Sexually dimorphic differences in angiogenesis markers are associated with brain aging trajectories in humans
Bibliographic record
Abstract
Aberrant angiogenesis could contribute to the development of cognitive impairment and represent a therapeutic target for preventing dementia. However, most studies addressing angiogenesis and cognitive impairment focus on model organisms. To test the relevance of angiogenesis to human cognitive aging, we evaluated associations of circulating blood markers of angiogenesis with brain aging trajectories in a pooled two-center sample from deeply phenotyped longitudinal human cohorts ( n = 435; female = 207, age = 74 ± 9) using cognitive assessments, biospecimens, structural brain imaging, and clinical data. Blood markers included ligands involved in angiogenesis and vascular function such as basic fibroblast growth factor (bFGF), members of the vascular endothelial growth factor family (VEGFA, VEGFB, and VEGFC), and placental growth factor (PlGF), in addition to their receptors VEGF receptor 1 (VEGFR1) and tyrosine kinase with immunoglobulin and EGF homology domain 2 (Tie2). Machine learning and traditional statistics revealed sexually dimorphic associations of plasma angiogenic growth factors with brain aging outcomes, including executive function and gray matter atrophy. Specifically, markers of angiogenesis were associated with higher executive function and less brain atrophy in younger women (not men), a directionality of association that reversed around age 75. Higher concentrations of bFGF, known for pleiotropic effects on multiple cell types, predicted favorable cognitive trajectories in both women and men. An independent sample from a multicenter dataset (MarkVCID; n = 80; female = 30, age = 73 ± 9) was used to externally validate these findings. In conclusion, this analysis demonstrates the association of angiogenesis to human brain aging, with potential therapeutic implications for vascular cognitive impairment and 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".