Vascular cognitive impairment (VCI) is predominately caused by vascular risk factors and cerebrovascular disease. VCI includes a broad spectrum of cognitive disorders, from mild cognitive impairment to vascular dementia caused by ischemic or hemorrhagic stroke, and vascular factors alone or in a combination with neurodegeneration including Alzheimer's disease (AD) and AD-related dementia. VCI accounts for at least 20-40% of all dementia diagnosis. Growing evidence indicates that cerebrovascular pathology is the most important contributor to dementia, with additive or synergistic interactions with neurodegenerative pathology. The most common underlying mechanism of VCI is chronic age-related dysregulation of CBF, although other factors such as inflammation and cardiovascular dysfunction play a role. Vascular risk factors are prevalent in VCI and if measured in midlife they predict cognitive impairment and dementia in later life. Particularly, hypertension, high cholesterol, diabetes, and smoking at midlife are each associated with a 20 to 40% increased risk of dementia. Control of these risk factors including multimodality strategies with an inclusion of lifestyle modification is the most promising strategy for treatment and prevention of VCI. In this review, we present recent developments in age-related VCI, its mechanisms, diagnostic criteria, neuroimaging correlates, vascular risk determinants, and current intervention strategies for prevention and treatment of VCI. We have also summarized the most recent and relevant literature in the field of VCI.

Background: The detection of subtle cognitive impairment in a clinical setting is difficult. Because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal. Objective: In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system. Methods: The best primary stage incorporated measures of short-term memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1,181 ADNI participants and tested on 127 patients from a memory clinic. Results: The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and diagnostic odds ratio of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression and gradient-boosted machine. Conclusion: The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm.

Background: The detection of subtle cognitive impairment in a clinical setting is difficult, and because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal. In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system. Methods: The best primary stage incorporated measures of short-term memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1181 ADNI participants and tested on 127 patients from a memory clinic. Results: The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and DOR of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression for stage 1 and gradient-boosted machine for stage 2. Conclusions: The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm.

Background: Perivascular spaces (PVS) are fluid-filled compartments surrounding small intracerebral vessels that transport fluid and clear waste. Objective: We examined associations between PVS count, vascular and neurodegenerative risk factors, and cognitive status among the predominantly Hispanic participants of the FL-VIP Study of Alzheimer’s Disease Risk. Methods: Using brain MRI (n = 228), we counted PVS in single axial image through the basal ganglia (BG) and centrum semiovale (CSO). PVS per region were scored as 0 (none), 1 (<10), 2 (11–20), 3 (21–40), and 4 (>40). Generalized linear models examined PVS associations with vascular risk factors and a composite vascular comorbidity risk (VASCom) score. Results: Our sample (mean age 72±8 years, 61% women, 60% Hispanic, mean education 15±4 years, 33% APOE4 carriers) was 59% hypertensive, 21% diabetic, 66% hypercholesteremic, and 30% obese. Mean VASCom score was 2.3±1.6. PVS scores ranged from 0–4 in the BG (mean 1.3±0.7) and CSO (mean 1.2±0.9), and 0–7 combined (mean 2.5±1.4). In multivariable regression models, BG PVS was associated with age (β= 0.03/year, p <  0.0001), Hispanic ethnicity (β= 0.29, p = 0.01), education (β= 0.04/year, p = 0.04), and coronary bypass surgery (β= 0.93, p = 0.02). CSO PVS only associated with age (β= 0.03/year, p <  0.01). APOE4 and amyloid-β were not associated with PVS. Conclusion: BG PVS may be a marker of subclinical cerebrovascular disease. Further research is needed to validate associations and identify mechanisms linking BG PVS and cerebrovascular disease markers. PVS may be a marker of neurodegeneration despite our negative preliminary findings and more research is warranted. The association between BG PVS and Hispanic ethnicity also requires further investigation.

Background: The 2021 Alzheimer's Association International Conference press release forecasted global dementia cases to triple by 2050, due to the major contribution of increases in vascular risk factors. The Global Vascular Risk Score (GVRS) was developed to predict vascular events using a point-based index for risk factor variables, though has demonstrated to be a significant predictor of global and domain-specific cognitive performance in a multi-ethnic cohort (Rundek, T. et al. Global Vascular Risk Score and CAIDE Dementia Risk Score Predict Cognitive Function in the Northern Manhattan Study. J Alzheimers Dis 73, 1221-1231 ( 2020)). However, more than half of the variance in global and domain-specific cognitive performance is still unexplained by GVRS variables. We sought to explain additional variance in cognitive performance by addition of MRI biomarkers of cerebral small vessel disease to the GVRS model. Method:We included 1290 subjects from the multiethnic Northern Manhattan Study population (age 64+/-8 years, 40% women, 66% Hispanic, 17% non-Hispanic Black, 15% non-Hispanic White, education 10+/-5 years). Multivariate linear regression models were employed to assess the added value of the cerebral small vessel disease MRI measures (white matter hyperintensity volume, silent brain infarcts, cerebral microbleeds, and perivascular spaces) beyond GVRS variables in explaining more variance in global and domain-specific (episodic memory, executive function, semantic memory, and processing speed) cognitive performance. The explained variance (R 2 ) of the GVRS model was compared to the proposed model with added imaging markers to determine their predictive performance. Statistical significance was determined using F-statistic difference test between the models. Result:The explained variance (R 2 ) and F-statistic for models of global and domainspecific cognition are in the table. Addition of MRI measures significantly improved the model for global cognition (F(4, 1108) = 2.68, p < 0.05) and domain-specific cognitive performance models of episodic memory (F(4, 1096) = 3.34, p < 0.01) and processing speed (F(4, 1096) = 2.40, p < 0.05).