4.5 Article

Certified normal: Alzheimer's disease biomarkers and normative estimates of cognitive functioning

期刊

NEUROBIOLOGY OF AGING
卷 43, 期 -, 页码 23-33

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.neurobiolaging.2016.03.014

关键词

Alzheimer's disease; Biomarkers; Cognition; Normative data; Memory; Preclinical disease

资金

  1. National Institute on Aging [P01-AG003991, P50-AG05681, P01-AG02676]
  2. National Institute of Diabetes and Digestive and Kidney Diseases [K23-DK094982]
  3. National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) [UL1 TR000448]
  4. Neuroimaging Informatics and Analysis Center [NIH 5P30NS048056]

向作者/读者索取更多资源

Normative samples drawn from older populations may unintentionally include individuals with preclinical Alzheimer's disease (AD) pathology, resulting in reduced means, increased variability, and overestimation of age effects on cognitive performance. A total of 264 cognitively normal (Clinical Dementia Rating = 0) older adults were classified as biomarker negative (Robust Normal, n = 177) or biomarker positive (Preclinical Alzheimer's Disease [PCAD], n = 87) based on amyloid imaging, cerebrospinal fluid biomarkers, and hippocampal volumes. PCAD participants performed worse than robust normals on nearly all cognitive measures. Removing PCAD participants from the normative sample yielded higher means and less variability on episodic memory, visuospatial ability, and executive functioning measures. These results were more pronounced in participants aged 75 years and older. Notably, removing PCAD participants from the sample significantly reduced age effects across all cognitive domains. Applying norms from the robust normal sample to a separate cohort did not improve Clinical Dementia Rating classification when using standard deviation cutoff scores. Overall, removing individuals with biomarker evidence of preclinical AD improves normative sample quality and substantially reduces age effects on cognitive performance but provides no substantive benefit for diagnostic classifications. (C) 2016 Elsevier Inc. All rights reserved.

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