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Record W4411549686 · doi:10.1101/2025.06.22.25330074

Mapping sex differences in brain and cognition in relation to <i>APOE4</i> and amyloid burden: A longitudinal normative modelling study

2025· preprint· en· W4411549686 on OpenAlexfundno aff
Sivaniya Subramaniapillai, Serena Verdi, Sarah E Keuss, Kirsty Lu, Sarah‐Naomi James, William Coath, David M. Cash, Frederik Barkhof, Marcus Richards, André F. Marquand, Jon M. Schott, James H. Cole, Ann‐Marie G. de Lange

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
FundersMedical Research CouncilNatural Sciences and Engineering Research Council of CanadaDementias Platform UKUK Dementia Research InstituteUniversity College London Hospitals NHS Foundation TrustSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungBritish Heart FoundationUniversity College LondonNational Institute for Health and Care ResearchBrain Research TrustCalifornia State University, BakersfieldBrain Research UKAlzheimer's SocietyNational Science FoundationWeston Brain InstituteAlzheimer's Association
KeywordsNormativeCognitionPsychologyRelation (database)Longitudinal studyCognitive psychologyDevelopmental psychologyCognitive scienceNeuroscienceComputer scienceEpistemologyMedicinePhilosophyPathology

Abstract

fetched live from OpenAlex

Abstract Sex differences in Alzheimer’s disease (AD) risk and progression are increasingly recognized, with females exhibiting higher global prevalence rates. Yet it remains unclear how genetic and biomarker indicators of Alzheimer’s risk, such as the apolipoprotein E-ɛ4 ( APOE4 ) allele and amyloid burden, relate to sex differences in brain and cognitive health during the preclinical stage. Using established normative models trained on ~58,000 healthy participants, we computed regional z-scores from T1-weighted MRI scans in 372 cognitively normal participants from the Insight 46 cohort. Scans were acquired at two timepoints, approximately three years apart, beginning at age 70. Regions with z-scores below −1.96 were classified as brain-structure outliers and summarized as total outlier count (tOC). We used linear mixed-effects models to examine how sex, age, and AD risk ( APOE4 status and amyloid burden) relate to tOC and cognitive outcomes measured by Preclinical Alzheimer Cognitive Composite (PACC) scores. Both cross-sectional associations and longitudinal changes in tOC and PACC scores were examined, and we tested whether the effects of APOE4 status and amyloid burden on brain and cognitive measures differed by sex. Cross-sectional analyses showed that males had greater tOC than females at younger ages. At timepoint 1, spatial maps showed more regions with outliers in males, though high outlier proportions were limited to occipital areas. By timepoint 2, group differences became more spatially distinct, with males and females showing deviations in different regions. Longitudinally, older males exhibited steeper increases in tOC over time compared to females. Females showed higher PACC scores overall, while no sex differences were observed in cognitive change over time. Greater tOC and amyloid burden were both associated with poorer cognitive outcomes, with the strongest association observed in female APOE4 carriers. However, we found no evidence that AD risk influenced age-related changes in tOC or cognition over time. These findings highlight the complex interplay between sex, age, and AD risk in shaping brain structure and cognition in later life. Some of the observed patterns may reflect emerging vulnerability not yet captured by short-term longitudinal change, underscoring the importance of continued observation. In conclusion, normative modelling provides a valuable approach for detecting subtle variation in brain and cognitive aging across risk groups.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.296
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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