High Fructose Corn Syrup and Sucrose do not Differ in Their Effects on Cardiometabolic Risk Factors: A Series of Systematic Reviews and Meta‐Analyses of Randomized Controlled Trials
Bibliographic record
Abstract
Background Increased consumption of fructose‐containing sugars has been implicated in the development of obesity, diabetes and their cardiometabolic complications. HFCS has come under particular scrutiny more than sucrose due to its higher ratio of fructose to glucose. Objective To compare the effects of HFCS and sucrose on cardiometabolic risk factors, we conducted a series of systematic reviews and meta‐analyses of controlled trials lasting 蠅7 days. Data Sources MEDLINE, EMBASE, CINAHL and the Cochrane Library (through February 27, 2014). Data Extraction Two independent reviewers extracted data from eligible trials. Data were pooled using the generic inverse variance method and expressed as mean differences with 95% confidence intervals. Data Synthesis Eligibility criteria were met by 7 randomized controlled trials (n=659) comparing the effects of HFCS versus sucrose over a wide dose range (8‐30% total energy) and median follow‐up of 10 weeks (range, 10‐12 weeks) on various cardiometabolic endpoints: body weight, measures of adiposity, serum lipids, blood pressure, glycemic control, uric acid and inflammation. Sucrose and HFCS did not differ in their effects across all cardiometabolic endpoints (P>0.05). Conclusion Pooled analyses show that HFCS and sucrose in isocaloric comparisons behave similarly in their effects on cardiometabolic risk factors. To inform public policy further, there is a need for high quality trials focusing on the effects of ad libitum substitution of HFCS or sucrose with other sources of calories likely to replace them under ‘real world’ conditions.
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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 | 0.049 | 0.122 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.054 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".