A comprehensive review of oral glucosamine use and effects on glucose metabolism in normal and diabetic individuals
Bibliographic record
Abstract
Glucosamine (GlcN) is a widely utilized dietary supplement that is used to promote joint health. Reports that oral GlcN supplementation at usual doses adversely affects glucose metabolism in subjects with impaired glucose tolerance have raised concerns that GlcN should be contraindicated in individuals with diabetes and those at risk for developing it. This review addresses its potential, when used at typical doses, to affect glucose metabolism and insulin sensitivity in healthy individuals and those with diabetes or 'pre-diabetes'. Publicly available scientific information and data on GlcN were systematically compiled using the electronic search tool, Dialog , and reviewed with special emphasis on human studies. In long-term clinical trials, including those containing subjects with type 2 diabetes or 'pre-diabetes', GlcN produced a non-significant lowering of fasting blood glucose concentrations in all groups of subjects treated for periods of up to 3 years. Owing to limitations in study design, conclusions based on studies that report adverse affects of GlcN on insulin sensitivity and glucose tolerance in pre-diabetic subjects are suspect. However, no definitive long-term studies of GlcN use for individuals with pre-diabetes are available. Nevertheless, based on available evidence, we conclude that GlcN has no effect on fasting blood glucose levels, glucose metabolism, or insulin sensitivity at any oral dose level in healthy subjects, individuals with diabetes, or those with impaired glucose tolerance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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".