Prevalence of Potentially Modifiable Risk Factors in Persons At-Risk for Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Addressing modifiable medical and lifestyle risk factors may reduce the incidence of Alzheimer’s disease and related dementias (ADRD). The Cedars-Sinai Memory & Health Aging Program (MHAP) promotes brain health via personalized risk profiling and risk reduction among asymptomatic adults aged 40 years and older at risk for ADRD. MHAP was launched with an email invitation to Cedars-Sinai Medical Center patients. Eligibility required age 40 or older, no cognitive or neurological diagnosis, and presence of two or more ADRD risk factors. Participants completed a 90-minute in-person visit assessing ADRD risk factors and cognitive performance. Of 123 enrolled patients, 84 (68.2%) had a family history of dementia or carried at least one ApoE4 allele. The mean age was 59.5 years; 63.1% were female. Most were White (77.4%) or Asian (9.5%), non-Hispanic (95.2%), and highly educated (91.7% college graduate or higher). Common risk factors included family history of ADRD in a first-degree relative (92.9%), weak social networks (52.4%), sleep disorder (51.2%), elevated LDL cholesterol (50%), and obstructive sleep apnea (50%). Among those completing the Montreal Cognitive Assessment (MoCA), the average score was 26.0, with frequent difficulties in delayed recall (91.9%), visuospatial/executive function (52.7%), language (36.5%), and attention (27.0%). Personalized risk profiling revealed high rates of modifiable ADRD risk factors, including sleep disorders, elevated cholesterol, and low physical and social activity. Targeted interventions addressing these factors may reduce future ADRD risk in at-risk populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".