0083 Longitudinal sleep instability is associated with increased MRI-visible perivascular space burden

Abstract

Abstract Introduction Emerging data from our group found longitudinal sleep variability within the Seattle Longitudinal Study was significantly associated with cognitive impairment and predicted cognitive impairment 10-years downstream. While this data supports a link between sleep instability, aging, and cognitive decline, the mechanism linking these processes is unknown. MRI-visible perivascular space (PVS) burden is a putative marker of glymphatic dysfunction, and previous studies suggest that sleep disruption is associated with PVS burden. Methods This a secondary analysis of subjects who a) participated in the Seattle Longitudinal Study and b) had T1-weighted 3 T MRI and FLAIR scans available for PVS analysis (n=250). We evaluated the variability (standard deviation) in each participant’s longitudinal self-reported sleep duration. White matter MRI-visible PVS were evaluated with a semi-automated segmentation algorithm, accounting PVS total burden (number/cm3), volume (mm3/cm3), as well as average PVS width, length, and median volume across participants, blinded by group. Participants were stratified into low or high-sleep variability groups. Results Preliminary analysis (n=37) demonstrated a significant association between high sleep variability and increased whole-brain PVS burden (p=0.04), with low sleep variability participants exhibiting an average of 7 PVS and high sleep variability participants an average of 11.33. Follow-up analysis evaluated PVS features within the wider cohort. Conclusion These findings suggest that variability in longitudinal sleep duration may exert a previously unappreciated influence on pathological processes, potentially including glymphatic dysfunction. Future studies within this cohort will seek to evaluate longitudinal changes in PVS burden across longitudinally-collected MRIs. Additional studies should be performed evaluating these findings on an independent cohort to assess whether sleep variability of different time scales (weeks, years, decades) exerts similar effects on these outcomes. Support (if any)