Comparison of Glycemic Variability by Using Insulin Glargine and Insulin Degludec in Japanese Patients With Type 1 Diabetes, Monitored by Continuous Glucose Monitoring: A Preliminary Report
Bibliographic record
Abstract
Background: Insulin degludec is a novel ultra-long-acting basal insulin, which is used clinically first in Japan. We aimed to study efficacy of insulin degludec compared with insulin glargine in patients with type 1 diabetes, by using continuous glucose monitoring (CGM). Methods: Patients studied were 4 Japanese patients with type 1 diabetes treated by the basal-bolus insulin therapy. We studied the influences of switching from insulin glargine to insulin degludec on parameters for glycemic variability using CGM for consecutive three days. Parameters studied for glycemic variability include the 24 h mean glucose levels, standard deviation (SD) values of 24 h glucose levels, M-values, mean amplitude of glycemic excursions (MAGE) values, 24 h area under the glucose curve (AUC), time in hypoglycemia and time in hyperglycemia. We compared these CGM data using insulin glargine with those using insulin degludec. Results: Although a statistically significant difference was not obtained, 24 h mean glucose levels, 24 h AUC, SD values of 24 h glucose levels, MAGE values and time in hyperglycemia were smaller in the insulin degludec treatment as compared with those in the insulin glargine treatment. M-values in the insulin degludec treatment were significantly smaller than those in the insulin glargine treatment. Although a significant difference was not observed in time in hypoglycemia, hypoglycemia was developed in the patient during the treatment using insulin degludec. Conclusions: The present study showed that the switching from insulin glargine to insulin degludec as basal insulin improved glycemic variability in patients with type 1 diabetes. To our knowledge, this is the first to report efficacy of insulin degludec compared with insulin glargine in patients with type 1 diabetes by using CGM. J Endocrinol Metab. 2013;3(6):138-146 doi: http://dx.doi.org/10.4021/jem193w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".