Search Results (12,010)

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques, neurofibrillary tau tangles, neuroinflammation, and progressive cognitive decline. Beyond these classical pathological features, emerging evidence implicates microbial dysbiosis as a contributing factor, with the gut and oral microbiomes currently providing the strongest evidence for microbiome AD associations. In contrast, the ocular microbiome represents a biologically plausible but largely unexplored candidate whose potential contribution to AD pathogenesis remains hypothetical and requires rigorous investigation. The ocular surface shares embryological, anatomical, and functional connections with the central nervous system, and the retina has emerged as a non-invasive window into neurodegenerative brain changes. Bacterial taxa detected in ocular specimens, including Cutibacterium acnes and Acinetobacter johnsonii, overlap with those reported in some studies of AD brain tissue, prompting speculation about an ocular–brain microbial interface. However, this overlap does not establish microbial trafficking, and no direct evidence currently supports the proposition that ocular microorganisms translocate to, colonize, or contribute causally to AD brain pathology. This review critically appraises the existing evidence, graded by methodological rigor and evidence strength, across gut, oral, nasal, ear, and ocular microbiome compartments. We address the substantial methodological challenges inherent in low-biomass microbiome research, emphasize the importance of distinguishing contamination artifacts from biological signals, and delineate the evidence gaps that separate association from causation. We conclude by proposing a research framework that places the ocular microbiome as an emerging hypothesis warranting experimental validation, rather than an established contributor to AD. Full article

Alzheimer’s disease (AD) is increasingly recognized as a disorder involving profound mitochondrial dysfunction. Although photobiomodulation (PBM) has shown neuroprotective efficacy in experimental AD models, whether restoration of mitochondrial architecture is mechanistically required for these effects remains unknown. Here, we investigated the role of mitochondrial network remodeling in PBM-mediated neuroprotection in primary mouse hippocampal neurons exposed to amyloid-β (Aβ). Neurons were treated for 24 h with oligomeric Aβ_1–42 and irradiated with 808 nm PBM (100 mW/cm², 30 J/cm²). Mitochondrial morphology was quantified using three-dimensional confocal microscopy and computational network analysis. Aβ exposure induced severe mitochondrial fragmentation and swelling, reflected by increased mitochondrial count and sphericity together with reduced mitochondrial volume, surface area, branch length, and network connectivity. These structural alterations were accompanied by elevated reactive oxygen species production, adenosine triphosphate depletion, membrane depolarization, and reduced neuronal viability. PBM significantly reversed these abnormalities, restoring mitochondrial network integrity and partially normalizing cellular bioenergetics and redox homeostasis. To determine whether mitochondrial dynamics contributes to PBM-induced effects, neurons in another experiment were pre-treated with the dynamin-related protein-1 (DRP-1) mitochondrial division inhibitor-1 (Mdivi-1). The physiological and morphological profiles of the Aβ + PBM + Mdivi-1 and Aβ + Mdivi-1 groups were found to be largely indistinguishable in this case, revealing that PBM failed to restore mitochondrial connectivity, cellular bioenergetics, or viability of neurons in the partially blocked fission–fusion machinery. These findings demonstrate that mitochondrial dynamics is essential for PBM-mediated neuroprotection and identify restoration of mitochondrial network integrity as a crucial mechanism linking PBM to improved neuronal bioenergetics, redox balance, and survival in AD. Full article

Alzheimer’s disease (AD) is a neurodegenerative disorder, which is characterized by several features, such as the deposition of amyloid β (Aβ) fibrils in the brain, leading to the formation of plaques and secondly tau-associated neurofibrillary tangles, resulting in the degeneration of the brain cells. Another feature of AD is mitochondrial dysfunction and oxidative stress, which are linked to the excessive production of reactive oxygen species (ROS). Under physiological conditions, ROS homeostasis is well controlled by the ROS generating system and the cellular antioxidant network. This antioxidant network is in part controlled by nuclear factor erythroid 2-related factor 2 (Nrf2), and its activation protects tissues against oxidative stress and chronic inflammation. Since oxidative stress, inflammation, and impaired proteostasis are significantly involved in the pathogenesis of AD, the KEAP1-Nrf2 system has emerged as a promising therapeutic target for the disease. Caenorhabditis elegans is an invaluable model organism among others for neurodegenerative disease research, due to its short life cycle, transparent body, and fully mapped nervous system and many transgenic C. elegans models have been developed to study different aspects of AD. This review attempts to provide comprehensive understanding of the molecular characteristics and pathological basis of AD, including the altered Nrf2-ARE signaling in AD and the molecular architecture and regulation of the Nrf2-SKN-1 pathway in C. elegans. An overview of plant-derived natural products and their effect on Nrf2/SKN-1 activation. Several classical and novel transgenic strains are described, translating findings from C. elegans to mammalian models and humans, including clinical translations and ongoing trials, as well as personalized medicinal approaches are discussed. Full article

Background: Over the last decade, numerous studies have investigated the administration of monoclonal anti-amyloid antibodies (AAAs) as a therapeutic approach in Alzheimer’s disease (AD). The purpose of this umbrella review is to summarize current knowledge concerning the efficacy and safety of FDA- and EMA-approved AAAs for AD, namely, lecanemab and donanemab. Methods: We conducted a literature search in the PubMed, Scopus, and Web of Science databases in English, focusing on systematic reviews and/or meta-analyses, assessed by AMSTAR 2, concerning clinical and imaging efficacy, i.e., cognitive improvement and reduction of beta-amyloid on PET scan, as well as safety, i.e., adverse events and amyloid-related imaging abnormalities (ARIA). The extracted review-level dataset was entered into SPSS Version 26 for descriptive statistical analysis. Results: This umbrella review included 11 systematic reviews and/or meta-analyses. Patients treated with lecanemab or donanemab showed statistically significant changes on cognitive scales such as the Clinical Dementia Rating-Sum of Boxes (CDR-SB) (mean effect estimate −0.485, SD = 0.152, −0.70 to −0.34) and the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), but most were below or around clinically meaningful thresholds. Lecanemab and donanemab showed a statistically significant decrease in amyloid PET outcomes, including centiloids or the standardized uptake value ratio. Both antibodies were linked to ARIA, including amyloid-related imaging abnormalities-edema (ARIA-E OR 8.32–12.26) and amyloid-related imaging abnormalities-hemorrhage (ARIA-H OR 2–5.77), especially in ApoEε4 carriers. Conclusions: Lecanemab and donanemab are biologically active drugs for the treatment of early AD, with cognitive benefits below or around the clinically meaningful threshold. They are, however, associated with increased ARIA risk. Their administration depends upon careful patient selection, shared decision-making, and clear communication regarding expectations. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of late-life dementia. As pathology spreads through the brain, affected individuals experience devastating cognitive decline, linguistic impairments, mood changes, and behavioral issues. Developing effective disease-modifying therapies remains a persistent challenge because diagnosis typically occurs at a late stage, long after extensive, irreversible neuronal and synaptic loss has taken place. To overcome these diagnostic and therapeutic bottlenecks, attention has shifted to microRNAs (miRNAs), the most prominent class of abundant, small non-coding RNAs that post-transcriptionally regulate eukaryotic gene expression networks. This review delineates the critical functions of specific miRNAs in orchestrating the three primary pathological pillars of AD: amyloid-β (Aβ) buildup, chronic neuroinflammation, and systemic oxidative stress. Unraveling these complex epigenetic interactions contributes fundamentally to understanding AD’s pathology, offering highly sensitive avenues for early blood-based biomarker discovery and highlighting promising targets for innovative, RNA-targeted clinical therapeutics. Full article

Background: Population aging is accompanied by progressive immune remodeling, chronic low-grade inflammation, and increased susceptibility to neurodegenerative diseases. Although the microbiota–gut–brain axis is increasingly recognized as a regulator of neuroimmune homeostasis, mechanisms linking age-associated dysbiosis with immunosenescence, barrier dysfunction, and brain aging remain incompletely understood. This review integrates current evidence into the proposed Gut–Immune–Brain Resilience Axis (GIBRA), a systems-level model describing how microbial signaling shapes neuroimmune resilience during aging. Methods: A structured narrative review was conducted using PubMed and the Web of Science Core Collection from database inception up to July 2026. Evidence from systematic reviews, meta-analyses, consensus statements, mechanistic and translational studies, longitudinal cohorts, randomized clinical trials, and observational studies was synthesized. Results: Current evidence supports an important role for the disruption of microbial functional signaling in neuroimmune aging, while microbial taxonomy and functional profiles provide complementary levels of biological information. Reduced short-chain fatty acid production, dysregulated tryptophan metabolism, microbial translocation, endotoxin-mediated innate immune activation, and gut-conditioned adaptive immune responses promote immunosenescence, inflammaging, barrier dysfunction, and microglial priming. The proposed Double-Barrier Hypothesis links intestinal and blood–brain barrier dysfunction as complementary mechanisms underlying chronic neuroinflammation. GIBRA highlights functional microbiome endotypes, biomarkers, multi-omics, and artificial intelligence as emerging tools for precision medicine. Conclusions: GIBRA provides an integrated systems biology perspective connecting microbial signaling, immune resilience, barrier integrity, and brain resilience during aging. Prioritizing functional resilience over microbial taxonomy may improve biomarker discovery, patient stratification, and microbiome-targeted interventions for neurodegenerative disease prevention. Prospective longitudinal studies integrating multi-omics are needed to support clinical translation. Full article

Background and Objectives: Alzheimer’s disease (AD) is the most common neurodegenerative disorder, and current therapies provide only limited symptomatic relief without effectively slowing its progression. Increasing evidence suggests that aberrant activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling cascade contributes to AD-associated neuroinflammation. This study investigated the therapeutic potential and molecular mechanisms of JAK inhibitors in AD using integrated bioinformatics and network pharmacology approaches. Materials and Methods: Potential anti-AD targets of JAK inhibitors were identified using the SwissTargetPrediction and GeneCards databases. Functional enrichment, protein–protein interaction (PPI) analysis, transcriptomic validation using public datasets, regulatory network construction, molecular docking, normal mode analysis (NMA), and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction were performed to investigate the potential mechanisms of action of these drugs in AD. Results: Our analysis identified 163 shared targets between JAK inhibitors and AD. Enrichment analysis revealed that these genes were primarily involved in protein phosphorylation and were enriched in key signaling pathways, including the neurotrophin, phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling pathways. PPI analysis identified AKT1, BCL2, SRC, STAT3, and TNF as five highly ranked hub targets across multiple topological algorithms. Transcriptomic validation confirmed significantly higher expression of these targets in the prefrontal cortex of individuals with AD compared with normal subjects. Molecular docking indicated that pacritinib and momelotinib showed relatively favorable predicted interactions with the hub proteins, while NMA revealed differences in the predicted flexibility of the docked complexes. Furthermore, ADMET prediction showed that pacritinib possesses favorable pharmacokinetic properties for the treatment of AD. Conclusions: Collectively, these findings provide mechanistic insights into the potential effects of JAK inhibitors in AD and identify pacritinib as a computationally prioritized candidate that warrants experimental validation in appropriate AD models. However, as this study is based solely on computational analyses without wet-lab validation, the findings should be considered hypothesis-generating in silico evidence, and the potential safety concerns of pacritinib require further investigation. Full article

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders with partially overlapping clinical manifestations, making early and differential diagnosis challenging. This study investigated whether electroencephalography (EEG)-derived spectral features and Granger-causality (GC)-based directed functional connectivity features can characterize and classify AD, FTD, and healthy control (HC) subjects. Resting-state eyes-closed EEG recordings from 88 participants were analyzed, including 36 AD, 23 FTD, and 29 HC subjects. Spectral features included absolute and relative band power and spectral ratios, while directed connectivity features were extracted from broadband and frequency-specific GC matrices. Statistical analyses identified theta/alpha ratio (TAR) as the dominant spectral marker, with the strongest three-group differences observed in frontal and global TAR features. GC analysis revealed group-related alterations mainly in alpha-band regional directed connectivity, although three-group GC features did not survive false discovery rate (FDR) correction at q < 0.05. In the main nested cross-validation analysis, the spectral-only model achieved the best three-class performance, with balanced accuracy of 0.572 and macro-F1 of 0.557. For dementia group (DEM) vs. HC classification, the combined GC + spectral feature (GC + SPEC) set achieved balanced accuracy of 0.710 and macro-F1 of 0.665. For AD vs. FTD classification, the combined GC + SPEC feature set achieved the highest numerical performance in the main nested cross-validation (CV) comparison, with balanced accuracy of 0.584 and macro-F1 of 0.559. In the separate long permutation-testing analysis, which used a reduced hyperparameter grid for computational feasibility, above-chance performance was confirmed for the three-class spectral model and the DEM vs. HC GC + SPEC model (p < 0.001), but not for AD vs. FTD (p = 0.270). An exploratory photobiomodulation (PBM) single-case analysis showed longitudinal EEG reorganization, including increased alpha power, reduced delta/alpha ratio (DAR) and beta/alpha ratio (BAR), mixed TAR changes, and HC-like GC/GC + SPEC centroid projections. Overall, the results support the value of spectral and directed connectivity EEG markers for dementia-related EEG characterization, while highlighting the persistent difficulty of AD vs. FTD differentiation. Full article

Neurological disorders like Alzheimer’s disease, Parkinson’s disease, and epilepsy are becoming major causes of disability and mortality worldwide, and their prevalence is expected to rapidly increase with the aging of the population. These diseases develop silently, with irreversible neuronal damage often occurring decades before any clinical signs of illness are noticed, making early diagnosis and treatment difficult. The presymptomatic period greatly restricts the effectiveness of therapeutic interventions and reduces the possibility of disease-modifying interventions. Traditional diagnostic methods based on clinical assessment, neuroimaging, and invasive biomarkers are not sensitive enough to identify the disease at an early stage and are expensive to the healthcare system. The latest artificial intelligence (AI) technology and machine learning (ML) approaches, together with digital biomarkers obtained from eye tracking, facial expressions, speech analysis, motor dynamics, electrophysiology, wearable devices, and passive sensing, offer promising non-invasive alternatives for early detection of diseases. However, most reported performance metrics are derived from retrospective or pre-validated datasets, and prospective external validation remains limited. This narrative review synthesizes current evidence on AI-driven digital biomarkers for early detection of neurological diseases, examining disease-specific applications, methodological approaches, and challenges in clinical practices. We emphasize that clinical utility is task specific and dependent on disease stage, validation design, clinical endpoints, cost, workflow integration, and availability of disease-modifying therapies. We also note that much of the evidence summarized here derives from retrospective, case-control, or internally validated datasets and that prospective, patient-independent, and external validation with clinically meaningful endpoints remains limited. Reported performance figures should be read as proof-of-concept evidence rather than as evidence of demonstrated clinical readiness. We highlight promising future directions, including federated learning, explainable AI, and precision neurology approaches, while acknowledging that most applications remain investigational and require prospective validation before broad clinical deployment. Full article

Background: Intracellular Ca²⁺ dysregulation and neurovascular dysfunction are increasingly recognized as important mechanisms underlying neurodegenerative disorders, including Alzheimer’s disease (AD). Restoration of pathological Ca²⁺ imbalance has therefore emerged as a potential therapeutic strategy. Although ginsenoside compound K (CK) has demonstrated neuroprotective and anti-inflammatory properties, its role in Ca²⁺ homeostasis remains unclear. Methods: SH-SY5Y neuronal cells, U373 astrocytes, BV2 microglia, bEND brain endothelial cells, and MOVAS vascular smooth muscle cells were exposed to trimethyltin chloride (TMT, 5 μM, 24 h). Cell viability, real-time cell confluence, intracellular Ca²⁺ influx, and endoplasmic reticulum (ER) Ca²⁺ store release were evaluated. Ca²⁺ dynamics were analyzed using Fura-2 fluorescence, and signaling associated with CK-mediated Ca²⁺ regulation was investigated using pharmacological inhibitors. Orai1 protein expression was evaluated by Western blot analysis in SH-SY5Y and U373 cells. Results: TMT increased store-operated Ca²⁺ entry (SOCE)-mediated Ca²⁺ influx in SH-SY5Y neuronal cells without significant cytotoxicity. In contrast, TMT reduced both Ca²⁺ influx and cell viability in U373 astrocytes, BV2 microglia, and MOVAS cells, whereas bEND cells showed minimal changes. CK restored abnormal Ca²⁺ responses in a cell type-specific manner, reducing elevated Ca²⁺ influx in neuronal cells while restoring suppressed Ca²⁺ signaling in astrocytes. Consistent with these functional findings, Orai1 protein expression was increased by TMT in SH-SY5Y cells and attenuated by CK, whereas TMT reduced Orai1 expression in U373 cells, which was restored by CK. Pharmacological analyses suggested involvement of PKA- and LTCC-associated signaling in neuronal cells and PLC- and PLD-associated signaling in astrocytes. In both cell types, CK-associated Ca²⁺ responses were sensitive to NAC and the SOCE inhibitor BTP2, supporting ROS- and SOCE-associated mechanisms. Additional controls showed no significant inhibitor effects under control or CK-only conditions, strengthening the pharmacological interpretation of responses under TMT-containing conditions. Conclusions: TMT-induced Ca²⁺ dysregulation differed markedly by cell type. CK restored disrupted Ca²⁺ homeostasis in association with distinct pharmacological signaling profiles in neuronal and astrocytic cells. These findings suggest that CK may function as a cell type-specific Ca²⁺ homeostatic regulator under neurotoxic stress conditions and highlight the importance of differential Ca²⁺ regulation in neurodegenerative disease models. Full article

Alzheimer’s disease and related dementias are projected to affect more than 150 million people worldwide by 2050. Early staging with validated instruments such as the Clinical Dementia Rating (CDR) scale is essential for timely intervention, yet access to clinician-administered CDR assessment remains constrained by workforce, time, and geographic barriers. This study complements a previously published machine learning pipeline for Alzheimer’s disease prediction by addressing the downstream task of dementia staging. Because the global CDR score is already derived from the six sub-domain ratings through an established rule-based procedure, the contribution reported here lies not in discovering that mapping but in encoding it in a transparent, deployable form: an explainable decision tree classifier embedded in DiAbot, a large-language-model-fronted conversational system that supports self-administered CDR-style assessment. We extracted 13,453 CDR records from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), removed administrative variables, invalid entries, and missing rows (final n = 13,290), and trained decision tree classifiers under two impurity criteria, Information Gain and Gini Index, using a 70/30 stratified record-level hold-out and ten-fold stratified record-level cross-validation. This classifier-level evaluation uses the six domain scores as recorded during ADNI’s clinician-administered assessment, not scores elicited by the DiAbot chatbot; the trained classifier was separately embedded in a web application in which a prompt-engineered large language model conducts a CDR-style interview and normalizes responses to ordinal domain scores, but the end-to-end accuracy of that full conversational pipeline (chatbot elicitation through to final CDGLOBAL) has not yet been measured, and is not what the headline accuracy figures below report. The Information Gain Decision Tree reproduced the established mapping from the six CDR sub-domain scores to the CDGLOBAL with 99.86% accuracy under the record-level hold-out protocol (matching macro-averaged precision, recall, and F1-score), with a ten-fold record-level cross-validated mean of 99.81% (SD 0.07); this result represents fidelity to the established CDR scoring rule rather than independent dementia-diagnosis accuracy. Gini-based trees performed almost identically (99.79% hold-out, 99.74% cross-validated). Memory dominated feature importance, consistent with its role as the primary domain in the official CDR scoring algorithm. Residual misclassifications were confined to adjacent CDR stages. Because the CDGLOBAL is deterministically derived from the six sub-domain scores, these figures should be read throughout as evidence of high-fidelity reconstruction of the established CDR scoring relationship, not as general dementia-diagnosis accuracy comparable to imaging- or biomarker-based classifiers; further, participant-independent generalization remains unverified under the record-level protocol evaluated here. An interpretable classifier embedded in a conversational front-end can nonetheless make standardized CDR-style staging more widely accessible while preserving clinical inspectability; the resulting system is positioned as a screening-stage adjunct to, and not a replacement for, clinician-administered CDR assessment. Full article

Hypoxia-inducible factors (HIF-1α, HIF-2α, HIF-3α) regulate cellular adaptation to oxygen deprivation, but their region-specific roles in Alzheimer’s disease (AD) and AD with cerebral amyloid angiopathy (CAA) remain unclear. Using post-mortem human brain tissue from aging, AD, and AD + CAA groups, we measured all three HIF isoforms in hippocampal subfields (CA1, CA2, CA4, dentate gyrus) and anterior cingulate cortex (ACC) layers 3 and 5. In the AD hippocampus, two distinct patterns emerged: ischemia-resistant regions (CA4, DG) maintained HIF-2α and showed relative resilience, whereas vulnerable regions (CA1, CA2) exhibited HIF-1α upregulation, HIF-3α loss, and HIF-2α dysregulation. The ACC contrasts sharply with the hippocampus by preserving coordinated HIF-1α/HIF-3α regulation during aging and AD, with layer-specific divergence (exhaustion in layer 3 vs. resilience in layer 5) emerging only upon addition of CAA. Notably, HIF-2α in ACC neurons remains stably elevated across all conditions. Taken together, our results highlight HIF-2α as a potential contributor to regional vulnerability and raise the possibility that maintaining HIF-2α levels, in addition to or instead of modulating HIF-1α, could be worthy of further investigation in the context of AD and related vascular changes. Full article

Background/Objectives: Self-awareness constitutes a key metacognitive construct supporting self-regulation and adaptive functioning in aging. This systematic review examined how self-awareness fluctuates across the Alzheimer’s disease (AD) continuum and explored its associations with cognitive performance and neuroimaging markers. Methods: A systematic search was conducted in databases including PubMed, Scopus, Science Direct and Web of Science covering the period from 2016 to 2026, and the review was registered on the Open Science Framework (OSF). The selection process followed PRISMA guidelines, and a total of 334 studies were screened for eligibility while 42 met the inclusion criteria. Studies were eligible if they examined self-awareness in relation to cognitive and/or neuroimaging parameters, with individuals in the preclinical and clinical spectrum of AD as the reference population. Results: The included studies highlighted self-awareness as a dynamic construct closely linked to cognitive performance and neural integrity, with measurable deviations emerging along the continuum from subjective cognitive decline to dementia. Accordingly, the findings suggest that alterations in self-awareness may reflect the stage-dependent cognitive and neurobiological changes that characterize the progression of AD. Conclusions: Converging evidence suggests that assessing fluctuations of self-awareness, ranging from heightened awareness to reduced awareness, may contribute to the early identification of individuals at risk of progression across the AD continuum. However, the substantial methodological heterogeneity across studies precludes definitive conclusions regarding its clinical utility. Future longitudinal studies employing standardized assessment protocols are needed to determine whether these changes can reliably predict disease progression. Full article

Alzheimer’s disease (AD) is the leading cause of dementia and a major cause of death worldwide, making early detection a critical clinical priority. Because pathological changes may begin 15–20 years before symptom onset, artificial intelligence (AI) has emerged as a promising tool for identifying and characterizing AD. In particular, multi-modal approaches that integrate cognitive, biological, and sensor-based data have attracted growing interest. This systematic review compares single- and multi-modal AI strategies for AD detection, covering machine learning and deep learning methods, feature representations, validation strategies, and classification tasks. Searches of major databases identified 568 studies published between 2016 and early 2026; 278 met the inclusion criteria according to PRISMA guidelines. Multi-modal approaches generally achieved higher performance than single-modal strategies, particularly for challenging tasks such as predicting progression between closely related disease stages, although direct comparisons under identical conditions remain scarce. Critically, only about 4% of studies evaluated their models on a genuinely independent external cohort, raising substantial concerns about model generalizability. Overall, current AI systems remain highly dependent on existing datasets and heterogeneous evaluation protocols, which limit generalizability and clinical applicability. Future research should prioritize representative multimodal datasets, rigorous external validation, and clinically interpretable AI systems. Full article

We conducted a systematic review and meta-analysis of associations between apolipoprotein E (APOE) alleles and frontotemporal lobar degeneration (FTLD)-spectrum disorders. MEDLINE, Embase, CENTRAL, and Google Scholar were searched. APOE2 and APOE4 carrier status were compared between FTLD-spectrum disorders and healthy controls (HCs) or individuals with Alzheimer’s disease (AD). Forty studies were included. APOE4 carriage was more frequent in frontotemporal dementia (FTD) compared with HC (OR = 1.72; 95%CI = 1.45–2.04) and less common than in AD (OR = 0.35; 95%CI = 0.29–0.42). In contrast, APOE2 carriage was less prevalent in FTD relative to HC (OR = 0.83; 95%CI = 0.70–0.98) but more frequent compared with AD (OR = 1.80; 95%CI = 1.29–2.52). APOE4 effects were most pronounced in behavioral variant FTD. In clinically confirmed progressive supranuclear palsy (PSP), APOE4 carriage was not associated with PSP. Analysis restricted to pathologically confirmed PSP cases, however, showed lower APOE4 carriage in PSP than in healthy controls (OR = 0.78, 95% CI = 0.65–0.94), although this association failed to reach the multiplicity-adjusted significance threshold. APOE2 carriage was not associated with PSP in either clinically established or pathologically confirmed samples. Evidence was insufficient to establish or exclude associations for other FTLD-spectrum disorders because of the limited available data. In conclusion, APOE alleles show distinct associations across the FTLD spectrum. Full article