A Greater Flavonoid Intake Is Associated with Lower Total and Cause-Specific Mortality: A Meta-Analysis of Cohort Studies
Bibliographic record
Abstract
Introduction: The links between flavonoid intake and mortality were previously evaluated in epidemiological studies. The aim of the present study was to perform a systematic review and meta-analysis of cohort studies evaluating the link of flavonoid consumption with total and cause-specific mortality. Methods: Prospective cohort studies reporting flavonoid intake and mortality data published up to 30th April 2019 (without language restriction) were searched using PubMed, Scopus and EMBASE database. Generic inverse variance methods and random effects models were used to synthesize pooled and quantitative data. Sensitivity analysis was also performed by a leave-one-out method. Results: Overall, 16 articles met the inclusion criteria (nine studies were performed in Europe, five in the USA, one in Asia and one in Oceania); a total of 462,194 participants (all adults aged >19 years) with 23,473 mortality cases were included in the final analysis. The duration of follow-up ranged from 4.8 to 28 years. Most of the studies assessed flavonoid intake using food frequency questionnaires, whereas four studies used interviews and 1 study used 4-day food records. The meta-analysis showed that flavonoid consumption was inversely and significantly associated with total (relative risk (RR): 0.87, 95% confidence interval (CI) = 0.77–0.99) and cardiovascular disease mortality risk (RR: 0.85, 95%CI = 0.75–0.97), but not cancer (0.86, 95%CI = 0.65–1.14) mortality risk. These findings remained robust in sensitivity analyses. Conclusions: The present findings highlight the potential protective role of flavonoids against total and cause-specific mortality. These results support the recommendations for flavonoid-rich foods intake to prevent chronic diseases.
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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.047 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".