Background: Alcohol use disorder (AUD) is associated with cognitive deficits but little is known to what degree this is caused by genetically influenced traits, i.e. endophenotypes, present before the onset of the disorder. The aim of the current study was to investigate to what degree family history (FH) of AUD is associated with cognitive functions.
Methods: Case-control cross-sectional study at an outpatient addiction research clinic. Treatment-seeking AUD patients (n = 106) were compared to healthy controls (HC; n = 90), matched for age and sex. The HC group was further subdivided into AUD FH positive (FH+; n = 47) or negative (FH-; n = 39) based on the Family Tree Questionnaire. Participants underwent psychiatric and substance use assessments, completed the Barratt Impulsiveness Scale and performed a comprehensive battery of neuropsychological tests assessing response inhibition, decision making, attention, working memory, and emotional recognition.
Results: Compared to HC, AUD patients exhibited elevated self-rated impulsivity (p < 0.001; d = 0.62), as well as significantly poorer response inhibition (p = 0.001; d = 0.51), attention (p = 0.021; d = 0.38) and information gathering in decision making (p = 0.073; d = 0.34). Similar to AUD patients, FH+ individuals exhibited elevated self-rated impulsivity (p = 0.096; d = 0.46), and in addition significantly worse future planning capacity (p < 0.001; d = 0.76) and prolonged emotional recognition response time (p = 0.010; d = 0.60) compared to FH-, while no other significant differences were found between FH+ and FH-.
Conclusions: Elevated impulsivity, poor performance in future planning and emotional processing speed may be potential cognitive endophenotypes in AUD. These cognitive domains represent putative targets for prevention strategies and treatment of AUD.
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http://dx.doi.org/10.1017/S003329172000238X | DOI Listing |
J Gen Intern Med
January 2025
Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
Background: Patients with substance use disorder (SUD) are frequently hospitalized and readmitted. Hospitalization is an opportunity for treatment initiation, including medications for alcohol (MAUD) and opioid use disorder (MOUD). Addiction consult teams are one model for increasing hospital-based SUD treatment.
View Article and Find Full Text PDFAm J Psychiatry
January 2025
Department of Psychiatry, NYU Langone Center for Psychedelic Medicine, NYU Grossman School of Medicine, New York (Pagni, Zeifman, Mennenga, Carrithers, Goldway, O'Donnell, Ross, Bogenschutz); School of Life Sciences, Arizona State University, Tempe (Mennenga); Department of Psychology, New York University, New York (Goldway); Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque (Bhatt).
JMIR Form Res
December 2024
Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States.
Background: Anxiety disorders are common in alcohol use disorder (AUD) treatment patients. Such co-occurring conditions ("comorbidity") have negative prognostic implications for AUD treatment outcomes, yet they commonly go unaddressed in standard AUD care. Over a decade ago, we developed and validated a cognitive behavioral therapy intervention to supplement standard AUD care that, when delivered by trained therapists, improves outcomes in comorbid patients.
View Article and Find Full Text PDFBMC Med Res Methodol
December 2024
Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, the Netherlands.
Background: The aim of this study is to develop a method we call "cost mining" to unravel cost variation and identify cost drivers by modelling integrated patient pathways from primary care to the palliative care setting. This approach fills an urgent need to quantify financial strains on healthcare systems, particularly for colorectal cancer, which is the most expensive cancer in Australia, and the second most expensive cancer globally.
Methods: We developed and published a customized algorithm that dynamically estimates and visualizes the mean, minimum, and total costs of care at the patient level, by aggregating activity-based healthcare system costs (e.
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