Real-World Effects of Second-Generation Versus Earlier Intermediate/Basal Insulin Analogues on Rates of Hypoglycemia in Adults with Type 1 and 2 Diabetes (iNPHORM, US)
Bibliographic record
Abstract
INTRODUCTION: Second-generation basal insulin analogues have been shown to reduce hypoglycemia in several trials and observational studies of select populations; however, it remains unclear whether these results persist in real-world settings. Using self-reported hypoglycemia events, we assessed whether second-generation basal insulin analogues reduce rates of hypoglycemia events (non-severe/severe; overall/daytime/nocturnal) compared to earlier intermediate/basal insulin analogues among people with insulin-treated type 1 or 2 diabetes. METHODS: We used prospectively collected data from the Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-World Models (iNPHORM) panel survey. This US-wide, 1-year internet-based survey assessed hypoglycemia experiences and related sociodemographic and clinical characteristics of people with diabetes (February 2020-March 2021). We estimated population-average rate ratios for hypoglycemia comparing second-generation to earlier intermediate/basal insulin analogues using negative binomial regression, adjusting for confounders. Within-person variability of repeated observations was addressed with generalized estimating equations. RESULTS: Among iNPHORM participants with complete data, N = 413 used an intermediate/basal insulin analogue for ≥ 1 month during follow-up. After adjusting for baseline and time-updated confounders, average second-generation basal insulin analogue users experienced a 19% (95% CI 3-32%, p = 0.02) lower rate of overall non-severe hypoglycemia and 43% (95% CI 26-56%, p < 0.001) a lower rate of nocturnal non-severe hypoglycemia compared to earlier intermediate/basal insulin users. Overall severe hypoglycemia rates were similar among second-generation and earlier intermediate/basal insulin users (p = 0.35); however, the rate of severe nocturnal hypoglycemia was reduced by 44% (95% CI 10-65%, p = 0.02) among second-generation insulin users compared to earlier intermediate/basal insulin users. CONCLUSION: Our real-world results suggest second-generation basal insulin analogues reduce rates of hypoglycemia, especially nocturnal non-severe and severe events. Whenever possible and feasible, clinicians should prioritize prescribing these agents over first-generation basal or intermediate insulin in people with type 1 and 2 diabetes.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".