| Title: | Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study. |
| Journal: | Circulation |
| Published: | 21 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42323953/ |
| DOI: | https://doi.org/10.1161/circulationaha.125.077394 |
| URL: | https://research-information.bris.ac.uk/en/publications/2829f610-1c96-4d12-bf71-5ff4eb64e3dd |
Publication 20786
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage increases risk of vascular events and death but can be difficult to identify in the clinic. We developed a novel machine learning approach that quantifies current hypertension-associated multiorgan damage, mapping progression from health to advanced disease, in a pseudotemporal manner and predicts organ-specific disease progression trajectories.</p>
METHODS: We analyzed 566 multimodal imaging and nonimaging variables from 27 099 participants in the UK Biobank imaging substudy to develop a semisupervised contrastive trajectory inference (cTI) framework that models multiorgan alterations associated with hypertension exposure, including heart, brain, kidneys, vasculature, lungs, liver, and metabolic information. Model stability was validated through multiple internal validation steps, and external validity was tested on 5507 participants from the Atherosclerosis Risk in Communities study (ARIC). Clinical relevance was evaluated against existing risk scores and through ability to predict survival and incident multiorgan disease for up to 7 years, across both UK Biobank and ARIC.</p>
RESULTS: In the UK Biobank (mean age 63.27±7.48 years; 53.4% women) our global organ damage score (HyperScore) achieved an area under the curve of 0.964 (0.941-0.987) for identification of individuals with severe end-organ disease and robust stability in cross-validation with a mean root mean square error of 0.104±0.084. Survival odds differed significantly across HyperScore stages (P<0.001), whereas stratification by blood pressure was nonsignificant. We further revealed 6 hypertensive disease phenotypes (HyperTrajectory), characterized by predominant cardiac, lipoprotein, atherothrombosis, brain, cardiorenal, and liver features, respectively. External testing in ARIC confirmed stability of the model, with Jensen-Shannon distances as low as 0.10 for HyperScore distributions, without significant deviation in organ damage progression patterns (P>0.05) and consistent end-organ and outcome characteristics between ARIC and UK Biobank across HyperTrajectories.</p>
CONCLUSIONS: Machine learning-derived global organ damage scores are feasible in hypertension and enable identification of distinct hypertension-associated organ-disease phenotypes. New frameworks for hypertension assessment and monitoring using imaging to derive personalized risk assessment and phenotype-specific intervention may be achievable.</p>
26 Authors
- Mohanad Alkhodari
- Winok Lapidaire
- Turkay Kart
- Zhaohan Xiong
- Samuel Krasner
- Andrew Fletcher
- Shakila Bibi
- Natalie Savage
- Katie Suriano
- Tobias Baumeister
- Eric Ohuma
- Ana I L Namburete
- Pablo Lamata
- Yasser Iturria-Medina
- Lucy C Chappell
- Christina Aye
- Basky Thilaganathan
- Abigail Fraser
- Lucy Mackillop
- Richard J McManus
- Ntobeko A B Ntusi
- Ahsan H Khandoker
- Leontios J Hadjileontiadis
- Adam J Lewandowski
- Abhirup Banerjee
- Paul Leeson
1 Application
| Application ID | Title |
|---|---|
| 58244 | Gender-specific multi-modal phenotyping of hypertensive disease |
Enabling scientific discoveries that improve human health