Functional Regression Methods for Estimating 24-hour Ambulatory Blood Pressure and Heart Rate Parameters in Narcolepsy Type 1

Introduction: The nocturnal dip, a physiological drop in nocturnal blood pressure (BP), is driven by the autonomic nervous system. A reduction of <10% during nocturnal sleep versus daytime wakefulness is considered a "non-dipping" BP pattern and associated with increased cardiovascular disease risk in the general population. This study aims to compare different methods for estimating BP and heart rate (HR) nocturnal dip from ambulatory BP monitoring (ABPM) data in individuals with narcolepsy type 1 (NT1). Methods: Baseline ABPM data were from participants with NT1 in the randomized TAK-994 phase 2 clinical trial (NCT04096560). Sleep period time (SPT) windows were estimated from raw accelerometer data overlapping with baseline and week 3 ABPM visits. Three approaches estimated BP and HR dip: (1) fixed-window, with daytime defined as 06:00 to 22:00, nighttime as 00:00 to 06:00, and dip defined as a drop from the daytime to nighttime window average; (2) 24-h pattern employing a two-component cosinor model to estimate a continuous 24-h pattern of BP and HR, and defining dip as a drop from pattern average to its lowest point; and (3) actigraphy-based, with dip defined as a drop from non-SPT to SPT average of BP and HR, utilizing algorithmically identified SPT aiming to best reflect participants’ actual sleep periods. Results: The analytic sample consisted of 31 participants with NT1. Comparing actigraphy-based dip with fixed-window and 24-h pattern dips, the 24-h pattern dip had higher Pearson's correlation than the fixed-window dip across all three parameters (0.91 vs. 0.87, 0.88 vs. 0.68, and 0.88 vs. 0.56 for systolic BP SBP, diastolic BP DBP, and HR, respectively). We found substantial between- and within-participant variability in SPT timing and duration. A total of 61% of participants had a fixed-window SBP dip <10%, and 41% had a fixed-window DBP dip <10%. The 30th percentile of SBP/DBP dip varied substantially across calculation methods: 3.8%/8.6% (fixed-window), 6.8%/14.1% (24-h pattern), and 6.7%/12.1% (actigraphy-based). Conclusion: Estimated dip values from the 24-h pattern approach with a two-component cosinor model for BP and HR were strongly correlated with actigraphy-based dip values, which utilized an objective algorithm to identify participants’ sleep. The 24-h pattern approach offers a robust alternative to the fixed-window method for assessing dipping, especially in populations with sleep timing variations and disturbances, like NT1, and does not require simultaneous actigraphy measurement. The classification of a “non-dipper” varies depending on both the dip type (SBP vs. DBP) and the dip estimation method.