Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study
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. 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. 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. 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.
Article Details
Authors (26)
Mohanad Alkhodari
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Winok Lapidaire
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Turkay Kart
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Zhaohan Xiong
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Samuel Krasner
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Andrew J. Fletcher
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Shakila Bibi
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Natalie Savage
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Katie Suriano
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Tobias R. Baumeister
Neuroinformatics for Personalized Medicine Lab, McGill University, Montreal, Canada (T.R.B., Y.I.-M.).
Eric O. Ohuma
Ana I.L. Namburete
Oxford Machine Learning in Neuroimaging Lab, Department of Computer Science, University of Oxford, United Kingdom (A.I.L.N.).
Pablo Lamata
Yasser Iturria-Medina
Lucy C. Chappell
Department of Women and Children’s Health, Kings College London, United Kingdom (L.C.C.).
Christina Y.L. Aye
Nuffield Department of Women’s and Reproductive Health, University of Oxford, United Kingdom (C.Y.L.A., L.M.).
Basky Thilaganathan
Molecular and Clinical Science Research Institute, St George’s University of London, United Kingdom (B.T).
Abigail Fraser
Lucy Mackillop
Nuffield Department of Women’s and Reproductive Health, University of Oxford, United Kingdom (C.Y.L.A., L.M.).
Richard J. McManus
Ntobeko A.B. Ntusi
South African Medical Research Council, Cape Town, South Africa (N.A.B.N.).
Ahsan H. Khandoker
Healthcare Engineering Innovation Group (HEIG), Department of Biomedical Engineering & Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates (M.A., A.H.K., L.J.H.).
Leontios J. Hadjileontiadis
Healthcare Engineering Innovation Group (HEIG), Department of Biomedical Engineering & Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates (M.A., A.H.K., L.J.H.).
Adam J. Lewandowski
Cardiovascular Clinical Research Facility (CCRF), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (M.A., W.L., T.K., Z.X., S.K., A.J.F., S.B., N.S., K.S., A.J.L., P.L.).
Abhirup Banerjee
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom (A.B.).
Paul Leeson