Association between Metabolic Syndrome and Risk of Hypopharyngeal Cancer: A Nationwide Cohort Study from Korea.
Jeong Wook KangHyeon-Kyoung CheongSu Il KimMin Kyeong LeeYoung Chan LeeIn-Hwan OhYoung Gyu EunPublished in: Cancers (2023)
This study evaluated the relationship between metabolic syndrome (MS) and the risk of hypopharyngeal cancer. This retrospective cohort study used data from the Korean National Health Insurance Research Database. A total of 4,567,890 participants who underwent a health checkup in 2008 were enrolled. The participants were followed until 2019, and the incidence of hypopharyngeal cancer was analyzed. We evaluated the risk of hypopharyngeal cancer according to the presence of MS, including obesity, dyslipidemia, hypertension, and diabetes, using a multivariate Cox proportional hazards model adjusted for age, sex, alcohol consumption, and smoking. During the follow-up period, 821 were newly diagnosed with hypopharyngeal cancer. MS was inversely associated with the risk of hypopharyngeal cancer (hazard ratio (HR), 0.83 [95% confidence interval (CI), 0.708-0.971]). Large waist circumference and high triglyceride levels among MS elements were both inversely related to the risk of hypopharyngeal cancer (HR: 0.82 [95% CI, 0.711-0.945] and 0.83 [95% CI, 0.703-0.978], respectively). The risk of hypopharyngeal cancer decreased with increasing comorbidity of MS in women (N = 0 vs. N = 1-2 vs. N ≥ 3; HR = 1 vs. HR = 0.511 [95% CI, 0.274-0.952] vs. HR = 0.295 [95% CI, 0.132-0.66]), but not in men. This study may improve our etiological understanding of hypopharyngeal cancer.
Keyphrases
- papillary thyroid
- metabolic syndrome
- squamous cell
- multiple sclerosis
- mass spectrometry
- health insurance
- type diabetes
- healthcare
- squamous cell carcinoma
- body mass index
- cardiovascular disease
- public health
- lymph node metastasis
- blood pressure
- insulin resistance
- newly diagnosed
- mental health
- childhood cancer
- machine learning
- climate change
- physical activity
- risk assessment
- social media
- deep learning
- data analysis
- cervical cancer screening