Response: Re: The Influence of Statin Medications on Prostate-Specific Antigen Levels
Bibliographic record
Abstract
We thank Drs Cicero, Derosa, and Gaddi for their insightful comments regarding our recent study. They asked how the results would be different if we excluded non-simvastatin users, who made up only 5% of our cohort. When this was done, the observed median prostate-specific antigen (PSA) decline increased slightly from 4.1% to 4.5%, though the association with PSA decline and statin dose, and low-density lipoprotein (LDL) decline were not substantially altered. Whether similar results would be obtained with more hydrophilic statins is unknown, and unfortunately, as less than 1% of our cohort used hydrophilic statins, we did not have the power to compare these two groups. Another question that was raised regarded the fact that our results may be confounded by outliers in the effect of statins on LDL cholesterol. After excluding 42 men (3% of study population) with LDL change outside of 2 SDs from the mean change in LDL (26% decline), the overall median PSA decline remained 4.1%. Moreover, the association between decline in LDL and decline in PSA was slightly strengthened. When examining all men, a 10% decline in LDL was associated with a multivariate-adjusted 1.64% decline in PSA ( P = .001), whereas after excluding the 42 outliers, a 10% decline in LDL was associated with a 2.03% multivariate-adjusted decline in PSA ( P < .001). Cicero and colleagues suggested that for some men, possible lifestyle changes may account for the PSA decline. We absolutely agree that lifestyle changes could influence LDL concentrations. However, change in body mass index (BMI) after starting a statin, which may serve as a marker for lifestyle changes, did not confound the relationship between statin use and PSA. Furthermore, LDL change was strongly associated with statin dose. Thus, we feel LDL change serves as an accurate marker of adherence to and efficacy of statins in our cohort. To address this more fully, we stratified patients by BMI at the time of starting a statin and found that within each strata of BMI, statin use was associated with PSA decline, with no significant differences among strata of BMI. Unfortunately, we did not have data on the prevalence of metabolic syndrome in our cohort. Finally, Cicero and colleagues suggested that we should control for nonsteroidal anti-inflammatory drug (NSAID) use. This is an excellent point. Unfortunately, ascertaining NSAID use, many of which are nonprescription medications, using the Veterans Affairs (VA) prescription database proves difficult. Although some men do purchase their NSAIDs through the VA, this is certainly an undercount of those on NSAIDs. However, as the change in PSA in the period before statin use was 0% as compared with a 4.1% decline after starting statins, unless an NSAID was started exactly at the time a statin was started, it would be unlikely that chronic NSAID use could explain the temporal association between starting a statin and PSA decline we observed. Furthermore, the observed association between PSA decline and statin dose and LDL decline makes it unlikely that our results could be explained by NSAID confounding.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.023 | 0.016 |
| Insufficient payload (model declined to judge) | 0.044 | 0.028 |
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".