P1-235: TOMM40 “523” Genotype Affects Age of Alzheimer's Disease Onset Differentially by Race

2011 ◽  
Vol 7 ◽  
pp. S186-S186
Author(s):  
Kathleen Hayden ◽  
Michael Lutz ◽  
Ann Saunders ◽  
Heather Romero ◽  
James Burke ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Onset

scholarly journals Conditional Deletion of Prnp Rescues Behavioral and Synaptic Deficits after Disease Onset in Transgenic Alzheimer's Disease

2017 ◽  
Vol 37 (38) ◽  
pp. 9207-9221 ◽  
Author(s):  
Santiago V. Salazar ◽  
Christopher Gallardo ◽  
Adam C. Kaufman ◽  
Charlotte S. Herber ◽  
Laura T. Haas ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Onset ◽  
Conditional Deletion

scholarly journals Prediction of Alzheimer's disease using multi-variants from a Chinese genome-wide association study

Brain ◽  
2020 ◽  
Author(s):  
Longfei Jia ◽  
Fangyu Li ◽  
Cuibai Wei ◽  
Min Zhu ◽  
Qiumin Qu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Predictive Models ◽  
Genome Wide Association Study ◽  
Disease Onset ◽  
Genome Wide Association ◽  
Nucleotide Polymorphisms ◽  
Risk Variants ◽  
Genome Wide

Abstract Previous genome-wide association studies have identified dozens of susceptibility loci for sporadic Alzheimer’s disease, but few of these loci have been validated in longitudinal cohorts. Establishing predictive models of Alzheimer’s disease based on these novel variants is clinically important for verifying whether they have pathological functions and provide a useful tool for screening of disease risk. In the current study, we performed a two-stage genome-wide association study of 3913 patients with Alzheimer’s disease and 7593 controls and identified four novel variants (rs3777215, rs6859823, rs234434, and rs2255835; Pcombined = 3.07 × 10−19, 2.49 × 10−23, 1.35 × 10−67, and 4.81 × 10−9, respectively) as well as nine variants in the apolipoprotein E region with genome-wide significance (P < 5.0 × 10−8). Literature mining suggested that these novel single nucleotide polymorphisms are related to amyloid precursor protein transport and metabolism, antioxidation, and neurogenesis. Based on their possible roles in the development of Alzheimer’s disease, we used different combinations of these variants and the apolipoprotein E status and successively built 11 predictive models. The predictive models include relatively few single nucleotide polymorphisms useful for clinical practice, in which the maximum number was 13 and the minimum was only four. These predictive models were all significant and their peak of area under the curve reached 0.73 both in the first and second stages. Finally, these models were validated using a separate longitudinal cohort of 5474 individuals. The results showed that individuals carrying risk variants included in the models had a shorter latency and higher incidence of Alzheimer’s disease, suggesting that our models can predict Alzheimer’s disease onset in a population with genetic susceptibility. The effectiveness of the models for predicting Alzheimer’s disease onset confirmed the contributions of these identified variants to disease pathogenesis. In conclusion, this is the first study to validate genome-wide association study-based predictive models for evaluating the risk of Alzheimer’s disease onset in a large Chinese population. The clinical application of these models will be beneficial for individuals harbouring these risk variants, and particularly for young individuals seeking genetic consultation.

scholarly journals Identification of genetic risk factors in the Chinese population implicates a role of immune system in Alzheimer’s disease pathogenesis

2018 ◽  
Vol 115 (8) ◽  
pp. 1697-1706 ◽  
Author(s):  
Xiaopu Zhou ◽  
Yu Chen ◽  
Kin Y. Mok ◽  
Qianhua Zhao ◽  
Keliang Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Immune System ◽  
Chinese Population ◽  
Disease Onset ◽  
The Elderly ◽  
Common Variants ◽  
Altered Expression ◽  
Risk Variants ◽  
Regulatory Effects

Alzheimer’s disease (AD) is a leading cause of mortality among the elderly. We performed a whole-genome sequencing study of AD in the Chinese population. In addition to the variants identified in or around the APOE locus (sentinel variant rs73052335, P = 1.44 × 10−14), two common variants, GCH1 (rs72713460, P = 4.36 × 10−5) and KCNJ15 (rs928771, P = 3.60 × 10−6), were identified and further verified for their possible risk effects for AD in three small non-Asian AD cohorts. Genotype–phenotype analysis showed that KCNJ15 variant rs928771 affects the onset age of AD, with earlier disease onset in minor allele carriers. In addition, altered expression level of the KCNJ15 transcript can be observed in the blood of AD subjects. Moreover, the risk variants of GCH1 and KCNJ15 are associated with changes in their transcript levels in specific tissues, as well as changes of plasma biomarkers levels in AD subjects. Importantly, network analysis of hippocampus and blood transcriptome datasets suggests that the risk variants in the APOE, GCH1, and KCNJ15 loci might exert their functions through their regulatory effects on immune-related pathways. Taking these data together, we identified common variants of GCH1 and KCNJ15 in the Chinese population that contribute to AD risk. These variants may exert their functional effects through the immune system.

scholarly journals Bridging the Gap between Alzheimer’s Disease and Alzheimer’s-like Diseases in Animals

2019 ◽  
Vol 20 (7) ◽  
pp. 1664 ◽  
Author(s):  
Anita Gołaszewska ◽  
Wojciech Bik ◽  
Tomasz Motyl ◽  
Arkadiusz Orzechowski
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Clinical Symptoms ◽  
Hot Spot ◽  
Senile Plaques ◽  
Disease Onset ◽  
Suitable Model ◽  
In Vivo Models ◽  
Average Life Span

The average life span steadily grows in humans and in animals kept as pets or left in sanctuaries making the issue of elderly-associated cognitive impairment a hot-spot for scientists. Alzheimer’s disease (AD) is the most prevalent cause of progressive mental deterioration in aging humans, and there is a growing body of evidence that similar disorders (Alzheimer’s-like diseases, ALD) are observed in animals, more than ever found in senescent individuals. This review reveals up to date knowledge in pathogenesis, hallmarks, diagnostic approaches and modalities in AD faced up with ALD related to different animal species. If found at necropsy, there are striking similarities between senile plaques (SP) and neurofibrillary tangles (NFT) in human and animal brains. Also, the set of clinical symptoms in ALD resembles that observed in AD. At molecular and microscopic levels, the human and animal brain histopathology in AD and ALD shows a great resemblance. AD is fatal, and the etiology is still unknown, although the myriad of efforts and techniques were employed in order to decipher the molecular mechanisms of disease onset and its progression. Nowadays, according to an increasing number of cases reported in animals, apparently, biochemistry of AD and ALD has a lot in common. Described observations point to the importance of extensive in vivo models and extensive pre-clinical studies on aging animals as a suitable model for AD disease.

scholarly journals Association of cancer history with Alzheimer's disease onset and structural brain changes

2014 ◽  
Vol 5 ◽  
Author(s):  
Kelly N. H. Nudelman ◽  
Shannon L. Risacher ◽  
John D. West ◽  
Brenna C. McDonald ◽  
Sujuan Gao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Onset ◽  
Cancer History ◽  
Brain Changes ◽  
Structural Brain

SIRT1 as a therapeutic target for Alzheimer’s disease

2016 ◽  
Vol 27 (8) ◽  
pp. 813-825 ◽  
Author(s):  
Siew Ying Wong ◽  
Bor Luen Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transcription Factors ◽  
Stress Response ◽  
Energy Metabolism ◽  
Cellular Senescence ◽  
Therapeutic Target ◽  
Disease Onset ◽  
Mechanisms Of Action ◽  
Neuron Function

AbstractAlzheimer’s disease (AD) is the most prevalent cause of dementia in the aging population worldwide. SIRT1 deacetylation of histones and transcription factors impinge on multiple neuronal and non-neuronal targets, and modulates stress response, energy metabolism and cellular senescence/death pathways. Collectively, SIRT1 activity could potentially affect multiple aspects of hippocampal and cortical neuron function and survival, thus modifying disease onset and progression. In this review, the known and potential mechanisms of action of SIRT1 with regard to AD, and its potential as a therapeutic target, are discussed.

scholarly journals Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

2019 ◽  
Author(s):  
Vipul K. Satone ◽  
Rachneet Kaur ◽  
Anant Dadu ◽  
Hampton Leonard ◽  
Hirotaka Iwaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Onset ◽  
Machine Learning Techniques ◽  
Progression Rate ◽  
Early Stages ◽  
Age Related ◽  
Learning Techniques

AbstractBackgroundAlzheimer’s disease (AD) is a common, age-related, neurodegenerative disease that impairs a person’s ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes.Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patient’s progression.ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

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