The association between hyperuricemia and cardiovascular disease history: A cross-sectional study using KoGES HEXA data.
Joo-Hee KimMi Jung KwonHyo Geun ChoiSang Jun LeeSung-Woo KimJi Hee KimBong Cheol KwonJung-Woo LeePublished in: Medicine (2023)
This cross-sectional study examines the association between hyperuricemia and cardiovascular diseases (CVDs). Data from the Korean Genome and Epidemiology Study from 2004 to 2016 were analyzed. Among the 173,209 participants, we selected 11,453 patients with hyperuricemia and 152,255 controls (non-hyperuricemia). We obtained the history of CVDs (stroke and ischemic heart disease [IHD]) from all participants. Crude and adjusted odds ratios (aORs) (age, income group, body mass index, smoking, alcohol consumption, anthropometry data, and nutritional intake) for CVDs were analyzed using a logistic regression model. Participants with hyperuricemia reported a significantly higher prevalence of stroke (2.4% vs 1.3%) and IHD (5.6% vs 2.8%) than controls did (P < .001). Participants with hyperuricemia had a significantly higher aOR for CVD than the controls. The aOR of hyperuricemia for stroke was 1.22 (95% confidence interval = 1.07-1.39, P = .004). When analyzed by subgroup according to age and sex, this result was only persistent in women. The aOR of hyperuricemia for IHD was 1.45 (95% confidence interval = 1.33-1.59, P < .001). In the subgroup analyses, the results were similar, except in young men. Hyperuricemia was significantly associated with CVD in the Korean population.
Keyphrases
- uric acid
- cardiovascular disease
- body mass index
- atrial fibrillation
- alcohol consumption
- metabolic syndrome
- electronic health record
- big data
- physical activity
- type diabetes
- clinical trial
- risk factors
- weight gain
- polycystic ovary syndrome
- cardiovascular risk factors
- coronary artery disease
- middle aged
- data analysis
- brain injury
- adipose tissue
- artificial intelligence
- blood brain barrier
- cardiovascular events
- insulin resistance
- subarachnoid hemorrhage
- deep learning
- weight loss
- cervical cancer screening