Dietary habits and esophageal cancer risk in India: a systematic review and meta-analysis of case–control studies
Bibliographic record
Abstract
Abstract Background: In a country as geographically and culturally diverse as India, understanding dietary patterns linked to esophageal cancer (EC) is essential. Given the inconsistencies in existing research, this study aims to evaluate common dietary practices nationwide and their association with the risk of EC. Materials and Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the PESO framework, a systematic review was conducted using databases including PubMed, Google Scholar, and Dimensions.ai, along with manual searches from October 27 to December 7, 2024. The quality of the included studies was assessed using the Newcastle–Ottawa Scale. Publication bias was evaluated using contour-enhanced funnel plots at 5% and 10% significance levels, and Egger’s test indicated no significant bias ( P > 0.05). Pooled odds ratios (OR-pooled) were computed to evaluate the association between dietary habits and EC risk. Results: A total of 29 case–control studies, encompassing 7434 cases and 11,992 controls, were analyzed. The pooled ORs indicated a significant association between the consumption of processed foods and an increased risk of EC (OR-pooled = 1.88; 95% confidence interval [CI] = 1.33–2.64). Subgroup analyses revealed elevated risks associated with the intake of dried and smoked foods, butter, and processed bread OR-pooled = 1.61; 95% CI = 1.02–2.53), as well as even higher risk with Kalakhar, dry fish, and pickles (OR-pooled = 2.19; 95% CI = 1.20–4.01). The highest risk was associated with drinking salted tea more than four times a day or in quantities exceeding 1250 mL/day (OR-pooled = 9.04; 95% CI = 5.52–14.82). Other high-risk factors included consuming very hot beverages (OR-pooled, 1.95; 95% CI = 1.35–2.83), extremely spicy foods (OR-pooled = 3.77; 95% CI = 1.17–12.16), and red chilies (OR-pooled = 2.43; 95% CI = 1.62–3.65). Interestingly, individuals consumed only fish, meat, or both demonstrated a protective effect against EC (OR-pooled = 0.64; 95% CI = 0.55–0.74). Conclusion: This review highlights key dietary risk factors for EC in the Indian population, particularly the frequent intake of salted tea, very spicy foods, and extremely hot beverages. However, caution is warranted in interpreting these findings due to considerable heterogeneity and the region-specific nature of dietary studies, which may affect the generalizability of the results.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".