The Relationship between Inflammatory Diet Score and Cancer Outcomes: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Cancer remains one of the most prevalent diseases in the United States and a leading cause of death. Large prospective studies have found significant correlations between dietary intake and cancer. Chronic inflammation promotes pro-cancer inflammatory environments promoting the formation and growth of tumors while preventing effective anti-tumor responses. Nutrition can impact inflammation, with the intake of certain food items increasing biomarkers for systemic inflammation thus, the objective of this research was to explore the relationship between inflammatory diet score measured by the Dietary Inflammatory index and all-cause mortality, cancer-specific mortality, and cancer recurrence among cancer survivors. Web of Science, Medline, CINHAL, and PsycINFO databases were searched to collect potentially eligible sources that focus on dietary inflammation and cancer outcomes. All sources were uploaded to Covidence software and screened by two independent blinded reviewers. The quality of the sources was assessed using the Newcastle Ottawa scale and relevant data was extracted and transferred to the Comprehensive Meta Analysis software and a random effects model was used to perform meta-analysis. Of the 1444 studies imported into the Covidence software, 13 passed all the screening stages and were included in the final analysis. Eight studies reported on pre-diagnosis diet while five others reported on post-diagnosis diet. Five studies reported on colorectal cancer, four on breast cancer, two on ovarian cancer, one on endometrial cancer and one on prostate cancer. Meta-analysis of the studies found that being in the highest postdiagnosis DII score indicating pro-inflammatory diet significantly increased the risk of all-cause death among cancer survivors by 33.5% (HR = 1.335, 95% CI = 1.049, 1.698, n = 6). Analysis did not show a statistically significant association between DII score and cancer mortality or recurrence (HR = 1.097, 95% CI = 0.939, 1.281, n = 6). Analysis by cancer subtype found a significant correlation between postdiagnosis DII score and all-cause mortality among the breast cancer survivors (HR = 1.335, 95% CI = 1.041, 1.711, n = 3) though there were no significant associations between DII and the outcomes of interest from the other cancer types. The meta-analysis concludes that being in the highest postdiagnosis DII score group significantly increased the risk of all-cause death among cancer survivors. This suggests that risk of all-cause mortality could be reduced for cancer survivors by consuming more anti-inflammatory food components and reducing consumption of pro-inflammatory foods. These findings also warrant more research in this field to clarify the relationship between dietary inflammation as measured by the DII and cancer outcomes, particularly cancer-specific mortality.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".