Journal of cardiology · 2026
Mathematical analysis of the breakdown of physiological stability preceding heart failure onset using longitudinal health check-up data.
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Inoue Naoya, Ohinata Ryo, Morikawa Shuji
Аннотация
BACKGROUND: Heart failure (HF) is a multifactorial syndrome, yet the dynamic processes through which physiological systems become unstable before HF remain incompletely understood.
METHODS: We analyzed longitudinal data from individuals undergoing health check-ups. In participants who developed HF, the date of first HF hospitalization was defined as the index date. All measurements were aligned to the index date. Using estimated glomerular filtration rate, systolic blood pressure, heart rate, and body mass index, we quantified dynamic indicators via rolling window analyses: resilience based on autoregressive [AR(1)] coefficients, irreversibility proxies, and multivariate instability assessed by the maximum eigenvalue (λ₁) of covariance matrices. Rather than formal hypothesis testing, we focused on the consistency of temporal patterns across analytical conditions.
RESULTS: The HF group showed a progressive increase in AR(1) coefficients several years before HF, suggesting declining physiological resilience. Visual inspection of irreversibility trajectories suggested possible time-asymmetric patterns before HF onset, although formal quantification in the HF group was limited by sparse data. Multivariate analyses further demonstrated an increase in λ₁ as HF approached, indicating an increase in shared variance structure across multiple physiological markers.
CONCLUSIONS: Before HF onset, physiological systems may undergo a dynamic loss of stability characterized by reduced resilience, emergence of irreversible dynamics, and breakdown of multivariate coordination, preceding marked changes in mean biomarker levels.
METHODS: We analyzed longitudinal data from individuals undergoing health check-ups. In participants who developed HF, the date of first HF hospitalization was defined as the index date. All measurements were aligned to the index date. Using estimated glomerular filtration rate, systolic blood pressure, heart rate, and body mass index, we quantified dynamic indicators via rolling window analyses: resilience based on autoregressive [AR(1)] coefficients, irreversibility proxies, and multivariate instability assessed by the maximum eigenvalue (λ₁) of covariance matrices. Rather than formal hypothesis testing, we focused on the consistency of temporal patterns across analytical conditions.
RESULTS: The HF group showed a progressive increase in AR(1) coefficients several years before HF, suggesting declining physiological resilience. Visual inspection of irreversibility trajectories suggested possible time-asymmetric patterns before HF onset, although formal quantification in the HF group was limited by sparse data. Multivariate analyses further demonstrated an increase in λ₁ as HF approached, indicating an increase in shared variance structure across multiple physiological markers.
CONCLUSIONS: Before HF onset, physiological systems may undergo a dynamic loss of stability characterized by reduced resilience, emergence of irreversible dynamics, and breakdown of multivariate coordination, preceding marked changes in mean biomarker levels.