John Budde

OBJECTIVE: A recent study provisionally identified numerous genetic variants as risk factors for the transition from smoking to the development of nicotine dependence, including an amino acid change in the alpha5 nicotinic cholinergic receptor (CHRNA5). The purpose of this study was to replicate these findings in an independent data set and more thoroughly investigate the role of genetic variation in the cluster of physically linked nicotinic receptors, CHRNA5-CHRNA3-CHRNB4, and the risk of smoking. METHOD: Individuals from 219 European American families (N=2,284) were genotyped across this gene cluster to test the genetic association with smoking. The frequency of the amino acid variant (rs16969968) was studied in 995 individuals from diverse ethnic populations. In vitro studies were performed to directly test whether the amino acid variant in the CHRNA5 influences receptor function. RESULTS: A genetic variant marking an amino acid change showed association with the smoking phenotype (p=0.007). This variant is within a highly conserved region across nonhuman species, but its frequency varied across human populations (0% in African populations to 37% in European populations). Furthermore, functional studies demonstrated that the risk allele decreased response to a nicotine agonist. A second independent finding was seen at rs578776 (p=0.003), and the functional significance of this association remains unknown. CONCLUSIONS: This study confirms that at least two independent variants in this nicotinic receptor gene cluster contribute to the development of habitual smoking in some populations, and it underscores the importance of multiple genetic variants contributing to the development of common diseases in various populations.

Abstract Animal studies show aging varies between individuals as well as between organs within an individual 1–4 , but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20–50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer’s disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5 ), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.