Inclisiran Lowers LDL-C and PCSK9 Irrespective of Diabetes Status: The ORION-1 Randomized Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy and safety of inclisiran by diabetes status. RESEARCH DESIGN AND METHODS: ORION-1 (ClinicalTrials.gov, NCT02597127) randomized 501 subjects with atherosclerotic cardiovascular disease (ASCVD) or ASCVD risk equivalents and high LDL cholesterol (LDL-C), despite maximally tolerated LDL-C-lowering therapies, to one or two doses of placebo or inclisiran. Levels of lipids and proprotein convertase subtilisin/kexin type 9 (PCSK9) at baseline and day 180 were compared. RESULTS: < 0.005 for all doses in the without- and with-diabetes groups, respectively) and PCSK9. The inclisiran-treated groups also had lower apolipoprotein B, non-HDL cholesterol, and lipoprotein(a) but higher HDL cholesterol. Inclisiran had an adverse profile similar to that of placebo, and adverse events were proportionally balanced in the baseline with- and without-diabetes groups. CONCLUSIONS: PCSK9-targeted siRNA-driven strategies may provide a novel therapeutic option for managing dyslipidemia in the presence and absence of diabetes.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".