Late-Life Depressive Symptomatology, Motoric Cognitive Risk Syndrome, and Incident Dementia: The “NuAge” Study Results
Bibliographic record
Abstract
Background: Late-life depressive symptomatology and motoric cognitive risk syndrome (MCR) have independently been associated with an increased risk for incident dementia. This study aimed to examine the association of late-life depressive symptomatology, MCR, and their combination on incident dementia in community-dwelling older adults living in Quebec (Canada). Methods: The study was carried out in a subset of 1,098 community dwellers aged ≥65 years recruited in the “Nutrition as a determinant of successful aging: The Quebec longitudinal study” (NuAge), an observational prospective cohort study with 3 years follow-up. At baseline, MCR was defined by the association of subjective cognitive complaint with slow walking speed, and late-life depressive symptomatology with a 30-item Geriatric Depression Scale (GDS) score >5/30. Incident dementia, defined as a Modified Mini-Mental State score ≤79/100 test and Instrumental Activity Daily Living score <4/4, was assessed at each annual visit. Results: The prevalence of late-life depressive symptomatology only was 31.1%, of MCR only 1.8%, and the combination of late-life depressive symptomatology and MCR 2.4%. The combination of late-life depressive symptomatology and MCR at baseline was associated with significant overall incident dementia (odds ratio (OR) = 2.31 with P ≤ 0.001) but not for MCR only (OR = 3.75 with P = 0.186) or late-life depressive symptomatology only (OR = 1.29 with P = 0.276). Conclusions: The combination of late-life depressive symptomatology and MCR is associated with incident dementia in older community dwellers. The results suggested an interplay between late-life depressive symptomatology and MCR exposing them to an increased risk for dementia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".