Population-Level Interest in Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss Using Google Trends Statistics in a 12-Month Retrospective Analysis: An Infodemiology and Infoveillance Study
Bibliographic record
Abstract
Obesity is an exacerbated public health challenge, increasing the risk of several diseases and mortality while deteriorating the quality of life. There is significant dedication to exploring obesity therapies using glucagon-like peptide-1 (GLP-1) agonists, which have shown efficacy in reducing the number of deaths and complications associated with type 2 diabetes. This research aimed to examine the recent search popularity of GLP-1 agonists using Google Trends at both national (in Iraq) and global levels. To quantify relative search volume (RSV), the total search query activity has been transformed to a percentage scale ranging from 0% to 100%. The word "Ozempic" was chosen because of its extensive coverage in social media and web/print publications pertaining to this subject matter. A comparative search was performed targeting the phrases "Wegovy," "Saxenda," and "Mounjaro" to identify a novel combination GLP-1 agonist from August 2023 to August 2024. The present study demonstrated a statistically significant difference in the RSV among the four drugs (P < 0.0001) nationally and globally. In Iraq, the highest RSV for Ozempic was documented in Duhok, followed by Sulaymaniyah and Erbil. A comparable RSV profile has been noted for Saxenda, while substantial interest in Wegovy is seen in Ninawa. Meanwhile, globally, the highest RSV for Ozempic was recorded in Canada, the United States of America, and Australia. A distinct RSV profile has been observed for Saxenda, with heightened search interest recorded in Latin America, Poland, Sweden, and Australia. By contrast, Mounjaro received search interest primarily in Greenland and the United States, while Mounjaro search interest was noted in Canada, the United States, and Australia. This study demonstrates a significant and growing public interest in GLP-1 agonists, namely, Ozempic, Saxenda, Wegovy, and Mounjaro. As the use of GLP-1 agonists for weight loss becomes more common, more knowledge, understanding, and continuous scientific research will make it more convenient to obtain the best patient outcomes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".