Conversely, both CPD and ND were negatively genetically associated with hundreds of other diseases in BioVU , including those known to be associated with smoking, such as chronic airway obstruction, lung cancer, and metabolic diseases . Most of the associations between CPD or ND and psychiatric disorders were attenuated, and no longer significant, when we adjusted for TUD or AUD . We repeated our analyses using a quantitative measure of ND and obtained very similar findings . All pairs of PRSs showed significant correlations, except for AUDIT-C PRS and ND PRS . All r coefficients were positive, the strongest association being between CPD and FTND , except for AUDIT-C’s associations with and CPD and with FTND, which showed a negative association . The current study examines smoking and alcohol consumption phenotypes as genetic surrogates for nicotine dependence and alcohol misuse, respectively, using PRSs constructed from well-powered GWAS in the UKB and other population-based non-UKB cohorts. In applying the PRSs to a large pheWAS, we found that smoking consumption was a good proxy for dependence, but alcohol consumption was not a good proxy for alcohol misuse . Ascertainment bias may explain some of the inverse genetic correlations between alcohol consumption and, for example, obesity and type 2 diabetes . UKB and other similar collections based on voluntary participation, are only available to individuals who are relatively healthy and who have both the means and opportunity to participate, resulting in an over representation of data from individuals with higher education levels and socioeconomic status and alcohol consumption than the general population but, crucially, lower levels of metabolic disorders and problem drinking. Importantly, both alcohol consumption and alcohol misuse were measured in UKB. Thus,mobile vertical grow racks the difference between alcohol consumption and misuse could indicate that the genetic overlap between alcohol consumption and AUD is dependent on the specific patterns of drinking .
For example, Polimanti et al identified a positive genetic correlation between alcohol dependence and alcohol drinking quantity , but not frequency. Similarly, Marees et al showed that high alcohol consumption frequency was associated with high socioeconomic status and low risk of substance use disorders and other psychiatric disorders, whereas the opposite applied for high alcohol consumption quantity. Furthermore, these genetic correlations may be dissimilar to those observed when analyzing alcohol consumption in alcohol dependent individuals; such studies have yet to be performed. Notably, even though studying alcohol consumption has shown some utility, it is apparent that this phenotype measured in volunteer collections is not an optimal proxy for AUD. Similar observations have recently been described for cannabis use versus disorder with regards to proxy measures of psychosocial and anthropometric indices . Initial stages of recreational use may be etiologically distinct from later stages of pathological use for commonly used substances such as alcohol and cannabis, with only the latter stages of dependence and abuse indexing vulnerability to psychiatric impairment. Whereas use of nicotine may be a more addictive, alcohol, particularly measured in population-based cohorts such as the UKB, may represent a social habit. In contrast with alcohol, the genetic correlation between smoking consumption and ND was almost identical, and both scores showed similar patterns of genetic association with psychiatric and smoking-related comorbid diseases. We speculate that consumption phenotypes represent distinct indices of use depending on the drug: cigarette smoking may be a more accurate phenotype than drinks consumed , in addition to being a better index outcome of problematic use, such that quantity of cigarettes smoked may reflect ND. Indeed, CPD is a major component of standard measures that are used to define ND, most notably the Fagerström Test for ND. Although studying the genetics of ND alongside other smoking traits is key to gaining a better understanding of the neurobiological processes that influence the trajectory of smoking behaviors and their treatment implications, our findings suggest that smoking consumption phenotypes measured in volunteer cohorts can capture relevant sources of genetic information applicable to later stages of dependence and abuse.
Our analyses are not without limitations. We lack information regarding the potencies of cigarette and alcohol products used by individuals in the discovery and target samples. High-potency substance use is associated with increased severity of dependence, especially at younger ages. Relatedly, our results may be restricted to PRSs calculated in populations with low levels of alcohol-related problems, like UKB. Furthermore, although we used a proxy measure of alcohol misuse , instead of a clinical diagnosis of AD, the pheWAS findings using problematic alcohol use in BioVU suggest that the results of AUDIT-P PRS are similar to AD PRS . In addition, results from our sensitivity analyses revealed that the associations were slightly attenuated after diagnosis of AUD or TUD were included as covariates. It is plausible that many of the relationships between alcohol misuse and ND and psychopathology, detected in BioVU, may be consequences of an AUD or TUD diagnosis rather than due to shared genetic risk. Alternatively, these associations could reflect pleiotropy or be a causal peripheral effect of alcohol/nicotine persistent/pathological use. The reduction in the number of statistically significant associations after adjustment for AUD or TUD may imply shared genetic liability between these disorders and comorbid psychopathology, or that, simply, our correction for AUD or TUD was too stringent considering that most of the effect sizes were essentially unchanged and that we expected some degree of collinearity between AUD/TUD diagnosis and the PRSs that we calculated. Future studies should aim at exploring causal mechanisms. Lastly, our estimates of genetic overlap may be sensitive to environmental factors, for example when comparing results from UKB to younger cohorts. In summary, we performed a pheWAS of consumption and dependence/misuse polygenic scores. We conclude that smoking consumption measured in healthy volunteer cohorts is a powerful proxy for genetic studies of ND . For alcohol consumption, by using multivariate approaches that give statistically-derived weights to alcohol phenotypes or by including further restrictions to the study cohort , we may be able to mitigate some of the inverse associations between alcohol consumption and poor health and, in doing so, we may realize the full potential of alcohol consumption phenotypes as proxies for AUD.
Moreover, and as a collateral finding, we identified very robust associations between well-characterized measures of alcohol and nicotine consumption, misuse, and clinical diagnoses from a real world medical-center setting . These series of analyses demonstrate the value of using broad electronic health record measures for genetic studies of substance use disorders. Adolescence is a time of subtle, yet dynamic brain changes that occur in the context of major physiological, psychological,vertical cannabis grow systems and social transitions. This juncture marks a gradual shift from guided to independent functioning that is analogized in the protracted development of brain structure. Growth of the prefrontal cortex, limbic system structures, and white matter association fibers during this period are linked with more sophisticated cognitive functions and emotional processing, useful for navigating an increasingly complex psychosocial environment. Despite these developmental advances, increased tendencies toward risk-taking and heightened vulnerability to psychopathology are well known within the adolescent milieu. Owing in large part to progress and innovation in neuroimaging techniques, appreciable levels of new information on adolescent neurodevelopment are breaking ground. The potential of these methods to identify biomarkers for substance problems and targets for addiction treatment in youth are of significant value when considering the rise in adolescent alcohol and drug use and decline in perceived risk of substance exposure . What are the unique characteristics of the adolescent brain? What neural and behavioral profiles render youth at heightened risk for substance use problems, and are neurocognitive consequences to early substance use observable? Recent efforts have explored these questions and brought us to a fuller understanding of adolescent health and interventional needs. This paper will review neurodevelopmental processes during adolescence, discuss the influence of substance use on neuromaturation as well as probable mechanisms by which these substances influence neural development, and briefly summarize factors that may enhance risk-taking tendencies. Finally, we will conclude with suggestions for future research directions.The developmental trajectory of grey matter follows an inverted parabolic curve, with cortical volume peaking, on average, around ages 12–14, followed by a decline in volume and thickness over adolescence . Widespread supratentorial diminutions are evident, but show temporal variance across regions . Declines begin in the striatum and sensorimotor cortices , progress rostrally to the frontal poles, then end with the dorsolateral prefrontal cortex , which is also late to myelinate . Longitudinal charting of brain volumetry from 13–22 years of age reveals specific declines in medial parietal cortex, posterior temporal and middle frontal gyri, and the cerebellum in the right hemisphere, coinciding with previous studies showing these regions to develop late into adolescence . Examination of developmental changes in cortical thickness from 8–30 years of age indicates a similar pattern of nonlinear declines, with marked thinning during adolescence. Attenuations are most notable in the parietal lobe, and followed in effect size by medial and superior frontal regions, the cingulum, and occipital lobe .
The mechanisms underlying cortical volume and thickness decline are suggested to involve selective synaptic pruning of superfluous neuronal connections, reduction in glial cells, decrease in neuropil and intra-cortical myelination . Regional variations in grey matter maturation may coincide with different patterns of cortical development, with allocortex, including the piriform area, showing primarily linear growth patterns, compared to transition cortex demonstrating a combination of linear and quadratic trajectories, and isocortex demonstrating cubic growth curves . Though the functional implications of these developmental trajectories are unclear, isocortical regions undergo more protracted development and support complex behavioral functions. Their growth curves may reflect critical periods for development of cognitive skills as well as windows of vulnerability for neurotoxic exposure or other developmental perturbations.In contrast to grey matter reductions, white matter across the adolescent years shows growth and enhancement of pathways . This is reflected in white matter volume increase, particularly in fronto-parietal regions . Diffusion tensor imaging , a neuroimaging technique that has gained widespread use over the past decade, relies on the intrinsic diffusion properties of water molecules and has afforded a view into the more subtle micro-structural changes that occur in white matter architecture. Two common scalar variables derived from DTI are fractional anisotropy , which describes the directional variance of diffusional motion, and mean diffusivity , an indicator of the overall magnitude of diffusional motion. These measures index relationships between signal intensity changes and underlying tissue structure, and provide descriptions of white matter quality and architecture . High FA reflects greater fiber organization and coherence, myelination and/or other structural components of the axon, and low MD values suggest greater white matter density .These trends continue through early adulthood in a nearly linear manner , though recent data suggest an exponential pattern of anisotropic increase that may plateau during the late-teens to early twenties . Areas with the most prominent FA change during adolescence are the superior longitudinal fasciculus, superior corona radiata, thalamic radiations, and posterior limb of the internal capsule . Other projection and association pathways including the corticospinal tract, arcuate fasciculus, cingulum, corpus callosum, superior and mid-temporal white matter, and inferior parietal white matter show anisotropic increases as well . Changes in subcortical and deep grey matter fibers are more pronounced, with less change in compact white matter tracts comprising highly parallel fibers such as the internal capsule and corpus callosum . Fiber tracts constituting the fronto-temporal pathways appear to mature relatively later , though comparison of growth rates among tracts comes largely from cross-sectional data that present developmental trends. The neurobiological mechanisms contributing to FA increases and MD decreases during adolescence are not entirely understood, but examination of underlying diffusion dynamics point to some probable processes. For example, decreases in radial diffusivity , diffusion that occurs perpendicular to white matter pathways, suggests increased myelination, axonal density, and fiber compactness , but have not been uniformly observed to occur during adolescence. Similarly, changes in axial diffusivity , diffusion parallel to the fibers’ principle axis, show discrepant trends, with some studies documenting decreases , and others increases in this index .