Cerebrovascular injury markers explain the effect of systemic vascular risk on cognitive decline in older adults with lower amyloid burden
Bibliographic record
Abstract
Abstract Background Systemic vascular risk is a well‐established contributor to late‐life cognitive decline, yet the mechanism is not completely understood. We investigated whether neuroimaging‐based measures of vascular injury (white matter hyperintensity (WMH) volume, Peak width of Skeletonized Mean Diffusivity (PSMD), and relative cerebral blood flow (rCBF)) could explain the effect of systemic vascular risk on cognitive decline using longitudinal data from the Harvard Aging Brain Study. Method We used the Framingham Heart Study cardiovascular disease risk score (FHS‐CVD) as an index of systemic vascular risk. We extracted WMH from structural MRI ( https://hypermapp3r.readthedocs.io/ ) and PSMD from diffusion MRI ( http://www.psmdmarker.com ). We performed kinetic modeling on dynamically‐acquired PiB‐PET data to extract 1) amyloid burden as the distribution volume ratio (PiB‐DVR) and 2) relative tracer delivery (PiB‐R1 via MRTM reference‐tissue analysis as a proxy of rCBF). Global cognition was assessed using Preclinical Alzheimer Cognitive Composite (PACC). We considered two linear mixed effect models to examine whether cerebrovascular injury markers could explain the FHS‐CVD effect on PACC change over time: 1) FHS‐CVD on PACC change controlling for age, sex, years of education, and PiB‐DVR; 2) the previous model including WMH, PSMD, and PIB‐R1. Analyses were stratified by high (PiB+) and low (PiB‐) baseline amyloid burden. Result Figure 1 illustrates the relationships between demographics and neuroimaging measures at baseline. Table 1 shows the demographics and study information. We observed a significant effect of FHS‐CVD on PACC change (PiB‐: t=‐3.9, p<0.001 (Table 2); PiB+: t=‐2.6, p=0.008). When the cerebrovascular injury markers were included in the model, the effect of FHS‐CVD was reduced in the PiB‐ group while PSMD and PiB‐R1 explained PACC change (Table 3; Figure 2). The second model fit was significantly better than the first model (L.ratio= 25.9, p<0.001). The effects remained significant after controlling for gray matter volume. In contrast, cerebrovascular injury measures did not explain the effect of FHS‐CVD on PACC change in the PIB+ group. Conclusion These results demonstrate that cerebrovascular injury largely explains the effect of systemic vascular risk on cognitive decline in older adults with lower amyloid burden suggesting mechanisms by which a higher systemic vascular risk impacts brain function.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".