ArticleFrontiers in oncology2026
Association between metabolic syndrome components and the risk of malignant neoplasms of the brain: a nationwide cohort study.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To examine the relationship between metabolic syndrome (MetS) components and the development of malignant neoplasms of the brain (MNBs) and to identify the most significant influence. Methods: We conducted a nationwide cohort study using the Korean National Health Insurance Service database, enrolling 3, 976, 961 individuals aged ≥40 years who completed a national health checkup in 2009. Participants were followed from 2010 to 2020 to assess MNB incidence using International Classification of Diseases, 10th Revision, codes C71.0-C71.9. Participants were stratified by the number of MetS components (0-5). Cox proportional hazards models were used to estimate hazard ratios (HRs), adjusted for age, sex, smoking, alcohol use, exercise, and comorbidities. Results: In multivariate analysis adjusting for age, sex, lifestyle factors, and comorbidities, individual component counts modeled as discrete categorical comparisons against the zero-component reference did not reach statistical significance at any level (HR for 5 components: 1.070; 95% CI: 0.941-1.217; p = 0.303). However, when participants were stratified by clinically established cumulative thresholds, those with ≥3 MetS components demonstrated a modestly but statistically significantly increased MNB risk (HR: 1.091; 95% CI: 1.017-1.170; ≥5 components, 1.153; 95% CI: 1.040-1.278). Among individual components, elevated triglycerides showed the strongest independent association with MNB risk (adjusted HR: 1.517; 95% CI: 1.179-1.953), whereas the other components showed weaker or non-significant associations. Conclusions: The cumulative burden of MetS was modestly but statistically significantly associated with MNB risk, with elevated triglycerides showing the strongest association. These findings suggest that metabolic health management, particularly lipid monitoring, may inform brain tumor risk assessment. Critically, however, the outcome variable encompasses a diagnostically heterogeneous group of brain malignancies identified by administrative ICD-10 coding, and no mechanistic inference or histological subtype-specific conclusion can be drawn from the present data. These findings should be considered hypothesis-generating and require prospective validation using pathologically and molecularly characterized tumor registries before any clinical recommendations regarding lipid management for brain tumor prevention can be made.
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