Author: Cindy Birck

“Deep normative modelling reveals insights into early-stage Alzheimer’s disease using multi-modal neuroimaging data”

Authors: Ana Lawry Aguila, Luigi Lorenzini, Mohammed Janahi, Frederik Barkhof, Andre Altmann

Abstract:

Background: Exploring the early stages of Alzheimer’s disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for affected individuals and their families. However, the study of the early stages of AD involves analysing heterogeneous disease cohorts which may present challenges for some modelling techniques. This heterogeneity stems from the diverse nature of AD itself, as well as the inclusion of undiagnosed or ‘at-risk’ AD individuals or the presence of comorbidities which differentially affect AD biomarkers within the cohort. Normative modelling is an emerging technique for studying heterogeneous disorders that can quantify how brain imaging-based measures of individuals deviate from a healthy population. The normative model provides a statistical description of the ‘normal’ range that can be used at subject level to detect deviations, which may relate to pathological effects.

Methods: In this work, we applied a deep learning-based normative model, pre-trained on MRI scans in the UK Biobank, to investigate ageing and identify abnormal age-related decline. We calculated deviations, relative to the healthy population, in multi-modal MRI data of non-demented individuals in the external EPAD (ep-ad.org) cohort and explored these deviations with the aim of determining whether normative modelling could detect AD-relevant subtle differences between individuals.

Results: We found that aggregate measures of deviation based on the entire brain correlated with measures of cognitive ability and biological phenotypes, indicating the effectiveness of a general deviation metric in identifying AD-related differences among individuals. We then explored deviations in individual imaging features, stratified by cognitive performance and genetic risk, across different brain regions and found that the brain regions showing deviations corresponded to those affected by AD such as the hippocampus. Finally, we found that ‘at-risk’ individuals in the EPAD cohort exhibited increasing deviation over time, with an approximately 6.4 times greater t-statistic in a pairwise t-test compared to a ‘super-healthy’ cohort.

Conclusion: This study highlights the capability of deep normative modelling approaches to detect subtle differences in brain morphology among individuals at risk of developing AD in a non-demented population. Our findings allude to the potential utility of normative deviation metrics in monitoring disease progression.

DOI: https://doi.org/10.1186/s13195-025-01753-3

Published online: 15 May 2025 in the journal Alzheimer’s Research & Therapy

“Blood pressure and Alzheimer’s disease biomarkers in cognitively unimpaired adults: a multicenter study”

Authors: Mariona Osset-Malla, Aitana Martínez-Velasco, Gonzalo Sánchez-Benavides , Mariateresa Buongiorno, Alejandro de la Sierra, Mahnaz Shekari, Carolina Minguillon, Gwendlyn Kollmorgen, Clara Quijano-Rubio, Henrik Zetterberg, Kaj Blennow, David Vállez García, Marc Suárez-Calvet, Juan Domingo Gispert, Oriol Grau-Rivera, ALFA Study

Abstract:

Background: Hypertension is a modifiable risk factor for dementia, potentially influencing Alzheimer’s disease (AD) pathology. Understanding this relationship is essential for developing interventions to reduce dementia risk.

Objectives: We investigated cross-sectional and longitudinal associations between blood pressure and AD biomarkers in cerebrospinal fluid (CSF) and amyloid (Aβ) positron emission tomography (PET) in cognitively unimpaired adults.

Design: Prospective observational study.

Setting: We analyzed data from cognitively unimpaired participants from three observational prospective European studies: ALFA+ (NCT02485730), EPAD-LCS (NCT02804789), and AMYPAD PNHS (EudraCT: 2018-002,277-22).

Measurements: ALFA+ participants had either CSF biomarkers (CSF Aβ42, Aβ40, p-tau181, t-tau) and/or Aβ PET data. EPAD participants had CSF biomarkers (CSF Aβ42, p-tau181, t-tau), and AMYPAD participants had Aβ PET data. All participants had available data about diabetes, use of hypertensive medication, and waist-to-hip ratio. Multivariable linear regression models were used to analyze cross-sectional associations between systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) with CSF biomarkers or Aβ PET (Centiloid units, CL); longitudinal associations were tested by means of delta CL scores between baseline and follow-up to assess Aβ PET changes over time.

Results: We included 405 participants from ALFA+ (mean age 61.1 years; 60 % female), 1104 from EPAD (mean age 64.8 years; 59.1 % female), and 340 from AMYPAD (mean age 71.8 years; 60 % female). In ALFA+, DBP was negatively associated with CSF Aβ40 (p = 0.016) and p-tau181 (p = 0.050), while there was a non-significant trend towards a positive association between SBP and CL over time (p = 0.058). In EPAD, DBP was negatively associated with CSF Aβ42 (p < 0.001) and p-tau181 (p = 0.014), while PP was positively associated with CSF Aβ42 (p = 0.024). In AMYPAD, SBP (p = 0.002) and PP (p = 0.003) were positively associated with CL at baseline, with a similar non-significant trend being found for DBP (p = 0.089). Higher DBP (p = 0.042) was significantly associated to increased CL over time, with a similar non-significant trend being found for SBP (p = 0.072). We did not find significant associations between blood pressure and longitudinal changes in CSF biomarkers.

Conclusions: Elevated blood pressure was associated with increased Aβ PET accumulation in cognitively unimpaired individuals. Further research is warranted to elucidate potential mechanisms underlying the negative associations between DBP and CSF biomarkers, which do not reflect the typical AD molecular signature. These findings highlight the relevance of high blood pressure as a potential risk factor for cognitive decline.

DOI: https://doi.org/10.1016/j.tjpad.2025.100304

Published online: 11 August 2025 in the journal of Prevention of Alzheimer’s disease

“Cortical thickness subtypes in cognitively unimpaired individuals: Differential network and transcriptomic vulnerability to cortical thinning”

Authors: Luigi Lorenzini, Mario Tranfa, Leonard Pieperhoff, Federico Masserini, Mara ten Kate, Lyduine E. Collij, Giuseppe Pontillo, Emma S. Luckett, Alle Meije Wink, Henk JMM Mutsaerts, Tiago Gil Oliveira, Daniele Altomare, Mercè Boada, Anouk den Braber, Cindy Birck, Christopher Buckley, Gill Farrar, Wiesje van der Flier, Giovanni B. Frisoni, Rossella Gismondi, Juan Domingo Gispert, Bernard J. Hanseeuw, Frank Jessen, Marta Marquié, Anja Mett, Craig Ritchie, Gemma Salvadó, Michael Schöll, Mahnaz Shekari, Andrew W. Stephens, Betty M. Tijms, David Vállez García, Rik Vandenberghe, Pieter Jelle Visser, Luca Roccatagliata, Neil P. Oxtoby, Matteo Pardini, Frederik Barkhof

Abstract:

Introduction: The emergence, stability, and contributing factors of Alzheimer’s disease (AD) gray matter subtypes remain unclear.

Methods: We analyzed data from 1323 individuals without a diagnosis of dementia (CDR < 1) with T1w-MRI and amyloid-PET, including 622 with longitudinal data (3.66 ± 1.78 years). Cortical thickness subtypes were identified using a non-negative matrix factorization (NMF) clustering algorithm. We examined clinical and demographic differences, subtype stability, and longitudinal thinning patterns using brain network models and imaging-transcriptomic analysis. Replication was performed with an alternative clustering approach and a validation cohort.

Results: Two stable subtypes emerged: limbic-predominant and hippocampal-sparing. Limbic-predominant participants were older, had higher amyloid burden, and faster memory decline, while hippocampal-sparing individuals showed greater attention and executive function decline. Distinct thinning patterns were linked to specific network properties and gene expression profiles.

Discussion: These MRI-based subtypes reflect underlying pathophysiological mechanisms and may aid in prognostication and clinical trial stratification.

DOI: https://doi.org/10.1002/alz.70762

Published online: 14 October 2025 in the journal Alzheimer’s & Dementia

“Early detection of Alzheimer’s disease using small RNAs. Results from the EPAD cohort”

Authors: Tobias Sikosek , Marco Heuvelman, Jagoda Mika, Mustafa Kahraman, Julia Jehn, Maurice Frank, Alberto Daniel-Moreno, Jessika Ceiler, Jasmin Skottke, Marta Sanchez-Delgado, Patrick Neubert, Christina Rudolf , Kaja Tikk, Rastislav Horos, Jeffrey L Cummings, Josie Butchart, Craig Ritchie, Jean Manson, Bruno R Steinkraus; EPAD consortium

Abstract:

Background: Alzheimer’s disease (AD) is the most common form of dementia, and early diagnosis is crucial to enable effective interventions. Currently, Alzheimer’s disease is diagnosed through cognitive assessments, brain imaging and fluid biomarkers focused on determining amyloid (A) and, tau (T) protein levels as well as neurodegeneration (N) in the AT(N) framework. Prognostic biomarkers for predicting cognitive decline within the amyloid positive (Aβ+) individuals would further strengthen the framework.

Objectives: This study evaluated small RNAs as novel auxiliary biomarkers, independent of the AT(N) framework, either alone or in combination with established protein markers, for detecting the earliest cognitive decline in AD.

Design: The European Prevention of Alzheimer’s Disease (EPAD) clinical trial platform is a prospective, multi-center study designed to investigate biomarkers for preclinical and prodromal AD.

Setting: Peripheral whole blood RNA sequencing was performed on participants across Europe with no cognitive impairment or very mild cognitive impairment (MCI), stratified by cerebrospinal fluid amyloid levels.

Participants: 1,913 participants, 50 years or older and free of dementia diagnosis at enrollment, were analyzed.

Intervention: (if any) Not applicable.

Measurements: Ultra-deep small RNA sequencing was performed on whole blood samples using a refined blocking protocol to eliminate highly abundant erythroid small RNAs, and thereby to open sequencing bandwidth for the discovery of less abundant biomarker RNAs. Biomarker RNAs were deconvolved into plasma or blood cell origin and analyzed for functional relevance. We define high and low amyloid groups based on a cutoff on the p-tau₁₈₁/Aβ_1-42 ratio as determined from cerebrospinal fluid.

Results: We identified a combination of small RNAs that predicted early cognitive decline (Clinical Dementia Rating of 0.5) with an area under the receiver-operator curve of ∼0.7. Notably, when focusing on individuals with cognitive decline and high amyloid burden (Aβ+), the predictive accuracy improved to an AUC of 0.77. This performance could be extended to the entire cohort when combining blood RNA and CSF amyloid markers (AUC 0.76). We conducted bioinformatic analyses to interrogate the likely functional relevance of these small RNAs, uncovering several links to dementia-relevant pathways, including neuronal, cardiovascular, and inflammatory activities. Our findings also suggest that small nucleolar RNAs warrant further investigation as potential disease-relevant markers, in addition to microRNAs.

Conclusions: Integrating small RNA biomarkers with protein-based assays offers preliminary evidence for stratifying MCI, particularly within the amyloid positive continuum. Small nucleolar RNAs and microRNAs warrant further exploration as complementary diagnostic tools, and their use may enable more precise and effective interventions.

DOI:https://doi.org/10.1016/j.tjpad.2025.100257

Published online: September 2025 in the journal of Prevention of Alzheimer’s disease

“Brain MRI signatures across sex and CSF Alzheimer’s disease biomarkers”

Authors: You Cheng, Yingnan He Karthik Gopinath, Benjamin Billot, Juan Eugenio Iglesias, Chao-Yi Wu, Hiroko Dodge, Anne-Marie Will, Becky Carlyle, Pia Kivisäkk, Bradley T Hyman, Steven E Arnold, Sudeshna Das; for the Alzheimer’s Disease Neuroimaging Initiative and for the European Prevention of Alzheimer’s Disease (EPAD) Consortium

Abstract:

The relationship between cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease and neurodegenerative effects is not fully understood. This study investigates neurodegeneration patterns across CSF Alzheimer’s disease biomarker groups, the association of brain volumes with CSF amyloid and tau status and sex differences in these relationships in a clinical neurology sample. MRI and CSF Alzheimer’s disease biomarkers data were analysed in 306 patients of the Mass General Brigham healthcare system aged 50+ (mean age = 68.4 ± 8.8 years; 43.1% female), who had lumbar punctures within 1 year of clinical MRI scans. We first analysed neurodegeneration patterns across four biomarker groups: 60 controls (A−T−&CU; amyloid negative, tau negative, cognitively unimpaired), 25 A+T− (amyloid positive, tau negative), 121 A+T+ (amyloid positive, tau positive) and 100 other dementia (A−T−&CI; amyloid negative, tau negative, cognitively impaired). Second, we examined volumetric associations with amyloid (amyloid positive, tau negative versus control) and tau in the presence of amyloid (amyloid positive, tau positive versus amyloid positive, tau negative) across 52 brain areas. Third, we examined sex differences in these relationships. Finally, we validated core analyses across three independent datasets—NACC (National Alzheimer’s Coordinating Center), ADNI (Alzheimer’s Disease Neuroimaging Initiative) and EPAD (European Prevention of Alzheimer’s Dementia)—totalling 3137 participants, and performed meta-analyses to obtain more robust estimates. We observed distinct neurodegeneration patterns across biomarker groups, with disrupted connectivity (brain volume covariance networks) in amyloid positive and other dementia groups, while amyloid and tau negative, cognitively unimpaired controls exhibited the most connected network. Amyloid was associated with subcortical, cerebellar and brainstem atrophy, with consistent association observations in the thalamus and amygdala across all four datasets. Tau in the presence of amyloid demonstrated general brain shrinkage through enlargement of extracerebral CSF, alongside unexpected ventricle shrinkages. Sex-based analyses revealed that A+T+ (amyloid positive, tau positive) had lower sex differences in connectivity patterns compared with other groups. Sex differences were also noted in amyloid-related ventricular volume changes. This study reveals how amyloid and tau affect brain connectivity and volume across sex and CSF biomarker groups, emphasizing global brain changes and sex differences. By leveraging automated pipelines and advanced MRI and biomarker analyses, we extracted meaningful and replicable findings from heterogeneous clinical samples from real-world data. The meta-analyses across four datasets enhance the generalizability of our results.

DOI: 10.1093/braincomms/fcaf210

Published online: 30 May 2025 in the journal Brain Communications

“Accrual of Alzheimer’s disease pathology as a function of proximity to parental dementia onset”

Authors: Elina T. Ziukelis, Elijah Mak, Craig Ritchie, John T. O’Brien, Dag Aarsland, for the European Prevention of Alzheimer’s Disease (EPAD) Consortium

Abstract:

Introduction: Whether temporal proximity to parental onset of dementia (PPO) can be used to estimate timing of the preclinical stage of sporadic Alzheimer’s disease (AD) remains uncertain.

Methods: We investigated cross-sectionally adults aged > 50 without dementia included in the European Prevention of Alzheimer’s Dementia (EPAD) study. PPO was tested as a predictor of quantitative levels of cerebrospinal fluid (CSF) β-amyloid (1-42) (Aβ1-42) in those with a parental history of dementia (n = 688) and of phosphorylated tau (p-tau) and EPAD neuropsychological examination (ENE) subscores in an amyloid positive subgroup (n = 226). Possible interactions were explored.

Results: Shorter PPO predicted lower CSF Aβ1-42 level (β = 9.357; T = 4.161; p < 0.001), interacting with apolipoprotein E (APOE) -𝜀4 carriage in a dose-dependent manner. Concomitant APOE-𝜀2 carriage appeared to provide protection. PPO did not predict p-tau levels or cognitive performance.

Discussion: PPO may provide a valid method of stratifying risk of early AD pathologic change in APOE-𝜀4 carriers, with empirical and clinical applications.

Highlights:

• Proximity to age of parental dementia onset can predict amyloid accrual
• The effect is APOE-𝜀4 dose-dependent and APOE-𝜀2 appears to provide protection
• PPO does not appear to predict further advancement along the AD continuum
• In the era of anti-amyloid treatments, this may inform timing of amyloid screening
• Used as an empirical metric, PPO could help elucidate the natural history of LOAD

DOI: 10.1002/dad2.70092

Published online: 30 May 2025 in the journal Alzheimer’s & Dementia

“Facilitating clinical use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative data and a diagnostic cutoff value”

Authors: Merel C. Postema, Mark A. Dubbelman, Jürgen Claesen, Craig Ritchie, Merike Verrijp, Leonie Visser, Pieter-Jelle Visser, Marissa D. Zwan, Wiesje M. van der Flier and Sietske A.M. Sikkes

Abstract:

Objective:The Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) is well validated and commonly used to assess difficulties in everyday functioning regarding dementia. To facilitate interpretation and clinical implementation across different European countries, we aim to provide normative data and a diagnostic cutoff for dementia.

Methods: Cross-sectional data from Dutch Brain Research Registry (N = 1,064; mean (M) age = 62 ± 11 year; 69.5% female), European Medial Information Framework-Alzheimer’s Disease 90 + (N = 63; Mage = 92 ± 2 year; 52.4% female), and European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study (N = 247; Mage = 63 ± 7 year; 72.1% female) were used. The generalized additive models for location, scale, and shape framework were used to obtain normative values (Z-scores). The beta distribution was applied, and combinations of age, sex, and educational attainment were modeled. The optimal cutoff for dementia was calculated using area under receiver operating curves (AUC-ROC) and Youden Index, using data from Amsterdam Dementia Cohort (N = 2,511, Mage = 64 ± 8 year, 44.4% female).

Results: The best normative model accounted for a cubic-like decrease of IADL performance with age that was more pronounced in low compared to medium/high educational attainment. The cutoff for dementia was 1.85 standard deviation below the population mean (AUC = 0.97; 95% CI [0.97–0.98]).

Conclusion:We provide regression-based norms for A-IADL-Q and a diagnostic cutoff for dementia, which help improve clinical assessment of IADL performance across European countries.

DOI: https://doi.org/10.1017/S1355617724000031

Published online: 8 March 2024 in the Journal of the International Neuropsychological Society