P2‐399: WHOLE BRAIN STRUCTURAL HEALTH IN RELATION TO CARDIOVASCULAR RISK FACTORS: AN EVALUATION USING THE BRAIN ATROPHY AND LESION INDEX
Bibliographic record
Abstract
Multiple structural changes on MRI in the brain can have interactive and additive impacts on aging and dementia. These changes can be collectively assessed using a semi-quantitative brain atrophy and lesion index (BALI). Previous studies have been focused on using the BALI to understand brain health of older adults, while it is not known how age and cardiovascular risk factors affect the accumulation of these various changes. In the present study, we address the question using a sample containing younger and middle-aged adults. Data of 239 subjects (men=71%; age range=20-80 years) who underwent regular health check and a routine anatomical MRI examination in Beijing Hospital of China were analyzed. Basic demographics and traditional cardiovascular risk factors (CVRF) of the subjects were reviewed. A BALI score was generated for each subject by evaluating T2 weighted MRI at 1.5T and 3T. Mean difference in BALI total and subcategory scores between subjects of difference age and CVRF conditions were examined using t-test and ANOVA, while associations between the BALI score and age or CVFR was evaluated by correlation and regression analyses. Over 89% of these subjects had at least one CVRF, while respectively 29%, 18%, 11%, 1% had two to five CVRFs. On average, older subjects had more CVRF. The BALI total score and subcategory scores were closely related to age (r's=0.41-0.69, p's<0.001). The BALI total and the categorical scores differed significantly by the number of CVRF (t's=4.16-14.83, p's<0.05). Subjects with a higher BALI score were more likely to be associated with at least one CVRF (Xs=6.9-43.9, p's<0.05). Multivariate analyses adjusting for various possible confounders demonstrated a strong impact of the CVRF on whole structural brain health as evaluated using BALI (Odds Ratio = 1.676, 95% CI=1.207-2.325), especially hypertension (OR=2.455, 95% CI=1.126-5.353), independent of the effect of age. The accumulation of deficits in brain structure can start at a younger age. Cardiovascular risk factors play a key role in affecting such accumulation. The data emphasize the importance of early control of cardiovascular risk factors on promoting brain health in aging-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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".