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https://doi.org/10.24966/aad-7...
Article
License: CC BY
Data sources: UnpayWall
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Addiction & Addictive Disorders
Article . 2019 . Peer-reviewed
Data sources: Crossref
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Addiction & Addictive Disorders
Article
License: CC BY
Data sources: UnpayWall
https://doi.org/10.1101/190041...
Article . 2019 . Peer-reviewed
Data sources: Crossref
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Peripheral Biomarkers of Tobacco-Use Disorder: A Systematic Review

Authors: Newton, Dwight F;

Peripheral Biomarkers of Tobacco-Use Disorder: A Systematic Review

Abstract

AbstractIntroductionTobacco use disorder (TUD) is a major worldwide healthcare burden resulting in 7 million deaths annually. TUD has few approved cessation aids, all of which are associated a high rate of relapse within one year. Biomarkers of TUD severity, treatment response, and risk of relapse have high potential clinical utility to identify ideal responders and guide additional treatment resources.MethodsA MEDLINE search was performed using the terms biomarkers, dihydroxyacetone phosphate, bilirubin, inositol, cotinine, adrenocorticotropic hormone, cortisol, pituitary-adrenal system, homovanillic acid, dopamine, pro-opiomelanocortin, lipids, lipid metabolism all cross-referenced with tobacco-use disorder.ResultsThe search yielded 424 results, of which 57 met inclusion criteria. The most commonly studied biomarkers were those related to nicotine metabolism, the hypothalamic-pituitary-adrenal (HPA) axis, and cardiovascular (CVD) risk. Nicotine metabolism was most associated with severity of dependence and treatment response, where as HPA axis and CVD markers showed less robust associations with dependence and relapse risk.ConclusionsNicotine-metabolite ratio, cortisol, and atherogenicity markers appear to be the most promising lead biomarkers for further investigation, though the body of literature is still preliminary. Longitudinal, repeated-measures studies are required to determine the directionality of the observed associations and determine true predictive power of these biomarkers. Future studies should also endeavour to study populations with comorbid psychiatric disorders to determine differences in utility of certain biomarkers.

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
gold