Hostname: page-component-76fb5796d-45l2p Total loading time: 0 Render date: 2024-04-26T12:15:09.805Z Has data issue: false hasContentIssue false
  • English
  • Français

The CERAD Neuropsychological Assessment Battery Is Sensitive to Alcohol-Related Cognitive Deficiencies in Elderly Patients: A Retrospective Matched Case-Control Study

Published online by Cambridge University Press:  06 November 2017

Liane Kaufmann ,
Stefan Huber ,
Daniel Mayer ,
Korbinian Moeller  and
Josef Marksteiner
Liane Kaufmann*
Affiliation:
Department of Psychiatry and Psychotherapy A, General Hospital Hall, Hall in Tyrol, Austria
Stefan Huber
Affiliation:
Junior Research Group Neuro-cognitive Plasticity, Leibniz Institut für Wissensmedien, Tuebingen, Germany
Daniel Mayer
Affiliation:
Department of Psychiatry and Psychotherapy A, General Hospital Hall, Hall in Tyrol, Austria
Korbinian Moeller
Affiliation:
Junior Research Group Neuro-cognitive Plasticity, Leibniz Institut für Wissensmedien, Tuebingen, Germany Department of Psychology, University of Tuebingen, Tuebingen, Germany LEAD Graduate School and Research Network, University of Tuebingen, Tuebingen, Germany
Josef Marksteiner
Affiliation:
Department of Psychiatry and Psychotherapy A, General Hospital Hall, Hall in Tyrol, Austria
*
Correspondence and reprint requests to: Liane Kaufmann, General Hospital Hall, Department of Psychiatry and Psychotherapy A, A-6060 Hall in Tyrol, Austria. E-mail: liane.kaufmann@tirol-kliniken.at
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Adverse effects of heavy drinking on cognition have frequently been reported. In the present study, we systematically examined for the first time whether clinical neuropsychological assessments may be sensitive to alcohol abuse in elderly patients with suspected minor neurocognitive disorder. Methods: A total of 144 elderly with and without alcohol abuse (each group n=72; mean age 66.7 years) were selected from a patient pool of n=738 by applying propensity score matching (a statistical method allowing to match participants in experimental and control group by balancing various covariates to reduce selection bias). Accordingly, study groups were almost perfectly matched regarding age, education, gender, and Mini Mental State Examination score. Neuropsychological performance was measured using the CERAD (Consortium to Establish a Registry for Alzheimer’s Disease). Classification analyses (i.e., decision tree and boosted trees models) were conducted to examine whether CERAD variables or total score contributed to group classification. Results: Decision tree models disclosed that groups could be reliably classified based on the CERAD variables “Word List Discriminability” (tapping verbal recognition memory, 64% classification accuracy) and “Trail Making Test A” (measuring visuo-motor speed, 59% classification accuracy). Boosted tree analyses further indicated the sensitivity of “Word List Recall” (measuring free verbal recall) for discriminating elderly with versus without a history of alcohol abuse. Conclusions: This indicates that specific CERAD variables seem to be sensitive to alcohol-related cognitive dysfunctions in elderly patients with suspected minor neurocognitive disorder. (JINS, 2018, 24, 360–371)

Keywords

ElderlyMinor neurocognitive disorderAlcohol abuseNeuropsychological performanceCERADVerbal recognition memoryVisuo-motor speed
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 24 , Issue 4 , April 2018 , pp. 360 - 371
Copyright
Copyright © The International Neuropsychological Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders ((4th ed.), Washington, DC: American Psychiatric Association Press.Google Scholar
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders ((5th ed.), Washington, DC: American Psychiatric Association Press.Google Scholar
Anstey, K.J., Mack, H.A., & Cherbuin, N. (2009). Alcohol consumption as a risk factor for dementia and cognitive decline: Meta-analysis of prospective studies. American Journal of Geriatric Psychiatry, 17(7), 542–555.Google Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. Retrieved from http://www.jstor.org/stable/2346101 Google Scholar
Bernardin, F., Maheut-Bosser, A., & Paille, F. (2014). Cognitive impairments in alcohol-dependent subjects. Frontiers in Psychiatry, 5, 78.Google Scholar
Bertoux, M., Ramanan, S., Slachevsky, A., Wong, S., Henriquez, F., Musa, G., & Dubois, B. (2016). So close but yet so far: Executive contribution to memory processes in behavioral variant of frontotemporal dementia. Journal of Alzheimers Disease, 54(3), 1005–1014.Google Scholar
Beydoun, M.A., Beydoun, H.A., Gamaldo, A.A., Teel, A., Zonderman, A.B., & Wang, Y. (2014). Epidemiological studies on modifiable factors associated with cognition and dementia: Systematic review and meta-analysis. BMC Public Health, 14, 643.Google Scholar
Butters, N. (1985). Alcoholic Korsakoff’s syndome: Some unresolved issues concerning etiology, neuropathology, and cognitive deficits. Journal of Clinical and Experimental Neuropsychology, 7(2), 181–210.Google Scholar
Caine, D., Halliday, G.M., Kril, J.J., & Harper, C.G. (1997). Operational criteria for the classification of chronic alcoholics: Identification of Wernicke’s encephalopathy. Journal of Neurology, Neurosurgery, and Psychiatry, 62(1), 51–60.Google Scholar
Chandler, M.J., Lacritz, L.H., Hynan, L.S., Barnard, H.D., Allen, G., Deschner, M., & Cullum, C.M. (2005). A total score for the CERAD neuropsychological battery. Neurology, 65(1), 102–106.Google Scholar
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MIning (KDD ‘16). ACM, New York, NY. (pp. 785–794). doi:http://dx.doi.org/10.1145/2939672.2939785.Google Scholar
Chen, T., He, T., & Benesty, M. (2016). xgboost: Extreme gradient boosting. R package version 0.4-4. Retrieved from https://CRAN.R-project.org/package=xgboost.Google Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159.Google Scholar
DeFrancesco, M., Marksteiner, J., Deisenhammer, E.A., Hinterhuber, H., & Weiss, E. M. (2009). Association of mild cognitive impairment (MCI) and depression. Neuropsychiatrie, 23(3), 144–150. [Article in German].Google Scholar
Dupuy, M., & Chanaud, S. (2016). Imaging the addicted brain: Alcohol. International Review of Neurobiology, 129, 1–31.Google Scholar
Ehrensperger, M.M., Berres, M., Taylor, K.I., & Monsch, A.U. (2010). Early detection of Alzheimer’s disease with a total score of the German CERAD. Journal of the International Neuropsychological Society, 16, 910–920.Google Scholar
Ferguson, C.J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532–538.Google Scholar
Folstein, M.F., Folstein, S.E., & McHugh, P.R. (1975). ‘Mini Mental State’ - a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.Google Scholar
Friedman, N.P., & Miyake, A. (2016). Unity and diversity of executive functions: Individual differences as a window to cognitive structure. Cortex, 86, 186–204.Google Scholar
Gauggel, S., & Birkner, B. (1999). Validität und Reliabilität einer deutschen Version der Geriatrischen Depressionsskala (GDS). Zeitschrift für Klinische Psychologie, 28, 18–27. [Article in German].Google Scholar
Gini, C. (1921). Measurement of inequality of incomes. The Economic Journal, 31(121), 124–126. http://doi.org/10.2307/2223319.Google Scholar
Heser, K., Bleckwenn, M., Wiese, B., Mamone, S., Riedel-Heller, S.G., Stein, J., & Wagner, M., for the AgeCoDe study group. (2016). Late-life depressive symptoms and lifetime history of major depression: Cognitive deficits are largely due to incipient dementia rather than depression. Journal of Alzheimers Disease, 54(1), 185–199.Google Scholar
Ho, D.E., Imai, K., King, G., & Stuart, E. (2011). MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42(8), 1–28.Google Scholar
Hughes, C.P., Berg, L., Danziger, W.L., Coben, L.A., & Martin, R.L. (1982). A new clinical scale for the staging of dementia. British Journal of Psychiatry, 140, 566–572.Google Scholar
Mayer, D., Diwo, A., Imarhiagbe, D., Erler, S., Marksteiner, J., & Kaufmann, L. (2015). About the differential diagnostic utility of the CERAD neuropsychological assessment battery in patients with mild cognitive impairment with and without depressive symptomatology. Zeitschrift für Neuropsychologie, 26(2), 131–142. [Article in German].Google Scholar
Milborrow, S. (2016). Rpart.plot: Plot ‘rpart’ models: An enhanced version of ‘plot.rpart’. R package version 2.0.1. Retrieved from https://CRAN.R-project.org/package=rpart.plot.Google Scholar
Morris, J.C., Heyman, A., Mohs, R.C., Hughes, J.P., van Belle, G., Fillenbaum, G., & Clark, C. (1989). The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology, 39, 1159–1165.Google Scholar
Oscar-Berman, M., & Marinkovic, K. (2007). Alcohol: Effects on neurobehavioral functions and the brain. Neuropsychological Reviews, 17(3), 239–257.Google Scholar
Oscar-Berman, M., Valmas, M.M., Sawyer, K.S., Ruiz, S.M., Luhar, R.B., & Gravitz, Z.R. (2014). Profiles of impaired, spared and recovered neuropsychological processes in alcoholism. Handbook of Clinical Neurology, 125, 183–210.Google Scholar
Paajanen, T., Hänninen, T., Tunnard, C., Mecocci, P., Sobow, T., Tsolaki, M., & Soininen, H. (2010). CERAD neuropsychological battery total score in multinational mild cognitive impairment and control populations: The AddNeuroMed study. Journal of Alzheimer’s Disease, 22, 1089–1097.Google Scholar
Panza, F., Frisardi, V., Seripa, D., Logroscino, C., Santamato, A, Imbimbo, B.P., & Solfrizzi, V. (2012). Alcohol consumption in mild cognitive impairment and dementia: Harmful or neuroprotective? International Journal of Geriatric Psychiatry, 27(12), 1218–1238.Google Scholar
Panza, F., Frisardi, V., Capurso, D., D’Introno, A., Colacicco, A.M., Imbimbo, B.P., & Solfrizzi, V. (2010). Late-life depression, mild cognitive impairment, and dementia: Possible continuum? American Journal of Geriatric Psychiatry, 18(2), 98–116.Google Scholar
Peters, R., Peters, J., Warner, J., Beckett, N., & Bulpitt, C. (2008). Alcohol, dementia and cognitive decline in the elderly: A systematic review. Age and Ageing, 37, 505–512.Google Scholar
R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing. Retrieved from http://www.r-project.org/.Google Scholar
Randolph, J.J., Falbe, K., Manuel, A.K., & Balloun, J.L. (2014). A step-by-step guide to propensity score matching in R information on the dataset used. Practical Assessment, Research & Evaluation, 19(18), 1–6.Google Scholar
Ridley, N.J., Draper, B., & Withall, A. (2013). Alcohol-related dementia: An update of the evidence. Alzheimer’s Research & Therapy, 5, 3.Google Scholar
Schmid, N.S., Ehrensperger, M.M., Berres, M., Beck, I.R., & Monsch, A.U. (2014). The extension of the German CERAD neuropsychological assessment battery with tests assessing subcortical, executive and frontal functions improves accuracy in dementia diagnosis. Dementia and Geriatric Cognitive Disorders Extra, 4, 322–334.Google Scholar
Sinforiani, E., Zucchella, C., Pasotti, C., Casoni, F., Bini, P., & Costa, A. (2011). The effects of alcohol on cognition in the elderly: From protection to neurodegeneration. Functional Neurology, 26(2), 103–106.Google Scholar
Smith, A. (1995). Medical manifestations of alcoholism in the elderly. The International Journal of the Addictions, 30(13&14), 1749–1798.Google Scholar
Sullivan, E.V., Harris, R.A., & Pfefferbaum, A. (2010). Alcohol’s effects on brain and behavior. Alcohol Research & Health, 33(1), 127–143.Google Scholar
Thalmann, B., Monsch, A.U., Schneitter, M., Bernasconi, F., Aebi, C., Camachova-Davet, Z., & Staehelin, H.B. (2000). The CERAD neuropsychological assessment battery (CERAD-NAB) - A minimal dataset as a common tool for German-speaking Europe. Neurobiology of Aging, 21, 30.Google Scholar
Therneau, T., Atkinson, B., & Ripley, B. (2015). Rpart: Recursive partitioning and regression trees. Retrieved from https://cran.r-project.org/package=rpart.Google Scholar
Wickham, H. (2009). Ggplot2: Elegant graphics for data analysis. New York, NY: Springer-Verlag.Google Scholar
Wolfsgruber, S., Jessen, F., Wiese, B., Stein, J., Bicke, H., Mösch, E., & Wagner, M., for the AgeCoDe study group. (2014). The CERAD neuropsychological assessment battery total score detects and predicts Alzheimer Disease dementia with high diagnostic accuracy. American Journal of Geriatric Psychiatry, 22(10), 1017–1028.Google Scholar
Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., Adey, M., & von Leirer, O. (1983). Development and validationof a Geriatric Depression Scale: A preliminary report. Journal of Psychiatric Research, 17, 37–49.Google Scholar