Glycemic Index, Glycemic Load and Cancer Risk: An Updated Meta-Analysis
Bibliographic record
Abstract
Diets high in glycemic index (GI) and glycemic load (GL) have been related to an increased risk of selected cancers, but additional quantification is required. We updated a systematic review and meta-analysis published in 2015 to May 2019 to provide quantitative information on GI/GL and cancer risk. Relative risks (RR) and the corresponding 95 % confidence intervals (CI) for the highest versus the lowest categories of GI and GL were extracted from selected studies and pooled using random-effects models. Twenty reports (>22,000 cancer cases) have become available after January 2015, and 15 were added to the meta-analyses by cancer sites, which considered a total of 88 investigations. The five additional reports were reviewed, but not included in the meta-analyses, since data were inadequate to be pooled. For hormone-related cancers, summary RRs for the highest versus lowest GI and GL intakes were moderately increased. They ranged from 1.04 (breast) to 1.12 (endometrium) for GI and from 1.03 (prostate) to 1.22 (ovary) for GL, of borderline significance. High GI was associated with small increased risks of colorectal (summary RR for GI: 1.20, 95% CI, 1.07-1.34-GL: 1.09, 95% CI, 0.97-1.22, 19 studies), bladder (GI: 1.25, 95% CI, 1.11-1.41-GL: 1.10, 95% CI, 0.85-1.42, four studies) and kidney cancers (GI: 1.16, 95% CI, 1.02-1.32-GL: 1.14, 95% CI, 0.81-1.60, five studies). GL was not significantly related to those cancer sites. Stomach, prostate and lung cancers were not associated with GI and GL. The present analysis, based on an updated comprehensive evaluation of the epidemiological literature, indicates moderate unfavorable effects of high versus low GI on colorectal, and possibly bladder and kidney cancers, and a possible moderate positive association between GL and endometrial cancer.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.055 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".