Abstract FR442: The Association of Nocturnal Blood Pressure Patterns with BMI, Diabetes, and Cardiovascular Dysfunction
Bibliographic record
Abstract
Background: Ambulatory Blood Pressure Monitoring (ABPM) offers valuable insights into 24-hour blood pressure (BP) patterns, providing a more comprehensive assessment of cardiovascular risk compared to office-based measurements. This study investigates the relationship between nocturnal BP fluctuations, BMI, diabetes, and cardiovascular dysfunction in a large cohort. Methods: A retrospective analysis of 8199 participants undergoing 24-hour ABPM was conducted. Nocturnal BP patterns were categorized into dipping, non-dipping, reverse-dipping, and extra-dipping. Associations between these patterns and BMI, diabetes, and cardiovascular dysfunction were examined. Additionally, the relationship between menopause status, early-morning hypertension, and diabetes was assessed in female participants. Results: Among participants, 40.7% exhibited hypertension linked to abnormal nocturnal BP patterns (non-dipping 19.16%, 6.22% reverse-dipping, and 1.74% extra-dipping); only 13.06% showed normal dipping pattern. Increased BMI, particularly in overweight and obese categories, was significantly associated with non-dipping and reverse-dipping patterns. These patterns correlated with heightened risks of cardiovascular events, early-morning hypertension, and diabetes. Conclusion: ABPM provides critical insights into abnormal nocturnal BP patterns and their associations with BMI, diabetes, and cardiovascular dysfunction. The findings support broader integration of ABPM into clinical settings to enhance cardiovascular risk assessment and management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".