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INTRODUCTION: Patients with subjective memory complaints (SMC) may include subgroups with different neuropsychological profiles and risks of cognitive impairment. METHODS: Cluster analysis was performed on two datasets (n: 630 and 734) comprising demographic and neuropsychological data from SMC and healthy controls (HC). Survival analyses were conducted on clusters. Bayesian model averaging assessed the predictive utility of clusters and other biomarkers. RESULTS: Two clusters with higher and lower than average cognitive performance were detected in SMC and HC. Assignment to the lower performance cluster increased the risk of cognitive impairment in both datasets (hazard ratios: 1.78 and 2.96; Plog‐rank: 0.04 and <0.001) and was associated with lower hippocampal volumes and higher tau/amyloid beta 42 ratios in cerebrospinal fluid. The effect of SMC was small and confounded by mood. DISCUSSION: This study provides evidence of the presence of cognitive clusters that hold biological significance and predictive value for cognitive decline in SMC and HC. Highlights: Patients with subjective memory complaints include two cognitive clusters.Assignment to the lower performance cluster increases risk of cognitive impairment.This cluster shows a pattern of biomarkers consistent with incipient Alzheimer's disease pathology.The same cognitive cluster structure is found in healthy controls.The effect of memory complaints on risk of cognitive decline is small and confounded. [ABSTRACT FROM AUTHOR]

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]

Background: There is a need to better understand the experience of patients living with Alzheimer's disease (AD) in the early stages.Objective: The aim of the study was to evaluate the perception of quality of life in patients with early-stage AD.Methods: A multicenter, non-interventional study was conducted including patients of 50-90 years of age with prodromal or mild AD, a Mini-Mental State Examination (MMSE) score ≥22, and a Clinical Dementia Rating-Global score (CDR-GS) of 0.5.-1.0. The Quality of Life in Alzheimer 's Disease (QoL-AD) questionnaire was used to assess health-related quality of life. A battery of self-report instruments was used to evaluate different psychological and behavioral domains. Associations between the QoL-AD and other outcome measures were analyzed using Spearman's rank correlations.Results: A total of 149 patients were included. Mean age (SD) was 72.3 (7.0) years and mean disease duration was 1.4 (1.8) years. Mean MMSE score was 24.6 (2.1). The mean QoL-AD score was 37.9 (4.5). Eighty-three percent (n = 124) of patients had moderate-to-severe hopelessness, 22.1% (n = 33) had depressive symptoms, and 36.9% (n = 55) felt stigmatized. The quality of life showed a significant positive correlation with self-efficacy and negative correlations with depression, emotional and practical consequences, stigma, and hopelessness.Conclusion: Stigma, depressive symptoms, and hopelessness are frequent scenarios in AD negatively impacting quality of life, even in a population with short disease duration and minimal cognitive impairment. [ABSTRACT FROM AUTHOR]

Objectives: Mild cognitive impairment (MCI) is associated with an increased risk of further cognitive decline, partly depending on demographics and biomarker status. The aim of the present study was to survey the clinical practices of physicians in terms of biomarker counseling, management, and follow‐up in European expert centers diagnosing patients with MCI. Methods: An online email survey was distributed to physicians affiliated with European Alzheimer's disease Consortium centers (Northern Europe: 10 centers; Eastern and Central Europe: 9 centers; and Southern Europe: 15 centers) with questions on attitudes toward biomarkers and biomarker counseling in MCI and dementia. This included postbiomarker counseling and the process of diagnostic disclosure of MCI, as well as treatment and follow‐up in MCI. Results: The response rate for the survey was 80.9% (34 of 42 centers) across 20 countries. A large majority of physicians had access to biomarkers and found them useful. Pre‐ and postbiomarker counseling varied across centers, as did practices for referral to support groups and advice on preventive strategies. Less than half reported discussing driving and advance care planning with patients with MCI. Conclusions: The variability in clinical practices across centers calls for better biomarker counseling and better training to improve communication skills. Future initiatives should address the importance of communicating preventive strategies and advance planning. [ABSTRACT FROM AUTHOR]

Objective: Alzheimer disease (AD) is the leading cause of dementia, and although its etiology remains unclear, it seems that type 2 diabetes mellitus (T2DM) and other prediabetic states of insulin resistance could contribute to the appearance of sporadic AD. As such, we have assessed whether tau and β-amyloid (Aβ) deposits might be present in pancreatic tissue of subjects with AD, and whether amylin, an amyloidogenic protein deposited in the pancreas of T2DM patients, might accumulate in the brain of AD patients.Methods: We studied pancreatic and brain tissue from 48 individuals with no neuropathological alterations and from 87 subjects diagnosed with AD. We examined Aβ and tau accumulation in the pancreas as well as that of amylin in the brain. Moreover, we performed proximity ligation assays to ascertain whether tau and/or Aβ interact with amylin in either the pancreas or brain of these subjects.Results: Cytoplasmic tau and Aβ protein deposits were detected in pancreatic β cells of subjects with AD as well as in subjects with a normal neuropathological examination but with a history of T2DM and in a small cohort of control subjects without T2DM. Furthermore, we found amylin deposits in the brain of these subjects, providing histological evidence that amylin can interact with Aβ and tau in both the pancreas and hippocampus.Interpretation: The presence of both tau and Aβ inclusions in pancreatic β cells, and of amylin deposits in the brain, provides new evidence of a potential overlap in the mechanisms underlying the pathogenesis of T2DM and AD. ANN NEUROL 2019;86:539-551. [ABSTRACT FROM AUTHOR]

There is increasing evidence of a vascular contribution to Alzheimer's disease (AD). In some cases, prior work suggests that chronic brain hypoperfusion could play a prime pathogenic role contributing to the accumulation of amyloid-β,while other studies favor the hypothesis that vascular dysfunction and amyloid pathology are independent, although synergistic, mechanisms contributing to cognitive impairment. Vascular dysfunction can be evaluated by assessing cerebral blood flow impairment. Phase contrast velocity mapping by MRI offers a non-invasive means of quantifying the total inflow of blood to the brain. This quantitative parameter could be a sensitive indicator of vascular disease at early stages of AD. In this work, phase contrast MRI was used to evaluate cerebral hemodynamics in patients with subjective memory complaints, amnestic mild cognitive impairment, and mild to moderate AD, and compare them with control subjects. Results showed that blood flow and velocity were decreased in the patients with cognitive dysfunction and the decrease correlated with the degree of cognitive impairment as assessed by means of neuropsychological tests. Total cerebral blood flow measurements were clearly reduced in AD patients, but more importantly appeared to be sensitive enough to distinguish between healthy subjects and those with mild cognitive impairment. A quantitative measurement of total brain blood flow could potentially predict vascular dysfunction and compromised brain perfusion in early stages of AD. [ABSTRACT FROM AUTHOR]