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From the Departments of Psychiatry (Drs. Giannakopoulos, Bouras, and Kövari) and Geriatrics (Drs. Herrmann and Gold), HUG Belle-Idée, University of Geneva School of Medicine; Department of Psychogeriatrics (Dr. Giannakopoulos), University of Lausanne School of Medicine, Switzerland; Kastor Neurobiology of Aging Laboratories and Fishberg Research Center for Neurobiology (Drs. Bussière, Bouras, Perl, Morrison, and Hof) and Departments of Geriatrics and Adult Development (Drs. Morrison and Hof), Ophthalmology (Dr. Hof), Pathology (Dr. Perl), and Psychiatry (Dr. Perl), Mount Sinai School of Medicine, New York, NY; and Elan Pharmaceuticals Inc. (Dr. Bussière), South San Francisco, CA.
Address correspondence and reprint requests to Prof. Panteleimon Giannakopoulos, Clinic of Geriatric Psychiatry, Department of Psychiatry, University of Geneva School of Medicine, 1225 Chêne-Bourg, Geneva, Switzerland; e-mail: Panteleimon.Giannakopoulos{at}medecine.unige.ch
Objective: To examine the relationship between stereologic estimates of AD-related pathology and severity of cognitive deficits in brain aging.
Background: Previous studies reported substantial contributions of neurofibrillary tangles (NFT), amyloid deposits, and neuronal loss to the development of dementia. However, the prediction of cognitive status based on nonstereologic quantification of these measures has led to conflicting results. Such studies have measured densities, rather than absolute numbers, and most do not take into account the potential interaction between the above pathologic hallmarks in a global multivariate analysis.
Methods: Clinicopathologic study in 22 elderly cases. Cognitive status assessed prospectively using the Mini-Mental State Examination (MMSE); stereologic assessment of NFT, unaffected neurons, and total amyloid volume in the CA1 field of the hippocampus, entorhinal cortex, and area 9. Statistical analysis was performed using both univariate and multivariate linear regression models.
Results: High total NFT counts but not amyloid volume were strongly associated with a lower number of unaffected neurons in all areas studied. A high proportion of variability in MMSE scores was explained by NFT and neuronal counts in the CA1 field (83% and 85.4%), entorhinal cortex (87.8% and 83.7%), and area 9 (87% and 79%); amyloid volume in the entorhinal cortex, but not in the CA1 field and area 9, accounted for 58.5% of MMSE variability. Multivariate analyses showed that total NFT counts in the entorhinal cortex and area 9 as well as neuron numbers in the CA1 field were the best predictors of MMSE score.
Conclusions: These new stereologic data indicate that neuronal pathology in hippocampal formation and frontal cortex closely reflects the progression of cognitive deficits in brain aging and AD. They also demonstrate that amyloid volume has no additional predictive value, in terms of clinicopathologic correlations, beyond its interaction with NFT.
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