Alzheimer Type Research Articles

Assessment of Anticholinergic and Antidiabetic Properties of Some Natural and Synthetic Molecules: An In vitro and In silico Approach.

This study aimed to determine the in vitro and in silico effects of some natural and synthetic molecules on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and α-glucosidase enzymes. Alzheimer's disease (AD) and Type II diabetes mellitus (T2DM) are considered the most important diseases of today's world. However, the side effects of therapeutic agents used in both diseases limit their use. Therefore, developing drugs with high therapeutic efficacy and better pharmacological profile is important. This study sets out to determine the related enzyme inhibitors used in treating AD and T2DM, considered amongst the most important diseases of today's world. In the current study, the in vitro and in silico effects of dienestrol, hesperetin, Lthyroxine, 3,3',5-Triiodo-L-thyronine (T3) and dobutamine molecules on AChE, BChE and α - glycosidase enzyme activities were investigated. All the molecules showed an inhibitory effect on the enzymes. The IC50 and Ki values of the L-Thyroxine molecule, which showed the strongest inhibition effect for the AChE enzyme, were determined as 1.71 μM and 0.83 ± 0.195 μM, respectively. In addition, dienestrol, T3, and dobutamine molecules showed a more substantial inhibition effect than tacrine. The dobutamine molecule showed the most substantial inhibition effect for the BChE enzyme, and IC50 and Ki values were determined as 1.83 μM and 0.845 ± 0.143 μM, respectively. The IC50 and Ki values for the hesperetin molecule, which showed the strongest inhibition for the α -glycosidase enzyme, were determined as 13.57 μM and 12.33 ± 2.57 μM, respectively. According to the results obtained, the molecules used in the study may be considered potential inhibitor candidates for AChE, BChE and α-glycosidase.

Pure-tone audiometry and dichotic listening in primary progressive aphasia and Alzheimer's disease.

Hearing is multifaceted, and the relative contributions of peripheral and central hearing loss are rarely considered together in the context of dementia. Here, we assessed peripheral (as measured with pure-tone audiometry) and central (as measured with dichotic listening) hearing in 19 patients with typical amnestic Alzheimer's disease (tAD), 10 patients with logopenic variant primary progressive aphasia (lvPPA), 11 patients with nonfluent/agrammatic variant PPA (nfvPPA), 15 patients with semantic variant PPA (svPPA), and 28 healthy age-matched individuals. Participants also underwent neuropsychological assessment and magnetic resonance image scanning, allowing us to use voxel-based morphometry to assess associations between hearing scores and grey matter volume. Dichotic listening was impaired in all patient groups relative to healthy controls. In the combined patient (but not healthy control) cohort, dichotic listening scores were significantly correlated with measures of global cognitive functioning and speech-based neuropsychological tasks. Pure-tone audiometry scores were not significantly elevated in any patient group relative to the healthy control group, and no significant correlations were observed between peripheral hearing and neuropsychological task performance in either the combined patient or healthy control cohorts. Neuroanatomically, dichotic listening performance was associated with grey matter volume in a bilateral fronto-temporoparietal network over the combined patient cohort, but no correlates were identified for pure-tone audiometry. Our findings highlight the importance of speech parsing mechanisms beyond elementary sound detection in driving cognitive test performance, underline the importance of assessing central hearing alongside peripheral hearing in people with dementia, and further delineate the complex auditory profiles of neurodegenerative dementias.

Behavioral Alterations of Spatial Cognition and Role of the Apolipoprotein E-ε4 in Patients with MCI Due to Alzheimer's Disease: Results from the BDSC-MCI Project.

Background: Beyond memory deterioration, spatial disorientation may occur along the continuum of normal aging-dementia of Alzheimer's type. The present study aims at detecting behavioral disorders of spatial cognition in prodromal Alzheimer's disease (AD) and verifying the association between Apolipoprotein E-ε4 (ApoE-ε4) genotype and gait patterns during a real-world naturalistic task. Methods: A sample of 58 elderly participants, of which 20 patients with mild cognitive impairment with CFS biomarker evidence of AD, 23 individuals with subjective cognitive decline (SCD), and 15 healthy controls (HCs), was tested by a modified version of the Detour Navigation Test (DNT-mv). Generalized linear models were run to explore the association between group belonging and wrong turns (WTs)/moments of hesitation (MsH) as behavioral disorientation scores of the DNT-mv as well as the effect of ApoE-ε4 genotype on time and walking speed registered by a smartphone app providing GPS tracking of body movement around urban environments. Results: Patients with MCI due to AD reported more WTs than individuals with SCD and HCs. Further, the ApoE-ε4 genotype determined a lower capacity in spatial information processing, influencing gait during naturalistic spatial navigation tasks. Conclusions: Behavior alterations of spatial cognition can be detected ecologically in prodromal AD. The use of technological solutions supporting gait analysis may help in corroborating the experimental observation.

Brain Microvascular Pericyte Pathology Linking Alzheimer's Disease to Diabetes.

The brain microvasculature, which delivers oxygen and nutrients and forms a critical barrier protecting the central nervous system via capillaries, is deleteriously affected by both Alzheimer's disease (AD) and type 2 diabetes (T2D). T2D patients have an increased risk of developing AD, suggesting potentially related microvascular pathological mechanisms. Pericytes are an ideal cell type to study for functional links between AD and T2D. These specialized capillary-enwrapping cells regulate capillary density, lumen diameter, and blood flow. Pericytes also maintain endothelial tight junctions to ensure blood-brain barrier integrity, modulation of immune cell extravasation, and clearance of toxins. Changes in these phenomena have been observed in both AD and T2D, implicating "pericyte pathology" as a common feature of AD and T2D. This review examines the mechanisms of AD and T2D from the perspective of the brain microvasculature, highlighting how pericyte pathology contributes to both diseases. Our review identifies voids in understanding how AD and T2D negatively impact the brain microvasculature and suggests future studies to examine the intersections of these diseases.

Baseline multimodal imaging to predict longitudinal clinical decline in atypical Alzheimer's disease

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures.Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons.We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score.Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies.

Islet amyloid polypeptide fibril catalyzes amyloid-β aggregation by promoting fibril nucleation rather than direct axial growth

Aberrant aggregation of amyloid-β (Aβ) and islet amyloid polypeptide (IAPP) into amyloid fibrils underlies the pathogenesis of Alzheimer's disease (AD) and type 2 diabetes (T2D), respectively. T2D significantly increases AD risk, with evidence suggesting that IAPP and Aβ co-aggregation and cross-seeding might contribute to the cross-talk between two diseases. Experimentally, preformed IAPP fibril seeds can accelerate Aβ aggregation, though the cross-seeding mechanism remains elusive. Here, we computationally demonstrated that Aβ monomer preferred to bind to the elongation ends of preformed IAPP fibrils. However, due to sequence mismatch, the Aβ monomer could not directly grow onto IAPP fibrils by forming multiple stable β-sheets with the exposed IAPP peptides. Conversely, in our control simulations of self-seeding, the Aβ monomer could axially grow on the Aβ fibril, forming parallel in-register β-sheets. Additionally, we showed that the IAPP fibril could catalyze Aβ fibril nucleation by promoting the formation of parallel in-register β-sheets in the C-terminus between bound Aβ peptides. This study enhances our understanding of the molecular interplay between Aβ and IAPP, shedding light on the cross-seeding mechanisms potentially linking T2D and AD. Our findings also underscore the importance of clearing IAPP deposits in T2D patients to mitigate AD risk.

Disentangling the relationship between cancer mortality and COVID-19 in the US

Cancer is considered a risk factor for COVID-19 mortality, yet several countries have reported that deaths with a primary code of cancer remained within historic levels during the COVID-19 pandemic. Here, we further elucidate the relationship between cancer mortality and COVID-19 on a population level in the US. We compared pandemic-related mortality patterns from underlying and multiple cause (MC) death data for six types of cancer, diabetes, and Alzheimer’s. Any pandemic-related changes in coding practices should be eliminated by study of MC data. Nationally in 2020, MC cancer mortality rose by only 3% over a pre-pandemic baseline, corresponding to ~13,600 excess deaths. Mortality elevation was measurably higher for less deadly cancers (breast, colorectal, and hematological, 2–7%) than cancers with a poor survival rate (lung and pancreatic, 0–1%). In comparison, there was substantial elevation in MC deaths from diabetes (37%) and Alzheimer’s (19%). To understand these differences, we simulated the expected excess mortality for each condition using COVID-19 attack rates, life expectancy, population size, and mean age of individuals living with each condition. We find that the observed mortality differences are primarily explained by differences in life expectancy, with the risk of death from deadly cancers outcompeting the risk of death from COVID-19.

Disentangling the relationship between cancer mortality and COVID-19 in the US.

Cancer is considered a risk factor for COVID-19 mortality, yet several countries have reported that deaths with a primary code of cancer remained within historic levels during the COVID-19 pandemic. Here, we further elucidate the relationship between cancer mortality and COVID-19 on a population level in the US. We compared pandemic-related mortality patterns from underlying and multiple cause (MC) death data for six types of cancer, diabetes, and Alzheimer's. Any pandemic-related changes in coding practices should be eliminated by study of MC data. Nationally in 2020, MC cancer mortality rose by only 3% over a pre-pandemic baseline, corresponding to ~13,600 excess deaths. Mortality elevation was measurably higher for less deadly cancers (breast, colorectal, and hematological, 2-7%) than cancers with a poor survival rate (lung and pancreatic, 0-1%). In comparison, there was substantial elevation in MC deaths from diabetes (37%) and Alzheimer's (19%). To understand these differences, we simulated the expected excess mortality for each condition using COVID-19 attack rates, life expectancy, population size, and mean age of individuals living with each condition. We find that the observed mortality differences are primarily explained by differences in life expectancy, with the risk of death from deadly cancers outcompeting the risk of death from COVID-19.

Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.

Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.

Characteristics of streptozotocin-induced and okadaic acid-induced models of Alzheimer's disease

alzheimer's disease is the main cause of dementia in people over the age of 65, which affects millions of patients worldwide. Since the prevalence of this pathology increases significantly with the increase in life expectancy, the societal consequences of Alzheimer's disease will be dire if no effective therapy is developed in the near future. Fundamental experimental work is a mandatory and necessary stage of preclinical research for the safe and effective development of new approaches to the treatment of dementia as part of clinical trials in the future. Streptozotocin is a natural chemical obtained from Streptomyces achromogenes, primarily classified as an antibiotic, the introduction of which leads to the activation of oxidative stress and damage to the myelin sheath. This chemical compound promotes the accumulation of characteristic abnormal proteins, called β-amyloid and tau protein, which leads to imitation of neuropathological, behavioral, metabolic and molecular symptoms that resemble human Alzheimer's disease. Okadaic acid is a C38 fatty acid polyester phycotoxin, which is a powerful and selective inhibitor of serine/threonine phosphatases: PP-1c, PP1, PP2A and PP2B, which leads to a decrease in dephosphorylation of phosphorylated tau protein and, accordingly, to hyperphosphorylation of tau protein. Hyperphosphorylation of tau protein is responsible for the deposition of neurofibrillary tangles in vivo and in vitro, which ultimately causes synaptic dysfunction and neuronal death. In addition, okadaic acid activates kinases that regulate tau protein phosphorylation: GSK3β, CDK5, MAPK, ERK1/2, AMPK. The activation of kinases significantly contributes to the phosphorylation of tau protein. Okadain administration is also reported to significantly activate the expression of BACE1, one of the key enzymes in the amyloidogenic cascade. The mechanisms of molecular and morphological changes of the Alzheimer's type in the brain of experimental animals when streptozotocin and okadaic acid are administered are similar in many respects. These include induction and activation of neurodegeneration, apoptosis and necrosis of neurons, development of oxidative stress and inhibition of antioxidant systems, deficiency of neurotransmitters and mitochondrial dysfunction, etc. Both proposed toxins effectively contribute to the simulation of dementia of the Alzheimer's type in the experiment.

Tannins-enriched fraction of TeMac™ protects against aluminum chloride induced Alzheimer's disease-like pathology by modulating aberrant insulin resistance and alleviating oxidative stress in diabetic rats

Ethnopharmacological relevanceAlzheimer's disease is the most common neurodegenerative disease with therapeutic limitations. Insulin resistance plays a role in the progression of Alzheimer's disease. Therapies that modulate insulin secretion and signaling, as well as oxidative stress in the brain are now being investigated for their potential role in the prevention of Alzheimer's disease (AD). Terminalia macroptera (Combretaceae) is a plant that different parts have been used traditionally for the treatment of metabolic and neurological conditions. Previous study has indicated that the crude extract exhibit anti-diabetic property. In addition, the plant is a rich source of tannins, phenolic acids, flavonoids, triterpenes. However, there is no study on its protective effect against biochemical alterations of AD in diabetic rats. Aim of the studyThe present research study investigated the neuroprotective effects of TeMac™ on Alzheimer-like pathology induced by aluminum chloride (AlCl3) in diabetic rats. MethodsA phytochemical analysis of TeMac™ was carried out to quantify tannins. The potential effect of the tannins-enriched fraction (TEF) of TeMac™ to prevent the formation of senile plaques was conducted by its ability to inhibit the activities of β-secretase (EC 3.4.23.46), monoamine oxidase A (EC 1.4.3.4) and the fibrillation of Aβ. A diabetic model was induced from female Wistar rats by a single intraperitoneal injection of streptozotocin (STZ, 35 mg/kg BW). After that, the blood glucose level was measured to confirm the induction of diabetes. Three days after induction, animals received AlCl3 (75 mg/kg BW) alone (AD control) or concomitantly with 400 mg/kg BW of TEF of TeMac™ or 5 mg/kg BW Daonil by daily gavage for 42 days. At the end of the experiment, rats were sacrificed, blood and brains were collected. The levels of amyloid fibrils, glucose, albumin and the activities of DPP4, β-secretase and phosphatase, and markers of oxidative stress in the brain were assessed. ResultsTEF of TeMac™ displays a potential ability to inhibit the activities of β-secretase, monoamine oxidase, and Aβ fibrillation. Treatment with TEF of TeMac™ significantly inhibited DPP4 and BACE1 activities and reduced brain glucose and amyloid fibril levels, and improved cerebral albumin levels and modulated oxidative stress markers. ConclusionOur findings indicate that TEF of TeMac™ prevents Alzheimer's-type pathology linked to insulin resistance in rats. TEF of TeMac™ may be a potential drug candidate for the treatment of diabetes-associated cognitive impairment.

Dementia incidence varied by anticancer drugs and molecular targeted therapy in a population-based cohort study

Anticancer drugs may affect the incidence of dementia by modulating the common pathophysiology between cancer and dementia. However, there is a paucity of research that focused on anticancer drugs with different mechanisms of action and their associations with subtypes of dementia. Therefore, we aimed to investigate the incidence of dementia according to various groups of anticancer drugs. From the Korea National Health Insurance Service database, our retrospective population-based cohort study enrolled 116,506 cancer patients aged 65 years and older who received anticancer drugs between January 1, 2008 and December 31, 2018. The hazard ratio was determined using Cox proportional hazards regression models, comparing each group of anticancer drugs to all other anticancer drugs, after adjusting for covariates. Antimetabolites (HR = 0.91; 95% CI 0.84–0.97) and molecular targeted therapies (HR = 0.60; 95% CI 0.49–0.74) were associated with a decreased incidence of dementia of the Alzheimer type (DAT), but not with vascular dementia. Among molecular targeted therapies, epidermal growth factor receptor inhibitors (HR = 0.60; 95% CI 0.46–0.79) and multikinase inhibitors (HR = 0.49; 95% CI 0.27–0.89) were associated with a low incidence of DAT only. Our findings highlight the potential for targeted repurposing of anticancer drugs to prevent dementia.

Cortical Thickness Correlates of Go/No-go and Motor Sequencing in Mild Cognitive Impairment and Suspected Alzheimer Disease Dementia.

While the cognitive hallmark of typical Alzheimer disease (AD) is impaired memory consolidation, increasing evidence suggests that the frontal lobes and associated executive functions are also impacted. We examined two neurobehavioral executive function tasks and associations with cortical thickness in patients diagnosed with mild cognitive impairment (MCI), suspected AD dementia, and a healthy control group. First, we compared group performances on a go/no-go (GNG) task and on Luria's Fist-Edge-Palm (FEP) motor sequencing task. We then examined correlations between neurobehavioral task performance and the thickness of frontal cortical regions, AD signature regions, broader unbiased brain regions, and white matter hyperintensities (WMH). Participants with MCI performed worse than healthy controls, but better than participants with suspected AD dementia on both tasks. Both GNG and FEP (to a slightly greater extent) tasks showed diffuse associations with most AD signature regions and multiple additional regions within the temporal, parietal, and occipital cortices. Similarly, both tasks showed significant associations with all other cognitive tasks examined. Of the frontal regions examined, only the middle frontal gyrus and pars opercularis were associated with performance on these tasks. Interactions between the precuneus and transtemporal gyri were most predictive of GNG task performance, while the interaction between superior temporal and lingual gyri was most predictive of FEP task performance. This study replicates difficulties with both GNG and FEP tasks in participants with MCI and AD dementia. Both tasks showed widespread associations with the cortical thickness of various brain structures rather than localizing to frontal regions, consistent with the diffuse nature of AD.

Innovative Approaches for Early Alzheimer’s Disease Detection through Novel Analysis of Brain MRI Images

Alzheimer's is one of the most well-known reasons for Dementia. Alzheimer's sickness (AD) is an irreversible, moderate cerebrum issue that gradually obliterates memory and thinking abilities. Alzheimer's and different types of dementia positioned seventh driving reason for death by WHO. Picture handling is broadly utilized in the clinical field to recognize illness and help specialists in dynamics dependent on perception. The paper means to recognize the Alzheimer's sickness at the most punctual with the goal that patients can be forestalled before irreversible changes happen in the mind. We propose the picture handling method to deal with the Magnetic Resonance Imaging (MRI) of the cerebrum from the pivotal plane, coronal plane, and sagittal plane. The picture division is utilized to feature the impacted locale in cerebrum MRI. The analyzed area in mind MRI incorporates the hippocampus and volume of the cerebrum. A similar ID of people impacted with the Alzheimer's illness, Healthy partner, and Mild Cognitive weakness is finished.

Exploring the complex nexus of Alzheimers disease in down syndrome

In recent years, the life expectancy of individuals with Down Syndrome (DS) has increased significantly thanks to the advancements in healthcare and treatments, giving rise to an older population of people with DS. However, this demographic shift has brought about new concerns: as individuals with DS age, they face a higher incidence of early-onset Alzheimers Disease (EOAD) (i.e. the development of AD before the age of 65). Research has revealed an accelerated progression of symptoms, from onset of dementia to death, in DS patients. Thus, it is essential to identify the factors (both genetic and environmental) that underlie the development of different stages of AD-DS. Concomitantly, there is a pressing need to initiate intervention trials that could slow down or prevent the rapid advancement of AD-DS. This paper underscores the growing importance of addressing AD-DS in an ageing DS population. By elucidating genetic and environmental factors, clinical features, diagnostic challenges, roles of genes in the development of AD-DS, and potential future research directions, the article aims to contribute to a deeper understanding of dementia of the Alzheimer Type (DAT) and facilitate the development of effective treatments that could improve the lives of DS individuals affected by DAT.

The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes.

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual-/group-level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer's Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer's disease and frontotemporal lobar degeneration. We included three time-point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer's disease and frontotemporal lobar degeneration variants (114 typical Alzheimer's disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On this data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer's disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualisable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and inter-relation of paradigmatic and mixed cases, capturing dynamic individual trajectories, and linking syndromic variability to neuropathology and key clinical end-points such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer's disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer's disease and frontotemporal lobar degeneration.

Unexpected Low Rate of Amyloid-β Pathology in Multiple Sclerosis Patients.

The life expectancy of people with multiple sclerosis (MS) has increased, yet we have noted that development of a typical Alzheimer disease dementia syndrome is uncommon. We hypothesized that Alzheimer disease pathology is uncommon in MS patients. In 100 MS patients, the rate of amyloid-β plasma biomarker positivity was approximately half the rate in 300 non-MS controls matched on age, sex, apolipoprotein E proteotype, and cognitive status. Interestingly, most MS patients who did have amyloid-β pathology had features atypical for MS at diagnosis. These results support that MS is associated with reduced Alzheimer disease risk, and suggest new avenues of research. ANN NEUROL 2024;96:453-459.

Comorbid neuropathology and atypical presentation of Alzheimer's disease.

Alzheimer's disease (AD) neuropathological changes present with amnestic and nonamnestic (atypical) syndromes. The contribution of comorbid neuropathology as a substratum of atypical expression of AD remains under investigated. We examined whether atypical AD exhibited increased comorbid neuropathology compared to typical AD and if such neuropathologies contributed to the accelerated clinical decline in atypical AD. We examined 60 atypical and 101 typical AD clinicopathological cases. The number of comorbid pathologies was similar between the groups (p=0.09). Argyrophilic grain disease was associated with atypical presentation (p=0.008) after accounting for sex, age of onset, and disease duration. Vascular brain injury was more common in typical AD (p=0.022). Atypical cases had a steeper Mini-Mental Status Examination (MMSE) decline over time (p=0.033). Comorbid neuropathological changes are unlikely to contribute to atypical AD presentation and the steeper cognitive decline seen in this cohort. Autopsy cohort of 60 atypical and 101 typical AD; does comorbid pathology explain atypical presentation?Atypical versus Typical AD: No significant differences in comorbid neuropathologies were found (p=0.09).Argyrophilic Grain Disease Association: significantly correlates with atypical AD presentations, suggesting a unique neuropathological pattern (p=0.008).Vascular Brain Injury Prevalence: Vascular brain injury is more common in typical AD than in atypical AD (p=0.022).Cognitive Decline in Atypical AD: Atypical AD patients experience a steepercognitive decline measured by MMSE than those with typical AD despite lacking more comorbid neuropathology, highlighting the severity of atypical AD pathogenesis (p=0.033).

Association Between SGLT2 Inhibitors and Risk of Dementia and Parkinson's Disease: A Meta-Analysis of 12 Randomized Controlled Trials

BackgroundSodium-glucose cotransporter 2 inhibitors (SGLT2i) have been shown to reduce the risk of hospitalizations from heart failure and cardiovascular mortality. However, SGLT2i therapy's potential effects on the risks of dementia and Parkinson's disease are not well established, with conflicting results based on observational studies. Hence, we sought to evaluate the association between SGLT2i and the risk of dementia and Parkinson's disease in patients with type 2 diabetes mellitus, heart failure, or chronic kidney disease. MethodsWe performed a systematic literature search on PubMed, and Clinicaltrial.gov for relevant randomized controlled trials from inception until March 2024 without any language restrictions. Odds ratios (OR) and 95% confidence intervals (CI) were pooled using a random-effect model. ResultsA total of 12 randomized controlled trials with 74,442 patients (40,784 in the SGLT2i group and 33,658 in the control group) were included in the analysis. The mean age of patients in SGLT2i and control was 65.3 and 65.2 years, respectively. Pooled analysis showed that there is no significant association between SGLT2i use and the risk of dementia (OR 1.37; 95% CI, 0.70-2.69; P = .36), dementia Alzheimer's type (OR 1.99; 95% CI, 0.59-6.71; P = .27), vascular dementia (OR 0.40; 95% CI, 0.09-1.85; P = .24), and Parkinson's disease (OR 0.63; 95% CI, 0.25-1.61; P = .33) when compared with the control groups. ConclusionOur study suggests that there is no significant association between SGLT2i use and the risk of dementia, its subtypes, and Parkinson's disease when compared with the control groups.

Computational Investigation of Coaggregation and Cross-Seeding between Aβ and hIAPP Underpinning the Cross-Talk in Alzheimer's Disease and Type 2 Diabetes.

The coexistence of amyloid-β (Aβ) and human islet amyloid polypeptide (hIAPP) in the brain and pancreas is associated with an increased risk of Alzheimer's disease (AD) and type 2 diabetes (T2D) due to their coaggregation and cross-seeding. Despite this, the molecular mechanisms underlying their interaction remain elusive. Here, we systematically investigated the cross-talk between Aβ and hIAPP using atomistic discrete molecular dynamics (DMD) simulations. Our results revealed that the amyloidogenic core regions of both Aβ (Aβ10-21 and Aβ30-41) and hIAPP (hIAPP8-20 and hIAPP22-29), driving their self-aggregation, also exhibited a strong tendency for cross-interaction. This propensity led to the formation of β-sheet-rich heterocomplexes, including potentially toxic β-barrel oligomers. The formation of Aβ and hIAPP heteroaggregates did not impede the recruitment of additional peptides to grow into larger aggregates. Our cross-seeding simulations demonstrated that both Aβ and hIAPP fibrils could mutually act as seeds, assisting each other's monomers in converting into β-sheets at the exposed fibril elongation ends. The amyloidogenic core regions of Aβ and hIAPP, in both oligomeric and fibrillar states, exhibited the ability to recruit isolated peptides, thereby extending the β-sheet edges, with limited sensitivity to the amino acid sequence. These findings suggest that targeting these regions by capping them with amyloid-resistant peptide drugs may hold potential as a therapeutic approach for addressing AD, T2D, and their copathologies.