Exploring sex‐specific risk factors in cognitive decline: a network analysis of modifiable and non‐modifiable determinants
Bibliographic record
Abstract
Abstract Background Dementia incidence is projected to nearly triple by 2050 worldwide 1 , posing unique challenges to healthcare systems. Identifying non‐modifiable and modifiable risk factors (RF) is crucial, including sex‐specific factors, given the higher prevalence of dementia among females (60%) 2 . This study employed to network analysis to examine common RF for dementia, as identified in the Lancet Commission 3,4 in individuals with cognitive decline (CD) comparing sex‐specific networks to identify unique RF and interactions. Method 411 individuals with CD were included (mild cognitive impairment and Alzheimer's dementia) from the Ontario Neurodegenerative Disease Research Initiative (30% of participants) and Canadian Consortium for Neurodegeneration in Aging (52% female – Table 1). A network analysis 5 was used to examine non‐modifiable (e.g., age), modifiable (e.g., sleep, alcohol use) and cognitive outcomes. Sex‐specific networks were created and compared, node centrality was investigated to determine the relative importance of each RF. Result No statistical difference in network structure between CD males and females was observed (M = 0.221; p = 0.256). The male network did have higher global strength than the females’, indicating greater connectivity among all nodes (S = 0.688; p = 0.044)[Figure 1&2). In the female network, nodes that were central (greatest impact on network structure) were systolic blood pressure, diagnosis (mild cognitive impairment vs Alzheimer's disease), age and sleep quality, while memory and cognitive medication use were central nodes for males ( p < 0.05). Specific edges, such as the relationship between systolic blood pressure and other RF (Figure 1 and 2), were significantly different between networks Conclusion Our findings reveal unique sex‐specific network patterns of RF for CD, emphasizing the importance of sex‐disaggregated analyses. Although the overall structure of the networks were not statistically different, key differences in patterns for the edges and nodes emerged, with males demonstrating higher connectivity among RF, and thus a more closely related network of RF. These findings point to significant gaps in our understanding of sex‐specific RF for CD and highlight the need for further investigation to disentangle these complex relationships. Future work should integrate biomarkers, such as neuroimaging to further explore how sex‐specific RF influence dementia risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".