Dietary inflammatory index and the risk of esophageal cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
It is well-recognized that inflammation is an adaptive pathophysiological response in many types of cancer. Research on nutrition's critical role in inflammation, a risk factor for all forms of cancer, is growing. The dietary inflammatory index (DII) was created lately to assess if a diet is pro- or anti-inflammatory in terms of inflammation. Indeed, several studies have demonstrated the correlation between DII and the risk of several cancer types. This meta-analysis set out to look into the relationship between DII and the different forms of esophageal cancer. PubMed, Cochrane library, Embase, Scopus, and Web of Science databases were searched up to May 2024 to retrieve relevant articles. RAYYAN intelligent tool for systematic reviews was incorporated for the screening of studies. Original articles written in English Studies that investigated the inflammatory index of diet in individuals who developed esophageal cancer were included in this study.STATA v18 software was used to conduct the meta-analysis. Egger's test for publication bias assessment was implemented. Newcastle Ottawa scale was used to evaluate the qualities of the included studies. A plot digitizer was used to extract digital data. A total of 13 studies were included in the systematic review, with 6 studies contributing to the meta-analysis, comprising 10,150 participants. The participants were categorized into high and low DII groups, with the low DII group (n = 3,403) serving as the reference. The meta-analysis demonstrated a significant association between high DII and increased risk of esophageal cancer. Specifically, individuals in the high DII group were 29% more likely to develop esophageal cancer, with a pooled odds ratio (OR) of 1.29 (95% Confidence Interval [CI]: 1.16–1.43), as calculated using a random-effects model. Moderate heterogeneity was observed (I2 > 50%). Egger’s test indicated evidence of publication bias (p < 0.05). Subgroup and sensitivity analyses confirmed the robustness of this association across populations and study designs. our study concludes that a higher level of DII is associated with a higher risk of esophageal cancer development. This study suggests that modifying inflammatory properties of dietary patterns can reduce the risk of incidence of esophageal cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".