Low glycemic index diet, exercise and vitamin D to reduce breast cancer recurrence (DEDiCa): design of a clinical trial
Bibliographic record
Abstract
BACKGROUND: Mechanisms influencing breast cancer (BC) development and recurrence include hyperglycemia, hyperinsulinemia, high insulin-like growth factor-1, high circulating estrogen, inflammation and impaired cellular differentiation/apoptosis. A lifestyle program that targets all the above mechanisms may be warranted. Low glycemic index (GI) foods produce lower post-prandial glucose and insulin responses and have been associated with lower BC risk. Moderate physical activity post-diagnosis reduces BC recurrence and mortality, partly explained by reduced insulin and estrogen levels. Vitamin D increases cell differentiation/apoptosis and high serum vitamin D levels improve BC survival. Yet no trial has evaluated the combined effect of a low GI diet, moderate physical activity and vitamin D supplementation on BC recurrence in the context of a Mediterranean lifestyle setting. METHODS: Women (30-74 yr) who had undergone surgery for primary histologically confirmed BC (stages I-III) within the previous 12 months, in cancer centres in Italy, will be randomized to follow, for a maximum of 33 months, either a high intensity treatment (HIT) composed of low GI diet + exercise + vitamin D (60 ng/mL serum concentration) or a lower intensity treatment (LITE) with general advice to follow a healthy diet and exercise pattern + vitamin D to avoid insufficiency. Both interventions are on a background of a Mediterranean diet. Considering a 20% recurrence rate within 3 years for BC cases and a predicted rate of 10% in the HIT group, with power of 80% and two-sided alpha of 0.05, the subject number required will be 506 (n = 253 in each arm). Clinic visits will be scheduled every 3 months. Dietary and exercise counselling and vitamin D supplements will be given at each clinic visit when blood samples, anthropometric measures and 7-day food records will be collected. DISCUSSION: DEDiCa study aims to reduce BC recurrence in women with BC using a lifestyle approach with additional vitamin D and to investigate possible cardio-metabolic benefits as well as epigenetic modifications according to lifestyle changes. Given the supporting evidence and safety of the components of our intervention we believe it is feasible and urgent to test it in cancer patients. TRIAL REGISTRATION: May 11, 2016; NCT02786875 . EUDRACT NUMBER: 2015-005147-14.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".