The cardiovascular effects of novel weight loss therapies
Bibliographic record
Abstract
The prevalence of overweight and obesity has reached pandemic proportions. Obesity is known to increase the risk for Type 2 diabetes and hypertension, as well as the risk for overt cardiovascular (CV) disease, including myocardial infarction, heart failure, and stroke. The rising prevalence of obesity may counteract the recent advances in primary and secondary prevention of CV disease. Overweight and obesity are common in patients with CV disease; however, cardiologists face several challenges in managing body weight in this population. Many may not consider obesity as a therapeutic target probably because there were no previous highly effective and safe pharmacologic interventions to consider. In addition, they may not have the expertise or resources to implement lifestyle interventions and may have limited familiarity with obesity pharmacotherapy. Moreover, the long-term CV effects of obesity pharmacotherapy remain uncertain due to limited CV outcome data with weight loss as the primary intervention. Although current CV guidelines recognize the importance of weight loss, they primarily focus on lifestyle modifications, with fewer details on strategies to utilize obesity pharmacotherapy and surgery. However, the recent 2022 American Diabetes Association/European Association for the Study of Diabetes consensus on the management of Type 2 diabetes has moved up weight management to the front of the treatment algorithm, by prioritizing the use of pharmacologic interventions such as glucagon-like peptide-1 receptor agonists and dual glucose-dependent insulinotropic polypeptide/glucagon-like peptide-1 receptor agonists, which have potent weight-lowering effects, in addition to glucose-lowering effects. This review appraises the current evidence regarding the CV effects of weight-loss interventions. Considering this evidence, practical guidance is provided to assist cardiologists in developing and implementing treatment plans, which may allow optimal weight management while maximizing CV benefits and minimizing side effects to improve the overall well-being of people with 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 | 0.000 |
| 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) | 0.000 | 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; a candidate call from one teacher head, 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".