Atherosclerosis and vascular cognitive impairment neuropathological guideline
Notice bibliographique
Résumé
Sir, We congratulate Skrobot et al. (2016) on their timely development of a pathological scoring system for diagnosis of vascular cognitive impairment (VCI). Their study has several strengths. Through a well-organized study, they began by developing terminology and definitions for pathologies putatively associated with VCI. Then, they agreed on consensus about brain areas that should be sampled, staining methods that should be used, and pathologies that should be scored. Finally, they studied their scoring system in a validation study, and found the scoring to be accurate in predicting cognitive impairment in 77.9% of cases. However, their study also has some limitations. Apart from the number of autopsies that were done for the validation study (only 113 brains), the lack of atherosclerosis in their proposed scoring system is the major drawback. In their final model for estimation of VCI, one or more large (>10 mm) subcortical cerebral infarcts was sufficient to bring the probability of VCI to at least moderate level. Their VCI estimation model has only two other components, moderate or severe arteriolosclerosis and moderate or severe leptomeningeal cerebral amyloid angiopathy. Finding the last two components in brain autopsies is proposed to make VCI probability as moderate, and finding at least one of them in addition to brain subcortical infarcts bring a high probability for a VCI pathological diagnosis. Atherosclerosis of major brain vessels and circle of Willis is a common pathological finding in elderly brains. Arvanitakis et al. (2016) reported pathological brain findings of 1143 older Americans from two population-based cohorts of ageing, who were cognitively evaluated at a mean of 9.2 months before death. Moderate to severe atherosclerosis was the most common vascular brain pathology (39%), and was followed by moderate to severe arteriolosclerosis (35%) and gross infarcts (28%). Dolan et al. (2010) reported pathological findings of 200 elderly brains from the Baltimore Longitudinal Study of Aging. They measured intracranial atherosclerosis through grades 1–3, with most severe atherosclerosis represented by grade 3. They found that 136 of 200 participants had intracranial atherosclerosis > grade 1 that was more common than 90 participants with brain infarcts. Apart from being the most common vascular brain pathology, atherosclerosis has been shown to be associated with cognitive impairment and dementia. Arvanitakis et al. (2016) showed that each unit increase in the severity of brain vessels’ atherosclerosis increased odds of dementia by 33%, after controlling for age, sex, education, Alzheimer’s disease and Lewy bodies pathologies, and brain macro- and microinfarcts. It was interesting that atherosclerosis had a stronger association with dementia compared with arteriolosclerosis, which increased odds of dementia by 20% with each unit increase in its severity. As a reminder, arteriolosclerosis, but not atherosclerosis, is included in the VCI scoring system proposed by Skrobot et al. (2016). Dolan et al. (2010) reported that one grade increase in the severity of brain vessels atherosclerosis was associated with 100% increase in odds of dementia that was present even after excluding subjects with brain infarcts. Of note, only brain vessel, not aortic or cardiac, atherosclerosis was associated with dementia (Dolan et al., 2010) Apart from pathological post-mortem studies, longitudinal population-based studies have shown that carotid intima media thickness (IMT) and carotid plaques, as markers of carotid atherosclerosis, are associated with incident dementia. After an average follow up of 9.2 years, Zhong et al. (2012) found carotid IMT to be associated with incident cognitive impairment among 1651 cognitively normal participants of a longitudinal study of ageing. Each 0.1 mm increase in the IMT was associated with 9% increase in the hazard of cognitive impairment, after control for demographic and vascular risk factors. In another longitudinal study, Carcaillon et al. (2015) followed 6025 dementia-free subjects for a mean 5.4 years of follow up. They found carotid plaques to be associated with 92% increased risk of developing vascular dementia, after controlling for demographic and vascular factors and vascular diseases. Indeed, Barnes et al. (2009) have included internal carotid IMT as a factor in their dementia risk score for older adults. In conclusion, we think that brain vessel atherosclerosis should be scored in any neuropathological guideline providing a VCI probability, because of the association of atherosclerosis with cognitive impairment, dementia, and brain atrophy (Crystal et al., 2014). No funding was received towards this work.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,083 | 0,041 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,007 |
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 ».