Background: To understand the earliest signs of cognitive decline caused by Alzheimer disease (AD) and other illnesses causing dementia, information is needed from well-characterized individuals without dementia studied longitudinally until autopsy.
Objective: To determine clinical and cognitive features associated with the development of AD or other dementias in older adults.
Design: Longitudinal study of memory and aging.
Setting: Alzheimer's Disease Research Center, St Louis, Mo.
Main Outcome Measures: Clinical Dementia Rating, its sum of boxes, and neuropathologic diagnosis of dementia.
Participants: Eighty control participants who eventually came to autopsy.
Results: Individuals who did not develop dementia showed stable cognitive performance. Entry predictors of dementia were age, deficits in problem solving as well as memory, slowed psychomotor performance, and depressive features. Minimal cognitive decline occurred prior to dementia diagnosis, after which sharp decline was noted. Even individuals who were minimally cognitively impaired (Clinical Dementia Rating = 0.5) typically had neuropathologic AD at autopsy. Histopathologic AD also was present in 34% of individuals who did not have dementia at death; these individuals without dementia showed an absence of practice effects on cognitive testing.
Conclusions: Increased age, depressive features, and even minimal cognitive impairment, as determined clinically by Clinical Dementia Rating sum of boxes and by slowed psychomotor performance, identify older individuals without dementia who develop dementia. Older adults who do not develop dementia have stable cognitive performance. The absence of practice effects may denote the subset of older adults without dementia with histopathologic AD, which may reflect a preclinical stage of the illness.
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http://dx.doi.org/10.1001/archneur.62.5.758 | DOI Listing |
Psychiatr Danub
December 2024
Saveetha College Of Physiotherapy, SIMATS, Chennai, Tamil Nadu, India.
Zh Nevrol Psikhiatr Im S S Korsakova
December 2024
Federal Center of Brain Research and Neurotechnologies, Moscow, Russia.
Objective: Study of neuroimaging changes according to MRI morphometry and their comparison with the structure and severity of cognitive impairment (CI) in patients with Alzheimer's disease (AD) and primary open-angle glaucoma (POAG).
Material And Methods: The study involved 90 patients who were divided into two equal groups of 45 people and who early had diagnosis of AD (group 1; median age - 71 [66; 77] years) and POAG (group 2; median age - 68 [64; 77] years). 71] years).
Nat Aging
December 2024
Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile.
Structural inequality, the uneven distribution of resources and opportunities, influences health outcomes. However, the biological embedding of structural inequality in aging and dementia, especially among underrepresented populations, is unclear. We examined the association between structural inequality (country-level and state-level Gini indices) and brain volume and connectivity in 2,135 healthy controls, and individuals with Alzheimer's disease and frontotemporal lobe degeneration from Latin America and the United States.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Public Health, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
The prevalence of Alzheimer's disease (AD) is on the rise globally, and everyone who develops AD eventually experiences mild cognitive impairment (MCI) first. Timely intervention at an early stage of the disease may mitigate disease progression. Recent studies indicate that BDNF and MMP-9 play a significant role in the pathogenesis of AD.
View Article and Find Full Text PDFSci Rep
December 2024
GIN, IMN-UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decline, dementia, and stroke. They are visible as spherical hypointense signals on T2*- or susceptibility-weighted magnetic resonance imaging (MRI) sequences. An increasing number of automated CMB detection methods being proposed are based on supervised deep learning (DL).
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