Impact of Nocturnal Blood Pressure Patterns on BMI, Diabetes, and Cardiovascular Health: A Comprehensive Analysis
Bibliographic record
Abstract
ABSTRACT Ambulatory Blood Pressure Monitoring (ABPM) provides valuable insights into a patient’s 24-hour blood pressure (BP) patterns, offering an enhanced assessment of cardiovascular health, particularly in relation to body mass index (BMI), diabetes, and menopausal status. This method provides a more comprehensive understanding of BP dynamics compared to traditional office-based measurements, assisting healthcare professionals with devising a more accurate assessment of a patient’s cardiovascular risk. ABPM is increasingly growing in adoption as a service in medical practices and pharmacies due to the numerous advantages. This retrospective cohort study analyzes 24-hour ABPM data from 5618 participants to investigate the relationship between nocturnal BP fluctuations found through ABPM with BMI, diabetes, hypertension in both male and female participants. It also investigates the relationship between nocturnal BP fluctuations and menopausal status in female participants. These conditions are studied to ultimately assess whether BP fluctuations are potentially connected to an increased risk of developing chronic conditions such as cardiovascular dysfunction. The ABPM nocturnal blood-pressure fluctuations are categorized within four distinct categories: normal dipping, non-dipping, reverse dipping, and extra dipping. The findings of this study show that 44.11% of participants exhibit hypertension, with most of these participants experiencing abnormal nocturnal blood pressure patterns. Increased BMI, particularly in overweight and moderately obese categories, was significantly associated with non-dipping patterns. In addition, the relationship between menopausal status for female participants and nocturnal dipping patterns, early morning hypertension and diabetes diagnoses are assessed. These findings underscore the importance of ABPM in detecting abnormal BP patterns and their associations with key health indicators, advocating for its broader integration in clinical settings.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".