2026-06-22 英国研究イノベーション機構（UKRI）
＜関連情報＞
- https://www.ukri.org/news/ai-tool-reveals-hidden-organ-damage-caused-by-high-blood-pressure/
- https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.125.077394
- https://ieeexplore.ieee.org/document/10822397
高血圧性多臓器障害を定量化し、新たな疾患表現型を特定するための対照機械学習：多国籍マルチモーダル研究 Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study
Mohanad Alkhodari, DPhil, Winok Lapidaire, PhD, Turkay Kart, MS, Zhaohan Xiong, PhD, Samuel Krasner, BA, Andrew Fletcher, DPhil, Shakila Bibi, PhD, … , and Paul Leeson, MB, PhD
Circulation Published:21 June 2026
DOI:https://doi.org/10.1161/CIRCULATIONAHA.125.077394
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.
単純な臨床指標に基づく不確実性定量化を伴う複雑な高血圧性多臓器障害の深層学習モデリング Deep Learning-based Modelling of Complex Hypertensive Multi-Organ Damage with Uncertainty Quantification from Simple Clinical Measures
Turkay Kart; Mohanad Alkhodari; Winok Lapidaire; Abhirup Banerjee; Adam J. Lewandowski; Paul Leeson
2024 IEEE International Conference on Bioinformatics and Biomedicine Date Added to IEEE Xplore: 10 January 2025
DOI:https://doi.org/10.1109/BIBM62325.2024.10822397
Abstract
Hypertension is a leading risk factor for a number of diseases and can cause severe damage to the vital organs such as the brain and heart. However, the level of hypertension itself does not necessarily reflect the full extent of underlying end-organ changes, which may hinder the development of effective treatment strategies. While recent research has demonstrated that these end-organ changes can be measured with deep phenotyping, its clinical translation may not be feasible. In this study, we propose a state-of-art deep learning approach that can quantify multi-organ (e.g., heart, brain, vasculature) phenotypical changes due to persistent hypertension from simple and popular clinical measures such as electrocardiogram (ECG), routinely acquired clinical data (age, BMI, diastolic and systolic blood pressures), and cardiac short axis (SAX) images from the UK Biobank, one of the largest open-access biomedical databases. Our proposed approach captures the intricate patterns of hypertensive disease state without resorting to the complex measures, which is hard to obtain in practical settings. It generates a numeric score between 0 and 1 of multi-organ damage, as well as provides an estimate of the overall uncertainty. The performance of our models is evaluated in different experimental settings and compared against the reference model. The results consistently demonstrate that the proposed approach can effectively model the multi-organ phenotypical changes from simple clinical measures with high performance (best-performing model MAE=0.108, MSE=0.019, variance=0.0005), and underscores its feasibility for potential clinical use.
