Back

Daily mindfulness practice with and without slow breathing has opposing effects on plasma amyloid beta levels

Nashiro, K.; Cahn, B. R.; Choi, P.; Lee, H. R. J.; Satchi, S.; Min, J.; Yoo, H. J.; Mercer, N.; Cho, C.; Sordo, L.; Head, E.; Choupan, J.; Mather, M.

2025-03-11 psychiatry and clinical psychology
10.1101/2025.03.10.25323695 medRxiv
Show abstract

Prior research suggests that meditation may slow brain aging and reduce the risk of Alzheimers disease (AD). However, we lack research systematically examining what aspect(s) of meditation may drive such benefits. In particular, it is unknown how breathing patterns during meditation might influence health outcomes associated with AD. In this study, we examined whether two types of mindfulness meditation practice - one with slow breathing and one with normal breathing - differently affect plasma amyloid beta (A{beta}) relative to a no-intervention control group. One week of daily mindfulness practice with slow breathing decreased plasma A{beta} levels whereas one week of daily mindfulness practice with normal breathing increased plasma A{beta} levels. The no-intervention control group showed no changes in plasma A{beta} levels. Slow breathing appears to be a factor through which meditative practices can influence pathways relevant for AD. Research Transparency StatementConflicts of interest: All authors declare no conflicts of interest. Funding: This study was supported by Epstein Breakthrough Alzheimers Research Fund (PI: Mather, co-PI: Choupan) and by R01AG080652 (PI: Mather). Artificial intelligence: No artificial intelligence assisted technologies were used in this research or the creation of this article. Ethics: This research received approval from the University of Southern California Institutional Review Board (ID: UP-23-00373).

Published in Psychophysiology (predicted rank #10) · training set

Matching journals

The top 11 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.