The effect of lowering cholesterol through diet on serum prostate-specific antigen levels: A secondary analysis of clinical trials
Bibliographic record
Abstract
IMPORTANCE: Statins appear to lower serum prostate-specific antigen (PSA) and improve prostate cancer (PCa) outcomes through cholesterol-dependent and independent mechanisms. While dietary modifications have an established role in serum cholesterol reduction, whether diet-driven cholesterol reductions yield similar PCa benefits to that observed with statins is unclear. We aimed to study the effect of dietdriven cholesterol reduction on serum PSA and estimated-PCa risk. METHODS: A total of 291 men from six published randomized controlled trials of dietary interventions were included. Men were aged ≥40 years, free of PCa, and had baseline PSA <10.0 ng/mL. Participants received one of four diets (high-fiber, low-glycemic index, low-glycemic load, or cholesterol-lowering) for 8-24 weeks. The primary outcome evaluated the association between change from baseline low-density lipoprotein cholesterol (LDL-C) and PSA. How cholesterol reduction modified PCa risk was estimated using the Prostate Cancer Prevention Trial (PCPT) risk calculator (limited to age ≥55 years, baseline PSA ≥1.0 ng/mL). RESULTS: Baseline PSA was 0.90 ng/mL (interquartile range [IQR] 0.55-1.60) and LDL-C was 90 mg/dL (IQR 69-125). In multivariate regression, PSA decreased 1.9% (95% confidence interval [CI] 0.55-3.2, p=0.005) per 10% reduction in LDL-C. This regression was greater in men with baseline PSA ≥2.0 ng/mL (-5.4%, 95% CI 2.2-8.6] per 10% LDL-C reduction, p-interaction=0.001). In men with estimable PCPT risk, statin-comparable LDL-C reductions (≥15%) reduced PSA by 12% (p<0.001) and estimated PCa risk by 6.5% (p=0.005). CONCLUSIONS: This is the first study to show that serum cholesterol reduction through dietary interventions significantly lowered serum PSA and estimated PCa risk. Whether cholesterol-lowering diets improve PCa outcomes warrants investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".