A comprehensive insight into the molecular effect of theobromine on cardiovascular‐related risk factors: A systematic review of in vitro and in vivo studies
Bibliographic record
Abstract
Theobromine may have beneficial effects on cardiovascular risk factors. This study aimed to find molecular effects of theobromine on lipid profile, glycemic status, inflammatory factors, and vascular function through a comprehensive assessment of all in vitro and in vivo studies. The search process was started at 18 July 2022. Databases including PubMed, Scopus, and Web of Science were searched to find all articles published up to 18 July 2022. Nineteen studies were included in this study. In vitro studies showed the improving effects of theobromine on inflammatory markers. Of four animal studies assessing the effect of theobromine on inflammatory markers, two reported favorable effects. Among five animal studies assessing the effects of theobromine on lipid profile, three reported improving effects on either triglyceride, total cholesterol, low- or high-density lipoprotein cholesterol. Of the three human studies, two revealed that theobromine had improving effects on lipid profile. A favorable effect of theobromine on augmentation index was also reported in two RCTs. The results for other outcomes were inconclusive. Theobromine may have favorable effects on inflammatory factors, lipid profile, and vascular function markers. However, studies with a longer duration and lower, dietary-relevant doses are required for future confirmation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".