Metabolic and Vascular Risk Factor Variability Over 25 Years Relates to Midlife Brain Volume and Cognition
Bibliographic record
Abstract
BACKGROUND: Metabolic and vascular risk factors (MVRF) are associated with neurodegeneration and poor cognition. There is a need to better understand the impact of these risk factors on brain health in the decades that precede cognitive impairment. Longitudinal assessments can provide new insight regarding changes in MVRFs that are related to brain imaging features. OBJECTIVE: To investigate whether longitudinal changes in MVRF spanning up to 25 years would be associated with midlife brain volume and cognition. METHODS: Participants were from the CARDIA study (N = 467, age at year 25 = 50.6±3.4, female/male = 232/235, black/white = 161/306). Three models were developed, each designed to capture change over time; however, we were primarily interested in the average real variability (ARV) as a means of quantifying MVRF variability across all available assessments. RESULTS: Multivariate partial least squares that used ARV metrics identified two significant latent variables (partial correlations ranged between 0.1 and 0.26, p < 0.01) that related MVRF ARV and regional brain volumes. Both latent variables reflected associations between brain volume and MVRF ARV in obesity, cholesterol, blood pressure, and glucose. Subsequent bivariate correlations revealed associations among MVRF factors, aggregate brain volume and cognition. CONCLUSION: This study demonstrates that MVRF variability over time is associated with midlife brain volume in regions that are relevant to later-life cognitive decline.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".