Atherosclerosis and vascular cognitive impairment neuropathological guideline
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.083 | 0.041 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".