[P4–495]: NEUROIMAGING BIOMARKERS MODERATE THE ASSOCIATION BETWEEN DEMOGRAPHIC RISK AND DEMENTIA RATING SCALE ACROSS NEURODEGENERATIVE DISEASES: THE SUNNYBROOK DEMENTIA STUDY
Bibliographic record
Abstract
A large spectrum of pathologies in the aging brain in combination with cardiovascular risk have led to an increased prevalence and diagnosis of mixed neurodegenerative diseases in late life. Risk Scores(RS) to quantitatively differentiate neurodegenerative patients and at risk adults for cognitive impairment presents an alternative risk assessment tool for individually tailored intervention programs and/or preventive measures. Previous studies have proposed risk scores to predict dementia incidence and cognitive decline using multiple domains. The goal of this study was to test a novel multimodal, integrative method to examine the synergistic influence of established demographic risk factors and neuroimaging biomarkers to predict Dementia Rating Scale (DRS) performance and change across neurodegenerative diseases. We employed a longitudinal design using patients and controls (N=833; range=38–90 years; Mage=70.47 years) from the Sunnybrook Dementia Study. The recruited patients represented Alzheimer's disease, Mild Cognitive Impairment, Vascular Cognitive Impairment, Parkinson's/Lewy body diseases, Frontotemporal lobar degeneration, and mixed neurodegenerative diagnoses. We tested (1)whether neuroimaging biomarkers (ventricular volume, white matter hyperintensity(WMH), lacunes) and demographic RS (age[0≤70years(mean),1>70years]+ sex[0=male,1=female]+ education[0>14years(mean),1≤14years]) predicted DRS performance at baseline and longitudinal change, and (2)whether neuroimaging biomarkers mediate or moderate the effect of demographic RS on the DRS. We used latent growth modeling to determine how DRS performance changed (random intercept and random slope model) and path analysis. First, higher ventricular volume predicted poorer baseline DRS performance(β=-2.77, SE=0.27, p<.001) and steeper decline(β=-1.07, SE=0.22, p<.001). Second, higher WMH(β=-1.37, SE=0.56, p=.014) and lacunes(β=-32.00, SE=14.61, p=.028) predicted poorer DRS baseline performance. Third, higher demographic RS predicted poorer DRS baseline performance(β=-1.46, SE=0.65, p=.024) and less decline(β=0.99, SE=0.48, p=.038). Fourth, neuroimaging biomarkers moderated the association between demographic RS and DRS performance. Specifically, higher demographic RS predicted poorer DRS performance in those with low ventricular volume(β=-1.81, SE=0.74, p=.015), WMH(β=-2.22, SE=0.79, p=.005), and lacunes(β=-1.66, SE=0.74, p=.025). Higher demographic RS predicted less DRS decline for those with low ventricular volume(β=1.13, SE=0.53, p=.034) and high WMH(β=1.71, SE=0.71, p=.016). Neuroimaging biomarkers may moderate the association between demographic risk and DRS performance and change. Innovative methodological approaches may aid in identifying potential complex and dynamic mechanisms underlying multifaceted clinical phenotypes across neurodegenerative diseases.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".