Abstract 17006: Is White Rice Consumption a Risk for Cardiovascular Disease? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Current proposed Dietary Guidelines for Americans, 2015 suggested replacing refined grains (e.g., white rice) with whole grains (e.g., brown rice) for improved health outcomes; however, the association between white rice consumption and risk of cardiovascular disease (CVD) was not fully investigated. The purpose of this study was to explore the associations between rice consumption, metabolic syndrome and cardiovascular disease. Methods: We conducted a comprehensive search of MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE, Scopus, and the Cochrane Central Register of Controlled Trials from database inception through May 2015. Observational studies were included if the studies reported hazard ratios (HRs) or odd ratios (ORs) of the associations between rice consumption, metabolic syndrome and cardiovascular disease. Data were extracted by one reviewer and then reviewed by two independent reviewers. Conflicts were resolved through consensus. Using the DerSimonian and Laird random effects models, we calculated pooled HRs and pooled ORs with 95% confidence intervals (95% CIs). Subgroup analyses were performed to explore potential sources of heterogeneity. The quality of the included studies and publication bias were assessed. Results: Out of 343 retrieved articles, we identified 6 cross-sectional and 9 prospective studies with a total of 575,852 individuals with 24,649 total CVD events and 4,409 metabolic syndrome cases. The highest category of white rice consumption (≥5 times/week) was associated with a 37% higher risk of metabolic syndrome (pooled OR, 1.37; 95% CI: 1.03-1.84; p<0.001; I 2 = 78.7%) but not a higher risk of cardiovascular disease (pooled HR, 1.01; 95% CI: 0.95-1.08; p=0.65; I 2 = 0%), compared to the lowest category (≤1 time/month). Conclusions: Higher white rice consumption has not been shown to increase the risk of cardiovascular diseases but may be associated with increased risk of metabolic syndrome.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".