Uncovering the relationship between coffee and cholesterol: a systematic review
Bibliographic record
Abstract
Background: Drinking coffee every day is one of the most common daily routines worldwide. The antioxidant content in coffee is believed to reduce the risk of cardiovascular disease (CVD). The objective was to systematically review and analyze the existing literature on the relationship between coffee consumption frequency and systemic cholesterol. Material and methods: A comprehensive literature search was conducted using databases such as PubMed, Cochrane, and Scopus to identify studies published between January 2020 and March 2024. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to ensure transparency and rigor in the review process. Data extraction focused on sample size, study design, frequency categories of coffee consumption, coffee subtypes, coffee preparation methods, and reported cholesterol outcomes [low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), and total cholesterol (TC)]. Quality assessment of the included studies was performed using the Newcastle-Ottawa Scale (NOS). Results: The review synthesized findings from 11 relevant studies involving 1.271.523 participants. The evidence suggests that moderate coffee consumption may be associated with increased HDL levels, while excessive intake could lead to elevated LDL and TC levels. Mechanistic insights suggest that coffee’s diterpenes, such as cafestol and kahweol, play a significant role in modulating cholesterol levels. Conclusion: The systematic review concludes that the frequency of coffee consumption has a complex relationship with systemic cholesterol levels. Moderate coffee intake may confer cardiovascular benefits by increasing HDL levels, whereas high consumption may elevate LDL and TC. Further research is warranted to elucidate these relationships and inform dietary recommendations for individuals at risk for CVD.
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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".