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Record W7117235125 · doi:10.1002/alz70857_101637

Personalized Alzheimer's disease risk profiling in healthy middle‐aged individuals: The Cedars‐Sinai Memory & Healthy Aging Program

2025· article· en· W7117235125 on OpenAlexaboutno aff
Zaldy S. Tan, Nabeel Qureshi, Andrew Hirsch, Stephanie Bray, Mitzi M. Gonzales, Celina H. Shirazipour, Hugo J. Aparicio, Fatemeh Ramezani, Sarah Kremen, Nancy L. Sicotte

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsProfiling (computer programming)Healthy agingDementiaDiseasePhysical activityCognitionRisk assessmentCognitive decline

Abstract

fetched live from OpenAlex

BACKGROUND: Addressing modifiable medical and lifestyle risk factors may have the potential to reduce incidence of Alzheimer's disease and related dementias (ADRD). The Cedars-Sinai Memory & Health Aging Program (MHAP) is a clinical and research program that promotes brain health through personalized risk profiling and risk reduction among at risk, asymptomatic adults. In this study, we describe the demographics and risk profiles of participants seen in the first year of the program. METHOD: All Cedars-Sinai Medical Center patients received an email message and a link to the MHAP website. Eligibility criteria included 1) Age 40+ years; 2) absence of a cognitive or neurological diagnosis; 3) 2 risk factors for ADRD (only applicable to adults <65 years). Participants were asked to complete a pre-visit questionnaire on family and medical history, and validated surveys assessing ADRD risk factors identified by the Lancet Commission. Data analysis included descriptive statistics of participant demographics and survey responses. RESULT: The mean age of the cohort (N = 64) was 59.5 years (range: 40-87). Most patients were female (59.4%), non-Hispanic white (75%) or Asian (9.4%), and highly educated (95.3% college graduate or higher). More than half had the following ADRD risk factors: sleep disorder (75%), family history of ADRD in a first degree relative (69%), low physical activity (61%), low levels of socialization (55%), obstructive sleep apnea (55%), elevated LDL/Cholesterol (53%), and low MIND diet score (52%). Between a third and half of patients had the following additional risk factors: high blood pressure (42%), history of traumatic brain injury (TBI) (36%), and depressed mood (33%). Fifty-one participants completed the Montreal Cognitive Assessment (MoCA) and the average score for this highly educated cohort was 26.1 (range: 15-30), with difficulties on items assessing delayed recall (86%), language (33%), visuospatial/executive (29%), orientation (28%), and attention (26%). CONCLUSION: Personalized risk profiling for ADRD risk showed high rates of modifiable ADRD risk factors, including sleep disorder, elevated LDL cholesterol, low MIND diet scores, and low levels of physical activity and socialization. Future research will examine the feasibility and effectiveness of ADRD risk mitigation in measures of cognitive and structural brain aging.

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)0.002
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.053
GPT teacher head0.370
Teacher spread0.317 · 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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