Back

Sleep and diurnal blood pressure in concurrent hypertension and type 2 diabetes

Zhao, Y.; Zha, Y.; Zheng, Y.; Wei, L.; Chen, B.; Zhang, Z.; Tan, X.

2025-10-22 cardiovascular medicine
10.1101/2025.10.20.25338423 medRxiv
Show abstract

BackgroundHypertension is a primary cardiovascular complication in type 2 diabetes, with a significantly increased risk of morbidity and mortality compared to the general population. Ambulatory blood pressure monitoring is essential because blood pressure exhibits circadian rhythmicity, and sleep plays a crucial role in modulating nocturnal blood pressure patterns and overall cardiovascular risk. MethodA total of 20 patients (63.75 {+/-} 4.44 years old, 40% female, duration of T2D: 12.3 {+/-} 6.24 years) underwent ambulatory blood pressure monitoring (ABPM) and actigraphy in a free-living condition. Multi-day sleep parameters including total sleep time, sleep efficiency, wake after sleep onset, sleep onset latency and number of awakenings were assessed by wrist actigraphy supplemented with sleep diary, ABPM was measured for 24 hours during the sleep assessment period. Associations between parameters and ABPM across total sleep measurement period and the exact date of ABPM measurement were investigated, respectively. ResultsSleep onset latency was associated with 24-hour coefficient of variance of systolic blood pressure ({beta}-coefficient: 0.323, 95% CI: [0.014, 0.632], p = 0.04). Inter-day sleep efficiency was inversely associated with SD and CV (SD: {beta}-coefficient: -0.373, 95% CI: [-0.676, -0.071], P = 0.02; CV: {beta}-coefficient: -0.588, 95% CI: [-1.141, -0.036], P = 0.04) of diastolic blood pressure. ConclusionIn individuals with T2D and hypertension, both acute and chronic sleep disturbances, particularly prolonged sleep onset latency and reduced multi-day sleep efficiency, are closely associated with increased blood pressure variability, highlighting the importance of sleep quality in cardiovascular risk management.

Published in Sleep Research · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.