Alcohol sensitivity, alcohol use and hypertension in an older Chinese population: the Guangzhou Biobank Cohort Study

Although the J-shaped association between alcohol consumption and blood pressure (BP) is well known, the effect of alcohol sensitivity on this relationship is less clear. We studied the association of alcohol sensitivity and alcohol use with BP and hypertension. This cross-sectional analysis included 19335 older participants from the Guangzhou Biobank Cohort Study recruited from 2003 to 2006, using clinically measured BP and self-reported alcohol use and alcohol sensitivity. Alcohol use was rare in women, in whom light-to-moderate drinkers (<140 g ethanol per week) without alcohol sensitivity had lower systolic and diastolic BPs (mean difference 5.3 (95% CI 3.8-6.9) mmHg and 1.9 (1.1-2.7) mm Hg, respectively) and a reduced risk of hypertension (0.62 (0.53-0.72)) relative to never drinkers. Similarly, excessive drinkers (≥140g ethanol per week) without alcohol sensitivity had a significantly higher systolic and diastolic BP and risk of hypertension than did nondrinkers (mean difference 5.1 (2.8-7.4) mm Hg, 2.7 (1.5-4.0) mm Hg and 34% (8-66%), respectively, for men). These differences were even greater for men with alcohol sensitivity (mean differences 12.0 (8.9-15.2) mm Hg, 6.2 (4.5-7.9) mmHg and 95% CI (46-159%), respectively). Alcohol sensitivity and alcohol use were both associated with elevated BP and risk of hypertension in an older Chinese population. Alcohol sensitivity may aggravate the effect of drinking on BP. Limiting alcohol use to two drinks per day for men and one drink a day for women may be suitable for East Asians. Reduction of alcohol consumption should be an important public health target.link_to_subscribed_fulltex

GWAS of Behavioral Traits

Over the past decade, genome-wide association studies (GWAS) have evolved into a powerful tool to investigate genetic risk factors for human diseases via a hypothesis-free scan of the genome. The success of GWAS for psychiatric disorders and behavioral traits have been somewhat mixed, partly owing to the complexity and heterogeneity of these traits. Significant progress has been made in the last few years in the development and implementation of complex statistical methods and algorithms incorporating GWAS. Such advanced statistical methods applied to GWAS hits in combination with incorporation of different layers of genomics data have catapulted the search for novel genes for behavioral traits and improved our understanding of the complex polygenic architecture of these traits. This chapter will give a brief overview on GWAS and statistical methods currently used in GWAS. The chapter will focus on reviewing the current literature and highlight some of the most important GWAS on psychiatric and other behavioral traits and will conclude with a discussion on future directions.</p