Sporadic Alzheimer's disease (AD), as opposed to its autosomal dominant form, is likely caused by a complex interaction of genetic, environmental, and health lifestyle factors. Twin studies indicate that sporadic AD heritability could be between 58% and 79%, around half of which is explained by the ε4 allele of the apolipoprotein E (APOE4). We hypothesized that genes associated with known risk factors for AD, namely hypertension, hypercholesterolemia, obesity, diabetes, and cardiovascular disease, would contribute significantly to the remaining heritability. We analyzed 22 AD-associated single-nucleotide polymorphisms (SNPs), associated with these risk factors, that were included in the sequencing data of the Alzheimer's Disease Neuroimaging Initiative 1 data set, which included 355 participants with mild cognitive impairment (MCI). We built survival models with the selected SNPs to predict progression of MCI to probable AD over the 10-year follow-up of the study. The rs391300 SNP, located on the serine racemase (SRR) gene and linked to increased susceptibility to type 2 diabetes, was associated with progression from MCI to probable AD.
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http://dx.doi.org/10.1016/j.neurobiolaging.2017.11.013 | DOI Listing |
J Integr Neurosci
January 2025
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 637000 Nanchong, Sichuan, China.
Background: Volume alterations in the parietal subregion have received less attention in Alzheimer's disease (AD), and their role in predicting conversion of mild cognitive impairment (MCI) to AD and cognitively normal (CN) to MCI remains unclear. In this study, we aimed to assess the volumetric variation of the parietal subregion at different cognitive stages in AD and to determine the role of parietal subregions in CN and MCI conversion.
Methods: We included 662 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 228 CN, 221 early MCI (EMCI), 112 late MCI (LMCI), and 101 AD participants.
Bioengineering (Basel)
January 2025
Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85281, USA.
Alzheimer's disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, given the limitations of current diagnostic biomarkers and the need for non-invasive, accessible tools. This study aims to address these gaps by exploring driving performance as a novel, non-invasive biomarker for MCI detection.
View Article and Find Full Text PDFFront Psychiatry
January 2025
School of Nursing, Nanjing University of Chinese Medicine, Nanjing, China.
Introduction: Self-management is crucial for individuals with mild cognitive impairment (MCI) to enhance cognitive health and mitigate the potential risk of dementia. However, maintaining consistent engagement in self-management strategies seems a challenge for older adults with MCI. This study sought to gain insights into the barriers to self-management engagement among community-dwelling older adults with MCI.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Creative Product Design, Asia University, Taichung, Taiwan.
Alzheimer's disease (AD) is a complex, progressive, and irreversible neurodegenerative disorder marked by cognitive decline and memory loss. Early diagnosis is the most effective strategy to slow the disease's progression. Mild Cognitive Impairment (MCI) is frequently viewed as a crucial stage before the onset of AD, making it the ideal period for therapeutic intervention.
View Article and Find Full Text PDFNeuroSci
January 2025
Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA.
Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease. Differentiating early MCI (EMCI) from late MCI (LMCI) is crucial for early diagnosis and intervention. This study used free-water diffusion tensor imaging (fw-DTI) to investigate white matter differences and voxel-based correlations with Mini-Mental State Examination (MMSE) scores.
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