Association of a Healthy Diet Score with prostate cancer severity in newly diagnosed men: A cross-sectional analysis of RADICAL PC
Bibliographic record
Abstract
Background: Prostate cancer remains the second most common cause of cancer-related death in men in the United States (Siegel et al. 2017). Observational studies of patients with prostate cancer have found associations between diet and prostate cancer severity, but the evidence is inconsistent or inconclusive. The purpose of this thesis is to implement a validated international healthy diet score and evaluate whether or not it is associated with prostate cancer severity. Objective: The objectives of this thesis were: Chapter 1: examine whether an association exists between diet quality, using the validated Healthy Diet Score, and the severity of prostate cancer, and Chapter 2: examine the agreement between two methods of dietary data collection (an abridged FFQ and a longer previously validated FFQ) with respect to macronutrients and main food groups. Methods: We used observational data from the Randomized Intervention for Cardiovascular and Lifestyle Risk Factors in Prostate Cancer Patients (RADICAL PC), a multi-centre Canadian prospective cohort study into which men with a new diagnosis of prostate cancer or who were being treated with androgen deprivation therapy were enrolled. To complete objective 1 (Chapter 1) of this dissertation, a cross-sectional analysis was completed using baseline data collected in the RADICAL PC study. In order to evaluate the association of diet with prostate cancer severity, the relationship between the Healthy Diet Score and prostate cancer severity (stage and grade) was assessed. The second objective (Chapter 2) is a comparability sub-study comparing an abridged FFQ with a long, validated FFQ in a subgroup of participant (N=130) enrolled in the RADICAL PC study. Results: Chapter 1: In the cross-sectional analysis of baseline data collected in RADICAL PC, a higher diet score was not significantly associated with prostate cancer severity. An association between age and the high-risk prostate cancer category was found to be statistically significant (OR: 1.04, 95%CI 1.02-1.05, p<0.00). Chapter 2: There was good agreement between the abridged FFQ and long FFQ for carbohydrates, proteins, whole wheat, refined grains, fish, dairy, potatoes, fruits, nuts, and soft drinks (Spearman rank correlation >0.5). Food groups including fried foods, processed meats, vegetables and total fats (nutrients) were found to have moderate correlation (Spearman rank correlation between 0.3-0.5). There was low correlation for legumes, sugars and oils. Bland-Altman plots showed good absolute agreements between the two methods, and reliability test using Spearman’s correlation showed moderate to good correlation (0.45 to 0.75 among most food groups. Conclusion: There was no clear association between a healthier diet and prostate cancer severity in men with newly diagnosed prostate cancer. There was adequate agreement between the abridged SFFQ and the long FFQ of the expected food groups, and thus the SFFQ can be considered an appropriate tool to use for measuring diet among prostate cancer patients for some food groups and nutrients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".