Strawberry Intake, Lipids, C-Reactive Protein, and the Risk of Cardiovascular Disease in Women
Bibliographic record
Abstract
OBJECTIVE: There is indirect evidence suggesting that strawberries, containing several key nutrients, may be associated with the risk of cardiovascular disease (CVD). In the Women's Health Study, we examined strawberry intake for both its prospective association with CVD risk in 38,176 women and its cross-sectional association with lipids and C-reactive protein (CRP) in a subset of 26,966 women. METHODS: Strawberry intake was assessed from a baseline semiquantitative food frequency questionnaire, along with other self-reported lifestyle, clinical and dietary factors. Participants returned baseline bloods which were assayed for lipids and CRP. We computed the relative risks (RRs) for total CVD (1,004 cases) (including confirmed myocardial infarction, stroke, revascularization, and cardiovascular death) occurring during 10.9 years of follow-up. RESULTS: At baseline, 25.6%, 41.9%, 24.8%, and 7.7% of women reported corresponding strawberry intake of none, 1-3 servings/month, 1 serving/week, and > or =2 servings/week. For total CVD, the multivariate RRs (95% confidence intervals) for increasing categories of strawberry intake were 1.00 (ref), 1.01 (0.85-1.19), 0.95 (0.77-1.17), and 1.27 (0.94-1.72) (P, trend = 0.06). We found a similar lack of an association for individual cardiovascular endpoints and comparing mean levels of lipids and CRP by category of strawberry intake. However, women consuming > or =2 servings/week versus none had a borderline significant, multivariate 14% lower likelihood of an elevated CRP of > or =3 mg/L. CONCLUSIONS: Strawberry intake was unassociated with the risk of incident CVD, lipids, or CRP in middle-aged and older women, though higher strawberry intake may slightly reduce the likelihood of having elevated CRP levels. Additional epidemiologic data are needed to clarify any role of strawberries in CVD prevention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".