Effect of Fructose on Blood Pressure
Bibliographic record
Abstract
Concerns have been raised about the adverse effect of fructose on blood pressure. International dietary guidelines, however, have not addressed fructose intake directly. A systematic review and meta-analysis was conducted to assess the effect of fructose in isocaloric exchange for other carbohydrates on systolic, diastolic, and mean arterial blood pressures. Studies were identified using Medline, Embase, and Cochrane databases (through January 9, 2012). Human clinical trials of isocaloric oral fructose exchange for other carbohydrate sources for ≥7 days were included in the analysis. Data were pooled by the generic inverse variance method using random-effects models and expressed as mean differences with 95% CI. Heterogeneity was assessed by the Q-statistic and quantified by I(2). Study quality was assessed using the Heyland Methodological Quality Score. Thirteen isocaloric (n=352) and 2 hypercaloric (n=24) trials met the eligibility criteria. Overall, fructose intake in isocaloric exchange for other carbohydrates significantly decreased diastolic (mean difference: -1.54 [95% CI: -2.77 to -0.32]) and mean arterial pressure (mean difference: -1.16 [95% CI: -2.15 to -0.18]). There was no significant effect of fructose on systolic blood pressure (mean difference: -1.10 [95% CI: -2.46 to 0.44]). The hypercaloric fructose feeding trials found no significant overall mean arterial blood pressure effect of fructose in comparison with other carbohydrates. To confirm these results, longer and larger trials are needed. Contrary to previous concerns, we found that isocaloric substitution of fructose for other carbohydrates did not adversely affect blood pressure in humans.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".