The antidiabetic properties of lignans: a comprehensive review
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) is a chronic metabolic disease with a high global prevalence. Lignans, a class of plant natural compounds found in commonly consumed foods, are well-tolerated by humans and have demonstrated promising potential in the management of DM. Consumption of lignan-rich foods has been associated with improved overall health and quality of life. PURPOSE: The clinical and preclinical evidence on the role of lignans in managing DM are critically examined. METHODS: A thorough literature search was conducted across major scientific databases, focusing on studies that reported the effects of individual lignans on key diabetes indicators, such as glucose utilisation and insulin sensitivity, in both human and animal models, as well as in cell-based studies. RESULTS: A total of 180 lignans were included in the review. Out of these, only three were investigated in randomised clinical trials in humans and 31 in animal models. The reviewed evidence suggests some beneficial effects of lignans in preventing the development of obesity-related diabetes. Their therapeutic benefits in preventing diabetes-related complications, particularly diabetic nephropathy, in both type 1 and type 2 diabetes, are also supported. Metabolites of various lignans, produced by microbial metabolism in the gut and serum enzymes, appear to be key bioactive forms, highlighting the need for detailed pharmacodynamic studies, optimised dosage designs, and the use of the appropriate lignan molecules for cell-based screening. CONCLUSION: Lignans and their microbial metabolites show promise in preventing obesity-related diabetes and mitigating diabetes-related complications such as diabetic nephropathy, though further clinical studies are needed to optimize their therapeutic potential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".