Sleep Bruxism Is Associated with a Rise in Arterial Blood Pressure
Bibliographic record
Abstract
STUDY OBJECTIVES: Sleep bruxism (SB) is a movement disorder identified by grinding of teeth and rhythmic masticatory muscle activity (RMMA). RMMA is associated with body movements and cortical arousals. Increases in autonomic sympathetic activities that characterize sleep cortical arousal precede RMMA/SB. Based on these findings, this study examined whether RMMA/SB episodes are also associated with significant changes in arterial blood pressure (BP). DESIGN: Participants underwent 3 nights of full polysomnography that included noninvasive beat-to-beat BP recording. Single RMMA/SB episodes and arousal episodes were analyzed in stage 2 sleep and categorized as: (i) RMMA/SB + arousal; (ii) RMMA/SB + body movement; (iii) RMMA/SB + arousal + body movement; or (iv) arousal alone. Sleep and RMMA/SB data were compared to a Non SB group. RMMA/SB clusters (RMMA/SB episodes ≤ 30 sec apart) were also analyzed. SETTING: Sleep Laboratory at l'Hôpital du Sacré-Coeur de Montréal. PARTICIPANTS: Ten young, healthy participants with SB (mean age = 26 ± 1.8 years) and 9 without SB (mean age = 29 ± 1.2 years). INTERVENTIONS: N/A MEASUREMENTS AND RESULTS: BP increased with all RMMA/SB and arousal episodes (P ≤ 0.05). The average maximum BP surges (systolic/diastolic ± SE mm Hg) were: 25.6 ± 3.3/12.6 ± 2.0 for RMMA/SB + arousal; 30.1 ± 1.7/19.1 ± 1.9 for RMMA/SB + body movement; 26.0 ± 2.8/15.1 ± 2.0 for RMMA/SB + arousal + body movement; 19.4 ± 2.3/8.9 ± 1.2 for arousal alone; and for RMMA/SB clusters: Episode: 1: 26.2 ± 8.7/16.4 ± 5.7; Episode 2: 21.1 ± 7.9/12.6 ± 6.4. CONCLUSION: Rhythmic masticatory muscle activity/sleep bruxism (RMMA/SB) is associated with blood pressure fluctuations during sleep. Arousals and body movements often occur with RMMA/SB and can impact the magnitude of this BP surge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".