Title |
Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence
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Published in |
Translational Psychiatry, December 2017
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DOI | 10.1038/s41398-017-0037-y |
Pubmed ID | |
Authors |
Matthias Guggenmos, Katharina Schmack, Maria Sekutowicz, Maria Garbusow, Miriam Sebold, Christian Sommer, Michael N. Smolka, Hans-Ulrich Wittchen, Ulrich S. Zimmermann, Andreas Heinz, Philipp Sterzer |
Abstract |
The premature aging hypothesis of alcohol dependence proposes that the neurobiological and behavioural deficits in individuals with alcohol dependence are analogous to those of chronological aging. However, to date no systematic neurobiological evidence for this hypothesis has been provided. To test the hypothesis, 119 alcohol-dependent subjects and 97 age- and gender-matched healthy control subjects underwent structural MRI. Whole-brain grey matter volume maps were computed from structural MRI scans using voxel-based morphometry and parcelled into a comprehensive set of anatomical brain regions. Regional grey matter volume averages served as the basis for cross-regional similarity analyses and a brain age model. We found a striking correspondence between regional patterns of alcohol- and age-related grey matter loss across 110 brain regions. The brain age model revealed that the brain age of age-matched AD subjects was increased by up to 11.7 years. Interestingly, while no brain aging was detected in the youngest AD subjects (20-30 years), we found that alcohol-related brain aging systematically increased in the following age decades controlling for lifetime alcohol consumption and general health status. Together, these results provide strong evidence for an accelerated aging model of AD and indicate an elevated risk of alcohol-related brain aging in elderly individuals. |
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Unknown | 4 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 89 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 14 | 16% |
Researcher | 10 | 11% |
Student > Bachelor | 10 | 11% |
Student > Master | 10 | 11% |
Professor > Associate Professor | 7 | 8% |
Other | 14 | 16% |
Unknown | 24 | 27% |
Readers by discipline | Count | As % |
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Psychology | 16 | 18% |
Neuroscience | 14 | 16% |
Medicine and Dentistry | 7 | 8% |
Computer Science | 4 | 4% |
Biochemistry, Genetics and Molecular Biology | 3 | 3% |
Other | 12 | 13% |
Unknown | 33 | 37% |