BMI1 is associated with CSF amyloid-β and rates of cognitive decline in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Accumulating evidence suggests that BMI1 confers protective effects against Alzheimer’s disease (AD). However, the mechanism remains elusive. Based on recent pathophysiological evidence, we sought for the first time to identify genetic variants in BMI1 as associated with AD biomarkers, including amyloid-β. Methods We used genetic, longitudinal cognition, and cerebrospinal fluid (CSF) biomarker data from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort ( N = 1565). First, we performed a gene-based association analysis of common single nucleotide polymorphisms (SNPs) (minor allele frequency (MAF) > 5%) located within ± 20 kb of the gene boundary of BMI1 , an optimal width for including potential regulatory SNPs in the 5′ and 3′ untranslated regions (UTR) of BMI1 , with CSF Aβ 1-42 levels. Second, we performed cross-sectional and longitudinal association analyses of SNPs in BMI1 with cognitive performance using linear and mixed-effects models. We replicated association of SNPs in BMI1 with cognitive performance in an independent cohort ( N =1084), Religious Orders Study and the Rush Memory and Aging Project (ROS/MAP). Results Gene-based genetic association analysis showed that BMI1 was significantly associated with CSF Aβ 1-42 levels after adjusting for multiple testing using permutation (permutation-corrected p value=0.005). rs17415557 in BMI1 showed the most significant association with CSF Aβ 1-42 levels. Participants with minor alleles of rs17415557 have increased CSF Aβ 1-42 levels compared to those with no minor alleles. Further analysis identified and replicated the minor allele of rs17415557 as being significantly associated with slower cognitive decline rates in AD. Conclusions Our findings provide fundamental evidence that BMI1 rs17415557 may serve as a protective mechanism related to AD pathogenesis, which supports the results of previous studies linking BMI1 to protection against AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".