Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease

AbstractThe aim of this study was to explore the association between genetically predicted circulating levels of immunity and inflammation, and the risk of Alzheimer’s disease (AD) and hippocampal volume, by conducting a two-sample Mendelian Randomization Study. We identified 12 markers of immune cells and derived ratios (platelet count, eosinophil count, neutrophil count, basophil count, monocyte count, lymphocyte count, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, CD4 count, CD8 count, CD4-to-CD8 ratio, and CD56) and 5 signaling molecules (IL-6, fibrinogen, CRP, and Lp-PLA2 activity and mass) as primary exposures of interest. Other genetically available immune biomarkers with a weaker a priori link to AD were considered secondary exposures. Associations with AD were evaluated in The International Genomics of Alzheimer’s Project (IGAP) GWAS dataset (21,982 cases; 41,944 controls of European ancestry). For hippocampal volume, we extracted data from a GWAS meta-analysis on 33,536 participants of European ancestry. None of the primary or secondary exposures showed statistically significant associations with AD or with hippocampal volume following P-value correction for multiple comparisons using false discovery rate < 5% (Q-value < 0.05). CD4 count showed the strongest suggestive association with AD (odds ratio 1.32, P < 0.01, Q > 0.05). There was evidence for heterogeneity in the MR inverse variance-weighted meta-analyses as measured by Cochran Q, and weighted median and weighted mode for multiple exposures. Further cluster analyses did not reveal clusters of variants that could influence the risk factor in distinct ways. This study suggests that genetically predicted circulating biomarkers of immunity and inflammation are not associated with AD risk or hippocampal volume. Future studies should assess competing risk, explore in more depth the role of adaptive immunity in AD, in particular T cells and the CD4 subtype, and confirm these findings in other ethnicities.

Download Full-text

Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Applied Sciences ◽

10.3390/app11041574 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1574

Author(s):

Shabana Urooj ◽

Satya P. Singh ◽

Areej Malibari ◽

Fadwa Alrowais ◽

Shaeen Kalathil

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Detection ◽

Differential Evolution ◽

Early Stage ◽

Selection Process ◽

Parameter Tuning ◽

Wavelet Neural Network ◽

Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

Download Full-text