Sex Differences in Depression as a Risk Factor for Alzheimer’s Disease: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Depression is an important risk factor for Alzheimer's disease (AD) but little is known about the mechanisms of this association. Given sex differences in both AD and depression, we sought to conduct a systematic review and meta-analysis to examine whether there are sex differences in their association, as this may improve understanding of underlying mechanisms. RESEARCH DESIGN AND METHODS: MEDLINE, PsycINFO, and Cochrane Reviews were searched for observational studies including both sexes and examining the association between history of depression and AD. RESULTS: Forty studies, including 62,729 women and 47,342 men, were identified. Meta-analysis was not possible because only 3 studies provided sufficient data. Seven studies provided information about the influence of sex for a qualitative synthesis. Two found an association in men only, 2 in women only, and 3 reported no sex differences. The 2 studies finding an association in women only were unique in that they had the shortest follow-up periods, and were the only clinic-based studies. DISCUSSION AND IMPLICATIONS: The findings of our systematic review show that there are important methodological differences among the few studies providing data on the influence of sex on depression as a risk factor for AD. Had all 40 studies provided sex-segregated data, these methodological differences and their impact on sex effects could have been examined quantitatively. We encourage researchers to report these data, as well as potential moderating factors, so that the role of sex differences can be better understood.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".