Genetic Risk, Vascular Function, and Subjective Cognitive Complaints Predict Objective Cognitive Function in Healthy Older Adults: Results From the Brain in Motion Study
Bibliographic record
Abstract
Aging is associated with subjective memory complaints. Approximately half of those with subjective memory complaints have objective cognitive impairment. Previous studies have provided evidence of an association between genetic risk for Alzheimer’s disease and dementia progression. In addition, aging is a significant risk factor for vascular pathology that may underlie at least some of the cognitive changes. This study investigates the relative contribution of subjective cognitive complaints, vascular function and genetic risk for dementia in predicting objective cognitive performance. Multiple regression and relative importance analysis were used to investigate the relative contribution of vascular function, self-reported subjective cognitive complaints, and dementia genetic risk, in predicting objective cognition in a sample of 238 healthy community dwelling older adults. Age, sex, premorbid cognitive abilities, subjective verbal memory complaints, higher cerebrovascular blood flow during submaximal exercise, and certain dementia risk alleles were significant predictors of worse objective verbal memory performance (p < .001, R2 = 35.2-36.4%). Using relative importance analysis, subjective verbal memory complaints and certain dementia risk alleles contributed more variance than cerebrovascular measures. These results suggest that age-related changes in memory in healthy older adults can be predicted by subjective memory complaints, genetic risk, and to a lesser extent, cerebrovascular function.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".