Bibliographic record
Abstract
An important aspect of cardiovascular (CV) therapeutics and disease prevention is optimization of therapy based on pathophysiology to interrupt disease progression and improve outcome. This not only involves selection of the appropriate drug, dose, and timing but also requires matching drug pharmacokinetics and circadian variations of specific physiologic markers to the disease process. As physicians, we tend to treat the tip of the iceberg, an approach that is true in hypertension. We lower blood pressure (BP) levels aggressively to reduce CV risk, as guided by the latest management guidelines.1–3 In both 2003 and 2005, the goal BP level in hypertensive patients with diabetes or kidney disease was set at <130/80 mm Hg.1,2 Indeed, in the Appropriate BP Control in Diabetes (ABCD) trial,4 intensive BP control reduced mortality compared with standard BP control. A host of observational studies have shown that CV risk in hypertensive patients bears a continuous relationship to systolic and diastolic BP values, down to 110 mm Hg and 70 mm Hg, respectively.3 How low to go and how low is safe, especially in the elderly with stiff arteries and comorbidities, remain pertinent issues, however. An additional consideration is that of BP level variations over hours, days, and months. Ambulatory BP monitoring (ABPM) has unmasked circadian variations in BP level with nocturnal dips and morning surges and their relation to CV risk.5 Of importance, the morning BP level surge coincides in timing with and predicts major CV events such as myocardial infarction, sudden cardiac death, and stroke.5 At the same time, nocturnal BP level dips correlate with mortality,6 nocturnal falls, and silent strokes in elderly hypertensive persons.7 Clearly, the time has come for modern CV therapeutics to seriously consider circadian BP level variations and to target the morning BP level surge and 24-hour BP control. In this issue, White8 makes a strong case for the assessment of early morning BP in hypertension. He provides a timely review of the circadian variation in BP and emphasizes the following 6 important points: (1) Although the early morning BP level surge in “extreme dippers” coincides with the morning peak of major adverse CV events,5 few antihypertensive drugs can control it. (2) Matching drug pharmacokinetics with timing of dosing is necessary to control the morning BP level surge, and ABPM is superior to the clinic BP assessment in detecting it. (3) The circadian variation in the renin-angiotensin-aldosterone system (RAAS) and autonomic activity and levels of sodium and potassium modulate nocturnal BP level and morning BP level surges. (4) The results of the Micardis Community Ambulatory Monitoring Trial (MICCAT-2) with telmisartan ± hydrochlorothiazide and the Prospective Randomised Investigation of the Safety and Efficacy of Micardis vs Ramipril Using ABPM (PRISMA) comparing telmisartan with ramipril showed reduction of the morning BP level surge.9 (5) Agents with long half-lives such as telmisartan, amlodipine, and bisoprolol can control the early morning BP level surge. (6) Nighttime chronotherapeutic preparations (ie, graded-release diltiazem; controlled-onset, extended-release verapamil) effectively suppress the morning BP level surge. Dr White's review8 touches on the important topic of circadian variation and therapy. Clearly, in hypertension, a prime objective of modern pharmacotherapeutics should be to optimize BP control throughout every hour of every day and match the pharmacokinetics of antihypertensive drugs and circadian variations to most effectively suppress the peaks in BP and lower CV risk. Certainly, future randomized clinical trials (RCTs) should focus on the efficacy of strategies during the important morning hours when CV risk is greatest; this is best accomplished via ABPM. Current ABPM devices provide the average 24-hour BP level as well as mean daytime, nighttime, and morning BP levels and should be used more widely in the clinic. Besides detecting morning BP level surges and identifying patients with high CV risk, ABPM can detect adverse effects of drugs, including hypertension and hypotension. In fact, ABPM detected small increases in BP levels in patients treated with rofecoxib, endorsing the concept that drugs that raise BP level also increase CV risk and stroke.10 Of importance, ABPM would be useful in detecting hypotension and severe nocturnal BP level dips that may lead to hypoperfusion of target organs including the heart. This would especially benefit elderly hypertensive patients, who are more prone to myocardial infarction, stroke, and other comorbidities (eg, diabetes mellitus) and often receive potent vasodilators. The future may offer development of “smart” drugs that target the peaks in RAAS and autonomic activity as well as major harmful cytokines, growth factors, and proteins to further reduce hypertensive CV disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".