The Association between Antihypertensive Medication Use and Blood Pressure Is Influenced by Obesity
Bibliographic record
Abstract
Introduction. One in three US adults is living with obesity or hypertension, and more than 75% of hypertensive individuals are using antihypertensive medications. Therefore, it is important to examine blood pressure (BP) differences in populations that are using these medications with differing obesity status. Aim. We examined whether BP attained when using various antihypertensive medications varies amongst different body mass index (BMI) categories and whether antihypertensive medication use is associated with differences in other metabolic risk factors, independent of BMI. Methods. Adults with hypertension from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014 were used ( n=15,285 ). Linear regression analyses were used to examine the main effects and interaction between antihypertensive use and BMI. Results. In general, users of antihypertensive medications had lower BP than those not taking BP medications (NoBPMed) ( P<0.05 ), whereby in women, the differences in systolic BP between angiotensin-converting-enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) users and NoBPMed were greater in those with obesity (ACE inhibitors: −14 ± 1 mmHg; ARB: −16 ± 1 mmHg) compared to normal weight individuals (ACE inhibitors: −9 ± 1 mmHg; ARB: −11 ± 1 mmHg) ( P<0.05 ). Diastolic BP differences between women ARB users and NoBPMed were also greatest in obesity (−5 ± 1 mmHg) ( P<0.05 ) whilst there were no differences in normal weight individuals (−1 ± 1 mmHg) ( P>0.05 ). Furthermore, glucose levels and waist circumference in women were higher in those using ACE inhibitors compared to diuretics ( P<0.05 ). Conclusion. ACE inhibitors and ARBs may be associated with more beneficial BP profiles in women with obesity, with no obesity-related BP differences for antihypertensive medication in men. However, there could be potential cardiometabolic effects for some antihypertensive medications that should be explored further.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".