Opportunities to optimize lifestyle interventions in combination with <scp>glucagon‐like peptide</scp> ‐1‐based therapy
Bibliographic record
Abstract
Obesity is a chronic multi-system disease and major driver of type 2 diabetes and cardiometabolic disease. Nutritional interventions form the cornerstone of obesity and type 2 diabetes management. Some interventions such as Mediterranean diet can reduce incident cardiovascular disease, probably independently of weight loss. Weight loss of 5% or greater can improve many adiposity-related comorbidities. Although this can be achieved with lifestyle intervention, it is often difficult to sustain in the longer term due to adaptive endocrine changes. In recent years glucagon-like-peptide-1 receptor agonists (GLP-1RAs) have emerged as effective treatments for both type 2 diabetes and obesity. Newer GLP-1RAs can achieve average weight loss of 15% or greater and improve cardiometabolic health. There is heterogeneity in the weight loss response to GLP-1RAs, with a substantial number of patients unable to achieve 5% or greater weight. Weight loss, on average, is lower in older adults, male patients and people with type 2 diabetes. Mechanistic studies are needed to understand the aetiology of this variable response. Gastrointestinal side effects leading to medication discontinuation are a concern with GLP-1RA treatment, based on real-world data. With weight loss of 20% or higher with newer GLP-1RAs, nutritional deficiency and sarcopenia are also potential concerns. Lifestyle interventions that may potentially mitigate the side effects of GLP-1RA treatment and enhance weight loss are discussed here. The efficacy of such interventions awaits confirmation with well-designed randomized controlled trials.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".