Using Machine Learning to Study the Effects of Genetic Predisposition on Brain Aging in the UK Biobank
Bibliographic record
Abstract
The influence of genetic predisposition on changes in brain morphology during aging remains largely unknown. This study explores the effects of genetic predisposition on three key brain regions: total brain volume (TBV), lateral ventricular volume (LVV), and total hippocampal volume (THV). The brain age gap estimate (BrainAGE) biomarker is used as an input to a genome-wide association study to determine which single nucleotide polymorphisms (SNPs) and genes are associated with accelerated brain aging. Six independent significant SNPs were found to contribute to accelerated morphological changes: TBV had associations on chromosome 17 linked with brain aging, and the total THV had independent significant associations in the APOC1 and TOMM40 gene regions related to neurodegeneration. Lastly, LVV presented a possible novel discovery in the gene NUAK1, known to play a role in cellular senescence. This study provides a framework to uncover complex associations between brain aging physiology and genetics.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".