The Effect of Fructose‐Containing Sugars on Glycemic Control: A Systematic Review and Meta‐Analysis of Controlled Trials
Bibliographic record
Abstract
BACKGROUND Fructose‐containing sugars have become a focus of widespread concern regarding their contribution to the development of diabetes. AIMS To assess the effect of fructose‐containing sugars on glycemic control, we conducted a systematic review and meta‐analysis of controlled trials. DATA SOURCES We searched MEDLINE, EMBASE, CINAHL and the Cochrane Library (through October 3, 2015). DATA EXTRACTION We included controlled trials ≥7 days. Two independent reviewers extracted relevant study data and assessed risk of bias (Cochrane Risk of Bias Tool). Glycemic control outcomes included glycated blood proteins (HbA1c, fructosamine, glycated albumin), fasting glucose, and fasting insulin. Data were pooled using the generic inverse variance method and expressed as mean differences (MDs) or standardized mean differences (SMDs) with 95% confidence intervals. Heterogeneity was assessed (Cochran Q statistic) and quantified (I 2 statistic). RESULTS Eligibility criteria were met by 126 trials (N=2186). We identified 3 types of trials: 98 substitution trials (N=1625), in which fructose containing sugars were compared with other macronutrients matched for energy; 23 addition trials (N=492), in which fructose‐containing sugars supplemented diets with excess calories compared to the same diets alone without the excess calories; and 6 ad libitum trials (N=69), in which fructose‐containing sugars freely replaced other macronutrients without any strict control of calories. Fructose‐containing sugars improved glycated blood proteins compared with other macronutrients in substitution trials (SMD −0.15 [95%CI, −0.29 to −0.01]). Provision of excess calories from fructose‐containing sugars, however, increased fasting insulin (MD 6.14 [95% CI, 1.30 to 10.97]) in addition trials. None of the other analyses were significant. LIMITATIONS There was no serious risk of bias, but most trials were small, short and several analyses were complicated by significant unexplained heterogeneity. Very few trials compared fructose‐containing sugars with macronutrients other than starch. CONCLUSIONS Pooled analyses suggest that fructose‐containing sugars are no worse in their effects on glycemic control than other macronutrients (mainly starch) in energy matched comparisons. Fructose‐containing sugars supplementing diets with excess energy, however, show an increasing‐effect on insulin, an effect which appears more attributable to the excess energy than the sugars. Larger, longer, higher quality trials with a range of macro‐nutrient comparators are required. Support or Funding Information Canadian Diabetes Association, PSI Foundation, CIHR Banting and Best Graduate Student Award, Ontario Graduate Scholarship, Banting and Best Diabetes Centre Novo Nordisk Studentship
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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 | 0.025 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.031 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".