Dietary carbohydrate and the risk of type 2 diabetes: an updated systematic review and dose–response meta-analysis of prospective cohort studies
Bibliographic record
Abstract
Abstract We did this study to clarify the association between carbohydrate intake and the risk of type 2 diabetes (T2D) and potential effect modification by geographical location. PubMed, Scopus and Web of Science were searched to find prospective cohort studies of dietary carbohydrate intake and T2D risk. A random-effects dose–response meta-analysis was performed to calculate the summary hazard ratios (HRs) and 95%CIs. The quality of cohort studies and the certainty of evidence was rated using the Newcastle–Ottawa Scale and GRADE tool, respectively. Eighteen prospective cohort studies with 29,229 cases among 607,882 participants were included. Thirteen studies were rated to have high quality, and five as moderate quality. The HR for the highest compared with the lowest category of carbohydrate intake was 1.02 (95%CI: 0.91, 1.15; I2 = 67%, GRADE = low certainty). The HRs were 0.93 (95%CI: 0.82, 1.05; I2 = 58%, n = 7) and 1.26 (95%CI: 1.11, 1.44; I2 = 6%, n = 6) in Western and Asian countries, respectively. Dose–response analysis indicated a J shaped association, with the lowest risk at 50% carbohydrate intake (HR50%: 0.95, 95%CI: 0.90, 0.99) and with risk increasing significantly at 70% carbohydrate intake (HR70%: 1.18, 95%CI: 1.03, 1.35). There was no association between low carbohydrate diet score and the risk of T2D (HR: 1.14, 95%CI: 0.89, 1.47; I2 = 90%, n = 5). Carbohydrate intake within the recommended 45–65% of calorie intake was not associated with an increased risk of T2D. Carbohydrate intake more than 70% calorie intake might be associated with a higher risk.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| 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) | 0.003 | 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".