Lowest Glucose Variability and Hypoglycemia Are Observed With the Combination of a GLP-1 Receptor Agonist and Basal Insulin (VARIATION Study)
Bibliographic record
Abstract
OBJECTIVE: There is a dearth of published literature comparing glucose variability (GV) between different insulin regimens in type 2 diabetes. This cohort study compares GV using continuous glucose monitoring (CGM) in patients with well-controlled type 2 diabetes using four common insulin regimens: basal insulin + oral drugs (BO), basal insulin + glucagon-like peptide 1 receptor agonist (GLP-1 RA) (BGLP), premixed insulin (PM), and basal-bolus insulin (BB). RESEARCH DESIGN AND METHODS: , stable insulin regimen for a minimum of 6 months, and stable A1C value ≤7.5% (58 mmol/mol) before study enrollment-underwent 6-day masked CGM. Hypoglycemia was defined as a sensor glucose concentration <70 mg/dL on CGM. RESULTS: A total of 160 patients with comparable baseline characteristics formed four equal insulin regimen cohorts. The daily glucose SD (the primary outcome) was significantly lower in the BGLP cohort versus the BO, PM, and BB cohorts (P = 0.03, P = 0.01, and P < 0.01, respectively), and remained so after adjusting for age, BMI, type 2 diabetes duration, and A1C. Similarly, daily hypoglycemia outcomes on CGM were least for the BGLP cohort. CONCLUSIONS: The lowest GV and lowest hypoglycemia were observed in patients using the combination of basal insulin with a GLP-1 RA, supporting the complementary glycemic action of these agents in type 2 diabetes. These observed benefits in GV and hypoglycemia may contribute to the cardiovascular outcome reduction seen with GLP-1 RA therapy and should be investigated further.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".