Alzheimer's Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases

Neuroprotective Effects of Phenolic Constituents from Drynariae Rhizoma

This study aimed to provide scientific data on the anti-Alzheimer's disease (AD) effects of phenolic compounds from Drynariae Rhizoma (DR) extract using a multi-component approach. Screening of DR extracts, fractions, and the ten phenolic compounds isolated from DR against the key AD-related enzymes acetylcholinesterase (AChE), butyrylcholinesterase (BChE), β-site amyloid precursor protein cleaving enzyme 1 (BACE1), and monoamine oxidase-B (MAO-B) confirmed their significant inhibitory activities. The DR extract was confirmed to have BACE1-inhibitory activity, and the ethyl acetate and butanol fractions were found to inhibit all AD-related enzymes, including BACE1, AChE, BChE, and MAO-B. Among the isolated phenolic compounds, compounds (2) caffeic acid 4-O-β-D-glucopyranoside, (6) kaempferol 3-O-rhamnoside 7-O-glucoside, (7) kaempferol 3-o-b-d-glucopyranoside-7-o-a-L-arabinofuranoside, (8) neoeriocitrin, (9) naringin, and (10) hesperidin significantly suppressed AD-related enzymes. Notably, compounds 2 and 8 reduced soluble Amyloid Precursor Protein β (sAPPβ) and β-secretase expression by over 45% at a concentration of 1.0 μM. In the thioflavin T assay, compounds 6 and 7 decreased Aβ aggregation by approximately 40% and 80%, respectively, and degraded preformed Aβ aggregates. This study provides robust evidence regarding the potential of DR as a natural therapeutic agent for AD, highlighting specific compounds that may contribute to its efficacy.

Astaxanthin and improvement of dementia: A systematic review of current clinical trials

Worldwide, the incidence of neurodegenerative diseases especially dementia is steadily increasing due to the aging population. Abundant research emerges on the probability of combating or preventing the degeneration process, with the most established one being to tackle the existence of oxidative stress and free radicals production due to their nature of aggravating dementia. Astaxanthin, a marine carotenoid, was proven to be a protective agent of cerebral ischemia through many animal model clinical trials. This review summarizes the evidence of Astaxanthin's benefits for cognitive function across clinical trials done in older age. The results are of interest as its supplementation does not exhibit unwanted issues on the consumer based on physical and laboratory examinations. Despite not being supported statistically, however, subjective and objective cognitive amelioration were reported according to the majority of this review's trial subjects. Although there is no clear and direct mechanism for cognitive improvement by Astaxanthin activity in the body systems, the encouragement of Astaxanthin supplementation should be considered as the elderly with dementia may highly benefit from the improved cognitive function.

Glycolytic enzymes in non-glycolytic web: functional analysis of the key players

To survive in the tumour microenvironment, cancer cells undergo rapid metabolic reprograming and adaptability. One of the key characteristics of cancer is increased glycolytic selectivity and decreased oxidative phosphorylation (OXPHOS). Apart from ATP synthesis, glycolysis is also responsible for NADH regeneration and macromolecular biosynthesis, such as amino acid biosynthesis and nucleotide biosynthesis. This allows cancer cells to survive and proliferate even in low-nutrient and oxygen conditions, making glycolytic enzymes a promising target for various anti-cancer agents. Oncogenic activation is also caused by the uncontrolled production and activity of glycolytic enzymes. Nevertheless, in addition to conventional glycolytic processes, some glycolytic enzymes are involved in non-canonical functions such as transcriptional regulation, autophagy, epigenetic changes, inflammation, various signaling cascades, redox regulation, oxidative stress, obesity and fatty acid metabolism, diabetes and neurodegenerative disorders, and hypoxia. The mechanisms underlying the non-canonical glycolytic enzyme activities are still not comprehensive. This review summarizes the current findings on the mechanisms fundamental to the non-glycolytic actions of glycolytic enzymes and their intermediates in maintaining the tumor microenvironment.

An Integrated Computational Approaches for Designing of Potential Piperidine based Inhibitors of Alzheimer Disease by Targeting Cholinesterase and Monoamine Oxidases Isoenzymes

The study aimed to evaluate the potential of piperidine-based 2H chromen-2-one derivatives against targeted enzymes, i.e., cholinesterase's and monoamine oxidase enzymes. The compounds were divided into three groups, i.e., 4a-m ((3,4-dimethyl-7-((1-methylpiperidin-4-yl)oxy)-2H-chromen-2-one derivatives), 5a-e (3,4-dimethyl-7-((1-methypipridin-3-yl)methoxy)-2H-chromen-2-one derivatives), and 7a-b (7-(3-(3,4-dihydroisoquinolin-2(1H)-yl)propoxy)-3,4-dimethyl-2H-chromen-2-one derivatives) with slight difference in the basic structure. The comprehensive computational investigations were conducted including density functional theories studies (DFTs), 2D-QSAR studies, molecular docking, and molecular dynamics simulations. The QSAR equation revealed that the activity of selected chromen-2-one-based piperidine derivatives is being affected by the six descriptors, i.e., Nitrogens Count, SdssCcount, SssOE-Index, T-2-2-7, ChiV6chain, and SssCH2E-Index. These descriptor values were further used for the preparation of chromen-2-one based piperidine derivatives. Based on this, 83 new derivatives were created from 7 selected parent compounds. The QSAR model predicted their IC50 values, with compound 4 k and 4kk as the most potent multi-targeted derivative. Molecular docking results exhibited these compounds as the best inhibitors; however, 4kk exhibited greater activity than the parent compounds. The results were further validated by molecular dynamic simulation studies along with the suitable physicochemical properties. These results prove to be an essential guide for the further design and development of new piperidine based chromen-2-one derivatives having better activity against neurodegenerative disorder.

Application of machine learning in dementia diagnosis: A systematic literature review

According to the World Health Organization forecast, over 55 million people worldwide have dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for patients to plan for the future and deal with the disease. Machine Learning algorithms allow us to solve the problems associated with early disease detection. This work attempts to identify the current relevance of the application of machine learning in dementia prediction in the scientific world and suggests open fields for future research. The literature review was conducted by combining bibliometric and content analysis of articles originating in a period of 20 years in the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly, of which a limited number focused on machine learning in dementia diagnosis. After the exclusion process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the past five years in the theme of machine learning in dementia shows that it is a relevant field for research with still open questions. The methods used to identify dementia or what features are used to identify or predict this disease are explored in this study. The literature review revealed that most studies used magnetic resonance imaging (MRI) and its types as the main feature, accompanied by demographic data such as age, gender, and the mini-mental state examination score (MMSE). Data are usually acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Classification of Alzheimer's disease is more prevalent than prediction of Mild Cognitive Impairment (MCI) or their combination. The authors preferred machine learning algorithms such as SVM, Ensemble methods, and CNN because of their excellent performance and results in previous studies. However, most use not one machine-learning technique but a combination of techniques. Despite achieving good results in the studies considered, there are new concepts for future investigation declared by the authors and suggestions for improvements by employing promising methods with potentially significant results.

Combined 18F-FDG PET-CT markers in dementia with Lewy bodies

INTRODUCTION

18F-Fluoro-deoxyglucose-positron emission tomography (FDG-PET) is a supportive biomarker in dementia with Lewy bodies (DLB) diagnosis and its advanced analysis methods, including radiomics and machine learning (ML), were developed recently. The aim of this study was to evaluate the FDG-PET diagnostic performance in predicting a DLB versus Alzheimer's disease (AD) diagnosis.

METHODS

FDG-PET scans were visually and semi-quantitatively analyzed in 61 patients. Radiomics and ML analyses were performed, building five ML models: (1) clinical features; (2) visual and semi-quantitative PET features; (3) radiomic features; (4) all PET features; and (5) overall features.

RESULTS

At follow-up, 34 patients had DLB and 27 had AD. At visual analysis, DLB PET signs were significantly more frequent in DLB, having the highest diagnostic accuracy (86.9%). At semi-quantitative analysis, the right precuneus, superior parietal, lateral occipital, and primary visual cortices showed significantly reduced uptake in DLB. The ML model 2 had the highest diagnostic accuracy (84.3%).

DISCUSSION

FDG-PET is a valuable tool in DLB diagnosis, having visual and semi-quantitative analyses with the highest diagnostic accuracy at ML analyses.

Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?

Although not classically considered together, there is emerging evidence that Alzheimer's disease (AD) and epilepsy share a number of features and that each disease predisposes patients to developing the other. Using machine learning, we have previously developed an automated fluorodeoxyglucose positron emission tomography (FDG-PET) reading program (i.e., MAD), and demonstrated good sensitivity (84%) and specificity (95%) for differentiating AD patients versus healthy controls. In this retrospective chart review study, we investigated if epilepsy patients with/without mild cognitive symptoms also show AD-like metabolic patterns determined by the MAD algorithm. Scans from a total of 20 patients with epilepsy were included in this study. Because AD diagnoses are made late in life, only patients aged ≥40 years were considered. For the cognitively impaired patients, four of six were identified as MAD+ (i.e., the FDG-PET image is classified as AD-like by the MAD algorithm), while none of the five cognitively normal patients was identified as MAD+ (χ² = 8.148, p = 0.017). These results potentially suggest the usability of FDG-PET in prognosticating later dementia development in non-demented epilepsy patients, especially when combined with machine learning algorithms. A longitudinal follow-up study is warranted to assess the effectiveness of this approach.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography

PURPOSE

Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value.

METHODS

A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[¹⁸F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored.

RESULTS

The newly identified DLBRP shared topographic similarities with ADRP (R² = 24%) and PDRP (R² = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time.

CONCLUSION

DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.

Monitoring Alzheimer's Disease Progression in Mild Cognitive Impairment Stage Using Machine Learning-Based FDG-PET Classification Methods

BACKGROUND

We previously introduced a machine learning-based Alzheimer's Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects.

OBJECTIVE

We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD.

METHODS

MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior.

RESULTS

The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7-0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01).

CONCLUSION

These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD.