Joint Effect of ABCA7 rs4147929 and Body Mass Index on Progression from Mild Cognitive Impairment to Alzheimer’s Disease: The Shanghai Aging Study

2020 ◽  
Vol 17 (2) ◽  
pp. 185-195
Author(s):  
Jianxiong Xi ◽  
Ding Ding ◽  
Qianhua Zhao ◽  
Xiaoniu Liang ◽  
Li Zheng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Body Mass Index ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Body Mass ◽  
Joint Effect ◽  
Genome Wide Association Studies ◽  
Risk Genotype ◽  
Aging Study

Background: Approximately 40 independent Single Nucleotide Polymorphisms (SNPs) have been associated with Alzheimer’s Disease (AD) or cognitive decline in genome-wide association studies. Methods: We aimed to evaluate the joint effect of genetic polymorphisms and environmental factors on the progression from Mild Cognitive Impairment (MCI) to AD (MCI-AD progression) in a Chinese community cohort. Conclusion: Demographic, DNA and incident AD diagnosis data were derived from the follow-up of 316 participants with MCI at baseline of the Shanghai Aging Study. The associations of 40 SNPs and environmental predictors with MCI-AD progression were assessed using the Kaplan-Meier method with the log-rank test and Cox regression model. Results: Rs4147929 at ATP-binding cassette family A member 7 (ABCA7) (AG/AA vs. GG, hazard ratio [HR] = 2.43, 95% confidence interval [CI] 1.24-4.76) and body mass index (BMI) (overweight vs. non-overweight, HR = 0.41, 95% CI 0.22-0.78) were independent predictors of MCI-AD progression. In the combined analyses, MCI participants with the copresence of non-overweight BMI and the ABCA7 rs4147929 (AG/AA) risk genotype had an approximately 6-fold higher risk of MCI-AD progression than those with an overweight BMI and a non-risk genotype (HR = 6.77, 95% CI 2.60-17.63). However, a nonsignificant result was found when participants carried only one of these two risk factors (nonoverweight BMI and AG/AA of ABCA7 rs4147929). Conclusion: ABCA7 rs4147929 and BMI jointly affect MCI-AD progression. MCI participants with the rs4147929 risk genotype may benefit from maintaining an overweight BMI level with regard to their risk for incident AD.

scholarly journals PVRL2 rs6859 Modifies the Effect of Triglyceride on Progression of Mild Cognitive Impairment: The Shanghai Aging Study

2020 ◽  
Author(s):  
Ruru Wang ◽  
Ding Ding ◽  
Abuduaili Atibaike ◽  
Jianxiong Xi ◽  
Qianhua Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cox Model ◽  
Association Studies ◽  
Interactive Effect ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Aging Study

Abstract Background Mild cognitive impairment (MCI) is an intermediate stage between normal cognition and Alzheimer’s disease (AD). Genome-wide association studies (GWAS) have identified many AD-risk variants and indicated the important role of lipid metabolism pathway in AD progression. This study aimed to investigate the effects of triglyceride (TG) and genetic risk factors on progression from MCI to AD (MCI-AD progression).Methods The current study sample comprised of 305 MCI subjects aged 50 and over who were prospectively followed up for average 4.5 years in a sub-cohort of the Shanghai Aging Study. A consensus diagnosis of incident AD was conducted according to Diagnostic and Statistical Manual of Mental Disorders-IV and the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association criteria. Fasting blood samples were obtained at baseline for analyzing serum TG. Single nucleotide polymorphisms (SNPs) genotyping was performed using a MassARRAY system. The effect of TG, genetic variants and their interaction on MCI-AD progression were analyzed using Cox proportional hazards regression model.Results During a mean (±SD) follow-up period of 4.5±1.3 y, 58 subjects developed incident AD. The SNP, rs6859 in the Poliovirus Receptor–Related 2 (PVRL2) gene, was significantly associated with incident AD (false discovery rate (FDR)-adjusted P = 0.018). In multivariate cox model, the PVRL2 rs6859 AG, AA and AG+AA genotypes were associated with significantly increased incident AD, compared with the GG genotype (hazard ratio [HR] = 2.29, P = 0.029, and HR = 2.92, P = 0.013, and HR = 2.47, P =0.012, respectively). In PVRL2 rs6859 AG/AA carriers, higher ln TG was significantly associated with increased risk of incident AD (adjusted HR =2.64, P = 0.034). Ln TG and PVRL2 rs6859 had interactive effect on the MCI-AD progression (P Ln TG × rs6859 = 0.001). Conclusion The present study indicated that PVRL2 rs6859 modified the effect of TG on MCI-AD progression. Precision prevention in MCI population based on genetic information should be considered to avoid progression to AD.

scholarly journals Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lanlan Li ◽  
Yeying Yang ◽  
Qi Zhang ◽  
Jiao Wang ◽  
Jiehui Jiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Association Studies ◽  
Genomic Research ◽  
Genome Wide Association Studies ◽  
Genetic Biomarkers ◽  
Gwas Analysis

Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50 % , 98.39 % ± 2.50 % , and 99.44 % ± 1.11 % using the DLG model, while 71.38 % ± 0.63 % , 63.13 % ± 2.87 % , and 85.59 % ± 6.66 % using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

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