93 Impact of Cardiovascular Risk on Cognitive and Brain Aging in Autosomal Dominant Frontotemporal Dementia
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».