Age-Related Whole-Brain Structural Changes in Relation to Cardiovascular Risks Across the Adult Age Spectrum
Bibliographic record
Abstract
Background: The brain atrophy and lesion index (BALI) has been developed to assess whole-brain structural deficits that are commonly seen on MRI in aging. It is unclear whether such changes can be detected at younger ages and how they might relate to other exposures. Here, we investigate how BALI scores, and the subcategories that make the total score, compare across adulthood and whether they are related to the level of cardiovascular risks, in both young and old adulthood. Methods: Data were from 229 subjects (72% men; 24-80 years of age) whose annual health evaluation included a routine anatomical magnetic resonance imaging (MRI) examination. A brain atrophy and lesion index (BALI) score was generated for each subject from T2-weighted MRI. Differences in the BALI total score and categorical subscores were examined by age and by the level of cardiovascular risk factors (CVRF). Regression analysis was used to evaluate relationships between continuous variables. Relative risk ratios (RRR) of CVRF on BALI were examined using a multinomial logistic regression. The area under the receiver operating characteristic curve was used to estimate the classification accuracy. Results: Nearly 90% of the participants had at least one CVRF. Mean CVRF scores increased with age (slope=0.03; r=0.36, 95% confidence intervals: 0.23-0.48; p<0.001). The BALI total score was closely related to age (slope=0.18; r=0.69, 95% confidence intervals: 0.59-0.78; p<0.001), as so were the categorical subscores (r’s= 0.41-0.61, p<0.001); each differed by the number of CVRF (t-test: 4.16-14.83, Chi2: 6.9-43.9, p’s<0.050). Multivariate analyses adjusted for age and sex suggested an independent impact of age and the CVRF on the BALI score (for each year of advanced age, RRR=1.20, 95% CI= 1.11-1.29; for each additional CVRF, RRR=3.63, 95% CI=2.12-6.23). The CVRF and BALI association remained significant even in younger adults. Conclusion: The accumulation of MRI-detectable structural brain deficits can be evident from young adulthood. Age and the number of CVFR are independently associated with BALI score. Further research is needed to understand the extent to which other age-related health deficits can increase the risk of abnormalities in brain structure and function, and how these, with BALI scores relate to cognition.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".