Females generally show better memory performance, particularly in episodic memory, than males across the lifespan [1-3]. However, two-thirds of Alzheimer's disease (AD) cases occur in females, who experience more rapid cognitive decline and brain atrophy in the presence of AD-related neuropathology [4]. The functional brain architecture underlying episodic and relational memory in middle-aged individuals—and whether this differs by sex—remains poorly understood. This study aimed to identify the functional brain architecture associated with episodic and relational memory using a data-driven approach, focusing on sex differences. Resting-state functional MRI data and neuropsychological assessments were obtained from 488 cognitively healthy individuals (316 F/172 M), aged 40-59 years, from the PREVENT-Dementia study. Connectome-based predictive modeling (CPM) was used to identify functional brain networks related to episodic and relational memory across the entire cohort, within female-only and male-only subgroups. Model generalizability was evaluated using data from the Cambridge Center for Ageing and Neuroscience (Cam-CAN) dataset. CPM identified both positive and negative networks significantly associated with episodic and relational memory in the entire cohort (positive: r = 0.16, p  < 0.001; negative: r = 0.10, p  = 0.017; Figure 1A). These networks were particularly characterized by within-network and between-network connections involving default mode and cingulo-opercular networks in the positive network (Figure 2A). Sex-stratified analyses revealed distinct model performance: both positive and negative networks predicted episodic and relational memory in the female-only group (positive: r = 0.18, p -corrected  = 0.002; negative: r  = 0.11, p -corrected  = 0.048; Figure 1B), whereas only the negative network predicted episodic and relational memory in the male-only group (positive: r = 0.01, p -corrected  = 0.836; negative: r = 0.16, p -corrected  = 0.048; Figure 1C) after Bonferroni correction. These results didn’t generalize to the external Cam-CAN dataset. We identified brain networks underlying episodic and relational memory in middle-aged individuals, revealing sex-specific differences. These findings suggest potential sex-specific mechanisms in memory-related brain networks during midlife, which may contribute to differing trajectories of cognitive decline in aging and Alzheimer's disease. However, the lack of generalizability to an external dataset underscores the need for further validation in diverse populations.

BACKGROUND:To model scenarios exploring potential impacts of disease-modifying therapies (DMTs) for Alzheimer's disease (AD) dementia on future health and social care costs in the United Kingdom. METHODS:A cohort Markov model was developed using population projections and published AD epidemiological data. Stage-specific transition rates (mild cognitive impairment due to AD and mild, moderate, severe AD dementia) and health and social care cost data were applied to estimate cost outcomes over 2020-2040. Potential proportion of eligible population receiving treatment (uptake) and follow-up care models (primary vs. specialist care) were elicited from expert opinion. Scenarios combined ranges of DMT efficacy estimates, uptake, and care model. DMT price was excluded due to no UK precedent. RESULTS:Without DMT access, 1,038,405 people (1.5%) were projected to have AD dementia by 2040. Under the various DMT treatment scenarios, the prevalence of AD dementia by 2040 was projected to be 34,000-98,000 cases lower. Associated cumulative cost offsets were higher, £4.4-12.9billion over 2020-2040, in scenarios where most individuals received primary care follow-up, compared with majority specialist care follow-up (-£2.3billion to +£3.2billion). Assuming DMT efficacy of 25%, 58% uptake and majority primary care follow-up cumulative cost offsets increased from £4.4billion to £10.1billion by 2040 but the UK Health Service would need to diagnose and provide DMT for over a million individuals by 2030 and two million by 2040 to achieve this. CONCLUSIONS:Potential cost offset from DMT are large but highly dependent on the model of healthcare delivery and the ability of healthcare systems to scale up diagnosis and treatment services.

INTRODUCTION:Early Alzheimer's disease (AD) involves subtle cortical changes that may precede atrophy. Magnetic resonance imaging (MRI) microstructural markers may detect earlier pathology than classical morphometry. METHODS:We analyzed cross-sectional MRI and amyloid-β (Aβ) positron emission tomography (PET) data from 1323 non-demented AMYPAD participants. Cortical volume, thickness, gray-white matter contrast (GWC), and mean diffusivity (MD) were related to global Aβ burden and estimated time to Aβ-positivity using regression, correlation, and change-point analyses. RESULTS:Microstructural measures showed stronger age associations than macrostructural measures, whereas all measures were unaffected by apolipoprotein E (APOE) -ε4 carriership. GWC and MD showed minimal overlap with volume and thickness. Higher Aβ burden was most strongly associated with reduced GWC and cortical thinning. Change-point analyses showed GWC alterations preceded Aβ-positivity by several years. DISCUSSION:Cortical microstructural MRI, particularly GWC, changes earlier than atrophy and may serve as an early in vivo marker of AD pathology.

The Amyloid Imaging to Prevent Alzheimer’s Disease Prognostic and Natural History Study (AMYPAD-PNHS) multimodal magnetic resonance imaging (MRI) dataset provides open-access longitudinal MRI data of 2759 cognitively normal or mild cognitive impairment individuals, encompassing (micro-)structural, physiological, and functional MRI sequences from 10 European parent cohorts. Processed and raw images, and image-derived (endo-)phenotypes, are organized in Brain Imaging Data Structure (BIDS) standards and accessible upon request, to enhance generalizability and comparability between future neuroimaging studies. This dataset supports preclinical Alzheimer’s disease (AD) and aging-related research by enabling robust multimodal analyses of neurodegeneration, microvascular pathology, and structural and functional connectivity changes, facilitating advanced investigations into preclinical AD mechanisms, informing early intervention strategies and allowing reproducible and centralized neuroimaging (endo-)phenotyping.

Background With the emergence of disease-modifying anti-amyloid-beta (Aβ) therapies for Alzheimer’s disease (AD), early and accurate quantitative measures of Aβ burden are critical. Blood-based biomarkers are a scalable and minimally invasive diagnostic solution; plasma tau phosphorylated at threonine 217 (pTau217) is a promising marker for Aβ pathology. The clinical performance of the prototype ElecsysⓇ Phospho-Tau (217P) Plasma immunoassay (Roche Diagnostics) to detect Aβ burden was investigated in an unselected cohort reflective of clinical practice. Methods Plasma was prospectively collected from participants aged 55 to 80 years with objective or subjective cognitive decline under evaluation for AD. Participants were recruited at multiple clinical sites spanning primary and secondary care. Plasma pTau217 concentrations measured using the prototype pTau217 plasma immunoassay were compared with amyloid positron emission tomography centiloid-based classification at different cutoffs, with further analyses performed at centiloid cutoff 30. Outcomes Among 588 participants, plasma pTau217 demonstrated high concordance with centiloid-based classification at selected cutoffs. The discriminative ability of plasma pTau217 to detect Aβ pathology peaked at centiloid cutoff 32 (area under the curve=0.933). Subgroup analyses at centiloid cutoff 30 demonstrated good discrimination of Aβ positivity/negativity by clinical diagnosis, age, and sex. Moderately decreased kidney function to kidney failure was found to influence plasma pTau217 levels. Interpretation The prototype pTau217 plasma immunoassay showed high accuracy in reflecting Aβ burden among individuals presenting with cognitive complaints across diverse clinical settings. These findings support its potential implementation into routine clinical practice for early detection of AD, alongside standard clinical and neuropsychologic assessments.

Abstract INTRODUCTION Plasma biomarker rule‐out tests can be used early in the Alzheimer's disease (AD) diagnostic pathway to identify people with low likelihood of amyloid pathology. METHODS A care setting‐agnostic cutoff for the Elecsys ® Phospho‐Tau (181P) plasma (pT181p) immunoassay was determined and validated in two independent, diverse cohorts, representative of real‐world clinical practice ( N = 604; N = 787, respectively). An additional cutoff was assessed in a subset of people reflective of primary care ( n = 312). RESULTS At a care setting‐agnostic cutoff of 0.934 pg/ml, high negative predictive value (NPV) (93.8%) and acceptable positive predictive value (PPV) (46.6%) were observed against amyloid‐positron emission tomography (PET). A primary care‐specific cutoff of 0.722 pg/ml showed high NPV (97.9%) against amyloid‐PET. Amyloid positivity prevalence for each cutoff population was 22.5% and 13.1%, respectively. DISCUSSION The pT181p immunoassay is a high‐performance test that may substantially help improve access to amyloid pathology testing for people requiring confirmation, potentially leading to timely clinical decisions and better outcomes.

Positron emission tomography combined with magnetic resonance imaging (PET/MR) has not yet achieved the level of adoption of PET/CT. This study aimed to harmonise PET imaging protocols across a national PET/MR network and to quantitatively assess whether PET/MR can achieve reliability comparable to PET/CT. While previous PET test-retest studies have demonstrated good repeatability, they have typically been limited to small cohorts or restricted site configurations. We conducted a multi-site harmonisation and rigorous test-retest study across the network of eight PET/MR scanners. Thirty-seven healthy older participants (65-90 years) underwent harmonised one-hour amyloid PET/MR scans using either [ ^18 F]flutemetamol or [ ^18 F]florbetaben on two occasions. Retest scans were performed under conditions of same-site repeatability or multi-site reproducibility. Harmonised acquisition and reconstruction protocols were applied, and amyloid burden was quantified on the Centiloid (CL) scale. CL values across 74 scans showed excellent test-retest agreement (ICC = 0.968), improving to 0.987 after exclusion of one attenuation correction related outlier. Mean test-retest variability was 2.58

Antibody therapies can remove amyloid plaques from the brain and slow cognitive decline in people with Alzheimer's disease who are mildly impaired. These drugs are now being evaluated in participants who are cognitively unimpaired but positive for a biomarker of Alzheimer's disease for their safety, tolerability, disease-modifying and cognitive preserving effects, and ability to avert the onset of cognitive impairment. If these studies are successful, and the drugs get regulatory approval, they could accelerate the evaluation and approval of related Alzheimer's disease-modifying treatments in people who are unimpaired with or without a biomarker of the disease. Preclinical Alzheimer's disease therapies that modify the underlying disease in people who are unimpaired with a biomarker of Alzheimer's disease and primary prevention therapies that avert the onset of amyloid plaques in those with a negative test have the potential to substantially prevent ensuing biological and clinical manifestations of Alzheimer's disease. In this Policy View, we assess the challenges and opportunities that trials of these drug treatments will bring, and consider the blood tests, cognitive assessments, and post-marketing strategies needed to enable the approval, affordability, health-care insurance coverage, and equitable use. Our recommendations are intended for consideration in the USA, and relevant refinement in other countries.

Abstract Speech biomarkers could form a critical step in improving the accessibility, scalability, and early detection of Alzheimer's disease and related dementias. However, ethical and practical challenges remain across regulatory and cultural contexts. In this paper, we briefly review the challenges in adopting speech biomarkers, relate our experiences globally to recent advances in the neurodegenerative field, and consider how speech assessments could be integrated into clinical care. Insights from high‐ and low‐ and middle‐income countries (Taiwan, Ghana, Colombia, Brazil, Greece, UK, and US) demonstrate the potential impact of speech technology. While there are common benefits, risks, incentives, and hurdles, many aspects are specific to country or region. There is a need for more speech data sets (particularly in languages other than English), standardization in data collection and analysis, stronger collaboration between machine learning and neurodegenerative disease experts, unified privacy regulation, and finally, a consensus on the clinical interpretation of speech biomarker data.

Poor self-reported sleep quality is associated with cognitive impairment. Alzheimer’s disease (AD) patients present sleep disruptions decades before they start to decline clinically. Similarly, amyloid-β (Aβ) starts accumulating during the preclinical phase of the disease. This study investigated associations between self-reported sleep quality and Aβ burden longitudinally in clinically unimpaired (CU) adults. Four hundred seventeen CU adults from the AMYPAD PNHS cohort were included, with baseline self-reported sleep quality assessments (Pittsburgh Sleep Quality Index, PSQI) and longitudinal Aβ PET scans. Participants were categorized by baseline Aβ levels as negative (A-), grey-zone (GZ), or positive (A+). Linear mixed-effects (LME) models tested the association between baseline sleep quality and Aβ burden over time, including interaction effects with baseline Aβ status. Global PSQI score was not associated with Aβ burden over time in the entire group. However, a significant interaction with baseline Aβ status was found, whereby poorer subjective sleep quality was linked to accelerated Aβ accumulation in GZ participants. Poorer subjective sleep quality is associated with faster Aβ accumulation in CU individuals with intermediate Aβ levels, highlighting sleep as a potential target for early AD prevention and identifying an optimal intervention window.

AIMS:To assess the long-term effects of lecanemab plus standard of care (SoC) compared with SoC alone in a cohort of patients with early Alzheimer's disease (AD; mild cognitive impairment [MCI] due to AD, or mild AD dementia) using different modeling approaches and data from Clarity AD (NCT0388745538). METHODS:A Markov model was employed using health states based on disease severity, long-term institutionalization, and death, with disease severity defined using the Clinical Dementia Rating - Sum of Boxes (CDR-SB) classification for MCI due to AD, and Mild, Moderate, and Severe AD. State transitions during the first 18 months of treatment were estimated using either patient count data (Approach 1) or multistate survival analysis (Approach 2). Transition probabilities beyond 18 months for the lifetime of the cohort were informed by longitudinal natural history data for the SoC arm with a hazard ratio for time-to-worsening health state applied to estimate outcomes in the lecanemab arm. RESULTS:Over a lifetime horizon, the model predicted a delayed time to Mild, Moderate, and Severe AD for patients treated with lecanemab compared to SoC by 1.31, 1.85, and 2.04 years, respectively when using Approach 1. Patients treated with lecanemab experienced a survival benefit of 1.36 years, comprised of an additional 1.85 years in early AD and 0.49 years less in moderate and severe AD, compared to patients treated with SoC alone. The model also predicted that compared to SoC, lecanemab increased the time in community care and reduced time spent in institutional care. Results were similar when using Approach 2. LIMITATIONS:Long-term disease progression was informed by constant annual transition probabilities derived from the published literature. CONCLUSIONS:Patients treated with lecanemab experience delayed progression to Moderate and Severe AD, resulting in additional life-years (LYs) and reduced time in institutional care.

INTRODUCTION:Early detection of Alzheimer's disease (AD) is critical for timely intervention as disease-modifying treatments emerge. Speech-based digital biomarkers offer scalable options for remotely capturing speech-derived functional changes associated with early cognitive decline, but validation across real-world populations remains limited. METHODS:We evaluated the speech biomarker for cognition (SB-C), an automated speech-derived measure associated with cognitive status, in 736 participants across five European cohorts (Barcelonaβeta Brain Research Center's Alzheimer's at-risk cohort, European Prevention of Alzheimer's Dementia Scotland, Dementia Study of Cognitive and Biomarker Dynamics, Longitudinal Cognitive Impairment and Dementia Study, and Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably [BioFINDER-Primary Care]). Participants completed verbal learning and semantic fluency tasks via automated phone or app-based platforms. SB-C performance was compared to Mini-Mental State Examination, Clinical Dementia Rating, Preclinical Alzheimer Cognitive Composite 5, and cerebrospinal fluid amyloid beta and phosphorylated tau181 biomarker status. RESULTS:SB-C significantly differentiated cognitively unimpaired and impaired groups (P < 0.001), correlated with standard cognitive measures, and showed moderate-to-high area under the curve (0.56-0.82) for classifying biomarker positivity, with strongest results in BioFINDER-Primary Care. DISCUSSION:SB-C is a scalable, remote speech-derived marker associated with cognitive status and AD biomarker group differences.

Harmonisation is widely used to mitigate site- and scanner-related batch variability in multisite neuroimaging studies and is particularly critical in longitudinal clinical trials, where detection of subtle biological or treatment-related changes depends on reliable measurement across scanners and timepoints. However, the effectiveness of harmonisation in small, heterogeneous clinical datasets remains insufficiently understood, particularly in relation to subject-level variability and consistency across acquisition settings, and its impact on both removal of technical variability and preservation of biological variation in pooled multisite analyses. We systematically evaluated a range of image-based and statistical harmonisation methods using a clinically realistic multisite, multiscanner structural T1-weighted (T1w) MRI test-retest dataset comprising three controlled acquisition scenarios: repeatability, intra-scanner reproducibility and inter-scanner reproducibility. Methods were applied under different batch specifications (site, scanner, or both) and performance was assessed within each scenario and in pooled data using a multi-metric framework capturing both technical and biological variability in volumetric imaging-derived phenotypes (IDPs) relevant to aging and dementia research. Across IDPs, before harmonisation variability was lowest in the repeatability scenario (median variability=0.6 to 2.7%, rank consistency ρ ≥0.9), with modest increases under intra-scanner reproducibility (0.5 to 3.2%, ρ=0.5 to 1.0) and substantially greater variability under inter-scanner reproducibility conditions (1.7 to 19.2%, ρ =−0.1 to 0.9). These results offer important information to consider for multisite study design, including sample size calculation in clinical trials. Harmonisation performance was strongly context dependent, with clearer benefits emerged in inter-scanner scenarios where both variability reduction and improvements in subject-level consistency were observed. In pooled data, approaches that explicitly modelled site as batch and accounted for repeated-measure structure showed greater consistency across IDPs in batch effect mitigation and more accurately reflected underlying biological variation. Our evaluation metrics enabled disentangling the removal of global batch effect while highlighting residual variability at the phenotype-specific or multivariate levels. These findings demonstrate that harmonisation cannot be treated as a one-size-fits-all solution and must be interpreted relative to the acquisition context, dataset structure, and downstream analytic goals. Multi-metric evaluation under realistic clinical constraints is essential to support reliable and translatable neuroimaging inference by ensuring appropriate correction of batch effects while preserving longitudinal biological signals and sensitivity to clinically meaningful change in multisite studies. ### Competing Interest Statement Author FB - Steering committee or Data Safety Monitoring Board member for Biogen, Merck, Eisai, Prothena and Idorsia. Advisory board member for Combinostics, Scottish Brain Sciences, IXICO and Alzheimer Europe. Consultant for Roche, Celltrion, Merck, Bracco. Research agreements with ADDI, Merck, Biogen, GE Healthcare, Icometrix, Roche. Co-founder and shareholder of Queen Square Analytics LTD. ### Funding Statement This study was supported by UK Medical Research Council Dementias Platform UK (MR/T033371/1), Alzheimers Association Grant (AARF-21-846366), and NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The Centre for Integrative Neuroimaging was supported by core funding from the Wellcome Trust (203139/Z/16/Z and 203139/A/16/Z). Author J-P.T is supported by the NIHR Newcastle Biomedical Research Centre (BRC). Author FB is supported by the NIHR Biomedical Research Centre at UCLH. DT is supported by the UCLH NIHR Biomedical Research Centre. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of the NHS Health Research Authority (NRA) and the UK Administration of Radioactive Substances Advisory Committee (ARSAC) gave ethical approval for this work; The study was approved by an NHS Health Research Authority (NRA) ethics committee (Ref: 18/NW/0102; IRAS 223411) and the UK Administration of Radioactive Substances Advisory Committee (ARSAC). The University of Manchester acted as the sponsor for the study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The DPUK PET/MR Network will make PET/MR imaging data and associated datasets available to the research community via controlled-access data-sharing procedures, in accordance with applicable data protection regulations.

INTRODUCTION:Inflammation contributes to Alzheimer's disease (AD), but its stage-specific and amyloid-dependent patterns remain unclear. METHODS:We analyzed 964 participants from the Bio-Hermes cohort (cognitively normal [CN] = 404, mild cognitive impairment [MCI] = 302, mild AD = 258). Plasma levels of 32 cytokines, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) were quantified alongside core AD biomarkers. Associations with cognition, amyloid, apolipoprotein E (APOE) ε4, and clinical outcomes were assessed using analysis of covariance, partial correlations, and regression models. RESULTS:Twenty-four cytokines, NfL, and GFAP differed across cognitive groups. Amyloid stratification revealed a core amyloid-independent profile (14 cytokines + NfL) and a broader amyloid-specific profile including GFAP, interleukin (IL)-1β, and IL-18, implicating microglial inflammasome and astrocytic activation. Stage-dependent patterns suggested inflammation may act as early driver, concurrent process, or late amplifier. Paradoxical associations (e.g., eotaxin-2, IL-2R with better memory) and APOE ε4-linked immune differences indicated context-dependent roles. DISCUSSION:This exploratory study reveals biologically plausible, inflammatory heterogeneity in AD and highlights plasma cytokine profiles as candidate biomarkers and therapeutic targets, warranting investigation.

AbstractINTRODUCTIONWe explored associations between measurements of the ocular choroid microvasculature and Alzheimer's disease (AD) risk.METHODSWe measured the choroidal vasculature appearing in optical coherence tomography (OCT) scans of 69 healthy, mid‐life individuals in the PREVENT Dementia cohort. The cohort was prospectively split into low‐, medium‐, and high‐risk groups based on the presence of known risk factors (apolipoprotein E [APOE] ε4 genotype and family history of dementia [FH]). We used ordinal logistic regression to test for cross‐sectional associations between choroidal measurements and AD risk.RESULTSChoroidal vasculature was progressively larger between ordinal risk groups, and significantly associated with risk group prediction. APOE ε4 carriers had thicker choroids and larger vascularity compared to non‐carriers. Similar trends were observed for those with a FH.DISCUSSIONSOur results suggest a potential link between the choroidal vasculature and AD risk. However, these exploratory findings should be replicated in a larger sample.Highlights Ocular choroidal microvasculature is of interest in relation to neurodegeneration due to its autonomic response to systemic, pathophysiological change. Choroidal changes in the prodromal stage of Alzheimer's disease (AD) are unexplored. The PREVENT Dementia cohort offers a unique, non‐invasive study of the microvasculature in mid‐life individuals at increased risk for developing AD. Significantly increased ocular choroidal vasculature was associated with increased risk (apolipoprotein E carrier and/or family history of dementia) for AD. These exploratory results suggest a potential association between the ocular choroidal vasculature and AD risk. However, findings should be replicated in a larger sample.

In the last decade, extensive research has emerged into understanding the impact of risk factors for Alzheimer’s Disease (AD) on brain function in pre-symptomatic stages. Here, we focused on the apolipoprotein e4 (APOEe4) gene, the major genetic risk factor for sporadic AD, and its effect on brain function in early adulthood. In the first part of the study, we systematically reviewed the multimodal functional neuroimaging literature, exploring its relationship with cognition, and the potential effects of other variables including the demographics, other risk factors, and methodological and analytical choices. While the studies demonstrated consistent alterations of APOEe4 carriers in brain connectivity and activity; the results of fMRI studies covered mostly the differences in the directionality using standard connectivity and activity measures. In the second part of this study, we aimed to address this gap by using the graph theory analysis to explore the dynamic behaviour of the six resting-state networks of interest in young APOEe4 carriers versus non-carriers (n=129, aged 17-22). Average Path Length and Closeness Centrality were consistently disrupted, pointing to network reorganisation in multiple resting-state networks, albeit using different mechanisms. This study is the first to demonstrate the restructuring of multiple resting-state networks in young adults modulated by the APOE genotype.

The emergence of disease-modifying drug therapies is expected to revolutionize the field of Alzheimer's disease (AD). Recent results from anti-amyloid clinical trials highlight the importance of early identification and accurate risk-stratification of individuals in early stages of the disease. In this context, the Amyloid Imaging to Prevent Alzheimer’s Disease (AMYPAD) Prognostic and Natural History Study (PNHS) was established, leveraging existing cohorts to alleviate the burden of recruiting de novo participants. Here, we describe the harmonization and integration efforts of brain imaging, clinical, cognitive, and fluid biomarker data. Access to the data is available through the Alzheimer’s Disease Data Initiative, with additional details provided at https://amypad.eu/data/ The AMYPAD PNHS integrates prospective and historical data from 32 European sites across 10 countries. These sites contribute data from 10 Parent Cohorts (PC), predominantly comprising non-demented at-risk subjects, including EPAD LCS, EMIF-AD (60++ and 90+), ALFA+, FACEHBI, FPACK, UCL-2010-412, Microbiota, DELCODE, and the AMYPAD Diagnostic and Patient Management Study. A meticulous data curation process was implemented, harmonizing metrics and questionnaires through strategies such as recoding into categories, Percentage of Maximum Possible Scores, and z-scores. Expert reviewers at each site conducted PET visual reads. Centralized quantification of static PET images, employing site-specific Gaussian smoothing, yielded harmonized Centiloid values. Parametric modelling of dynamic PET scans was also performed providing metrics such as the distribution volume ratio. The initial data set includes 3366 participants (55% females, 67±8 years), with 2629 having at least one follow-up visit (2.6±1.9 years). Of those, 1618 underwent baseline amyloid PET, 888 with follow-up. The dataset incorporates clinical outcomes, biomarkers, risk factors, and other relevant variables (Figure 1). Distribution of participants based on amyloid PET status at baseline yielded 60% negative (<12CL), 24% grey-zone (12-50CL), and 16% positive (>50CL) cases. The AMYPAD PNHS represents the largest European longitudinal dataset phenotyping individuals at risk of AD-related progression. The consortium is currently evolving into its new phase, namely the Euro-PAD collaborative framework, and the dataset will be expanded in terms of variables (e.g., currently integrating GWAS and advanced MRI data) and number of cohorts. Interested to join, please contact us at https://amypad.eu/

Alzheimer’s disease is a devastating neurodegenerative disorder with a complex pathogenesis. One main pathological feature utilised in diagnosis is neurodegeneration or neuronal injury, which is reflected in reductions in cerebral glucose metabolism measured by [18F]Fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET). Here we evaluated the involvement of glial reactivity measured with magnetic resonance spectroscopy (MRS) and cerebral blood flow measured with arterial spin labelling (ASL) on [18F]FDG PET as a measure of cerebral glucose metabolism. 123 people living with early Alzheimer’s disease who completed baseline evaluations on the evaluating liraglutide in Alzheimer’s disease trial were enrolled. Participants completed [18F]FDG PET scans with arterial input, T1 weighted MRI, single-voxel 1 HMRS, and pulsed ASL scans at Imperial College London Clinical Imaging Facility. The Totally Automatic Robust Quantitation in NMR (TARQUIN) package was used to process MRS scans and identify the concentration of myo-inositol within the posterior cingulate cortex (PCC), a marker of glial activation. Oxford-ASL was utilised to process ASL and quantify cerebral blood flow in the PCC. Finally, spectral analysis was performed on the [18F]FDG PET scans to assess the cerebral metabolic rate of glucose in the PCC. Pearson’s correlations were performed between the cerebral metabolic rate of glucose, cerebral blood flow and glial activity measured by the level of myo-inositol in the PCC. Increased cerebral glucose metabolism was correlated with higher myo-inositol in this sample of Alzheimer’s disease participants. In contrast, cerebral blood flow was not associated with cerebral glucose metabolism. Here we demonstrate that increased glial reactivity contributes to [18F]FDG PET signal in the early stages of Alzheimer’s disease. In response to early neuronal injury, astrocytes and microglia may become activated and enhance regional rates of glucose consumption. Hence, the contribution from these cells in addition to neurons should be considered in interpreting [18F]FDG PET as a measure of cerebral glucose metabolism. Interestingly, cerebral blood flow did not influence glucose metabolism. Microglia and astrocyte reactivity may contribute to an increase the cerebral glucose metabolism while neuronal loss and synaptic function may contribute to lower glucose metabolism measured by [18F]FDG in the early stages of Alzheimer's disease.