Late-Onset Depression and Dementia: A Systematic Review of the Temporal Relationships and Predictive Associations
Bibliographic record
Abstract
Background: Late-onset depression (LOD) has been increasingly recognized as a risk factor for dementia, yet the temporal and causal nature of this relationship remains unclear. Objective: The purpose of this review is to investigate the temporal association between LOD and dementia. Methods: A comprehensive search for studies examining the temporal relationship between LOD and dementia was conducted using MEDLINE via Ovid. The end date of the search was 9 September 2024. A total of 3450 studies were identified, of which 27 met the inclusion criteria. This review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and an article quality assessment was completed. Results: The review demonstrated a significant temporal association between LOD and the risk of dementia, with the highest risk observed within the first decade following depression onset. LOD was consistently associated with an increased likelihood of developing dementia, particularly Alzheimer’s disease, compared to depression at earlier life stages. Conclusions: This systematic review highlights the significant association between LOD and dementia risk, emphasizing the need for early recognition and intervention. Future research should investigate the age at which LOD becomes a risk factor for dementia, the relationship between depression severity, family history of dementia, and dementia risk, as well as the efficacy of preventative treatments.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".