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Record W4392604238 · doi:10.1101/2024.03.06.24303892

Publication bias in pharmacogenetics of statin-associated muscle symptoms, an umbrella review with a meta-epidemiological study

2024· review· en· W4392604238 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuemedRxiv · 2024
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsUniversity of British Columbia
FundersCentre National de la Recherche Scientifique
KeywordsFunnel plotSLCO1B1Publication biasStatinMeta-analysisMedicineOdds ratioInternal medicinePharmacogeneticsPharmacologyGenotypeGeneticsBiology

Abstract

fetched live from OpenAlex

Abstract Background Statin-associated muscle symptoms (SAMS) are a major cause of treatment discontinuation. Adjusting statin dosages for solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotype has been proposed to reduce SAMS. We hypothesized that the association between SLCO1B1 genotype and SAMS is misestimated because of publication bias. Methods We searched for published systematic reviews evaluating the association between SLCO1B1 genotype and SAMS. We collected the odds ratio (OR) of this association in each clinical study. We assessed the presence of publication bias using the visual inspection of a funnel plot and Egger’s test and used the Bayes Factor (BF Publication-bias ) of the Robust Bayesian Meta-Analysis (RoBMA) as a sensitivity analysis. We evaluated the effect of publication bias by comparing qualitatively and quantitatively (ratio of OR [ROR]) OR of the meta-analysis i) uncorrected for potential publication bias (OR Uncorrected ) and ii) corrected using the trim-and-fill (OR Trim&Fill ). We also used the RoBMA (OR RoBMA ) for corrected OR as a sensitivity analysis. Our primary analysis covered the associations between any SLCO1B1 genotype and any statin drug. Secondary analysis focused on SLCO1B1 genotypes and statin drug subgroups. Results We included 8 cohort and 11 case-control studies, totaling 62 OR of three SLCO1B1 genotypes and five statin drugs plus one ‘mixed’ statin treatment. All controls were statin-tolerant patients. In the primary analysis, the funnel plot was suggestive of publication bias, confirmed by Egger’s test (p=0.001) and RoBMA (BF Publication-bias =18). Correcting the estimate for publication bias resulted in loss of the association, from a significant OR Uncorrected (1.31 95% CI [1.13– 1.53]) to corrected ORs suggesting no difference: i) OR Trim&Fill (1.07 95% CI [0.89–1.30]) and ii) OR RoBMA (1.02 95% CI [1.00–1.33]). The ROR Trim&Fill and the ROR RoBMA suggested that publication bias overestimated the association by 18% and 23%, respectively. The results were similar for the most studied SLCO1B1 genotype, as for simvastatin and atorvastatin. Conclusion The effect of the SLCO1B1 genotype on the risk of developing SAMS is overestimated in the published literature. This could lead prescribers to incorrectly decreasing statin doses or even avoiding statin use, leading to a loss of the potential cardiovascular benefit of statins. Clinical perspective What is new? There is significant publication bias in the available literature regarding the association between SLCO1B1 genotype and statin-associated muscle symptoms. The available literature overestimates the importance of the SLCO1B1 genotype on statin-associated muscle symptoms. What are the clinical implications? The cardiovascular benefit of statins might be wrongly lost when adjusting statin therapy with the SLCO1B1 genotype. The effect of publication bias should be considered when writing guidelines.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad)Research integrity
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysislow
gptMetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad)
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.282
GPT teacher head0.446
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it