Integrated actigraphy‐based biomarker for Alzheimer’s dementia progression
Bibliographic record
Abstract
Abstract Background Actigraphy derived measures including amplitude, regularity, and variability of daily rhythm, have been shown to predict incident Alzheimer’s dementia. Here we developed an integrated actigraphy biomarker (IAB) for Alzheimer’ risk and investigated whether the IAB predicted the conversion from mild cognitive impairment (MCI) to Alzheimer’ dementia. Method We studied 1195 participants (age 80.8±7.2yrs [SD]) from the Rush Memory and Aging Project who had finished baseline actigraphy assessment (∼10 days), were free from Alzheimer’s dementia at actigraphy baseline and had been followed for up to 15 years with annual cognitive assessment and clinical diagnoses. Ten sleep/circadian related features were derived from baseline actigraphy recordings and were fed into a random forest survival model for prediction of time and incident Alzheimer’s dementia. IAB was derived from the model as the relative risk. Cox proportional hazards and logistic regression models were used to evaluate the performance of IAB in predicting incident Alzheimer’s dementia (all 1195 participants) or MCI (858 without MCI at baseline) and predicting the conversion from MCI to Alzheimer’s dementia. Demographic variables including age, sex, and education were controlled in all Cox and logistic regression models. Result Total 287 participants developed Alzheimer’s dementia during the follow‐up. The derived IAB was 0.6 SD larger in the participants developed Alzheimer’s dementia as compared with the controls. Larger IAB was associated with increased risk of AD with a hazard ratio (HR) = 1.63 (95% CI = 1.46‐1.81, P<0.0001) for 1‐SD increase in IAB. IAB did not predict incident MCI (P = 0.8). Within participants with MCI (337 at baseline and 308 developed), larger IAB was associated with a higher risk for AD, i.e., HR = 1.34 for 1 SD increase (95% CI = 1.18‐1.51, P<0.0001). The logistic model using the cutoff of 3 years for the MCI‐Alzheimer’s dementia conversion gives the odd ratio = 1.56 for 1 SD increase of IAB, and results in AUC = 0.68, with a sensitivity = 0.68 and specificity = 0.61. Conclusion Derived actigraphy biomarker was predictive of Alzheimer’s risk at preclinical stages, and the conversion from MCI. Actigraphy provides useful information for early prediction and detection of AD thought its performance needs to be improved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".