Heterogeneous Effects of Fructose on Blood Lipids in Individuals With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Because of blood lipid concerns, diabetes associations discourage fructose at high intakes. To quantify the effect of fructose on blood lipids in diabetes, we conducted a systematic review and meta-analysis of experimental clinical trials investigating the effect of isocaloric fructose exchange for carbohydrate on triglycerides, total cholesterol, LDL cholesterol, and HDL cholesterol in type 1 and 2 diabetes. RESEARCH DESIGN AND METHODS: We searched MEDLINE, EMBASE, CINAHL, and the Cochrane Library for relevant trials of > or =7 days. Data were pooled by the generic inverse variance method and expressed as standardized mean differences with 95% CI. Heterogeneity was assessed by chi(2) tests and quantified by I(2). Meta-regression models identified dose threshold and independent predictors of effects. RESULTS: Sixteen trials (236 subjects) met the eligibility criteria. Isocaloric fructose exchange for carbohydrate raised triglycerides and lowered total cholesterol under specific conditions without affecting LDL cholesterol or HDL cholesterol. A triglyceride-raising effect without heterogeneity was seen only in type 2 diabetes when the reference carbohydrate was starch (mean difference 0.24 [95% CI 0.05-0.44]), dose was >60 g/day (0.18 [0.00-0.37]), or follow-up was < or =4 weeks (0.18 [0.00-0.35]). Piecewise meta-regression confirmed a dose threshold of 60 g/day (R(2) = 0.13)/10% energy (R(2) = 0.36). A total cholesterol-lowering effect without heterogeneity was seen only in type 2 diabetes under the following conditions: no randomization and poor study quality (-0.19 [-0.34 to -0.05]), dietary fat >30% energy (-0.33 [-0.52 to -0.15]), or crystalline fructose (-0.28 [-0.47 to -0.09]). Multivariate meta-regression analyses were largely in agreement. CONCLUSIONS: Pooled analyses demonstrated conditional triglyceride-raising and total cholesterol-lowering effects of isocaloric fructose exchange for carbohydrate in type 2 diabetes. Recommendations and large-scale future trials need to address the heterogeneity in the data.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".