Latent class typology of nicotine withdrawal: genetic contributions and association with failed smoking cessation and psychiatric disorders.

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Title: Latent class typology of nicotine withdrawal: genetic contributions and association with failed smoking cessation and psychiatric disorders.
Authors: Xian H (AUTHOR), Scherrer JF (AUTHOR), Madden PAF (AUTHOR), Lyons MJ (AUTHOR), Tsuang M (AUTHOR), True WR (AUTHOR), Eisen SA (AUTHOR)
Source: Psychological Medicine. Mar2005, Vol. 35 Issue 3, p409-419. 11p.
Abstract: BACKGROUND: Nicotine withdrawal is associated with failed smoking cessation and thus contributes to continuance of the habit and increases risk of smoking-related illnesses. Withdrawal is also associated with psychiatric disorders such as depression and alcoholism. However, relatively little is known about how to characterize the severity of withdrawal, including whether withdrawal subtypes exist in male smokers. If so, do these subtypes represent quantitative or qualitative differences? METHOD: Smoking and withdrawal data were obtained from 4112 male-male twin pairs of the Vietnam Era Twin Registry during a 1992 administration of the Diagnostic Interview Schedule. Latent Class Analysis (LCA) was used to derive significantly different nicotine withdrawal profiles, and their association with psychiatric disorders was assessed. Genetic and environmental contributions and the correlation between these contributions were evaluated using bivariate biometrical modeling of the withdrawal phenotype and failed smoking cessation. RESULTS: The LCA model which best fit the data was a four-class severity continuum. Psychiatric disorders were significantly associated with more severe classes and the magnitude of the association increased as withdrawal severity increased. Genetics accounted for 31% and 51% of the variance in risk for withdrawal and failed cessation, respectively. The genetic contributions were significantly correlated (r = 0.37). CONCLUSIONS: Nicotine withdrawal classes are characterized by quantitative differences. The strong association between psychiatric disorders and withdrawal severity and the significant genetic correlation between withdrawal and cessation highlight the importance of withdrawal severity. Further refinement of the DSM definition of withdrawal to incorporate severity ratings may be warranted. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Latent class typology of nicotine withdrawal: genetic contributions and association with failed smoking cessation and psychiatric disorders.
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  Data: <searchLink fieldCode="AR" term="%22Xian+H%22">Xian H</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Scherrer+JF%22">Scherrer JF</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Madden+PAF%22">Madden PAF</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lyons+MJ%22">Lyons MJ</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tsuang+M%22">Tsuang M</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22True+WR%22">True WR</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Eisen+SA%22">Eisen SA</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Psychological+Medicine%22">Psychological Medicine</searchLink>. Mar2005, Vol. 35 Issue 3, p409-419. 11p.
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: BACKGROUND: Nicotine withdrawal is associated with failed smoking cessation and thus contributes to continuance of the habit and increases risk of smoking-related illnesses. Withdrawal is also associated with psychiatric disorders such as depression and alcoholism. However, relatively little is known about how to characterize the severity of withdrawal, including whether withdrawal subtypes exist in male smokers. If so, do these subtypes represent quantitative or qualitative differences? METHOD: Smoking and withdrawal data were obtained from 4112 male-male twin pairs of the Vietnam Era Twin Registry during a 1992 administration of the Diagnostic Interview Schedule. Latent Class Analysis (LCA) was used to derive significantly different nicotine withdrawal profiles, and their association with psychiatric disorders was assessed. Genetic and environmental contributions and the correlation between these contributions were evaluated using bivariate biometrical modeling of the withdrawal phenotype and failed smoking cessation. RESULTS: The LCA model which best fit the data was a four-class severity continuum. Psychiatric disorders were significantly associated with more severe classes and the magnitude of the association increased as withdrawal severity increased. Genetics accounted for 31% and 51% of the variance in risk for withdrawal and failed cessation, respectively. The genetic contributions were significantly correlated (r = 0.37). CONCLUSIONS: Nicotine withdrawal classes are characterized by quantitative differences. The strong association between psychiatric disorders and withdrawal severity and the significant genetic correlation between withdrawal and cessation highlight the importance of withdrawal severity. Further refinement of the DSM definition of withdrawal to incorporate severity ratings may be warranted. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1017/s0033291704003289
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        Text: English
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              Text: Mar2005
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