Machine learning integration of multimodal data identifies key features of blood pressure regulation
Open accessAbstract
BACKGROUND: Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP). METHODS: We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB). FINDINGS: Our model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts. INTERPRETATION: We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies. FUNDING: Chronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.
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Multimodal AI that integrates clinical and laboratory features can further improve personalized management of hypertension.
“Multimodal AI that integrates clinical and laboratory features can further improve personalized management of hypertension.40”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [40]
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Machine Learning and Artificial Intelligence for Research on Hypertension
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- Authors
- Louca, P., Tran, T. Q. B., Toit, C. D., Christofidou, P., Spector, T. D., Mangino, M., Suhre, K., Padmanabhan, S., & Menni, C.
- Affiliations
- King's College London (GB)
- University of Glasgow (GB)
- Cornell University (US)
- Corresponding author
- Padmanabhan, Sandosh
- Menni, Cristina
- ORCID
- Venue
- EBioMedicine
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 84
- Pages
- 104243
- ISSN
- 2352-3964 (ISSN-L)
- PMC
- PMC9463529
- OpenAlex
- W4294816208
- Cited by
- 37
- References
- 73
- Citations by year
- 2026: 4 · 2025: 17 · 2024: 9 · 2023: 5 · 2022: 2
- FWCI
- 5.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EBioMedicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Qatar Foundation (NPRP11C-0115-180010)
- Wellcome Trust (733100, NPRP11C-0115-180010, 212904, 212904/Z/18/Z, MR/M016560/1, 212904/Z/18/)
- National Institute for Health and Care Research (MR/M016560/1, 733100)
- British Heart Foundation (18/6/34217, PG/12/85/29925, RE/18/6/34217, MR/M016560/1, 733100, NPRP11C-0115-180010, CS 16131878)
- King's College London
- European Commission (H2020, 733100, MR/M016560/1, 212904/Z/18/Z)
- Fonds National de la Recherche Luxembourg
- Weill Cornell Medical College
- Qatar National Research Fund (NPRP11C‐0115‐180010)
- Horizon 2020 Framework Programme (733100)
- Weill Cornell Medicine - Qatar
- Medical Research Council (MR/M004422/1, MR/M016560/1, 212904/Z/18/Z, CS/16/1/31878, PG/12/85/29925)
- Health Data Research UK
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Health Sciences › Nursing › Nutrition and Dietetics › Sodium Intake and Health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- blood pressure regulation, systolic blood pressure, hypertension, biomarkers, metabolomics, dietary intake, multi-omics integration, lactate, uric acid, creatinine, dietary fat, XGBoost, TwinsUK
- MeSH terms
- Machine Learning, Blood Pressure, Carnitine, Chlorides, Creatinine, Humans, Hypertension, Lactates, Uric Acid, Biomarkers, alpha-Linolenic Acid
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