Abstract 11033: Efficacy and Safety of Inclisiran by Baseline Glycemic Status: A Post Hoc Pooled Analysis of the ORION-10 and ORION-11 Phase III Randomized Controlled Trials
Bibliographic record
Abstract
Introduction: Disorders of glucose metabolism increase in prevalence globally and contribute to excess risk of ASCVD, necessitating intensive lipid lowering. Aim: To analyze efficacy and safety of inclisiran (small interfering RNA targeting hepatic PCSK9 mRNA) across the strata of glycemic disorders. Methods: In this post hoc, pooled analysis of ORION-10 and ORION-11, 3174 patients (pts) with ASCVD/ASCVD risk equivalent (including diabetes mellitus [DM]) were randomized 1:1 to receive 300 mg inclisiran sodium (equivalent to 284 mg inclisiran) or placebo (pbo) at baseline (BL), Day (D) 90, and 6-monthly thereafter. Analyses were stratified by BL glycemic status (normoglycemia, pre-DM or DM). LDL-C percentage (%) change from BL to D510 and time-adjusted % change from D90 to D540 were evaluated. Safety was assessed over 540 days. Results: BL characteristics were generally balanced between the treatment arms across the glycemic strata ( Table 1 ). LDL-C % change and time-adjusted % change were greater with inclisiran vs pbo and were similar across all strata ( Table 2 ). Treatment-emergent adverse events (TEAE) and treatment-emergent serious adverse events were generally similar between the treatment arms; TEAEs were reported more frequently in pts with vs without glycemic disorders (not shown). Clinically relevant TEAEs at the injection site were reported more frequently with inclisiran vs pbo across the strata but all were mild or moderate ( Table 2 ). Conclusions: Twice-yearly dosing with inclisiran (after the initial and 3-month doses) provided effective and sustained LDL-C lowering irrespective of BL glycemic status, and was generally well tolerated.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".