Abstract 16863: Alirocumab Effect on Glycemic Measures in Patients Without Diabetes at Baseline
Bibliographic record
Abstract
Background: Statin use has been reported to have modest adverse effects on glycemia and incidence of diabetes mellitus (DM). We assessed effects of alirocumab (ALI), a PCSK9 inhibitor with substantial effect on LDL-C, on development of incident DM or impaired glucose control (IGC) in patients without diabetes at baseline in 10 trials in the ODYSSEY Phase 3 program. Methods: Five trials were controlled with placebo (N=2318 ALI vs 1174 placebo), and 5 with ezetimibe (N=864 ALI vs 618 ezetimibe), ranging in length from 24 to 104 wks. Most patients were receiving background statin. Different ways of assessing ALI effect on glycemic measures were used, including occurrence of treatment-emergent adverse events (TEAEs) related to DM, and fasting plasma glucose (FPG) and HbA1c measured at baseline and every 12-24 wks thereafter. Additional analysis using all 3 parameters was conducted (see Table footnote). Results: Duration of exposure is shown in the Table. At baseline, 32.8% of patients had DM, 93% based on medical history. Another 37.4% had IGC at baseline, based on laboratory data (93.6%) or medical history (6.4%). In patients without DM at baseline, there was no difference between ALI and control groups in occurrence of TEAEs related to DM with HR (95% CI) = 0.79 (0.44-1.43) in the placebo-controlled pool and 0.55 (0.22-1.41) in the ezetimibe-controlled pool. Mean change in FPG and HbA1c over time showed no meaningful differences between treatment groups by baseline glycemic status. In the additional analysis shown in the Table, there was no notable difference between ALI and control groups in % of patients with baseline normoglycemia developing DM or IGC, or those with baseline IGC developing DM. Conclusions: In 10 studies involving 4974 patients comparing ALI to either placebo or ezetimibe (median exposure 66 or 27 wks, respectively), data show no significant effect of ALI on transition to IGC or DM. Longer follow up with larger numbers is needed to conclusively rule out an effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".