93 Impact of Cardiovascular Risk on Cognitive and Brain Aging in Autosomal Dominant Frontotemporal Dementia
Bibliographic record
Abstract
Objective: Poor cardiovascular health occurs with age and is associated with increased dementia risk, yet its impact on frontotemporal lobar degeneration (FTLD) and autosomal dominant neurodegenerative disease has not been well established. Examining cardiovascular risk in a population with high genetic vulnerability provides an opportunity to assess the impact of lifestyle factors on brain health outcomes. In the current study, we examined whether systemic vascular burden associates with accelerated cognitive and brain aging outcomes in genetic FTLD. Participants and Methods: 166 adults with autosomal dominant FTLD (C9orf72 n= 97; GRN n= 34; MAPT n= 35; 54% female; Mage = 47.9; Meducation = 15.6 years) enrolled in the Advancing Research and Treatment for Frontotemporal Lobar Degeneration (ARTFL) and Longitudinal Evaluation of Familial Frontotemporal Dementia Longitudinal FTD study (ALLFTD) were included. Participants completed neuroimaging and were screened for cardiovascular risk and functional impairment during a comprehensive neurobehavioral and medical interview. A vascular burden score (VBS) was created by summing vascular risk factors (VRS) [diabetes, hypertension, hyperlipidemia, and sleep apnea] and vascular diseases (VDS) [cerebrovascular disease (e.g., TIA, CVA), cardiac arrhythmia (e.g., atrial fibrillation, pacemaker, defibrillator), coronary artery disease (e.g., myocardial infarction, cardiac bypass, stent), and congestive heart failure] following a previously developed composite (range 0 to 8). We examined the interaction between each vascular health metric (VBS, VDS, VRS) and age (vascular health*age) on clinical severity (CDR plus NACC FTLD-SB), and white matter hyperintensity (WMH) volume outcomes, adjusting for age and sex. Vascular risk, disease, and overall burden scores were examined in separate models. Results: There was a statistically significant interaction between total VBS and age on both clinical severity (ß=0.20, p=0.044) and WMH burden (ß=0.20, p=0.032). Mutation carriers with higher vascular burden evidenced worse clinical and WMH outcomes for their age. When breaking down the vascular burden score into (separate) vascular risk (VRS) and vascular disease (VDS) scores, the interaction between age and VRS remained significant only for WMH (ß=0.26, p=0.009), but not clinical severity (ß=0.04, p=0.685). On the other hand, the interaction between VDS and age remained significant only for clinical severity (ß=0.20, p=0.041) but not WMH (ß=0.17, p=0.066). Conclusions: Our results demonstrate that systemic vascular burden is associated with an “accelerated aging” pattern on clinical and white matter outcomes in autosomal dominant FTLD. Specifically, mutation carriers with greater vascular burden show poorer neurobehavioral outcomes for their chronological age. When separating vascular risk from disease, risk was associated with higher age-related WMH burden, whereas disease was associated with poorer age-related clinical severity of mutation carriers. This pattern suggests preferential brain-related effects of vascular risk factors, while the functional impact of such factors may be more closely aligned with fulminant vascular disease. Our results suggest cardiovascular health may be an important, potentially modifiable risk factor to help mitigate the cognitive and behavioral disturbances associated with having a pathogenic variant of autosomal dominant FTLD. Future studies should continue to examine the neuropathological processes underlying the impact of cardiovascular risk in FTLD to inform more precise recommendations, particularly as it relates to lifestyle interventions.
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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 | 0.001 | 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".