Zinc supplementation alleviates hyperglycemia and associated metabolic abnormalities in streptozotocin-induced diabetic rats
Bibliographic record
Abstract
The cause and effect relationship between diabetes and zinc is complex and unclear. This animal study has examined the potential of zinc supplementation in beneficial modulating hyperglycemia, insulin secretion, and metabolic abnormalities associated with diabetes. The study was conducted in streptozotocin-induced diabetic rats. Groups of hyperglycemic rats were subjected to dietary interventions for 6 weeks with zinc supplementation (5 times and 10 times the normal level). Supplemental-zinc-fed diabetic groups showed significant control on hyperglycemia and hypoinsulinemia. There was a significant reduction in protein glycosylation, glucosuria, and urinary excretion of proteins and urea in diabetic animals maintained on a zinc-supplemented diet. Diabetic rats showed significantly higher plasma albumin and lower plasma urea and creatinine levels upon zinc supplementation. Significant alterations in insulin sensitivity indices HOMA-IR, HOMA-B, and QUICKI were also indicated by zinc supplementation. The pathological abnormalities in pancreatic islets of diabetic animals were significantly alleviated by dietary zinc intervention. This study provides the first evidence that zinc supplementation can partially ameliorate the severity of diabetic hyperglycemia and associated metabolic abnormalities, hypoinsulinemia, insulin resistance, and altered pancreatic morphology. Thus, zinc supplementation may offer a significant potential for clinical application in managing diabetic hyperglycemia and related metabolic complications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".