Beneficial Effects of Addition of Oral Spray Insulin (Oralin) on Insulin Secretion and Metabolic Control in Subjects with Type 2 Diabetes Mellitus Suboptimally Controlled on Oral Hypoglycemic Agents
Bibliographic record
Abstract
The aim of this study was to determine the metabolic effect and the safety of a novel oral insulin spray (Oralin) formulation at breakfast-time in subjects with type 2 diabetes with suboptimal glucose control and maintained on a combination therapy of oral hypoglycemic agents (OHAs). This was an open-label, crossover, randomized study design in subjects (n = 21) with type 2 diabetes (glycated hemoglobin A1c >8.0%). Subjects received each of the following treatments, in random order: metformin + glyburide and placebo puffs at time 0 min; or metformin + glyburide and Oralin spray (100 U) at time 0 min. Fifteen minutes later, subjects were given a standard breakfast containing 360 calories [Sustacal (Mead Johnson, Evansville, IN) liquid meal]. Blood samples were taken at regular intervals to measure serum glucose, insulin, and C-peptide. Time-averaged postprandial glucose increments (PPGIs) between 0 and 240 min were calculated for each treatment. Group mean PPGIs to OHAs versus Oralin in combination with OHAs were compared to determine the mean efficacy of the active treatment. The Oralin spray lowered the 2-h postprandial glucose rise significantly in comparison with the OHAs alone. The serum insulin levels were significantly higher with faster onset of action in the Oralin spray treatment when compared with the OHAs treatment. The reductions in C-peptides were also significantly greater in the Oralin arm than in the OHAs treatment. This study results demonstrated that Oralin could be used as meal insulin as an add-on therapy in combination with failing OHAs treatment in subjects with type 2 diabetes to regulate postprandial glucose levels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".