Whole Brain Atrophy and Sample Size Estimate via Iterative Principal Component Analysis for Twelve-month Alzheimer's Disease Trials

Neuroprotective Role of DPP-4 Inhibitor Linagliptin Against Neurodegeneration, Neuronal Insulin Resistance and Neuroinflammation Induced by Intracerebroventricular Streptozotocin in Rat Model of Alzheimer's Disease

Alzheimer's disease (AD) is an age-related, multifactorial progressive neurodegenerative disorder manifested by cognitive impairment and neuronal death in the brain areas like hippocampus, yet the precise neuropathology of AD is still unclear. Continuous failure of various clinical trial studies demands the utmost need to explore more therapeutic targets against AD. Type 2 Diabetes Mellitus and neuronal insulin resistance due to serine phosphorylation of Insulin Receptor Substrate-1 at 307 exhibits correlation with AD. Dipeptidyl Peptidase-4 inhibitors (DPP-4i) have also indicated therapeutic effects in AD by increasing the level of Glucagon-like peptide-1 in the brain after crossing Blood Brain Barrier. The present study is hypothesized to examine Linagliptin, a DPP-4i in intracerebroventricular streptozotocin induced neurodegeneration, and neuroinflammation and hippocampal insulin resistance in rat model of AD. Following infusion on 1st and 3rd day, animals were treated orally with Linagliptin (0.513 mg/kg, 3 mg/kg, and 5 mg/kg) and donepezil (5 mg/kg) as a standard for 8 weeks. Neurobehavioral, biochemical and histopathological analysis was done at the end of treatment. Dose-dependently Linagliptin significantly reversed behavioral alterations done through locomotor activity (LA) and morris water maze (MWM) test. Moreover, Linagliptin augmented hippocampal GLP-1 and Akt-ser473 level and mitigated soluble Aβ (1-42), IRS-1 (s307), GSK-3β, TNF-α, IL-1β, IL-6, AchE and oxidative/nitrosative stress level. Histopathological analysis also exhibited neuroprotective and anti-amylodogenic effect in Hematoxylin and eosin and Congo red staining respectively. The findings of our study concludes remarkable dose-dependent therapeutic potential of Linagliptin against neuronal insulin resistance via IRS-1 and AD-related complication. Thus, demonstrates unique molecular mechanism that underlie AD.

Linagliptin, a DPP-4 inhibitor, ameliorates Aβ (1-42) peptides induced neurodegeneration and brain insulin resistance (BIR) via insulin receptor substrate-1 (IRS-1) in rat model of Alzheimer's disease

Alzheimer's disease (AD) is the most devastating neurodegenerative disorder, accounting over 46 million cases of dementia globally. Evidence supports that Brain Insulin Resistance (BIR) due to serine phosphorylation of Insulin Receptor Substrate-1 (IRS-1) has an association with AD. GLP-1 an incretin hormone, rapidly degraded by Dipeptidyl Peptidase-4 (DPP-4) has also confirmed its efficacious role in AD. Linagliptin, a DPP-4 inhibitor is hypothesized to increase GLP-1 level, which then crosses Blood Brain Barrier (BBB), decreases Amyloid-beta (Aβ) and insulin resistance in hippocampus. Thus, the present study was designed to evaluate Linagliptin in Aβ (1-42) peptides induced rat model of AD. Following 1 week of induction, rats were administered with Linagliptin (0.513 mg/kg, 3 mg/kg, and 5 mg/kg) orally for 8 weeks and donepezil (5 mg/kg) as a reference standard. At the end of scheduled treatment neurobehavioral parameters were assessed. After this, rats were sacrificed, hippocampus was isolated from the whole brain for histopathological analysis and biochemical parameters estimation. Linagliptin dose-dependently and significantly reversed motor and cognitive impairment, assessed through locomotor activity (LA) and Morris water maze (MWM) test respectively. Moreover, Linagliptin augmented GLP-1 level and attenuated soluble Aβ (1-42), IRS-1 (s307), GSK-3β, TNF-α, IL-1β, IL-6, AchE and oxidative/nitrosative stress level in hippocampus. H&E and Congo red staining also exhibited neuroprotective and anti-amylodogenic effect respectively. Our study findings implies the significant effect of Linagliptin in reversing the behavioural and biochemical deficits by altering Aβ (1-42) and BIR via IRS-1 confirming one of the mechanism underlying the pathophysiology of AD.

Visualizing Alzheimer's disease progression in low dimensional manifolds

While tomographic neuroimaging data is information rich, objective, and with high sensitivity in the study of brain diseases such as Alzheimer's disease (AD), its direct use in clinical practice and in regulated clinical trial (CT) still has many challenges. Taking CT as an example, unless the relevant policy and the perception of the primary outcome measures change, the need to construct univariate indices (out of the 3-D imaging data) to serve as CT's primary outcome measures will remain the focus of active research. More relevant to this current study, an overall global index that summarizes multiple complicated features from neuroimages should be developed in order to provide high diagnostic accuracy and sensitivity in tracking AD progression over time in clinical setting. Such index should also be practically intuitive and logically explainable to patients and their families. In this research, we propose a new visualization tool, derived from the manifold-based nonlinear dimension reduction of brain MRI features, to track AD progression over time. In specific, we investigate the locally linear embedding (LLE) method using a dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI), which includes the longitudinal MRIs from 562 subjects. About 20% of them progressed to the next stage of dementia. Using only the baseline data of cognitively unimpaired (CU) and AD subjects, LLE reduces the feature dimension to two and a subject's AD progression path can be plotted in this low dimensional LLE feature space. In addition, the likelihood of being categorized to AD is indicated by color. This LLE map is a new data visualization tool that can assist in tracking AD progression over time.

Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns

OBJECTIVE

Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in analyzing magnetic resonance images (MRI).

METHOD

Longitudinal percentage of brain volume changes (PBVC) in two-year follow up and its intermediate counterparts in early 6-month and late 18-month are used as features in supervised and unsupervised classification procedures based on K-mean, fuzzy clustering method (FCM) and support vector machine (SVM). The most relevant features for classification are selected using discriminative analysis (DA) of features and their principal components (PC). Accuracy of the proposed method is evaluated in a group of 30 patients with AD (16 males, 14 females, age±standard-deviation (SD)=75±1.36 years) and 30 normal controls (15 males, 15 females, age±SD=77±0.88 years) using leave-one-out cross-validation.

RESULTS

Results indicate superiority of supervised machine learning techniques over unsupervised ones in diagnosing AD and withal, predominance of RBF kernel over lineal one. Accuracies of 83.3%, 83.3%, 90% and 91.7% are achieved in classification by K-mean, FCM, linear SVM and SVM with radial based function (RBF) respectively.

CONCLUSION

Evidence that SVM classification of longitudinal atrophy rates may results in high accuracy is given. Additionally, it is realized that use of intermediate atrophy rates and their principal components improves diagnostic accuracy.