Search Results (51)

Introduction: Alzheimer’s disease (AD), and more specifically late-onset Alzheimer’s disease (LOAD), represents a considerable challenge in terms of early and timely diagnosis and treatment. Early diagnosis is crucial to improve the efficacy of the therapies and patients’ quality of life. The current challenge is to accurately identify at-risk individuals before the manifestations of the first symptoms of AD. Methods and results: Here, we present an improved model for LOAD risk prediction, which applies the k-nearest neighbors (KNN) algorithm. We have achieved a sensitivity of 0.80 and an area under the curve (AUC) of 0.71, which represents a high performance especially when compared to an AUC of 0.66 reported previously in 2019 using a KNN model. Discussion: The application of a mathematical model that combines genetic and clinical covariates showed a good prediction of the AD/LOAD risk, with the higher weight being the polygenic genetic risk, APOE haplotype, and age. Compared to previous studies, our model integrates and correlates genetic prediction together with phenotypic information by fine-tuning the parameters of the model in order to achieve the best performance. This algorithm can be used in the general population and does not require the manifestation of any symptoms for its effective application. Thus, we present here an advanced model for risk prediction of LOAD. Full article

The rising prevalence of Alzheimer’s disease (AD), particularly among older adults, has driven increased research into its underlying mechanisms and risk factors. Aging, genetic susceptibility, and cardiovascular health are recognized contributors to AD, but how the age of onset affects disease progression remains underexplored. This study investigates the role of early- versus late-onset Alzheimer’s disease (EOAD and LOAD, respectively) in shaping the trajectory of cognitive decline. Leveraging data from the Religious Orders Study and Memory and Aging Project (ROSMAP), two cohorts were established: individuals with early-onset AD and those with late-onset AD. Comprehensive analyses, including differential gene expression profiling, pathway enrichment, and gene co-expression network construction, were conducted to identify distinct molecular signatures associated with each cohort. Network modularity learning algorithms were used to discern the inner structure of co-expression networks and their related functional features. Computed network descriptors provided deeper insights into the influence of age at onset on the biological progression of AD. Full article

Alzheimer’s disease (AD) is a leading cause of dementia in the elderly, with late-onset AD (LOAD) accounting for 95% of the cases. The etiology underlying LOAD, however, remains unclear. Using a humanized mouse model, we showed previously that exposure to ozone (O₃), a potential environment risk factor, in a cyclic exposure protocol that mimics a human exposure scenario, accelerated AD-like neuropathophysiology in old humanized male ApoE3 (E3) but not ApoE4 (E4) mice. Using RNA sequencing (RNA-seq) techniques, we further demonstrate here that the ApoE genotype has the greatest influence on transcriptional changes, followed by age and O₃ exposure. Notably, AD-related genes were expressed even at baseline and in young mice, but the differences in the expression levels are obvious in old age. Importantly, although both E3 and E4 mice exhibited some AD-related transcriptomic alterations, old E3 mice exposed to O₃, which showed memory impairment, experienced more pronounced disruptions in the expression of genes related to redox balance, neurogenesis, neuroinflammation, and cellular senescence in the hippocampus, compared with O₃-exposed old E4 mice. These results provide new insights into the molecular mechanisms underlying memory loss in O₃-exposed old E3 male mice and emphasize the complexity of interactions between gene, environment, and aging in AD pathophysiology. Full article

Late-onset Alzheimer’s disease (LOAD) is a chronic, multifactorial, and progressive neurodegenerative disease that associates with aging and is highly prevalent in our older population (≥65 years of age). This hypothesis generating this narrative review will examine the important role for the use of sodium thiosulfate (STS) as a possible multi-targeting treatment option for LOAD. Sulfur is widely available in our environment and is responsible for forming organosulfur compounds that are known to be associated with a wide range of biological activities in the brain. STS is known to have (i) antioxidant and (ii) anti-inflammatory properties; (iii) chelation properties for calcium and the pro-oxidative cation metals such as iron and copper; (iv) donor properties for hydrogen sulfide production; (v) possible restorative properties for brain endothelial-cell-derived bioavailable nitric oxide. Thus, it becomes apparent that STS has the potential for neuroprotection and neuromodulation and may allow for an attenuation of the progressive nature of neurodegeneration and impaired cognition in LOAD. STS has been successfully used to prevent cisplatin oxidative-stress-induced ototoxicity in the treatment of head and neck and solid cancers, cyanide and arsenic poisoning, and fungal skin diseases. Most recently, intravenous STS has become part of the treatment plan for calciphylaxis globally due to vascular calcification and ischemia-induced skin necrosis and ulceration. Side effects have been minimal with reports of metabolic acidosis and increased anion gap; as with any drug treatment, there is also the possibility of allergic reactions, possible long-term osteoporosis from animal studies to date, and minor side-effects of nausea, headache, and rhinorrhea if infused too rapidly. While STS poorly penetrates the intact blood–brain barrier(s) (BBBs), it could readily penetrate BBBs that are dysfunctional and disrupted to deliver its neuroprotective and neuromodulating effects in addition to its ability to penetrate the blood–cerebrospinal fluid barrier of the choroid plexus. Novel strategies such as the future use of nano-technology may be helpful in allowing an increased entry of STS into the brain. Full article

Aging and apolipoprotein E4 (APOE4) are the two most significant risk factors for late-onset Alzheimer’s disease (LOAD). Compared to APOE3, APOE4 disrupts cholesterol homeostasis, increases cholesteryl esters (CEs), and exacerbates neuroinflammation in brain cells, including microglia. Targeting CEs and neuroinflammation could be a novel strategy to ameliorate APOE4-dependent phenotypes. Toll-like receptor 4 (TLR4) is a key macromolecule in inflammation, and its regulation is associated with the cholesterol content of lipid rafts in cell membranes. We previously demonstrated that in normal microglia expressing APOE3, inhibiting the cholesterol storage enzyme acyl-CoA:cholesterol acyltransferase 1 (ACAT1/SOAT1) reduces CEs, dampened neuroinflammation via modulating the fate of TLR4. We also showed that treating myelin debris-loaded normal microglia with ACAT inhibitor F12511 reduced cellular CEs and activated ABC transporter 1 (ABCA1) for cholesterol efflux. This study found that treating primary microglia expressing APOE4 with F12511 also reduces CEs, activates ABCA1, and dampens LPS-dependent NFκB activation. In vivo, two-week injections of nanoparticle F12511, which consists of DSPE-PEG₂₀₀₀, phosphatidylcholine, and F12511, to aged female APOE4 mice reduced TLR4 protein content and decreased proinflammatory cytokines, including IL-1β in mice brains. Overall, our work suggests nanoparticle F12511 is a novel agent to ameliorate LOAD. Full article

Late-onset Alzheimer’s disease (LOAD) is a subtype of dementia that manifests after the age of 65. It is characterized by progressive impairments in cognitive functions, behavioral changes, and learning difficulties. Given the progressive nature of the disease, early diagnosis is crucial. Early-onset Alzheimer’s disease (EOAD) is solely attributable to genetic factors, whereas LOAD has multiple contributing factors. A complex pathway mechanism involving multiple factors contributes to LOAD progression. Employing a systems biology approach, our analysis encompassed the genetic, epigenetic, metabolic, and environmental factors that modulate the molecular networks and pathways. These factors affect the brain’s structural integrity, functional capacity, and connectivity, ultimately leading to the manifestation of the disease. This study has aggregated diverse biomarkers associated with factors capable of altering the molecular networks and pathways that influence brain structure, functionality, and connectivity. These biomarkers serve as potential early indicators for AD diagnosis and are designated as early biomarkers. The other biomarker datasets associated with the brain structure, functionality, connectivity, and related parameters of an individual are broadly categorized as clinical-stage biomarkers. This study has compiled research papers on Alzheimer’s disease (AD) diagnosis utilizing machine learning (ML) methodologies from both categories of biomarker data, including the applications of ML techniques for AD diagnosis. The broad objectives of our study are research gap identification, assessment of biomarker efficacy, and the most effective or prevalent ML technology used in AD diagnosis. This paper examines the predominant use of deep learning (DL) and convolutional neural networks (CNNs) in Alzheimer’s disease (AD) diagnosis utilizing various types of biomarker data. Furthermore, this study has addressed the potential scope of using generative AI and the Synthetic Minority Oversampling Technique (SMOTE) for data augmentation. Full article

The microvessel neurovascular unit, with its brain endothelial cells (BEC) and blood–brain barrier remodeling, is important in the development of impaired cognition in sporadic or late-onset Alzheimer’s disease (LOAD), which is associated with aging and is highly prevalent in older populations (≥65 years of age). It is also linked with vascular dementia and vascular contributions to cognitive impairment and dementia, including cerebral amyloid angiopathy in neurodegeneration. LOAD is considered to be the number one cause of dementia globally; however, when one considers the role of mixed dementia (MD)—the combination of both the amyloid cascade hypothesis and the vascular hypothesis of LOAD—it becomes apparent that MD is the number one cause. Microvessel BECs are the first cells in the brain to be exposed to peripheral neurotoxins from the systemic circulation and are therefore the brain cells at the highest risk for early and chronic injury. Therefore, these cells are the first to undergo injury, followed by excessive and recurrent wound healing and remodeling processes in aging and other age-related diseases such as cerebrocardiovascular disease, hypertension, type 2 diabetes mellitus, and Parkinson’s disease. This narrative review explores the intricate relationship between microvessel remodeling, cerebral small vessel disease (SVD), and neurodegeneration in LOAD. It also discusses the current understanding of how microvessel dysfunction, disruption, and pathology contribute to the pathogenesis of LOAD and highlights potential avenues for therapeutic intervention. Full article

Early-onset Alzheimer’s disease (EOAD), defined as Alzheimer’s disease onset before 65 years of age, has been significantly less studied than the “classic” late-onset form (LOAD), although EOAD often presents with a more aggressive disease course, caused by variants in the APP, PSEN1, and PSEN2 genes. EOAD has significant differences from LOAD, including encompassing diverse phenotypic manifestations, increased genetic predisposition, and variations in neuropathological burden and distribution. Phenotypically, EOAD can be manifested with non-amnestic variants, sparing the hippocampi with increased tau burden. The aim of this article is to review the different genetic bases, risk factors, pathological mechanisms, and diagnostic approaches between EOAD and LOAD and to suggest steps to further our understanding. The comprehension of the monogenic form of the disease can provide valuable insights that may serve as a roadmap for understanding the common form of the disease. Full article

The purpose of this study was to investigate whether plasma biomarkers can help to diagnose, differentiate from Alzheimer disease (AD), and stage cognitive performance in patients with positron emission tomography (PET)-confirmed primary age-related tauopathy, termed tau-first cognitive proteinopathy (TCP) in this study. In this multi-center study, we enrolled 285 subjects with young-onset AD (YOAD; n = 55), late-onset AD (LOAD; n = 96), TCP (n = 44), and cognitively unimpaired controls (CTL; n = 90) and analyzed plasma Aβ42/Aβ40, pTau181, neurofilament light (NFL), and total-tau using single-molecule assays. Amyloid and tau centiloids reflected pathological burden, and hippocampal volume reflected structural integrity. Receiver operating characteristic curves and areas under the curves (AUCs) were used to determine the diagnostic accuracy of plasma biomarkers compared to hippocampal volume and amyloid and tau centiloids. The Mini-Mental State Examination score (MMSE) served as the major cognitive outcome. Logistic stepwise regression was used to assess the overall diagnostic accuracy, combining fluid and structural biomarkers and a stepwise linear regression model for the significant variables for MMSE. For TCP, tau centiloid reached the highest AUC for diagnosis (0.79), while pTau181 could differentiate TCP from YOAD (accuracy 0.775) and LOAD (accuracy 0.806). NFL reflected the clinical dementia rating in TCP, while pTau181 (rho = 0.3487, p = 0.03) and Aβ42/Aβ40 (rho = −0.36, p = 0.02) were significantly correlated with tau centiloid. Hippocampal volume (unstandardized β = 4.99, p = 0.01) outperformed all of the fluid biomarkers in predicting MMSE scores in the TCP group. Our results support the superiority of tau PET to diagnose TCP, pTau181 to differentiate TCP from YOAD or LOAD, and NFL for functional staging. Full article

The pathophysiology of many neuropsychiatric disorders is still poorly understood. Identification of biomarkers for these diseases could benefit patients due to better classification and stratification. Exosomes excreted into the circulatory system can cross the blood–brain barrier and carry a cell type-specific set of molecules. Thus, exosomes are a source of potential biomarkers for many diseases, including neuropsychiatric disorders. Here, we investigated exosomal proteins produced from human-induced pluripotent stem cells (iPSCs) and iPSC-derived neural stem cells, neural progenitors, neurons, astrocytes, microglia-like cells, and brain capillary endothelial cells. Of the 31 exosome surface markers analyzed, a subset of biomarkers were significantly enriched in astrocytes (CD29, CD44, and CD49e), microglia-like cells (CD44), and neural stem cells (SSEA4). To identify molecular fingerprints associated with disease, circulating exosomes derived from healthy control (HC) individuals were compared against schizophrenia (SCZ) patients and late-onset Alzheimer’s disease (LOAD) patients. A significant epitope pattern was identified for LOAD (CD1c and CD2) but not for SCZ compared to HC. Thus, analysis of cell type- and disease-specific exosome signatures of iPSC-derived cell cultures may provide a valuable model system to explore proteomic biomarkers for the identification of novel disease profiles. Full article

Adiponectin, a hormone secreted by adipose tissue, plays a complex role in regulating metabolic homeostasis and has also garnered attention for its potential involvement in the pathogenesis of late-onset Alzheimer’s disease (LOAD). The objective of this study was to investigate the association of ADIPOQ variants with plasma adiponectin levels and LOAD risk in subjects from the Slovak Caucasian population. For this purpose, 385 LOAD patients and 533 controls without cognitive impairment were recruited and genotyped for a total of eighteen ADIPOQ single nucleotide polymorphisms (SNPs). Both single-locus and haplotype-based logistic regression analyses were employed to assess the association of SNPs with LOAD risk, while linear regression analysis was used to explore their influence on adiponectin levels in LOAD patients. ADIPOQ variants rs822395 and rs2036373 in intron 1 were found to significantly elevate total adiponectin levels after accounting for several potential confounders. Additional SNPs in the 5′ region and intron 1 exhibited a non-significant trend of association with adiponectin. However, none of the ADIPOQ SNPs showed an association with LOAD risk, neither in the whole-group analysis nor in subgroup analyses after stratification for sex or the APOE ε4 allele, a well-established LOAD risk factor. In summary, while adiponectin has emerged as a potential contributor to the development of LOAD, this study did not unveil any significant involvement of its gene variants in susceptibility to the disease. Full article

Alzheimer’s Disease (AD) is an age-related neurodegenerative disorder characterized by progressive memory loss and cognitive impairment, affecting 35 million individuals worldwide. Intracerebroventricular (ICV) injection of low to moderate doses of streptozotocin (STZ) in adult male Wistar rats can reproduce classical physiopathological hallmarks of AD. This biological model is known as ICV-STZ. Most studies are focused on the description of behavioral and morphological aspects of the ICV-STZ model. However, knowledge regarding the molecular aspects of the ICV-STZ model is still incipient. Therefore, this work is a first attempt to provide a wide proteome description of the ICV-STZ model based on mass spectrometry (MS). To achieve that, samples from the pre-frontal cortex (PFC) and hippocampus (HPC) of the ICV-STZ model and control (wild-type) were used. Differential protein abundance, pathway, and network analysis were performed based on the protein identification and quantification of the samples. Our analysis revealed dysregulated biological pathways implicated in the early stages of late-onset Alzheimer’s disease (LOAD), based on differentially abundant proteins (DAPs). Some of these DAPs had their mRNA expression further investigated through qRT-PCR. Our results shed light on the AD onset and demonstrate the ICV-STZ as a valid model for LOAD proteome description. Full article

Alzheimer’s Disease (AD) is a complex neurodegenerative disease resulting in progressive loss of memory, language and motor abilities caused by cortical and hippocampal degeneration. This review captures the landscape of understanding of AD pathology, diagnostics, and current therapies. Two major mechanisms direct AD pathology: (1) accumulation of amyloid β (Aβ) plaque and (2) tau-derived neurofibrillary tangles (NFT). The most common variants in the Aβ pathway in APP, PSEN1, and PSEN2 are largely responsible for early-onset AD (EOAD), while MAPT, APOE, TREM2 and ABCA7 have a modifying effect on late-onset AD (LOAD). More recent studies implicate chaperone proteins and Aβ degrading proteins in AD. Several tests, such as cognitive function, brain imaging, and cerebral spinal fluid (CSF) and blood tests, are used for AD diagnosis. Additionally, several biomarkers seem to have a unique AD specific combination of expression and could potentially be used in improved, less invasive diagnostics. In addition to genetic perturbations, environmental influences, such as altered gut microbiome signatures, affect AD. Effective AD treatments have been challenging to develop. Currently, there are several FDA approved drugs (cholinesterase inhibitors, Aß-targeting antibodies and an NMDA antagonist) that could mitigate AD rate of decline and symptoms of distress. Full article

Junk foods are typically low in essential nutrients, such as vitamins, minerals, and antioxidants. They are also loaded with trans fats and saturated fats, which can increase the level of triglycerides in the blood. High triglyceride levels can contribute to the development of non-alcoholic fatty liver disease (NAFLD), a condition where excess fat accumulates in the liver. A high intake of junk foods can lead to insulin resistance, a condition where the body’s cells become less responsive to insulin. A diet lacking in nutrients and loaded with unwanted toxins can impair the liver’s ability to detoxify harmful substances and damage its overall function. It is known that the regular consumption of junk food can be linked to memory impairment and cognitive decline. Several studies have shown that diets high in unhealthy fats, sugars, and processed foods can negatively impact brain health, including memory function. In this study, Wistar rats were used to model Late-Onset Alzheimer’s Disease (LOAD), which was inspired by knowledge of the liver–brain axis’s role in causing dementia. The model mimicked junk-food-induced liver–brain damage, and was developed by using the toxins d-galactosamine, ethanol and d-galactose. To begin with, the model rats demonstrated insulin resistance, a characteristic of LOAD patients. Glucose levels in both the brain and liver tissues were significantly elevated in the model, paralleling clinical findings in LOAD patients. High glucose levels in the brain lead to the increased production of advanced glycation end-products (AGEs), which, along with amyloid beta, harm neighbouring neurons. Histopathological analysis revealed deformed glial nodules, apoptotic neurons, and amyloid plaques in the brain section in the later stages of the disease. Simultaneously, the liver section displayed features of cirrhosis, including an effaced lobular architecture and the extravasation of red blood cells. Liver enzymes ALT, AST and ALP were consistently elevated with disease progression. Furthermore, immunohistochemistry confirmed the presence of amyloid precursor protein (APP) in the diseased brain. The positive expression of Hypoxia-Inducible Factor 3-Alpha (HIF3A) in the brain indicated hypoxic conditions, which is consistent with other LOAD studies. This model also exhibited damaged intestinal villi and excessive bowel and urinary incontinence, indicating malnutrition and a disturbed gut microbiome, which is also consistent with LOAD patients. Bioinformatics analysis on serum protein suggests a few affected molecular pathways, like the amyloid secretase pathway, androgen/oestrogen/progesterone biosynthesis, the apoptosis signalling pathway, the insulin/IGF pathway-protein kinase B signalling cascade, the Metabotropic glutamate receptor group I pathway, the Wnt signalling pathway, etc. Behavioural analysis confirmed memory decline and the loss of muscle strength with disease progression. Overall, this rat model of LOAD sheds valuable light on LOAD pathology and highlights the potential link between liver dysfunction, particularly induced by the excessive consumption of junk food, and LOAD. This study contributes to a deeper understanding of the complex molecular mechanisms involved in LOAD, paving the way for new possibilities in therapeutic interventions. Full article

Telomeres are structures at the ends of eukaryotic chromosomes that help maintain genomic stability. During aging, telomere length gradually shortens, producing short telomeres, which are markers of premature cellular senescence. This may contribute to age-related diseases, including Alzheimer’s disease (AD), and based on this, several studies have hypothesized that telomere shortening may characterize AD. Current research, however, has been inconclusive regarding the direction of the association between leukocyte telomere length (LTL) and disease risk. We assessed the association between LTL and AD in a retrospective case–control study of a sample of 255 unrelated patients with late-onset AD (LOAD), including 120 sporadic cases and 135 with positive family history for LOAD, and a group of 279 cognitively healthy unrelated controls, who were all from Calabria, a southern Italian region. Following regression analysis, telomeres were found significantly shorter in LOAD cases than in controls (48% and 41% decrease for sporadic and familial cases, respectively; p < 0.001 for both). Interestingly, LTL was associated with disease risk independently of the presence of conventional risk factors (e.g., age, sex, MMSE scores, and the presence of the APOE-ε4 allele). Altogether, our findings lend support to the notion that LTL shortening may be an indicator of the pathogenesis of LOAD. Full article