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Purpose of Review: This review describes the evolution of the clinical criteria for Alzheimer's disease over the past 25 years, with special emphasis on those recently published that have incorporated the use of biomarkers., Recent Findings: One of the most important advances in the knowledge of Alzheimer's disease was the development of cerebrospinal fluid, PET and MRI biomarkers. These have shown that the Alzheimer's disease is present in cognitively normal individuals, suggesting that there is a long incubation process that precedes the onset of the symptoms. Although there are diagnostic criteria for Alzheimer's disease, the National Institute on Aging and the Alzheimer's Association has proposed a set of diagnostic criteria oriented to provide a unified vision of the pathological process from preclinical, to mild cognitive impairment, and to full-blown dementia. These new criteria take advantage of different biomarkers to support the clinical diagnosis of the different stages of the disease., Summary: The new guidelines provide a definition of the dementia syndrome and core diagnostic features to be used in research and clinical practice, although they caution about the use of biomarkers, since they still require validation, and the longitudinal interaction and dynamics of these biomarkers in relationship to the manifestation of the symptoms are not fully understood.

Background: Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge. Objective: To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD. Methods: Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model. Results: Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11). Conclusion: A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables. [ABSTRACT FROM AUTHOR]

Introduction: People with subjective cognitive decline (SCD) report cognitive deterioration. However, their performance in neuropsychological evaluation falls within the normal range. The present study aims to analyse whether structural magnetic resonance imaging (MRI) reveals grey matter changes in the SCD population compared with healthy normal controls (HC). Methods: Parallel systematic searches in PubMed and Web of Science databases were conducted, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Quality assessment was completed using the Newcastle-Ottawa Scale (NOS). Results: Fifty-one MRI studies were included. Thirty-five studies used a region of interest (ROI) analysis, 15 used a voxel-based morphometry (VBM) analysis and 10 studies used a cortical thickness (CTh) analysis. Ten studies combined both, VBM or CTh analysis with ROI analysis. Conclusions: Medial temporal structures, like the hippocampus or the entorhinal cortex (EC), seemed to present grey matter reduction in SCD compared with HC, but the samples and results are heterogeneous. Larger sample sizes could help to better determine if these grey matter changes are consistent in SCD subjects. [ABSTRACT FROM AUTHOR]

(1) Background: Both cerebral vascular disorders and cognitive decline increase in incidence with age. The role of cerebral vascular disease and hemodynamic changes in the development of cognitive deficits is controversial. The objective of this study was to assess the cardiovascular response during cardiac stress testing in neurologically asymptomatic individuals who developed cognitive impairment several years after previous cardiac stress testing. (2) Methods: This was a retrospective cohort study of patients who underwent cardiac stress testing between January 2001 and December 2010. Patients were followed up until May 2015, and we selected those who developed cognitive dysfunction including dementia, mild cognitive impairment, and subjective cognitive decline, after the stress test. Heart rate and blood pressure both at rest and at peak exercise, and the mean R-R interval at rest were recorded. For each patient who developed cognitive impairment, we selected one matched control who did not show cognitive decline by the end of the follow-up period. (3) Results: From the cohort of 7224 patients, 371 developed cognitive impairment; of these, 186 (124 men) met the inclusion criteria, and 186 of the other patients were selected as matched controls. During follow-up, cognitive impairment appeared 6.2 ± 4.7 years after the cardiac stress test. These patients who had subsequently developed cognitive impairment had significantly lower at-rest systolic, diastolic, and mean blood pressure than controls (p < 0.05). Further, compared with controls, their maximum heart rate was significantly higher at peak exercise. (4) Conclusion: The results from this study suggest that differences in cardiovascular response to stress might be present in individuals who develop cognitive decline. These findings challenge the possibility of assessing blood pressure and heart rate variability at rest and during cardiac stress as potential risk factors associated with cognitive impairment. [ABSTRACT FROM AUTHOR]