Patterns of postprandial hyperglycemia after basal insulin therapy: individual and regional differences
Bibliographic record
Abstract
BACKGROUND: Treatment of postprandial hyperglycemia could be needed when basal insulin added to oral therapy does not maintain glycated haemoglobin (HbA1C ) targets in type 2 diabetes mellitus. Knowing individual and regional patterns of postprandial hyperglycemia in this setting might improve therapeutic decisions. METHODS: Patient-level self-monitored blood glucose data were pooled from six studies of insulin glargine for patients with HbA1C ≥ 7.0% after 24 weeks. Percentages of participants with highest daily postprandial blood glucose and greatest postprandial increments after each of the three daily meals were calculated and compared between four geographical regions; USA, Canada, Germany, and other European countries. RESULTS: For 494 participants (mean age 60.1 years, diabetes duration 9.6 years, and BMI 29.8 kg/m(2) ), mean endpoint HbA1C was 7.8%. On insulin glargine treatment, highest postprandial blood glucose most often occurred post-dinner (44% of participants) and greatest postprandial increments post-breakfast (46% of participants) in all regions. Participants with greatest postprandial increments post-breakfast were older and experienced less HbA1C improvement with insulin glargine than those with greatest postprandial increments after other meals. Post-breakfast and post-dinner postprandial blood glucose was higher in the USA and Canada versus Germany, and in the USA versus Other European countries (all p < 0.05). Postprandial increments after dinner were greater in the USA versus all other regions. CONCLUSIONS: Generally, highest postprandial blood glucose follows dinner and greatest postprandial increments follow breakfast. Variations in patient characteristics and eating patterns might underlie differences both within and between regions. Awareness of regional differences and evaluation of an individual's typical eating pattern might facilitate appropriate prandial therapy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".