799-P: Predictors of Glycemic Control by Derived Time in Range for People with Type 2 Diabetes Advancing with iGlarLixi—A Pooled Analysis
Bibliographic record
Abstract
This post-hoc, pooled analysis of 3 randomized controlled trials aimed to identify factors associated with achieving a derived Time in Range (dTIR) ≥70% at the end of treatment (EOT) with iGlarLixi in people with type 2 diabetes (T2D). dTIR was calculated from 7-point self-monitored plasma glucose (SMPG) profiles and assessed at baseline (BL) and EOT for people with T2D advancing from oral (LixiLan-O), insulin (LixiLan-L), or GLP-1 RA (LixiLan-G) therapy to once-daily iGlarLixi. Participants at EOT were classified as either achieving or not achieving dTIR ≥70%, and corresponding predictive BL characteristics were analyzed with univariable and multivariable stepwise logistic regression. Analyses (N=880) showed that 86% of participants achieved dTIR ≥70% with iGlarLixi; T2D duration and HbA1c at BL were greater in those with dTIR <70% (mean [SD] 11.35 [7.31] years and 8.26 [0.68] %) than those with dTIR ≥70% (10.40 [6.51] years and 7.96 [0.68] %), respectively. Post-meal SMPG at BL was higher in those not achieving dTIR ≥70%. Lower BL HbA1c (p=0.0205), BL insulin dose (p<0.0001), and hypoglycemia level 2 frequency (p=0.0139 for 1-3 vs 0 events; p=0.0172 for ≥4 vs 0) were predictors of attaining dTIR ≥70% (Table). HbA1c, insulin dose, and hypoglycemia frequency at BL were predictors of achieving target dTIR ≥70% in people with T2D advancing therapy with iGlarLixi. Disclosure I.Hramiak: Research Support; Eli Lilly and Company, Novo Nordisk, Sanofi, Speaker's Bureau; Canadian Medical & Surgical Knowledge Translation Research Group (CMS), Insulet Corporation, Medtronic, Merck & Co., Inc., Bayer Inc. J.P.Frias: Advisory Panel; Becton, Dickinson and Company, Pfizer Inc., Sanofi, Consultant; Akero Therapeutics, Inc., 89bio, Inc., Aimmune, Boehringer Ingelheim Inc., Eli Lilly and Company, Carmot Therapeutics, Inc., Echosens, Merck & Co., Inc., Metacrine, Inc., Novo Nordisk, Pfizer Inc., Sanofi, Employee; Ionis Pharmaceuticals, Research Support; Akero Therapeutics, Inc., 89bio, Inc., Altimmune, Axcella Health Inc., Boehringer Ingelheim Inc., Eli Lilly and Company, Intercept Pharmaceuticals, Inc., Carmot Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Madrigal Pharmaceuticals, Inc., Merck & Co., Inc., Metacrine, Inc., Novo Nordisk, Oramed Pharmaceuticals, Novartis, Pfizer Inc., Sanofi, Speaker's Bureau; Eli Lilly and Company, Sanofi. H.Aydin: Advisory Panel; Sanofi, Novo Nordisk, Boehringer Ingelheim Inc., Speaker's Bureau; Novo Nordisk. F.Lauand: Employee; Sanofi. L.Melas-melt: None. E.Souhami: Employee; Sanofi, Stock/Shareholder; Sanofi. M.Haluzik: Advisory Panel; Novo Nordisk, Lilly Diabetes, Boehringer-Ingelheim, Research Support; Sanofi, Speaker's Bureau; Abbott, AstraZeneca. J.Rosenstock: Advisory Panel; Applied Therapeutics Inc., Boehringer Ingelheim Inc., Eli Lilly and Company, Novo Nordisk, Oramed Pharmaceuticals, Sanofi, Zealand Pharma A/S, Intarcia Therapeutics, Inc., Hanmi Pharm. Co., Ltd., Research Support; Applied Therapeutics Inc., Boehringer Ingelheim Inc., Eli Lilly and Company, Merck & Co., Inc., Novartis, Novo Nordisk, Pfizer Inc., Sanofi, Intarcia Therapeutics, Inc. Funding Sanofi
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.034 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".