Abstract 37: The Brain Care Score and Neuroimaging Markers in Asymptomatic Individuals in the UK Biobank
Bibliographic record
Abstract
The 21-point Brain Care Score (BCS) is a novel tool designed to motivate individuals and care providers to take action to reduce the risk of stroke and dementia by motivating lifestyle changes (Fig 1). In this study we aimed to assess if the BCS is also associated with brain changes on MRI in people who have not yet developed dementia or stroke. Methods: This study was conducted within the MRI substudy of the longitudinal cohort study UK Biobank. The assessed MRI neuroimaging markers included: brain volume, white matter hyperintensities (WMH) volume, fractional anisotropy (FA) and mean diffusivity (MD). FA/MD metrics were calculated as the average across 48 discrete brain regions. We used multivariable linear regression to test for association between the BCS computed using baseline data (2006-2010) and neuroimaging markers, measured both during first (2014+) and repeat (2019+) MRI assessments. Results: There were 34,772 study participants with MRIs and available BCS data (mean age 55, 53% female). Every five-point increase in the BCS was associated with an 11% increase in brain volume (Beta 0.11, SE 0.01), a 26% reduction in WMH volume (Beta -0.26, SE 0.01), a 13% increase in average FA (Beta 0.13, SE 0.02), and a 9% decrease in average MD (Beta -0.09, SE 0.01). There were 3,778 study participants with first and repeat MRI (mean age 53, 53% female). Comparing the first and repeat imaging assessments, every five-point increase in baseline BCS was associated with a slower growth in WMH volume (Beta -0.08, SE 0.03) and a slower reduction in average FA (Beta 0.11, SE 0.03). Discussion: Among middle-aged adults without dementia or stroke, a higher BCS is strongly associated with better neuroimaging brain health profiles and slower rates of brain health decline. Given that the neuroimaging markers evaluated in our study are recognized risk factors that precede stroke and dementia by many years, our results support the BCS is a promising tool for early intervention to prevent these conditions.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".