Rates of Hypoglycemia Predicted in Patients with Type 2 Diabetes on Insulin Glargine 300 U/ml Versus First- and Second-Generation Basal Insulin Analogs: The Real-World LIGHTNING Study
Bibliographic record
Abstract
The LIGHTNING study applied conventional and advanced analytic approaches to model, predict, and compare hypoglycemia rates of people with type 2 diabetes (T2DM) on insulin glargine 300 U/ml (Gla-300) with those on first-generation (insulin glargine 100 U/ml [Gla-100]; insulin detemir [IDet]) or second-generation (insulin degludec [IDeg]) basal-insulin (BI) analogs, utilizing a large real-world database. Data were collected between 1 January 2007 and 31 March 2017 from the Optum Humedica US electronic health records [EHR] database. Patient-treatments, the period during which a patient used a specific BI, were analyzed for patients who switched from a prior BI or those who newly initiated BI therapy. Data were analyzed using two approaches: propensity score matching (PSM) and a predictive modeling approach using machine learning. A total of 831,456 patients with T2DM receiving BI were included from the EHR data set. Following selection, 198,198 patient-treatments were available for predictive modeling. The analysis showed that rates of severe hypoglycemia (using a modified definition) were approximately 50% lower with Gla-300 than with Gla-100 or IDet in insulin-naïve individuals, and 30% lower versus IDet in BI switchers (all p < 0.05). Similar rates of severe hypoglycemia were predicted for Gla-300 and IDeg, regardless of prior insulin experience. Similar results to those observed in the overall cohorts were seen in analyses across subgroups at a particularly high risk of hypoglycemia. PSM (performed on 157,573 patient-treatments) revealed comparable reductions in HbA 1c with Gla-300 versus first- and second-generation BI analogs, alongside lower rates of severe hypoglycemia with Gla-300 versus first-generation BI analogs ( p < 0.05) and similar rates versus IDeg in insulin-naïve and BI-switcher cohorts. Based on real-world data, predicted rates of severe hypoglycemia with Gla-300 tended to be lower versus first-generation BI analogs and similar versus IDeg in a wide spectrum of patients with T2DM. Sanofi, Paris, France.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".