Effect of proprotein convertase subtilisin/kexin type 9 ( <scp>PCSK9</scp> ) monoclonal antibodies on new‐onset diabetes mellitus and glucose metabolism: <scp>A</scp> systematic review and meta‐analysis
Bibliographic record
Abstract
Aims To investigate the effect of two clinically applied proprotein convertase subtilisin/kexin type 9 monoclonal antibodies (PCSK9‐mAbs) on glycaemia and new‐onset diabetes mellitus (NODM). Materials and Methods PubMed, MEDLINE, Embase, Cochrane databases and ClinicalTrials.gov websites were systematically searched for randomized controlled trials that reported data on fasting plasma glucose (FPG), glycated haemoglobin (HbA1c) or NODM incidence. Risk ratios (RRs) for NODM and mean difference (MD) for FPG and HbA1c with 95% confidence intervals (CIs) were calculated using a fixed‐effect model. Heterogeneity was examined using the I 2 statistic and potential publication bias was assessed using funnel plots and Egger’s test. Results A total of 18 studies including 26 123 participants without diabetes were identified. No significant difference was observed in the PCSK9‐mAb treatment groups in terms of NODM (RR 1.05, 95% CI 0.95‐1.16), FPG (MD 0.00 mmol/L, 95% CI −0.02 to 0.02) or HbA1c (MD 0.00% [0 mmol/L], 95% CI −0.01 to 0.01) compared with control groups. Subgroup (PCSK9‐mAb type, participant characteristics, treatment duration, treatment method and differences in control treatment) and sensitivity analyses did not significantly alter the results. Meta‐regression analyses showed that risk of NODM was not associated with baseline age, baseline body mass index (BMI), proportion of men, treatment duration or percent LDL cholesterol reduction. Conclusions Alirocumab and evolocumab, two types of PCSK9‐mAb approved by the US Food and Drug Administration and the European Medicines Agency, had no significant impact on NODM and glucose homeostasis, regardless of PCSK9‐mAb type, participant characteristics, treatment duration, treatment method and differences in control treatment. Baseline age, BMI, proportion of men, treatment duration, and percent change of LDL cholesterol did not influence diabetes risk.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".