Dose response association of glycemic index with CHD risk: a systematic review and meta‐analysis of prospective cohorts
Bibliographic record
Abstract
Coronary Heart Disease (CHD) events are responsible for about a third of all deaths in developed countries. Glycemic index (GI) and glycemic load (GL) have been associated with CHD risk in some cohort studies but a dose response has not been established. Aims To conduct a meta‐analysis of GI and GL dose response association with CHD risk in cohort studies. Methods We searched MEDLINE, EMBASE, and CINAHL for prospective cohorts of GI and GL associations with primary incidence of CHD events. Measures of risk estimation with their respective GI and GL exposure doses were pooled using random effects generalized least squares linear models and linear spline piecewise regression models to investigate dose response trends. Data were expressed as change in percent risk of CHD per unit GI with 95% confidence intervals (CI). Results 9 studies were eligible, 7 had complete data for dose response analyses. No significant linear trends were observed for GI or GL dose, but in female cohorts the gradient of GI versus CHD risk was 0.15% [95%CI; 0.008–0.284] up to a GI of 78 and significantly increased to 3.82% [95%CI; 0.621–7.021] thereafter. Conclusion These results suggest a threshold effect may exist for the association of GI with CHD events in women. Funding: Canadian Institutes of Health Research. Grant Funding Source : ASN
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".