The effect of alpha-linolenic acid on glycemic control in individuals with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Polyunsaturated fats (PUFAs) have been shown to reduce type 2 diabetes (T2DM) risk and improve insulin responsiveness in T2DM subjects, but whether the plant sources of omega-3 PUFA (alpha-linolenic acid [ALA]) have an effect on glycemic control requires further investigation. METHODS: The parameters of interest were glycated hemoglobin (HbA1c), fasting blood glucose (FBG), fasting blood insulin (FBI), homeostatic model assessment for insulin resistance (HOMA-IR), fructosamine, and glycated albumin. A comprehensive search was conducted with MEDLINE, Embase, CINAHL, and Cochrane. Eligible studies included randomized controlled trials (RCTs) ≥1 month in duration that compared diets enriched in ALA with usual diets on glycemic parameters. For each study, the risk of bias as well as the study quality was assessed. Using the statistical software RevMan (v5.3), data were pooled using the generic inverse method with random effects model, and final results were expressed as mean differences (MD) with 95% confidence intervals (CI). Heterogeneity was assessed by the Cochran Q statistic and quantified by the I statistic. RESULTS: A total of 8 trials (N = 212) were included in the meta-analysis. Compared to a control diet, a median dose of 4.4 g/day of ALA intake for a median duration of 3 months did not affect HbA1c (%) (MD = -.01; [95%: -.32, .31], P = .96). A median ALA dose of 5.4 g/day did not lower FBG (MD = .07; [95% CI: -.61, .76], P = .84) or FBI (MD = 7.03, [95% CI: -5.84, 19.89], P = .28). Summary effect estimates were generally compromised by considerable and unexplained heterogeneity (I ≥75%). In the subgroup analysis of continuous predictors, a reduction in HbA1c (%) and FBG (mmol/L) was significantly associated with an increased intake of ALA. Further adjustment for Publication Bias using Duval and Tweedie's trim-and-fill analysis provided an adjusted, significant MD of -.25 (95% CI: -.38, -.12; P <.001) for HbA1c (%). CONCLUSIONS: ALA-enriched diets did not affect HbA1c, FBG, or FBI. The scarce number of existing RCTs and the presence of heterogeneity in our meta-analysis limit the ability to make firm conclusions about ALA in T2DM management. The potential for ALA to have dose-dependent effects warrants further research in this area.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".