Case–control and prospective studies of dietary α-linolenic acid intake and prostate cancer risk: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: α-Linolenic acid (ALA) is considered to be a cardioprotective nutrient; however, some epidemiological studies have suggested that dietary ALA intake increases the risk of prostate cancer. The main objective was to conduct a systematic review and meta-analysis of case-control and prospective studies investigating the association between dietary ALA intake and prostate cancer risk. DESIGN: A systematic review and meta-analysis were conducted by searching MEDLINE and EMBASE for relevant prospective and case-control studies. INCLUDED STUDIES: We included all prospective cohort, case-control, nested case-cohort and nested case-control studies that investigated the effect of dietary ALA intake on the incidence (or diagnosis) of prostate cancer and provided relative risk (RR), HR or OR estimates. PRIMARY OUTCOME MEASURE: Data were pooled using the generic inverse variance method with a random effects model from studies that compared the highest ALA quantile with the lowest ALA quantile. Risk estimates were expressed as RR with 95% CIs. Heterogeneity was assessed by χ(2) and quantified by I(2). RESULTS: Data from five prospective and seven case-control studies were pooled. The overall RR estimate showed ALA intake to be positively but non-significantly associated with prostate cancer risk (1.08 (0.90 to 1.29), p=0.40; I(2)=85%), but the interpretation was complicated by evidence of heterogeneity not explained by study design. A weak, non-significant protective effect of ALA intake on prostate cancer risk in the prospective studies became significant (0.91 (0.83 to 0.99), p=0.02) without evidence of heterogeneity (I(2)=8%, p=0.35) on removal of one study during sensitivity analyses. CONCLUSIONS: This analysis failed to confirm an association between dietary ALA intake and prostate cancer risk. Larger and longer observational and interventional studies are needed to define the role of ALA and prostate cancer.
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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 | 0.031 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.070 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".