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Record W4407979755 · doi:10.1101/2025.02.21.639584

Multi-omic subtypes of Alzheimer’s dementia are differentially associated with psychological traits

2025· preprint· en· W4407979755 on OpenAlexaff
Andrea R. Zammit, Lei Yu, Victoria N. Poole, Konstantinos Arfanakis, Julie A. Schneider, Vladislav Petyuk, Philip L. De Jager, Rima Kaddurah-Daouk, Yasser Iturria‐Medina, David A. Bennett

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsDementiaPsychologyNeuroscienceClinical psychologyBiologyMedicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Abstract Importance Psychological traits reflecting neuroticism, depressive symptoms, loneliness, and purpose in life are risk factors of AD dementia; however, the underlying biologic mechanisms of these associations remain largely unknown. Objective To examine whether one or more multi-omic brain molecular subtypes of AD is associated with neuroticism, depressive symptoms, loneliness, and/or purpose in life. Design Two cohort-based studies; Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP), both ongoing longitudinal clinical pathological studies that began enrollment in 1994 and 1997. Setting Older priests, nuns, and brothers from across the U.S. (ROS) and older adults from across the greater Chicago metropolitan area (MAP). Participants 822 decedents with multi-omic data from the dorsolateral prefrontal cortex. Exposure(s) Pseudotime, representing molecular distance from no cognitive impairment (NCI) to AD dementia, and three multi-omic brain molecular subtypes of AD dementia representing 3 omic pathways from no cognitive impairment (NCI) to AD dementia that differ by their omic constituents. Main outcome(s) and measure(s) We first ran four separate linear regressions with neuroticism, depressive symptoms, loneliness, purpose in life as the outcomes, and pseudotime as the predictor, adjusting for age, sex and education. We then ran four separate analyses of covariance (ANCOVAs) with Bonferroni-corrected post-hoc tests to test whether the three multi-omic AD subtypes are differentially associated with the four traits, adjusting for the same covariates. Result Pseudotime was positively associated ( p <0.05) with neuroticism and loneliness. AD subtypes were differentially associated with the traits: AD subtypes 1 and 3 were associated with neuroticism; AD subtype 2 with depressive symptoms; AD subtype 3 with loneliness, and AD subtype 2 with purpose in life. Conclusions and Relevance Three multi-omic brain molecular subtypes of AD dementia differentially share omic features with four psychological risk factors of AD dementia. Our data provide novel insights into the biology underlying well-established associations between psychological traits and AD dementia. Key points Question Are three distinct multi-omic brain molecular subtypes of Alzheimer’s disease (AD) dementia associated with four well-established psychological AD risk factors (neuroticism, depressive symptoms, loneliness and purpose in life)? Findings We found differential associations: AD subtypes 1 and 3 were associated with neuroticism, AD subtype 2 was associated with depressive symptoms, AD subtype 3 was associated with loneliness; and AD subtype 2 was associated with purpose in life. Meaning Psychological risk factors might be associated with AD dementia via shared multi-omic molecular pathways.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.037
GPT teacher head0.298
Teacher spread0.261 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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