Interaction between Education and Alzheimer’s Disease Biomarkers in Longitudinal Clinical Impairment
Bibliographic record
Abstract
Abstract Background Higher education is often associated with reduced risk of cognitive decline. These findings fueled the conceptualization of cognitive reserve to explain individual variabilities in clinical trajectory. However, our understanding of the biological basis for this phenomenon is still not precise. In this sense, we assessed the interaction between Alzheimer’s biomarkers and cognitive reserve. Method We included 85 individuals from the Translational Biomarkers of Aging and Dementia (TRIAD) cohort presenting normal cognition or mild cognitive impairment with global Clinical Dementia Rating (CDR) ≤ 0.5. All individuals underwent positron emission tomography (PET) for amyloid‐β (A) and tau (T) assessed with [18F]AZD4694‐PET and [18F]MK6240‐PET, respectively. A positivity was defined as neocortical Aβ‐PET SUVR ≥ 1.55. T positivity was defined as temporal meta‐ROI tau‐PET SUVR ≥ 1.24. We calculated a change in CDR sum of boxes (CDR‐SB) by subtracting baseline from their scores after 2 years of follow‐up. Analysis were conducted with the CDR sum of boxes (CDRSB) as well as within specific domains, always correcting for age, sex, APOEε4 carriership and baseline score. Result Demographics information is described in Table 1. Linear regression analysis showed that years of education interacted with A+T+ status to reduce cognitive decline (β = ‐0.19, p < 0.001) but did not interact with any other pathological status as shown in Figure 1. We also found that communication, a specific domain of the CDR scale, had a longitudinal association with education in the A+T+ group (β = ‐0.09, p = 0.0303, Figure 2). Conclusion The protective effect of education, as measure of cognitive reserve, occurred specifically in A+T+ individuals. Moreover, this effect was mostly on the communication domain. These results contribute to our understanding of cognitive reserve as they may suggest that education protects communication from the synergistic damage of amyloid and tau pathologies. Further investigations in additional cohorts with longer follow‐up is warranted.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".