Associations of peak width of skeletonized mean diffusivity with cardiovascular disease risk and cognitive decline in clinically normal older adults
Bibliographic record
Abstract
Abstract Background Cardiovascular disease (CVD) is associated with cognitive decline, both alone and synergistically with Aβ burden, and can have a stronger relationship with cognitive trajectories than many standard imaging biomarkers. However, the effects of CVD and Aβ on cognitive decline have not been examined alongside diffusion markers of small vessel disease (SVD). With data from the Harvard Aging Brain Study (HABS), we examined the influence of CVD risk and peak width of skeletonized mean diffusivity (PSMD; a DTI measure of SVD) on cognitive trajectories in clinically normal participants, while accounting for Aβ burden. Method 264 clinically normal participants from HABS (mean [SD]: age=73.2[7.1] years; follow‐up range=6.3[1.8] years) underwent at least four longitudinal assessments of the Preclinical Alzheimer’s Cognitive Composite (PACC96), as well as PiB‐PET imaging and DTI at baseline. PSMD was calculated from skeletonized mean diffusivity maps using FSL. CVD risk was quantified using the Framingham Heart Study‐CVD risk score. We used linear mixed‐effect models to examine the influence of baseline PSMD and CVD on longitudinal PACC, while controlling for, or interacting with, PiB DVR, correcting for baseline age, years of education, and biological sex. Result PSMD and CVD were significantly correlated (r=0.38, p<1.0x10‐9), though this weakened when correcting for age (r=0.15, p=0.01), while PSMD and PiB showed no association (r=0.09, p=0.13). Higher PSMD was associated with faster cognitive decline (t(1808)=‐2.40, p=0.02) and this effect remained (t(1806)=‐2.26, p=0.02) after correcting for PiB. The effect of PSMD was marginally significant (t(1804)=‐1.93, p=0.05) when correcting for both CVD (t(1804)=‐4.44, p<1.0x10‐5) and PiB (t(1804)=‐7.79, p<1.0x10‐13). No significant interactive effect of PSMD and PiB on cognitive decline was observed. Conclusion CVD risk and PSMD were significantly correlated, though this was largely driven by age, while PiB and PSMD were not. PSMD was significantly associated with faster cognitive decline. After adjusting for both CVD and PiB however, the effect of PSMD on cognitive decline was attenuated. It is possible that CVD risk may capture variance related to cognitive decline beyond that of PSMD. However, future research is needed to understand which domains of the CVD risk score are driving these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".