Associations Between Plasma, Imaging, Cerebrospinal Fluid Biomarkers and Naturalistic Driving Behaviors, Cognitive Tests Among Cognitively Normal Persons
Bibliographic record
Abstract
Abstract Background Previous work indicates that imaging and cerebrospinal fluid (CSF) amyloid and tau biomarkers are associated with driving behaviors, and that driving behaviors can be used to identify preclinical Alzheimer’s disease (AD). Along with traditional biomarkers, we examined the extent to which newer plasma and CSF biomarker are related to driving and cognitive metrics among cognitively normal older drivers. Method Mean driving and cognitive metrics were computed for all available time points in cognitively normal (Clinical Dementia Rating ® = 0) participants (N=167, mean age=73.3±4.9). Participants had a data logger installed in their personal vehicles and drove at their discretion for at least 6 months (mean follow‐up 2.2y±0.9y, range=0.6y‐4.7y) between 6/2/2015 and 2/29/2020 (pre‐COVID data) and took part in cognitive testing yearly. Participants took part in biomarker testing including, imaging (positron emission tomography [PET] amyloid, PET tau, normalized hippocampal volume [nHV]), CSF (Aβ42, Aβ40, tau, ptau181, neurofilament light chain [NfL]), and/or plasma (Aβ42, Aβ40, Aβ42/Aβ40, NfL) within 2 years of data logger installation. Fisher’s Z transformation was used to examine the correlations between the biomarker variables and the driving and cognitive measures. Results (Figure attached) Individual biomarkers were differentially related to different driving behaviors (e.g., number of trips was associated with PET amyloid [p<0.035], plasma Aβ42 [p<0.012] and Aβ40 [p<0.004], but not the other imaging, CSF, and plasma biomarkers [p>0.275]); and to different cognitive scores (e.g., Montreal Cognitive Assessment [MoCA] total scores were associated with amyloid imaging [p<.026], nHV [p<0.033], and plasma Aβ42/Aβ40 [p<0.035] but to no other biomarkers [p>0.080]). Some biomarkers were associated with driving behaviors but not cognitive scores (e.g., CSF Aβ40, tau, NfL; plasma Aβ42 and Aβ40 considered separately) whereas others were related to cognitive scores but not to any of the individual driving metrics (e.g., CSF Aβ42/Aβ40 ratio). Conclusions Different biomarkers are associated with different aspects of driving and cognitive functioning. These distinct relationships may help in understanding how different biological changes that occur during the preclinical stage of AD impact various sensorimotor and cognitive processes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".