Dietary Zinc Supplementation Attenuates Hyperglycemia in db/db Mice
Bibliographic record
Abstract
Although zinc (Zn) deficiency has been associated with insulin resistance, and altered Zn metabolism (e.g., hyperzincuria, low-normal plasma Zn concentrations) may be present in diabetes, the potential effects of Zn on modulation of insulin action in Type II diabetes have not been established. The objective of this study was to compare the effects of dietary Zn deficiency and Zn supplementation on glycemic control in db/db mice. Weanling db/db mice and lean littermate controls were fed Zn-deficient (3 ppm Zn; dbZD and InZD groups), Zn-adequate control (30 ppm Zn; dbC and InC groups) or Zn-supplemented (300 ppm Zn; dbZS and InZS groups) diets for 6 weeks. Mice were assessed for Zn status, serum and urinary indices of diabetes, and gastrocnemius insulin receptor concentration and tyrosine kinase activity. Fasting serum glucose concentrations were significantly lower in the dbZS group compared with the dbZD group (19.3 +/- 2.9 and 27.9 +/- 4.1 mM, respectively), whereas the dbC mice had an intermediate value. There was a negative correlation between femur Zn and serum glucose concentrations (r = -0.59 for lean mice, P = 0.007). The dbZS group had higher pancreatic Zn and lower circulating insulin concentrations than dbZC mice. Insulin-stimulated tyrosine kinase activity in gastrocnemius muscle was higher in the db/db genotype, and insulin receptor concentration was not altered. In summary, dietary Zn supplementation attenuated hyperglycemia and hyperinsulinemia in db/db mice, suggesting that the roles of Zn in pancreatic function and peripheral tissue glucose uptake need to be further investigated.
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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 | 0.001 | 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".