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- Cohen, Ann D, et al.
(författare)
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Fluid and PET biomarkers for amyloid pathology in Alzheimer's disease.
- 2019
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Ingår i: Molecular and cellular neurosciences. - : Elsevier BV. - 1095-9327 .- 1044-7431. ; 97, s. 3-17
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Forskningsöversikt (refereegranskat)abstract
- Alzheimer's disease (AD) is characterized by amyloid plaques and tau pathology (neurofibrillary tangles and neuropil threads). Amyloid plaques are primarily composed of aggregated and oligomeric β-amyloid (Aβ) peptides ending at position 42 (Aβ42). The development of fluid and PET biomarkers for Alzheimer's disease (AD), has allowed for detection of Aβ pathology in vivo and marks a major advancement in understanding the role of Aβ in Alzheimer's disease (AD). In the recent National Institute on Aging and Alzheimer's Association (NIA-AA) Research Framework, AD is defined by the underlying pathology as measured in patients during life by biomarkers (Jack et al., 2018), while clinical symptoms are used for staging of the disease. Therefore, sensitive, specific and robust biomarkers to identify brain amyloidosis are central in AD research. Here, we discuss fluid and PET biomarkers for Aβ and their application.
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| 2. |
- Morin, Ruth T., et al.
(författare)
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Latent Classes of Cognitive Functioning among Depressed Older Adults Without Dementia
- 2019
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Ingår i: Journal of the International Neuropsychological Society. - 1355-6177. ; 25:8, s. 811-820
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Tidskriftsartikel (refereegranskat)abstract
- Objective:Use latent class analysis (LCA) to identify patterns of cognitive functioning in a sample of older adults with clinical depression and without dementia and assess demographic, psychiatric, and neurobiological predictors of class membership.Method:Neuropsychological assessment data from 121 participants in the Alzheimer's Disease Neuroimaging Initiative-Depression project (ADNI-D) were analyzed, including measures of executive functioning, verbal and visual memory, visuospatial and language functioning, and processing speed. These data were analyzed using LCA, with predictors of class membership such as depression severity, depression and treatment history, amyloid burden, and APOE e4 allele also assessed.Results:A two-class model of cognitive functioning best fit the data, with the Lower Cognitive Class (46.1% of the sample) performing approximately one standard deviation below the Higher Cognitive Class (53.9%) on most tests. When predictors of class membership were assessed, carrying an APOE e4 allele was significantly associated with membership in the Lower Cognitive Class. Demographic characteristics, age of depression onset, depression severity, history of psychopharmacological treatment for depression, and amyloid positivity did not predict class membership.Conclusion:LCA allows for identification of subgroups of cognitive functioning in a mostly cognitively intact late life depression (LLD) population. One subgroup, the Lower Cognitive Class, more likely to carry an APOE e4 allele, may be at a greater risk for subsequent cognitive decline, even though current performance on neuropsychological testing is within normal limits. These findings have implications for early identification of those at greatest risk, risk factors, and avenues for preventive intervention.
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