Login / Signup

Liver-function parameters are associated with incident hypertension in a large Taiwanese population follow-up study.

Yi-Hsueh LiuSzu-Chia ChenWen-Hsien LeeYing-Chih ChenJiun-Chi HuangPei-Yu WuChih-Hsing HungChao-Hung KuoHo-Ming Su
Published in: Journal of human hypertension (2022)
Previous studies demonstrated inconsistent results regarding the association between liver function and hypertension. In addition, large cohort follow-up studies are lacking. Therefore, this longitudinal study aimed to investigate the association between liver function and incident hypertension using data from the Taiwan Biobank (TWB). We evaluated liver biomarkers, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin, alpha-fetoprotein (AFP), total bilirubin, and gamma-glutamyl transferase (GGT) in this study. A total of 21,293 participants without hypertension at baseline were analyzed. During the mean 3.9-year follow-up, 3002 participants developed hypertension (defined as incident hypertension). Multivariable analysis revealed that high AST (odds ratio [OR], 1.004; 95% confidence interval [CI], 1.001-1.007; p = 0.014), high ALT (OR, 1.004; 95% CI, 1.002-1.006; p < 0.001), high albumin (OR, 1.897; 95% CI, 1.573-2.286; p < 0.001), and high GGT (OR, 1.004; 95% CI, 1.003-1.005; p < 0.001) were significantly associated with incident hypertension in all study participants. In subgroup analysis of the participants with an ALT level ≤2 times the normal limit (80 u/l) (n = 20,983), multivariable analysis demonstrated that high ALT (OR, 1.009; 95% CI, 1.005-1.012; p < 0.001) and high GGT (OR, 1.005; 95% CI, 1.003-1.006; p < 0.001) were significantly associated with incident hypertension. In conclusion, we found that elevated AST, ALT, albumin, and GGT were associated with incident hypertension in a large Taiwanese cohort. A greater understanding of potential risk factors for hypertension may help to reduce the burden of hypertension in this Taiwanese population.
Keyphrases
  • blood pressure
  • cardiovascular disease
  • randomized controlled trial
  • type diabetes
  • clinical trial
  • machine learning
  • risk factors
  • big data
  • open label