Healthful and Unhealthful Plant-Based Diets and Their Association with Cardiometabolic Targets in Women Diagnosed with Breast Cancer: A Cross-Sectional Analysis of a Lifestyle Trial
Bibliographic record
Abstract
BACKGROUND: Plant-based diets are recommended in guidelines for the prevention of cancer and cardiometabolic diseases, which remain major causes of death in breast cancer survivors (BCS). Since not all plant foods are healthy, we calculated the plant-based dietary index (PDI), healthy (hPDI) and unhealthy (uPDI), and their associations with cardiometabolic targets in BCS. METHODS: Baseline dietary and cardiometabolic data were derived from 492 (median age 51, IQR 46-59) female BCS participating in a multicentric lifestyle trial conducted in Italy. Dietary data were collected with 7-day food records. PDI, hPDI, and uPDI were calculated by assigning positive scores to all plant foods, healthy plant foods or less healthy plant foods, respectively, as defined by the literature (scores ranged from 18 to 90). Using logistic or multinomial regression models, we estimated the odds ratios (OR) and the corresponding 95% confidence intervals (CI) between PDIs and cardiometabolic risk factors. RESULTS: ) was 0.47 (95%CI: 0.29-0.77), 0.37 (95%CI: 0.22-0.61) and 1.38 (95%CI: 0.83-2.28) with higher PDI, hPDI and uPDI, respectively. The OR of having a large waist circumference (≥88 cm) was 0.64 (95%CI: 0.42-1.00) with higher hPDI. The OR for hypercholesterolemia (≥200 mg/dL) was 1.80 (95%CI: 1.16-2.78) with higher uPDI. The ORs of hypertriglyceridemia (≥150 mg/dL) and metabolic syndrome were 0.38 (95%CI: 0.20-0.71) and 0.59 (95%CI: 0.35-0.97), respectively, with higher PDI. No other significant association was observed. CONCLUSIONS: Maintaining cardiometabolic risk factors within normal ranges is clinically relevant in BCS, and this may be more likely when a plant-based diet is consumed, especially if low in unhealthy plant foods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".