Yuan Ma, PhD; Albert Hofman, MD, PhD
Competition Sponsor: US National Academy of Medicine
Dementia is the most common neurodegenerative disease in older people, associated with high disability and dependency. Hypertension is a key player in the vascular etiology, but the relationship between blood pressure (BP) and dementia is not fully understood. Emerging evidence suggests that long-term BP variability contributes to dementia risk independent of BP level, possibly through autonomic disturbance or hemodynamic mechanisms. However, little is known regarding dementia risk in relation to short-term BP variability and other hemodynamic features of BP waveforms, which can be obtained more conveniently vias wearable devices. Therefore, we hypothesized that beat-to-beat BP variability predicts dementia risk independent of BP levels and that a “dynamic BP signature” derived from BP waveforms through novel prediction algorithms predicts dementia risk better than BP variability alone. We will test our hypothesis in a population-based cohort study among ~2,000 dementia-free older adults who underwent beat-to-beat BP measurements and subsequent long follow-up of dementia cases for up to 20 years. We will select the most informative set of dynamic features of BP waveforms and develop a novel dynamic BP signature using Cox models and random survival forest models. Model performance will be compared using model fit parameters. Our proposal is highly transformative with the rapid advances of wearable devices and cuffless BP monitoring techniques that enable us to quantify dynamic BP waveforms conveniently with low cost. Our proposal will inform the potential of dynamic BP measures as novel targets for dementia prediction and prevention.