Exploring sex‐specific risk factors in cognitive decline: Insights into modifiable and non‐modifiable determinants using a network analysis
Bibliographic record
Abstract
BACKGROUND: How sex-specific risk factors for dementia interact remains understudied. Network analysis provides a novel approach to examine these relationships, offering insights into sex-specific risk factor connectivity in healthy controls (HCs) and individuals with mild cognitive impairment or Alzheimer's disease (cognitive decline [CD]). METHODS: A network analysis of 896 participants examined associations among modifiable, non-modifiable, and cognitive risk factors. Network invariance and global strength tests assessed structural and connectivity differences. RESULTS: In males, network invariance differed between HC and CD groups, but connectivity was unchanged. In females, network invariance and connectivity were significantly altered, with HC females exhibiting stronger overall connectivity. Key risk factors in females included systolic blood pressure and apolipoprotein E ε4, whereas males' networks were primarily influenced by cognitive outcomes. DISCUSSION: Sex-specific networks suggest distinct mechanisms underlying cognitive decline. Future work differentiating mild cognitive impairment and Alzheimer's disease stages will refine our understanding of risk factor evolution and inform precision medicine approaches to dementia prevention. HIGHLIGHTS: Sex-stratified networks of risk factors differ in healthy and cognitively impaired adults. Female networks show greater global connectivity in healthy versus cognitively decline groups. Apolipoprotein E, family history, and lifestyle factors show sex-specific centrality in networks. Network structure changes more in females across the cognitive decline spectrum. Findings support sex-specific modeling of dementia risk for precision prevention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".