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Risk factors for cancer among patients with type 2 diabetes: a retrospective cohort study
Hui Liu1,2, Tian-Yi Zhang1,2, Ya-Wen Wang1,2
1Biobank, The First Affiliated Hospital of Xi 'an Jiaotong University, Xi'an, Shaanxi, 710061, China.
Background:
Type 2 diabetes mellitus (T2DM) and cancer pose significant public health challenges worldwide. This study investigated the distribution of cancer cases and associated risk factors among patients with T2DM, aiming to inform clinical practices and reduce the burden of co-morbidities.
Methods:
A retrospective cohort study analyzed electronic medical records of 70,073 T2DM patients admitted between 2013 and 2019. After exclusions, 3,284 individuals were included, with a median follow-up of 27 months. Associations between 38 baseline characteristics and cancer risk were first assessed using univariate Cox proportional hazards models (reported as hazard ratios [HRs] with 95% confidence intervals [CIs]). Significant predictors (p < 0.05) were further analyzed in adjusted multivariable Cox models by backward elimination (retaining 2 significant covariates).
Results:
Among the participants, 467 (14.2%) developed cancer, with digestive system cancers being the most prevalent (5.8%). The median age of participants was 58 years, with 59% being male. Risk factors for cancer among T2DM patients identified in multivariate analysis included lower serum uric acid levels (HR: 0.9970, 95% CI: 0.9942-0.9998), and higher AST/ALT ratio (HR: 1.84, 95% CI: 1.16-2.92). Specific risk factors for different types of cancer were also identified, such as lower apolipoprotein A for digestive system cancers and older age and lower serum uric acid levels for lung cancer.
Conclusions:
These findings highlight the potential value of integrating cancer screening into T2DM management for high-risk patients, such as individuals with altered uric acid or liver enzyme profiles. However, the study's retrospective design and reliance on single-center data necessitate further prospective studies to validate these associations and refine biomarker-guided monitoring strategies.
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