024)
Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort.
in Arteriosclerosis, thrombosis, and vascular biology
Lannelongue L
(2023)
Environmental Impacts of Machine Learning Applications in Protein Science.
in Cold Spring Harbor perspectives in biology
Larsson SC
(2023)
Mendelian randomization for cardiovascular diseases: principles and applications.
in European heart journal
Lee WH
(2024)
Genetically predicted plasma cortisol and common chronic diseases: A Mendelian randomization study.
in Clinical endocrinology
Marx N
(2023)
2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes.
in European heart journal
McGeoch LJ
(2023)
Cigarette smoking and risk of severe infectious respiratory diseases in UK adults: 12-year follow-up of UK biobank.
in Journal of public health (Oxford, England)
Oguntade A
(2023)
Body fat distribution, fat-free mass and cardiovascular function in the UK Biobank
in European Heart Journal
Oguntade AS
(2023)
Body Composition and Risk of Incident Heart Failure in 1 Million Adults: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies.
in Journal of the American Heart Association
Patel A
(2023)
MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data.
in Wellcome open research
Patel AP
(2023)
A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease.
in Nature medicine
Pennells L
(2024)
Adapting cardiovascular risk prediction models to different populations: the need for recalibration
in European Heart Journal
Schuermans A
(2024)
Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia
in JAMA Cardiology
Silcocks M
(2023)
Evolution and transmission of antibiotic resistance is driven by Beijing lineage Mycobacterium tuberculosis in Vietnam
in Microbiology Spectrum
Toivonen J
(2023)
The value of genetic data from 665,460 individuals in managing iron deficiency anaemia and suitability to donate blood
in Vox Sanguinis
Tong TYN
(2024)
Dietary amino acids and risk of stroke subtypes: a prospective analysis of 356,000 participants in seven European countries.
in European journal of nutrition
Tschiderer L
(2023)
Age at Menopause and the Risk of Stroke: Observational and Mendelian Randomization Analysis in 204 244 Postmenopausal Women.
in Journal of the American Heart Association
Wall J
(2023)
South Asian medical cohorts reveal strong founder effects and high rates of homozygosity
in Nature Communications
Zhao J
(2023)
Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets
in Nature Immunology
Zhao S
(2023)
Associations between vitamin D and autoimmune diseases: Mendelian randomization analysis
in Seminars in Arthritis and Rheumatism
Zhong H
(2023)
Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.
in Human molecular genetics
Zhu J
(2024)
Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models.
in Alzheimer's research & therapy