Bridging Integrator 1 ( <i>BIN1</i> , rs6733839) and Sex Are Moderators of Vascular Health Predictions of Memory Aging Trajectories
Bibliographic record
Abstract
BACKGROUND: A promising risk loci for sporadic Alzheimer's disease (AD), Bridging Integrator 1 (BIN1), is thought to operate through the tau pathology pathway. OBJECTIVE: We examine BIN1 risk for a moderating role with vascular health (pulse pressure; PP) and sex in predictions of episodic memory trajectories in asymptomatic aging adults. METHODS: The sample included 623 participants (Baseline Mean age = 70.1; 66.8% female) covering a 44-year longitudinal band (53-97 years). With an established memory latent variable arrayed as individualized trajectories, we applied Mplus 8.5 to determine the best fitting longitudinal growth model. Main analyses were conducted in three sequential phases to investigate: 1) memory trajectory prediction by PP, 2) moderation by BIN1 genetic risk, and 3) stratification by sex. RESULTS: We first confirmed that good vascular health (lower PP) was associated with higher memory level and shallower decline and males were more severely affected by worsening PP in both memory performance and longitudinal decline. Second, the PP prediction of memory trajectories was significant for BIN1 C/C and C/T carriers but not for persons with the highest AD risk (T/T homozygotes). Third, when further stratified by sex, the BIN1 moderation of memory prediction by PP was selective for females. CONCLUSION: We observed a novel interaction whereby BIN1 (linked with tauopathy in AD) and sex sequentially moderated a benchmark PP prediction of differential memory decline in asymptomatic aging. This multi-modal biomarker interaction approach, disaggregated by sex, can be an effective method for enhancing precision of AD genetic risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".