Association Between Metabolic Syndrome and Risk of Laryngeal Cancer: A Systematic Review
Bibliographic record
Abstract
Background: Metabolic syndrome (MetS) is a significant global health burden and a known risk factor of cardiovascular disease and diabetes. Growing evidence also links MetS to cancer development, likely via chronic inflammation, insulin resistance, and hormone disruption. However, its association with laryngeal cancer remains largely unclear and underexplored. Methods: For this review, we thoroughly searched PubMed/MEDLINE, Scopus, and Web of Science for observational studies investigating associations of MetS with laryngeal or head and neck cancers (HNCs) until 1 August 2025. Five large population-based studies were found to meet inclusion criteria, and risk of bias was assessed using the Joanna Briggs Institute checklist (JBI). Results: Three Korean cohort studies consistently found that MetS increased the risk of laryngeal cancer (HR 1.13–1.32), independent of smoking and alcohol use. Hypertension and hyperglycemia were the most consistent components associated with increased risk, and chronic MetS conferred the highest hazard. In contrast, analyses from the UK Biobank (HNC) and SEER-Medicare (HNSCC) cohorts showed null and inverse associations, respectively. Additional findings included dose–response effects with increasing MetS components, U-shaped associations for HDL-C and waist circumference and increased risk associated with elevated C-reactive protein. Conclusions: Current evidence suggests a possible association between MetS and risk of laryngeal cancer, although the direction and strength of effect vary across populations. Findings from Korean cohorts provide consistent signals of increased risk, whereas Western datasets have not replicated this pattern. Overall, the certainty of evidence is low to moderate, warranting cautious interpretation and further validation in diverse populations before inferring causality.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".