Effect of ethnicity on glycaemic index: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Low glycaemic index (GI) foods are recommended to improve glycaemic control in diabetes; however, Health Canada considers that GI food labeling would be misleading and unhelpful, in part, because selected studies suggest that GI values are inaccurate due to an effect of ethnicity. Therefore, we conducted a systematic review and meta-analysis to compare the GI of foods when measured in Caucasians versus non-Caucasians. METHODS: We searched MEDLINE, EMBASE and Cochrane databases for relevant articles. GI differences were aggregated using the generic inverse variance method (random effects model) and expressed as mean difference (MD) with 95% confidence intervals (95% CI). Study quality was assessed based on how well studies complied with official international GI methodology. RESULTS: Review of 1288 trials revealed eight eligible studies, including 28 comparisons of GI among 585 non-Caucasians and 971 Caucasians. Overall, there was borderline significant evidence of higher GI in non-Caucasians than Caucasians (MD, 3.3 (95% CI, -0.1, 6.8); P=0.06) with significant heterogeneity (I(2), 46%; P=0.005). The GI of eight types of rice was higher in non-Caucasians than Caucasians (MD, 9.5 (95% CI, 3.7, 23.1); P=0.001), but there was no significant difference for the other 20 foods (MD, 1.0 (95% CI, -2.5, 4.6); P=0.57). MD was significantly greater in the four low-quality studies (nine comparisons) than the four high-quality studies (19 comparisons; 7.8 vs 0.7, P=0.047). CONCLUSIONS: With the possible exception of rice, existing evidence suggests that GI values do not differ when measured in Caucasians versus non-Caucasians. To confirm these findings high-quality studies using a wide range of foods are required.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".