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Alzheimer’s disease (AD) is characterized pathologically by amyloid-beta (Aβ) deposition in brain parenchyma and blood vessels (as cerebral amyloid angiopathy (CAA)) and by neurofibrillary tangles of hyperphosphorylated tau. Compelling genetic and biomarker evidence supports Aβ as the root cause of AD. We previously reported human transmission of Aβ pathology and CAA in relatively young adults who had died of iatrogenic Creutzfeldt–Jakob disease (iCJD) after childhood treatment with cadaver-derived pituitary growth hormone (c-hGH) contaminated with both CJD prions and Aβ seeds. This raised the possibility that c-hGH recipients who did not die from iCJD may eventually develop AD. Here we describe recipients who developed dementia and biomarker changes within the phenotypic spectrum of AD, suggesting that AD, like CJD, has environmentally acquired (iatrogenic) forms as well as late-onset sporadic and early-onset inherited forms. Although iatrogenic AD may be rare, and there is no suggestion that Aβ can be transmitted between individuals in activities of daily life, its recognition emphasizes the need to review measures to prevent accidental transmissions via other medical and surgical procedures. As propagating Aβ assemblies may exhibit structural diversity akin to conventional prions, it is possible that therapeutic strategies targeting disease-related assemblies may lead to selection of minor components and development of resistance. A small number of patients who received growth hormone preparations contaminated with seeds of the amyloid-beta protein developed Alzheimer’s disease many years after treatment.

The causes of phenotypic heterogeneity in familial Alzheimer’s disease with autosomal dominant inheritance are not well understood. We aimed to characterise clinical phenotypes and genetic associations with APP and PSEN1 mutations in symptomatic autosomal dominant familial Alzheimer’s disease (ADAD). We retrospectively analysed genotypic and phenotypic data (age at symptom onset, initial cognitive or behavioural symptoms, and presence of myoclonus, seizures, pyramidal signs, extrapyramidal signs, and cerebellar signs) from all individuals with ADAD due to APP or PSEN1 mutations seen at the Dementia Research Centre in London, UK. We examined the frequency of presenting symptoms and additional neurological features, investigated associations with age at symptom onset, APOE genotype, and mutation position, and explored phenotypic differences between APP and PSEN1 mutation carriers. The proportion of individuals presenting with various symptoms was analysed with descriptive statistics, stratified by mutation type. Between July 1, 1987, and Oct 31, 2015, age at onset was recorded for 213 patients (168 with PSEN1 mutations and 45 with APP mutations), with detailed history and neurological examination findings available for 121 (85 with PSEN1 mutations and 36 with APP mutations). We identified 38 different PSEN1 mutations (four novel) and six APP mutations (one novel). Age at onset differed by mutation, with a younger onset for individuals with PSEN1 mutations than for those with APP mutations (mean age 43·6 years [SD 7·2] vs 50·4 years [SD 5·2], respectively, p<0·0001); within the PSEN1 group, 72% of age at onset variance was explained by the specific mutation. A cluster of five mutations with particularly early onset (mean age at onset <40 years) involving PSEN1’s first hydrophilic loop suggests critical functional importance of this region. 71 (84%) individuals with PSEN1 mutations and 35 (97%) with APP mutations presented with amnestic symptoms, making atypical cognitive presentations significantly more common in PSEN1 mutation carriers (n=14; p=0·037). Myoclonus and seizures were the most common additional neurological features; individuals with myoclonus (40 [47%] with PSEN1 mutations and 12 [33%] with APP mutations) were significantly more likely to develop seizures (p=0·001 for PSEN1; p=0·036 for APP), which affected around a quarter of the patients in each group (20 [24%] and nine [25%], respectively). A number of patients with PSEN1 mutations had pyramidal (21 [25%]), extrapyramidal (12 [14%]), or cerebellar (three [4%]) signs. ADAD phenotypes are heterogeneous, with both age at onset and clinical features being influenced by mutation position as well as causative gene. This highlights the importance of considering genetic testing in young patients with dementia and additional neurological features in order to appropriately diagnose and treat their symptoms, and of examining different mutation types separately in future research. Medical Research Council and National Institute for Health Research.

Amyloid-beta (Aβ) plaque formation in Alzheimer’s disease (AD) pathology is morphologically diverse. Understanding the association of polymorphic Aβ pathology with AD pathogenesis and progression is critical in light of emerging Aβ-targeting therapies. In this work, functional amyloid microscopy enhanced by deep learning was integrated with mass spectrometry imaging to delineate polymorphic plaques and to identify their associated Aβ make-up. In both sporadic AD ( n  = 12) and familial AD ( n  = 6), dense-core plaques showed higher levels of Aβ1-40 and N-terminal pyroglutamated Aβx-42 compared to diffuse plaques and plaques in non-demented, amyloid positive individuals ( n  = 5). Notably, a distinct dense-core plaque subtype, coarse-grained plaque, was observed in AD but not in non-demented, amyloid positive patients. Coarse-grained plaques were more abundant in early onset AD, showed increased neuritic dystrophy and higher levels of Aβ1-40 and Aβ3pE-40, an Aβ-pattern similar to cerebral amyloid angiopathy. The correlative chemical imaging paradigm presented here allowed to link structural and biochemical characteristics of Aβ plaque polymorphism across various AD etiologies. Deep learning enhanced chemical imaging delineated polymorphic Aβ plaques in situ. Cored plaques were associated with increased Aβ1-40 and 3pE-42 deposition. A distinct, coarse-grained plaque phenotype was specific to AD, showed increased neuritic dystrophy and an Aβ-pattern similar to CAA.

Tract-based spatial statistics (TBSS) is a popular method for the analysis of diffusion tensor imaging data. TBSS focuses on differences in white matter voxels with high fractional anisotropy (FA), representing the major fibre tracts, through registering all subjects to a common reference and the creation of a FA skeleton. This work considers the effect of choice of reference in the TBSS pipeline, which can be a standard template, an individual subject from the study, a study-specific template or a group-wise average. While TBSS attempts to overcome registration error by searching the neighbourhood perpendicular to the FA skeleton for the voxel with maximum FA, this projection step may not compensate for large registration errors that might occur in the presence of pathology such as atrophy in neurodegenerative diseases. This makes registration performance and choice of reference an important issue. Substantial work in the field of computational anatomy has shown the use of group-wise averages to reduce biases while avoiding the arbitrary selection of a single individual. Here, we demonstrate the impact of the choice of reference on: (a) specificity (b) sensitivity in a simulation study and (c) a real-world comparison of Alzheimer's disease patients to controls. In (a) and (b), simulated deformations and decreases in FA were applied to control subjects to simulate changes of shape and WM integrity similar to what would be seen in AD patients, in order to provide a \"ground truth\" for evaluating the various methods of TBSS reference. Using a group-wise average atlas as the reference outperformed other references in the TBSS pipeline in all evaluations.

Background Autosomal Dominant Alzheimer's Disease (ADAD), caused by mutations in Presenilins ( PSEN1/2 ) and Amyloid Precursor Protein ( APP ) genes, typically manifests with early onset (< 65 years). Age at symptom onset (AAO) is relatively consistent among carriers of the same PSEN1 mutation, but more variable for PSEN2 and APP variants, with these mutations associated with later AAOs than PSEN1 . Understanding this clinical variability is crucial for understanding disease mechanisms, developing predictive models and tailored interventions in ADAD, with potential implications for sporadic AD. Methods We performed biochemical assessment of γ-secretase dysfunction on 28 PSEN2 and 19 APP mutations, including disease-associated, unclear and benign variants. This analysis has been valuable in the assessment of PSEN1 variant pathogenicity, disease onset and progression. Results Our analysis reveals linear correlations between the molecular composition of Aβ profiles and AAO for both PSEN2 (R 2  = 0.52) and APP (R 2  = 0.69) mutations. The integration of PSEN1, PSEN2 and APP correlation data shows parallel but shifted lines, suggesting a common pathogenic mechanism with gene-specific shifts in onset. We found overall “delays” in AAOs of 27 years for PSEN2 and 8 years for APP variants, compared to PSEN1. Notably, extremely inactivating PSEN1 variants delayed onset, suggesting that reduced contribution to brain APP processing underlies the later onset of PSEN2 variants. Conclusion This study supports a unified model of ADAD pathogenesis wherein γ-secretase dysfunction and the resulting shifts in Aβ profiles are central to disease onset across all causal genes. While similar shifts in Aβ occur across causal genes, their impact on AAO varies in the function of their contribution to APP processing in the brain. This biochemical analysis establishes quantitative relationships that enable predictive AAO modelling with implications for clinical practice and genetic research. Our findings also support the development of therapeutic strategies modulating γ-secretase across different genetic ADAD forms and potentially more broadly in AD.

Background To investigate how serum neurofilament light (NfL) concentration changes through the course of disease in familial Alzheimer’s disease (FAD) and to assess when NfL concentration first increases. Methods NfL was measured using an ultrasensitive immunoassay in 117 serum samples from 61 individuals from families with PSEN1 or APP mutations in a longitudinal study (mean ± SD = 1.9 ± 1.1 visits/patient; inter-visit interval = 1.8 ± 1.1 years). The relationship between NfL concentration and estimated years to/from symptom onset (EYO) was modelled using linear regression, including all time points and robust standard errors to allow for repeated measurements, adjusting for age at visit and sex. Also, for the 27 participants who became symptomatic (during or before the study), NfL concentration was also modelled against known actual years to/from onset (AYO). Results There were 15 non-carriers and 46 mutation carriers (21 symptomatic; 25 presymptomatic). NfL concentration was increased ( p  = 0.045) in mutation carriers compared with non-carriers 15 years prior to expected symptom onset, increasing progressively thereafter. There was a significant inter- and intra-individual variability in the longitudinal pattern of change. Modelling NfL for the 27 mutation carriers with known AYO also showed a progressive increase over time. Conclusions There is evidence that serum NfL is increased more than a decade before the onset of clinical symptoms in FAD and rises thereafter. While there is variability in change over time, both within and between individuals, and more work is needed to understand the sources of this variability, serum NfL remains a promising, accessible biomarker of early neurodegeneration in presymptomatic Alzheimer’s disease.

Background More than 300 mutations in presenilin 1 (PSEN1) lead to autosomal dominant Alzheimer's disease (ADAD). PSEN1, as the catalytic subunit of γ-secretase, generates amyloid-β (Aβ) peptides through a sequential proteolysis of the amyloid precursor protein (APP). While ADAD typically presents with progressive cognitive decline, ~ 25% of PSEN1 mutation carriers develop spastic paraparesis (SP), a debilitating motor condition. The molecular basis of this phenotypic heterogeneity remains unknown. This study examines Aβ profiles generated by PSEN1 variants associated with different clinical presentations with the aim of exploring potential associations between different Aβ profiles and clinical heterogeneity. Methods We analysed reported Aβ peptide profiles generated in vitro by 160 PSEN1 variants, categorized by their associated AD or AD + SP phenotype. We employed an integrated analytical approach combining univariate comparisons of Aβ profiles with machine learning classification. Results AD + SP-linked mutations showed significantly higher Aβ43 levels and more severe impairments in γ-secretase processivity compared to pure dementia associated variants. Machine learning consistently identified Aβ43 as the most important feature allowing for the phenotypic classification. Unlike processivity impairments, total Aβ production was comparable between groups, suggesting specific rather than global alterations in γ-secretase function. Conclusions Our analysis reveals a robust association between elevated Aβ43 levels and SP development in PSEN1 mutation carriers. While this correlation does not establish causation, the distinct impact of SP-associated mutations on γ-secretase function, resulting in elevated Aβ43 production, suggests that mutation-specific mechanisms may underlie clinical heterogeneity in ADAD, with potential implications for biomarker and translational research.

Background There is increasing interest in improving understanding of the timing and nature of early neurodegeneration in Alzheimer’s disease (AD) and developing methods to measure this in vivo. Autosomal dominant familial Alzheimer’s disease (FAD) provides the opportunity for investigation of presymptomatic change. We assessed early microstructural breakdown of cortical grey matter in FAD with diffusion-weighted MRI. Methods Diffusion-weighted and T1-weighed MRI were acquired in 38 FAD mutation carriers (17 symptomatic, 21 presymptomatic) and 39 controls. Mean diffusivity (MD) was calculated for six cortical regions previously identified as being particularly vulnerable to FAD-related neurodegeneration. Linear regression compared MD between symptomatic and presymptomatic carriers and controls, adjusting for age and sex. Spearman coefficients assessed associations between cortical MD and cortical thickness. Spearman coefficients also assessed associations between cortical MD and estimated years to/from onset (EYO). Across mutation carriers, linear regression assessed associations between MD and EYO, adjusting for cortical thickness. Results Compared with controls, cortical MD was higher in symptomatic mutation carriers (mean ± SD CDR = 0.88 ± 0.39) for all six regions ( p  < 0.001). In late presymptomatic carriers (within 8.1 years of predicted symptom onset), MD was higher in the precuneus ( p  = 0.04) and inferior parietal cortex ( p  = 0.003) compared with controls. Across all presymptomatic carriers, MD in the precuneus correlated with EYO ( p  = 0.04). Across all mutation carriers, there was strong evidence ( p  < 0.001) of association between MD and cortical thickness in all regions except entorhinal cortex. After adjusting for cortical thickness, there remained an association ( p  < 0.05) in mutation carriers between MD and EYO in all regions except entorhinal cortex. Conclusions Cortical MD measurement detects microstructural breakdown in presymptomatic FAD and correlates with proximity to symptom onset independently of cortical thickness. Cortical MD may thus be a feasible biomarker of early AD-related neurodegeneration, offering additional/complementary information to conventional MRI measures.

Background Understanding the earliest manifestations of Alzheimer’s disease (AD) is key to realising disease-modifying treatments. Advances in neuroimaging and fluid biomarkers have improved our ability to identify AD pathology in vivo. The critical next step is improved detection and staging of early cognitive change. We studied an asymptomatic familial Alzheimer’s disease (FAD) cohort to characterise preclinical cognitive change. Methods Data included 35 asymptomatic participants at 50% risk of carrying a pathogenic FAD mutation. Participants completed a multi-domain neuropsychology battery. After accounting for sex, age and education, we used event-based modelling to estimate the sequence of cognitive decline in presymptomatic FAD, and uncertainty in the sequence. We assigned individuals to their most likely model stage of cumulative cognitive decline, given their data. Linear regression of estimated years to symptom onset against model stage was used to estimate the timing of preclinical cognitive decline. Results Cognitive change in mutation carriers was first detected in measures of accelerated long-term forgetting, up to 10 years before estimated symptom onset. Measures of subjective cognitive decline also revealed early abnormalities. Our data-driven model demonstrated subtle cognitive impairment across multiple cognitive domains in clinically normal individuals on the AD continuum. Conclusions Data-driven modelling of neuropsychological test scores has potential to differentiate cognitive decline from cognitive stability and to estimate a fine-grained sequence of decline across cognitive domains and functions, in the preclinical phase of Alzheimer’s disease. This can improve the design of future presymptomatic trials by informing enrichment strategies and guiding the selection of outcome measures.