The effects of chromium and vitamin D<sub>3</sub> co-supplementation on insulin resistance and tumor necrosis factor-alpha in type 2 diabetes: a randomized placebo-controlled trial
Bibliographic record
Abstract
The current study was conducted to assess the effects of simultaneous usage with vitamin D3 and chromium picolinate (CrPic) supplementations on homeostasis model assessment of insulin resistance (HOMA-IR), fasting blood glucose (FBS), hemoglobin A1c (HbA1c), tumor necrosis factor-α (TNF-α), and lipid profile in type 2 diabetes mellitus (T2DM). Ninety-two patients with T2DM were randomly allocated to the following 4 groups for 4 months: (I) placebo of vitamin D3 (n = 23); (II) vitamin D3 supplement at a dose of 50 000 IU/week (n = 23); (III) CrPic supplement at a dose of 500 μg/day (n = 23); and (IV) both vitamin D3 at a dose of 50 000 IU/week and CrPic at a dose of 500 μg/day (n = 23). HOMA-IR levels increased significantly in groups I and II after the intervention. However, this increase in group I was significantly higher than that in group II after the treatment. HOMA-IR levels were controlled in groups III and IV during the intervention. TNF-α decreased significantly in groups II, III, and IV after the intervention. FBS, HbA1c, and lipid profile did not change significantly in total groups after the intervention. It seems that chromium and vitamin D3 co-supplementation are probably effective in controlling HOMA-IR by decreasing TNF-α in T2DM. Novelty Chromium alone and/or in simultaneous pretreatment with vitamin D3 is more effective than vitamin D3 in controlling HOMA-IR in T2DM. Chromium and vitamin D3 alone and/or in simultaneous pretreatment decrease TNF-α in T2DM.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".