Blood Pressure and Heart Rate Patterns Identified by Unsupervised Machine Learning and Their Associations with Subclinical Cerebral and Renal Damage in a Japanese Community: The Masuda Study.

We applied unsupervised machine learning to analyze blood pressure (BP) and resting heart rate (HR) patterns measured during a 1-year period to assess their cross-sectional relationships with subclinical cerebral and renal target damage. Dimension reduction via uniform manifold approximation and projection, followed by K-means++ clustering, was used to categorize 362 community-dwelling participants (mean age, 56.2 years; 54.9% women) into three groups: Low BP and Low HR (Lo-BP/Lo-HR), High BP and High HR (Hi-BP/Hi-HR), and Low BP and High HR (Lo-BP/Hi-HR). Cerebral vessel lesions were defined as the presence of at least one of the following magnetic resonance imaging findings: lacunar infarcts, white matter hyperintensities, cerebral microbleeds, or intracranial artery stenosis. A high urinary albumin-to-creatinine ratio (UACR) was defined as the top 10% (≥ 12 mg/g) of the mean value from ≥2 measurements. Poisson regression with robust error variance, adjusted for demographics, lifestyle, and medical history, showed that the Hi-BP/Hi-HR group had relative risks of 3.62 (95% confidence interval, 1.75-7.46) for cerebral vessel lesions and 3.58 (1.33-9.67) for high UACR, and the Lo-BP/Hi-HR group had a relative risk of 3.09 (1.12-8.57) for high UACR, compared with the Lo-BP/Lo-HR group. These findings demonstrate the utility of an unsupervised, data-driven approach for identifying physiological patterns associated with subclinical target organ damage.