The effect of fructose on risk of incident hypertension: a systematic review and meta‐analysis of 3 large U.S. prospective cohorts
Bibliographic record
Abstract
Background There is concern that fructose contributes to the development of hypertension. Aims To determine the association between fructose and incident hypertension, we performed a systematic review and meta‐analysis of prospective cohort studies. Methods We searched MEDLINE, EMBASE, CINAHL and the Cochrane Library (through November 1, 2012) for relevant prospective cohort studies. We pooled relative risks (RR) using a generic inverse variance random effects model, and assessed (Q‐statistic) and quantified (I 2 ) between‐cohort heterogeneity. The Newcastle‐Ottawa scale assessed study quality. Results Three prospective cohort studies (n=37,375 men & 185,855 women) were included with 58,162 confirmed cases of incident hypertension (11,192 in men; 46,970 in women) over 2,502,357 person‐years follow‐up. Median energy‐adjusted fructose intake was 13.9 to 14.3% of total calories in the highest quintiles and 5.7 to 6.0% in the lowest quintiles, assessed by validated semi‐quantitative food‐frequency questionnaires. The RR of incident hypertension (highest vs. lowest quintile) was 1.02 (95% CI: 0.99 to 1.04), with no evidence of heterogeneity (I 2 =0%; P=0.59). Limitations Only 3 cohorts from a single country (U.S.) were represented. Conclusion Fructose was not associated with risk of hypertension in 3 large U.S. prospective cohorts. Funding: CIHR, Calorie Control Council
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".