Relationship for co‐trajectories of cognitive decline and late‐life depression with postmortem neuropathology
Bibliographic record
Abstract
Abstract Background The etiopathological relationship between late‐life cognitive decline and symptoms of depression remains unclear. Existing longitudinal studies have mostly grouped participants into distinct groups based on clinical diagnoses, and very few have analyzed postmortem neuropathology. Here we identified trajectories of both cognition and depressive symptoms in an elderly cohort over up to 27 years using growth mixture modeling (GMM), identifying co‐trajectory subgroups, and characterizing them with respect to postmortem neuropathologies. Method We analyzed 3,010 participants enrolled without dementia (mean age = 78) from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP). All included participants were evaluated for at least three annual visits for both depressive symptoms (using a modified Center for Epidemiologic Studies Depression Scale) and 19 cognitive tasks (yielding a global cognitive function summary score). A subset of 1,732 participants had complete postmortem neuropathological assessment data available. Latent trajectory subgroups were identified independently for global cognition and depressive symptoms using GMM with non‐linear time effects. Interaction effects of cognitive and depressive trajectory subgroups on neuropathologies were assessed using linear models. Result Three trajectory subgroups (Figure 1) were identified for both cognitive decline (minimal (n = 1,232), slow (n = 1,325) and fast (n = 302)) and depressive symptoms (sustained low (n = 1,815), moderately increasing (n = 762) and consistently elevated (n = 433)). These subgroups showed significant overlap (chi‐square p = 0.0001), and their intersections defined nine co‐trajectories. A significant interaction effect (p = 0.0062) was identified between cognitive decline and depressive symptom trajectory subgroups for brain‐wide levels of paired helical filament tau; among only individuals experiencing fast cognitive decline, sustained lower depressive symptoms were associated with higher levels of tau (Figure 2). Conclusion Longitudinal cognitive decline and depressive symptom trajectories in late‐life are not independent. Intriguingly, we observed the highest tau burden in those individuals with both sustained low depressive symptoms and fast rates of cognitive decline. Given that use of antidepressants may lower tau levels (Dafsari & Jessen, 2020), future models of neuropathology in trajectory‐based analyses will incorporate patterns of medication status.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".