Search Results (302)

Accurate detection of Alzheimer’s disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative combination of MRI slice orientation and anatomical location for AD classification. We propose an automated framework that first selects the most relevant slices using a feature entropy-based method applied to activation maps from a pretrained CNN model. For classification, we employ a lightweight CNN architecture based on depthwise separable convolutions to efficiently analyze the selected 2D MRI slices extracted from preprocessed 3D brain scans. To further interpret model behavior, an attention mechanism is integrated to analyze which feature level contributes the most to the classification process. The model is evaluated on three binary tasks: AD vs. mild cognitive impairment (MCI), AD vs. cognitively normal (CN), and MCI vs. CN. The experimental results show the highest accuracy (97.4%) in distinguishing AD from CN when utilizing the selected slices from the ninth axial segment, followed by the tenth segment of coronal and sagittal orientations. These findings demonstrate the significance of slice location and orientation in MRI-based AD diagnosis and highlight the potential of lightweight CNNs for clinical use. Full article

Type 10 17β-hydroxysteroid dehydrogenase (17β-HSD10) is the HSD17B10 gene product. It plays an appreciable part in the carcinogenesis and pathogenesis of neurodegeneration, such as Alzheimer’s disease and infantile neurodegeneration. This mitochondrial, homo-tetrameric protein is a central hub in various metabolic pathways, e.g., branched-chain amino acid degradation and neurosteroid metabolism. It can bind to other proteins carrying out diverse physiological functions, e.g., tRNA maturation. It has also previously been proposed to be an Aβ-binding alcohol dehydrogenase (ABAD) or endoplasmic reticulum-associated Aβ-binding protein (ERAB), although those reports are controversial due to data analyses. For example, the reported k_m value of some substrate of ABAD/ERAB was five times higher than its natural solubility in the assay employed to measure k_m. Regarding any reported “one-site competitive inhibition” of ABAD/ERAB by Aβ, the k_i value estimations were likely impacted by non-physiological concentrations of 2-octanol at high concentrations of vehicle DMSO and, therefore, are likely artefactual. Certain data associated with ABAD/ERAB were found not reproducible, and multiple experimental approaches were undertaken under non-physiological conditions. In contrast, 17β-HSD10 studies prompted a conclusion that Aβ inhibited 17β-HSD10 activity, thus harming brain cells, replacing a prior supposition that “ABAD” mediates Aβ neurotoxicity. Furthermore, it is critical to find answers to the question as to why elevated levels of 17β-HSD10, in addition to Aβ and phosphorylated Tau, are present in the brains of AD patients and mouse AD models. Addressing this question will likely prompt better approaches to develop treatments for Alzheimer’s disease. Full article

Glaucoma is a trans-synaptic neurodegenerative disease, and the pathological increase in intraocular pressure (IOP) is a major risk factor of glaucoma. High IOP alters microstructure and morphologies of the brain tissue. Since mechanical properties of the brain are sensitive to the alteration of the tissue microstructure, we investigate how varying durations of chronic elevated IOP alter brain mechanical properties. A chronic high IOP rat model was induced by episcleral vein cauterization with subconjunctival injection of 5-Fluorouracil. At 2, 4 and 8 weeks after induction, indentation tests were performed on the brain slices to measure mechanical properties in the hippocampus, lateral geniculate nucleus and occipital lobe of both hemispheres. Meanwhile, the brain’s microstructure was assessed via F-actin and myelin staining. Compared to the blank control group, the Young’s modulus decreased in all three brain regions in the highIOP experimental groups. F-actin fluorescence intensity and myelin area fraction were reduced in the hippocampus, while β-amyloid levels and tau phosphorylation were elevated in the experimental groups. Our study provides insight into Alzheimer’s disease pathogenesis by demonstrating how chronic high IOP alters the brain’s mechanical properties. Full article

Background and Objectives: Alzheimer’s disease (AD) has become a serious health problem. Agomelatine (Ago) is a neuroprotective antidepressant. This study aimed to assess how Ago influences behavioral outcomes in AD-like rat model. Materials and Methods: Forty-eight Wistar albino rats were allocated into four groups: Control (C), Alzheimer’s disease-like model (AD), Alzheimer’s disease-like model treated with Ago (ADAgo), and Ago alone (Ago). Physiological saline was injected intrahippocampally in C and Ago animals, whereas Aβ peptide was delivered similarly in AD and ADAgo rats. On day 15, 0.9% NaCl was administered to the C and AD groups, and Agomelatine (1 mg/kg/day) was infused into ADAgo and Ago rats via osmotic pumps for 30 days. Behavioral functions were evaluated using Open Field (OF), Forced Swim (FST), and Morris Water Maze (MWM) tests. Brain tissues were examined histopathologically. Neuritin, Nestin, DCX, NeuN, BDNF, MASH1, MT1, and MT2 transcripts were quantified by real-time PCR. Statistical analyses were performed in R 4.3.1, with p < 0.05 deemed significant. Results: In the FST, swimming, climbing, immobility time, and mobility percentage differed significantly among groups (p < 0.05). In the MWM, AD rats exhibited impaired learning and memory that was ameliorated by Ago treatment (p < 0.05). DCX expression decreased in AD rats but was elevated by Ago (p < 0.05). Nestin levels differed significantly between control and AD animals; MT1 expression varied between control and AD cohorts; and MT2 transcript levels were significantly lower in AD, ADAgo, and Ago groups compared to C (all p < 0.05). Conclusions: Ago exhibits antidepressant-like activity in this experimental AD model and may enhance cognitive function via mechanisms beyond synaptic plasticity and neurogenesis. Full article

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article

The selection of appropriate biomarkers in clinical practice aids in the early detection, treatment, and prevention of disease while also assisting in the development of targeted therapeutics. Recently, multi-omics data generated from advanced technology platforms has become available for disease studies. Therefore, the integration of this data with associated clinical data provides a unique opportunity to gain a deeper understanding of disease. However, the effective integration of large-scale multi-omics data remains a major challenge. To address this, we propose a novel deep learning model—the Multi-Omics Graph Attention biomarker Discovery network (MOGAD). MOGAD aims to efficiently classify diseases and discover biomarkers by integrating various omics data such as DNA methylation, gene expression, and miRNA expression. The model consists of three main modules: Multi-head GAT network (MGAT), Multi-Graph Attention Fusion (MGAF), and Attention Fusion (AF), which work together to dynamically model the complex relationships among different omics layers. We incorporate clinical data (e.g., APOE genotype) which enables a systematic investigation of the influence of non-omics factors on disease classification. The experimental results demonstrate that MOGAD achieves a superior performance compared to existing single-omics and multi-omics integration methods in classification tasks for Alzheimer’s disease (AD). In the comparative experiment on the ROSMAP dataset, our model achieved the highest ACC (0.773), F1-score (0.787), and MCC (0.551). The biomarkers identified by MOGAD show strong associations with the underlying pathogenesis of AD. We also apply a Hi-C dataset to validate the biological rationality of the identified biomarkers. Furthermore, the incorporation of clinical data enhances the model’s robustness and uncovers synergistic interactions between omics and non-omics features. Thus, our deep learning model is able to successfully integrate multi-omics data to efficiently classify disease and discover novel biomarkers. Full article

This study introduces a multi-task prediction model, MT-MBLAE, designed to use DNA methylation data from blood to predict the advancement of Alzheimer’s disease. By integrating various modules, including bi-directional long short-term memory (BiLSTM), long short-term memory (LSTM), and RepeatVector, among others, the model encodes DNA methylation profile data, capturing temporal and spatial information from instantaneous DNA methylation spectra data. Leveraging the network properties of BiLSTM and LSTM enables the consideration of both preceding and subsequent information in sequences, facilitating the extraction of richer features and enhancing the model’s comprehension of sequential data. Moreover, the model employs LSTM, time distributed to reconstruct time series DNA methylation profiles. The time-distributed layer applies identical layers at each time step of the sequence, sharing weights and biases uniformly across all time steps. This approach achieves parameter sharing, reduces the model’s parameter count, and ensures consistency in handling time series data. Experimental findings show the excellent performance of the MT-MBLAE model in predicting cognitively normal (CN) to mild cognitive impairment (MCI), and mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Full article

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice samples were collected under standardized conditions, ensuring data reliability and diversity. The proposed ternary classification algorithm integrates the predictions of binary CNN classifiers through a majority voting ensemble strategy. ResNet, DenseNet, and EfficientNet architectures were employed for model development. The experimental results show that the ensemble model based on ResNet achieves a weighted F1 score of 91.31%, demonstrating superior performance compared to existing approaches. To the best of our knowledge, this is the first large-scale study to perform three-class classification of neurodegenerative diseases using voice data. Full article

Alzheimer’s disease (AD) is a leading cause of dementia and a growing public health concern worldwide. Despite decades of research, effective disease-modifying treatments remain elusive, partly due to limitations in current experimental models. The purpose of this review is to critically assess and compare existing murine and alternative models of AD to identify key strengths, limitations, and future directions for model development that can enhance translational relevance and therapeutic discovery. Traditional transgenic mouse models have advanced the understanding of amyloid-beta and tau pathologies, but often fail to capture the complexity of sporadic, late-onset AD. In response, alternative models—including zebrafish, Drosophila melanogaster, Caenorhabditis elegans, non-human primates, and human brain organoids—are gaining traction due to their complementary insights and diverse experimental advantages. This review also discusses innovations in genetic engineering, neuroimaging, computational modelling, and drug repurposing that are reshaping the landscape of AD research. By integrating these diverse approaches, the review advocates for a multi-model, multidisciplinary strategy to improve the predictive power, accelerate clinical translation, and inform personalised therapeutic interventions. Ethical considerations and equitable access to diagnostics and emerging treatments are also emphasised. Ultimately, this work aims to support the development of more accurate, effective, and human-relevant models to combat AD. Full article

Background/Objectives: Structural magnetic resonance imaging (MRI) and 18-fluoro-deoxy-glucose positron emission tomography (PET) reveal the structural and functional information of the brain from different dimensions, demonstrating considerable clinical and practical value in the computer-aided diagnosis of Alzheimer’s disease (AD). However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. Most of the existing methods start from the single-modal data and assume that different datasets meet the same distribution, but they fail to fully consider the complementary information between the multi-modal data and fail to effectively solve the problem of domain distribution difference. Methods: In this study, we propose a multi-modal deep domain adaptation (MM-DDA) model that integrates MRI and PET modal data, which aims to maximize the utilization of the complementarity of the multi-modal data and narrow the differences in domain distribution to boost the accuracy of AD classification. Specifically, MM-DDA comprises three primary modules: (1) the feature encoding module, which employs convolutional neural networks (CNNs) to capture detailed and abstract feature representations from MRI and PET images; (2) the multi-head attention feature fusion module, which is used to fuse MRI and PET features, that is, to capture rich semantic information between modes from multiple angles by dynamically adjusting weights, so as to achieve more flexible and efficient feature fusion; and (3) the domain transfer module, which reduces the distributional discrepancies between the source and target domains by employing adversarial learning training. Results: We selected 639 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and considered two transfer learning settings. In ADNI1→ADNI2, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 92.40%, 81.81%, 81.13%, and 85.45%, respectively. In ADNI2→ADNI1, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 94.73%, 81.48%, 85.48%, and 81.69%, respectively. Conclusions: MM-DDA is compared with other deep learning methods on two kinds of transfer learning, and the performance comparison results confirmed the superiority of the proposed method in AD prediction tasks. Full article

As life expectancy continues to increase, it becomes increasingly important to extend healthspan by targeting mechanisms associated with aging. Cellular senescence is recognized as a significant contributor to aging and neurodegenerative disorders. This review examines the emerging role of nutraceuticals and functional foods as potential modulators of cellular senescence, which may, in turn, influence the development of neurodegenerative diseases. An analysis of experimental studies indicates that bioactive compounds, including polyphenols, vitamins, and spices, possess substantial antioxidants, anti-inflammatory and epigenetic properties. These nutritional senotherapeutic agents effectively scavenge reactive oxygen species, modulate gene expression, and decrease the secretion of senescence-associated secretory phenotype factors, minimizing cellular damage. Nutraceuticals can enhance mitochondrial function, reduce oxidative stress, and regulate inflammation, key factors in aging and diseases like Alzheimer’s and Parkinson’s. Furthermore, studies reveal that specific bioactive compounds can reduce senescence markers in cellular models, while others exhibit senostatic and senolytic properties, both directly and indirectly. Diets enriched with these nutraceuticals, such as the Mediterranean diet, have been correlated with improved brain health and the deceleration of aging. Despite these promising outcomes, direct evidence linking these compounds to reducing senescent cell numbers remains limited, highlighting the necessity for further inquiry. This review presents compelling arguments for the potential of nutraceuticals and functional foods to promote longevity and counteract neurodegeneration by exploring their molecular mechanisms. The emerging relationship between dietary bioactive compounds and cellular senescence sets the stage for future research to develop effective preventive and therapeutic strategies for age-related diseases. Full article

Alzheimer’s disease, a complex neurodegenerative disease, is characterized by the pathological aggregation of insoluble amyloid β and hyperphosphorylated tau. Multiple models of this disease have been employed to investigate the etiology, pathogenesis, and multifactorial aspects of Alzheimer’s disease and facilitate therapeutic development. Mammals, especially mice, are the most common models for studying the pathogenesis of this disease in vivo. To date, the scientific literature has documented more than 280 mouse models exhibiting diverse aspects of Alzheimer’s disease pathogenesis. Other mammalian species, including rats, pigs, and primates, have also been utilized as models. Selected aspects of Alzheimer’s disease have also been modeled in simpler model organisms, such as Drosophila melanogaster, Caenorhabditis elegans, and Danio rerio. It is possible to model Alzheimer’s disease not only by creating genetically modified animal lines but also by inducing symptoms of this neurodegenerative disease. This review discusses the main methods of creating induced models, with a particular focus on modeling Alzheimer’s disease on cell cultures. Induced pluripotent stem cell (iPSC) technology has facilitated novel investigations into the mechanistic underpinnings of diverse diseases, including Alzheimer’s. Progress in culturing brain tissue allows for more personalized studies on how drugs affect the brain. Recent years have witnessed substantial advancements in intricate cellular system development, including spheroids, three-dimensional scaffolds, and microfluidic cultures. Microfluidic technologies have emerged as cutting-edge tools for studying intercellular interactions, the tissue microenvironment, and the role of the blood–brain barrier (BBB). Modern biology is experiencing a significant paradigm shift towards utilizing big data and omics technologies. Computational modeling represents a powerful methodology for researching a wide array of human diseases, including Alzheimer’s. Bioinformatic methodologies facilitate the analysis of extensive datasets generated via high-throughput experimentation. It is imperative to underscore the significance of integrating diverse modeling techniques in elucidating pathogenic mechanisms in their entirety. Full article

The present Perspective analyzes the remarkable evolution of the Amyloid Cascade Hypothesis 2.0 (ACH2.0) theory of Alzheimer’s disease (AD) since its inception a few years ago, as reflected in the diminishing role of amyloid-beta (Aβ) in the disease. In the initial iteration of the ACH2.0, Aβ-protein-precursor (AβPP)-derived intraneuronal Aβ (iAβ), accumulated to neuronal integrated stress response (ISR)-eliciting levels, triggers AD. The neuronal ISR, in turn, activates the AβPP-independent production of its C99 fragment that is processed into iAβ, which drives the disease. The second iteration of the ACH2.0 stemmed from the realization that AD is, in fact, a disease of the sustained neuronal ISR. It introduced two categories of AD—conventional and unconventional—differing mainly in the manner of their causation. The former is caused by the neuronal ISR triggered by AβPP-derived iAβ, whereas in the latter, the neuronal ISR is elicited by stressors distinct from AβPP-derived iAβ and arising from brain trauma, viral and bacterial infections, and various types of inflammation. Moreover, conventional AD always contains an unconventional component, and in both forms, the disease is driven by iAβ generated independently of AβPP. In its third, the current, iteration, the ACH2.0 posits that proteolytic production of Aβ is suppressed in AD-affected neurons and that the disease is driven by C99 generated independently of AβPP. Suppression of Aβ production in AD seems an oxymoron: Aβ is equated with AD, and the later is inconceivable without the former in an ingrained Amyloid Cascade Hypothesis (ACH)-based notion. But suppression of Aβ production in AD-affected neurons is where the logic leads, and to follow it we only need to overcome the inertia of the preexisting assumptions. Moreover, not only is the generation of Aβ suppressed, so is the production of all components of the AβPP proteolytic pathway. This assertion is not a quantum leap (unless overcoming the inertia counts as such): the global cellular protein synthesis is severely suppressed under the neuronal ISR conditions, and there is no reason for constituents of the AβPP proteolytic pathway to be exempted, and they, apparently, are not, as indicated by the empirical data. In contrast, tau protein translation persists in AD-affected neurons under ISR conditions because the human tau mRNA contains an internal ribosomal entry site in its 5′UTR. In current mouse models, iAβ derived from AβPP expressed exogenously from human transgenes elicits the neuronal ISR and thus suppresses its own production. Its levels cannot principally reach AD pathology-causing levels regardless of the number of transgenes or the types of FAD mutations that they (or additional transgenes) carry. Since the AβPP-independent C99 production pathway is inoperative in mice, the current transgenic models have no potential for developing the full spectrum of AD pathology. What they display are only effects of the AβPP-derived iAβ-elicited neuronal ISR. The paper describes strategies to construct adequate transgenic AD models. It also details the utilization of human neuronal cells as the only adequate model system currently available for conventional and unconventional AD. The final alteration of the ACH2.0, introduced in the present Perspective, is that AβPP, which supports neuronal functionality and viability, is, after all, potentially produced in AD-affected neurons, albeit not conventionally but in an ISR-driven and -compatible process. Thus, the present narrative begins with the “omnipotent” Aβ capable of both triggering and driving the disease and ends up with this peptide largely dislodged from its pedestal and retaining its central role in triggering the disease in only one, although prevalent (conventional), category of AD (and driving it in none). Among interesting inferences of the present Perspective is the determination that “sporadic AD” is not sporadic at all (“non-familial” would be a much better designation). The term has fatalistic connotations, implying that the disease can strike at random. This is patently not the case: The conventional disease affects a distinct subpopulation, and the basis for unconventional AD is well understood. Another conclusion is that, unless prevented, the occurrence of conventional AD is inevitable given a sufficiently long lifespan. This Perspective also defines therapeutic directions not to be taken as well as auspicious ways forward. The former category includes ACH-based drugs (those interfering with the proteolytic production of Aβ and/or depleting extracellular Aβ). They are legitimate (albeit inefficient) preventive agents for conventional AD. There is, however, a proverbial snowball’s chance in hell of them being effective in symptomatic AD, lecanemab, donanemab, and any other “…mab” or “…stat” notwithstanding. They comprise Aβ-specific antibodies, inhibitors of beta- and gamma-secretase, and modulators of the latter. In the latter category, among ways to go are the following: (1) Depletion of iAβ, which, if sufficiently “deep”, opens up a tantalizing possibility of once-in-a-lifetime preventive transient treatment for conventional AD and aging-associated cognitive decline, AACD. (2) Composite therapy comprising the degradation of C99/iAβ and concurrent inhibition of the neuronal ISR. A single transient treatment could be sufficient to arrest the progression of conventional AD and prevent its recurrence for life. Multiple recurrent treatments would achieve the same outcome in unconventional AD. Alternatively, the sustained reduction/removal of unconventional neuronal ISR-eliciting stressors through the elimination of their source would convert unconventional AD into conventional one, preventable/treatable by a single transient administration of the composite C99/iAβ depletion/ISR suppression therapy. Efficient and suitable ISR inhibitors are available, and it is explicitly clear where to look for C99/iAβ-specific targeted degradation agents—activators of BACE1 and, especially, BACE2. Directly acting C99/iAβ-specific degradation agents such as proteolysis-targeting chimeras (PROTACs) and molecular-glue degraders (MGDs) are also viable options. (3) A circumscribed shift (either upstream or downstream) of the position of transcription start site (TSS) of the human AβPP gene, or, alternatively, a gene editing-mediated excision or replacement of a small, defined segment of its portion encoding 5′-untranslated region of AβPP mRNA; targeting AβPP RNA with anti-antisense oligonucleotides is another possibility. If properly executed, these RNA-based strategies would not interfere with the protein-coding potential of AβPP mRNA, and each would be capable of both preventing and stopping the AβPP-independent generation of C99 and thus of either preventing AD or arresting the progression of the disease in its conventional and unconventional forms. The paper is interspersed with “validation” sections: every conceptually significant notion is either validated by the existing data or an experimental procedure validating it is proposed. Full article

In addition to the classically accepted pathophysiological features of Alzheimer’s disease (AD), increasing attention is paid to the role of the insulin-resistant state of the central nervous system. Glucagon-like peptide-1 receptor (GLP-1R) agonism demonstrated neuroprotective consequences by mitigating neuroinflammation and oxidative damage. The present review aims to offer a comprehensive overview of the neuroprotective properties of GLP-1R agonists (GLP-1RAs), with a particular focus on experimental animal models of AD. Ameliorated amyloid-β plaque and neurofibrillary tangle formation and deposition following exenatide, liraglutide, and lixisenatide treatment was confirmed in several models. The GLP-1RAs studied alleviated central insulin resistance, as evidenced by the decreased serine phosphorylation of insulin receptor substrate 1 (IRS-1) and restored downstream phosphoinositide 3-kinase/RAC serine/threonine–protein kinase (PI3K/Akt) signaling. Furthermore, the GLP-1RAs influenced multiple mitogen-activated protein kinases (extracellular signal-regulated kinase: ERK; c-Jun N-terminal kinase: JNK, p38) positively and suppressed glycogen synthase kinase 3 (GSK-3β) hyperactivation. A lower proportion of reactive microglia and astrocytes was associated with better neuronal preservation following their administration. Finally, restoration of cognitive functions, particularly spatial memory, was also observed for semaglutide and dulaglutide. GLP-1RAs, therefore, hold promising disease-modifying potential in the management of AD. Full article

Chronic pain, defined by persistent pain beyond normal healing time, is a pervasive and debilitating condition affecting up to 30–50% of adults globally. In parallel, neurodegenerative diseases (NDs) such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS) are characterized by progressive neuronal loss and cognitive or motor decline, often underpinned by pathological protein misfolding and aggregation. Emerging evidence suggests a potential mechanistic link between chronic pain and NDs, with persistent pain contributing to neuroinflammatory states and protein homeostasis disturbances that mirror processes in neurodegeneration. This review explores the hypothesis that protein misfolding and aggregation serve as a mechanistic bridge between chronic pain and neurodegeneration. We systematically examine molecular pathways of protein misfolding, proteostasis dysfunction in chronic pain, and shared neuroimmune mechanisms, highlighting prion-like propagation of misfolded proteins, chronic neuroinflammation, and oxidative stress as common denominators. We further discuss evidence from experimental models and clinical studies linking chronic pain to accelerated neurodegenerative pathology—including tau accumulation, amyloid dysregulation, and microglial activation—and consider how these insights open avenues for novel therapeutics. Targeting protein aggregation, enhancing chaperone function, modulating the unfolded protein response (UPR), and attenuating glial activation are explored as potential strategies to mitigate chronic pain and possibly slow neurodegeneration. Understanding this intersection not only elucidates chronic pain’s role in cognitive decline but also suggests that interventions addressing proteostasis and inflammation could yield dual benefits in pain management and neurodegenerative disease modification. Full article