NPJ dementia最新文献

Variation in the APOE gene strongly affects Alzheimer's disease (AD) risk. However, the proportion of AD burden attributable to this variation requires clarification, which would help to elucidate the scope of strategies targeting apolipoprotein E (APOE) for AD prevention and treatment. We estimated the extents to which clinically diagnosed AD, AD neuropathology and all-cause dementia are attributable to the common APOE alleles in four large studies. First, we used data on 171,105 and 289,150 participants aged ≥60 years from UK Biobank (UKB) and FinnGen, respectively. AD and all-cause dementia were ascertained from linked electronic health records in these cohorts. Second, we examined amyloid-β positivity from amyloid positron emission tomography scans of 4415 participants of the A4 Study. Third, we analysed data from the Alzheimer's Disease Genetics Consortium (ADGC), where neuropathologically confirmed AD cases were compared to pathology-negative, cognitively intact controls (N = 5007). In each analysis, we estimated outcome risk among carriers of APOE risk alleles ε3 and ε4, relative to individuals with an ε2/ε2 genotype, and calculated attributable fractions to show the proportions of the outcomes due to ε3 and ε4. For AD, fractions ranged from 71.5% (95% confidence interval: 54.9%, 81.7%) in FinnGen to 92.7% in the ADGC (82.4, 96.5%). In A4, 85.4% (17.5, 94.5%) of cerebral amyloidosis was attributable to ε3 and ε4. The proportions of all-cause dementia attributable to ε3 and ε4 in UKB and Fin-Gen were 44.4% (95% CI: 18.2%, 62.2%) and 45.6% (30.6%, 56.9%), respectively. Without strong underlying risks from APOE ε3 and ε4, almost all AD and half of all dementia would not occur. Intervening on APOE should be prioritised to facilitate dementia prevention.

An ongoing pragmatic, randomized, unblinded clinical trial compares pimavanserin and quetiapine for psychosis in Lewy body diseases over 6 months (n = 60). Relative to local explanatory clinical trials, retention is higher (93% vs 40%, p < 0.001) and Hispanic enrollment greater (38% vs 21%, p = 0.043), reflecting the clinic demographics. Embedding pragmatic trials within routine care can enhance retention and diversity, thereby improving representativeness and real-world applicability of clinical research. Registry: ClinicalTrials.gov, TRN: NCT05590637; Registration Date: 18 October 2022.

Astrogliosis is increasingly recognized as a critical component of Alzheimer's disease (AD) pathology, but its precise mechanistic contribution to the interplay among amyloid-β (Aβ), tau, and neurodegeneration remains unclear. Glial fibrillary acidic protein (GFAP), a widely used biomarker of astrocytic activation, shows strong associations with both Aβ and tau pathologies; however, its causal and modulatory involvement in disease progression has yet to be fully delineated. In this study, we investigated whether plasma GFAP functions as a mediator and/or moderator of the relationships linking Aβ deposition, tau accumulation, and neurodegenerative changes in AD. Ninety-two older adults from the KBASE cohort underwent multimodal imaging including Aβ-PET, tau-PET, FDG-PET, and MRI-based hippocampal volumetry, along with blood sampling for plasma GFAP assessment. Elevated plasma GFAP levels were associated with increased Aβ and tau burden, reduced cerebral glucose metabolism, and smaller hippocampal volume, as well as poorer cognitive performance. Mediation analyses demonstrated that GFAP significantly explained the relationships between Aβ and tau, and between tau and metabolic dysfunction. Moderation analyses further revealed that GFAP strengthened the Aβ-tau association, while mitigating tau-related metabolic decline. Overall, these results support a dual role of GFAP as both a conduit and regulator of disease progression.

Alzheimer's disease (AD) is not only defined by amyloid-β and tau pathology but by early metabolic disruptions and hyperexcitability. How tau independently reshapes the coupling of metabolism-excitability to impact processes like sleep remains unclear. Here, hyperphosphorylated tau preserves whole-body metabolic function while driving cortical hyperexcitability and sleep loss in mouse models of tauopathy. Tau pathology prevented age-related decline in glucose tolerance and maintained diurnal hippocampal interstitial fluid (ISF) glucose and lactate rhythms, which were lost in aging wildtype mice. Stable isotope-resolved metabolomics revealed that tau pathology preferentially shunts glucose toward glutamate synthesis at the expense of GABA, suggesting an excitatory/inhibitory (E/I) imbalance not explained by synaptic mitochondrial deficits but by glycolytic flux. Hallmarks of hyperexcitability and impaired inhibitory tone were confirmed by continuous EEG/EMG recordings where decreased beta power, reduced cortical coherence, a flatter aperiodic slope, and abnormal gamma oscillations were associated with NREM and REM sleep loss. Widefield optical imaging confirmed exaggerated glutamatergic calcium activity during whisker stimulation. Together, these findings show that tau pathology drives glucose-dependent hyperexcitability while impairing network synchrony and sleep/wake architecture. This work identifies E/I imbalance as a mechanistic link between tau, metabolism, and sleep loss, highlighting a therapeutic target for tauopathies like AD.

Proper name anomia (PNA) is a common experience that can become unpleasantly amplified in people with Alzheimer's disease (AD). In this systematic review, we discuss the key cognitive stages where naming can fail: facial recognition, person-specific semantics, and proper-name retrieval. We examine claims that PNA is an early indicator of AD and review studies that have attempted to treat PNA in individuals with AD. Twenty-two eligible studies were included. The main findings are that individuals with AD frequently experience difficulties in recalling proper names at any point in the disease process, with the distribution of functional breakdown between the three key cognitive stages involved in successful naming being: facial recognition (19%), person-specific semantics (30%), and proper-name retrieval (40%). PNA can be an early manifestation of AD. Effective behavioural treatments are available for those whose naming difficulties occur at the retrieval stage, including trial-by-trial practice using vanishing cues and spaced retrieval. We also provide clinical recommendations regarding the diagnosis and treatment of PNA.

Latin America's diverse genetic landscape provides a unique opportunity to study Alzheimer's disease (AD) and frontotemporal dementia (FTD). The Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat) recruited 2162 participants with AD, FTD, or healthy controls from six countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru. Participants underwent genomic sequencing, and population structure analyses were conducted using Principal Component Analysis and ADMIXTURE. The study revealed a predominant mix of American, African, and European ancestries, with an additional East Asian component in Brazil. Variant curation identified 17 pathogenic variants, a pathogenic C9orf72 expansion, and 44 variants of uncertain significance. Seventy families showed autosomal dominant inheritance, with 48 affected by AD and 22 by FTD. This represents the first large-scale genetic study of AD and FTD in Latin America, highlighting the need to consider diverse ancestries, social determinants of health, and cultural factors when assessing genetic risk for neurodegenerative diseases.

Dementia encompasses many neurodegenerative disorders. While some causal coding variants are known, most GWAS variants are in non-coding regions of the genome, making understanding functional impacts challenging. This review explores the role of non-coding variation in dementia, covering methods to identify enhancers and their target genes, prioritize GWAS variants, and validate the functional effects of variation, providing a comprehensive framework for investigating non-coding variation and its implications in dementia research.

Accurate differential diagnosis of dementia is essential for guiding timely treatment, particularly as anti-amyloid therapies become more widely available and require precise patient characterization. Here, we developed a radiomics-based machine learning (ML) approach to enhance neuroimaging assessments in distinguishing Alzheimer's disease (AD) from other imaging-evident dementias (OIED). We retrospectively analyzed 1041 individuals from the National Alzheimer's Coordinating Center with confirmed dementia diagnoses and at least one T1 or T2/FLAIR MRI scan. Using FastSurfer and a Lesion Prediction Algorithm, we extracted volumetric and lesion features, which were then used to train ML models. Model performance was compared to the independent evaluations of seven fellowship-trained neuroradiologists. The classifier achieved an AUROC of 0.79 ± 0.01 for AD and 0.66 ± 0.03 for OIED, performing comparably to expert assessments. Interpretation using SHAP values showed strong alignment with imaging features known to align with AD or OIED, respectively. These findings highlight the potential of radiomics to augment neuroimaging workflows.

Comprehensively studying modifiable risk factors to understand their contributions to dementia mechanisms is imperative. This study used natural language processing (NLP) models to pre-select candidate risk factors for dementia from 5505 baseline variables in the UK Biobank. We then applied causal discovery approaches to examine the relationships among the selected variables and their links to dementia in later life, presenting these connections in a causal network. We identified eight risk factors that directly or indirectly influence dementia, with mental disorders due to brain dysfunction (ICD-10 F06) acting as direct causes and mediators in pathways from other neurological disorders to dementia. Although evidence for the direct link between biological age and dementia was less pronounced, its potential value in dementia management remains non-negligible. This study advances our understanding of dementia mechanisms and highlights the potential of NLP and machine learning for the causal discovery of complex diseases from high-dimensional data.

Brain diseases have complex patterns of genetic and environmental risk factors, and better understanding of these risks is required for more effective prevention strategies. Participants of the Dutch Brain Research Registry provided detailed information on family structure and occurrence of brain diseases. A total of 12,040 participants (73% female, aged 64.9 ± 11 years) provided information on 101,379 family members (53% female, aged 62 ± 25 years). We estimated heritability (h ²) of the nine most common brain diseases using polygenic modeling in SOLAR and assessed variations in h ² through bootstrapping; Alzheimer's disease (AD) (h ² = 73, range 53-86, P _fdr < 0.001), ALS (h ² = 72, range 10-98, P _fdr = 0.030), frontotemporal dementia (FTD) (h ² = 48, range 0-97, P _fdr = 0.132), vascular dementia (VaD) (h ² = 41, range 7-64, P = 0.003), Lewy Body dementia (h ² = 34, range 0-58, P = 0.132), iCVA (h ² = 27, 6-59, P _fdr = 0.013), hCVA (h ² = 29, 8-57, P _fdr = 0.007), Parkinson's disease (PD) (h ² = 38, 6-66, P _fdr = 0.013), and multiple sclerosis (h ² = 10, 10-97, P _fdr < 0.001). Shared environmental effects could be estimated for AD (c ² = 5.8%, P _fdr = 0.011), VaD (c ² = 9.0%, P _fdr = 0.021), FTD (c ² = 9.7%, P _fdr = 0.33), iCVA (c ² = 15.9%, P _fdr < 0.001), hCVA (c ² = 14.9%, P _fdr = 0.005), and PD (c ² = 7.5%, P _fdr = 0.25). These findings underscore the significance of genetic contribution to most brain diseases and the important role of shared environments in AD and vascular-related conditions, highlighting initiatives to mitigate modifiable risk factors.