Adding vitamin D3 to the dipeptidyl peptidase‐4 inhibitor saxagliptin has the potential to protect β‐cell function in LADA patients: A 1‐year pilot study
Bibliographic record
Abstract
AIMS: This trial was conducted to explore the protective effect on β-cell function of adding vitamin D3 to DPP-4 inhibitors to treat patients with latent autoimmune diabetes in adults (LADA). METHODS: 60 LADA patients were randomized to group A (n = 21) - conventional therapy with metformin (1-1.7 g/day) and/or insulin treatment; group B (n = 20) - saxagliptin (5 mg/day) plus conventional therapy; and group C (n = 19) - vitamin D3 (2000 IU/day) plus saxagliptin and conventional therapy for 12 months. Fasting and 2-hour postprandial blood samples were collected to measure blood glucose, glycosylated hemoglobin and C-peptide levels at baseline and after 3, 6 and 12 months of treatment. RESULTS: During the 12 months of follow-up, the levels of fasting C-peptide (FCP), 2-hour postprandial C-peptide (PCP) and the C-peptide index (CPI, serum C-peptide-to-plasma glucose level ratio) were maintained in group C. In contrast to those in group A and group B, FCP levels decreased significantly in group B, and CPI levels declined significantly in group A during the 1-year treatment (P < .05). Additionally, the levels of GADA titers in group C significantly decreased compared with those at baseline (P < .05), but no significant differences in GADA titers levels were detected in group A and group B. No significant differences were found among the three groups in the levels of FCP, PCP, the CPI or GADA titers. CONCLUSIONS: The data suggested that adding 2000 IU/day vitamin D3 to saxagliptin might preserve β-cell function in patients with LADA.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".