Journal of the American Heart Association · 2026
Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study.
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Fei Yuan, Liu Siwei, Tong Tianlang, Zhang Xuemei, Wang Hui, Liu Jun, Zheng Xiaoqi
Аннотация
BACKGROUND: Hypertension is a leading risk factor for cardiovascular, cerebrovascular, and renal diseases, significantly worsening prognosis and quality of life. We aimed to develop and validate multimodal machine learning models integrating clinical and proteomic features for early prediction of hypertensive complications.
METHODS: We analyzed 502 166 participants from the UKB (UK Biobank). Proteomic profiling was performed using the Olink Explore platform. Clinical variables and complication outcomes were obtained from electronic health records. Features were selected using Cox proportional hazards models and light gradient boosting machine classifiers. Multimodal predictive models were constructed using random forest, with Shapley Additive Explanations applied for model interpretation.
RESULTS: During follow-up, 1232, 166, and 549 participants developed heart, brain, and kidney complications, respectively. Among 3244 candidate features, 774, 600, and 1227 were associated with these outcomes. The integrated models achieved an area under the curve of 0.73 (95% CI, 0.68-0.77) for heart disease, 0.83 (95% CI, 0.73-0.92) for brain disease, and 0.79 (95% CI, 0.73-0.85) for kidney disease. Growth/differentiation factor 15 (hazard ratio [HR], 2.16 [95% CI, 1.93-2.42]), adaptor protein 3 complex subunit σ-2 (HR, 0.57 [95% CI, 0.42-0.78]), and tumor necrosis factor receptor superfamily member 10B (HR, 4.06 [95% CI, 3.40-4.85]) were significantly associated with their respective complications, effectively predicting the risk of clinical progression (all P<0.001).
CONCLUSIONS: Multimodal machine learning models combining proteomic and clinical data enable early identification of hypertensive complications. Growth/differentiation factor 15, adaptor protein 3 complex subunit σ-2, and tumor necrosis factor receptor superfamily member 10B may serve as potential biomarkers for risk prediction and early intervention.
METHODS: We analyzed 502 166 participants from the UKB (UK Biobank). Proteomic profiling was performed using the Olink Explore platform. Clinical variables and complication outcomes were obtained from electronic health records. Features were selected using Cox proportional hazards models and light gradient boosting machine classifiers. Multimodal predictive models were constructed using random forest, with Shapley Additive Explanations applied for model interpretation.
RESULTS: During follow-up, 1232, 166, and 549 participants developed heart, brain, and kidney complications, respectively. Among 3244 candidate features, 774, 600, and 1227 were associated with these outcomes. The integrated models achieved an area under the curve of 0.73 (95% CI, 0.68-0.77) for heart disease, 0.83 (95% CI, 0.73-0.92) for brain disease, and 0.79 (95% CI, 0.73-0.85) for kidney disease. Growth/differentiation factor 15 (hazard ratio [HR], 2.16 [95% CI, 1.93-2.42]), adaptor protein 3 complex subunit σ-2 (HR, 0.57 [95% CI, 0.42-0.78]), and tumor necrosis factor receptor superfamily member 10B (HR, 4.06 [95% CI, 3.40-4.85]) were significantly associated with their respective complications, effectively predicting the risk of clinical progression (all P<0.001).
CONCLUSIONS: Multimodal machine learning models combining proteomic and clinical data enable early identification of hypertensive complications. Growth/differentiation factor 15, adaptor protein 3 complex subunit σ-2, and tumor necrosis factor receptor superfamily member 10B may serve as potential biomarkers for risk prediction and early intervention.
Предметные рубрики MeSH
HumansProteomicsFemaleHypertensionMalePredictive Learning ModelsMiddle AgedLongitudinal StudiesRisk FactorsMachine LearningKidney DiseasesPrognosisRisk AssessmentAgedPredictive Value of TestsRandom ForestBoosting Machine Learning AlgorithmsHeart DiseasesBrain DiseasesBiomarkersClassification AlgorithmsPrediction Algorithms